Keywords: Multi-agent systems
Abstract: Systematic design of distributed feedback for coordinated control of multi-agent systems has much to gain from the rigorous examination of the nonlinear dynamics of collective animal behavior. Animals in groups, from bird flocks to fish schools, employ distributed strategies with constraints on sensing, computation, and actuation. Yet, at the level of the group, they are known to manage a variety of challenging tasks quickly, robustly and adaptively in an uncertain and changing environment. I will review recent work on models and methods for studying the mechanisms of collective dynamics in animal groups. I will describe system theoretic tools used to prove how collective behavior depends on parameters that model the individual agents, the network interconnections, and the environment. And I will discuss how these developments lay foundations for systematic control design methodologies that endow engineered multi-agent systems with the remarkable features of animal groups.

Biography: Naomi Ehrich Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and an associated faculty member of the Program in Applied and Computational Mathematics at Princeton University. She is currently Director of Princeton's Council on Science and Technology and an affiliated faculty member of the Princeton Neuroscience Institute and Program on Quantitative and Computational Biology. Her research and teaching are in control and dynamical systems with current interests in coordinated control of multi-agent systems, mobile sensor networks, collective animal behavior, and human decision dynamics. In 2013 she was elected to the American Academy of Arts and Sciences. She received a John D. and Catherine T. MacArthur Foundation Fellowship in 2004, the UCSB Mohammed Dahleh Award in 2005, and an Inaugural Distinguished ECE Alumni Award from the University of Maryland in 2012. She is a Fellow of the IEEE, ASME, SIAM, and IFAC. She received the B.S.E. degree in Mechanical Engineering from Princeton University in 1985 and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland in 1991 and 1994. From 1985 to 1989, she worked as an engineer in the electric power industry.

Keywords: Smart grids, Modeling and simulation of power systems, Intelligent control of power systems
Abstract: One part of the process of development and application of methods and principles of Smart Grids in electricity and heat production is numerical modeling and simulation of Power and Heating Plants, Power Systems, and also District Heating Networks. Due to the venue of the IFAC Congress this paper deals with the comparison between Czech and South Africa. Similarity is given by the fact, that basic fuel is coal. In the Czech Republic, it is produced 64% of its electricity from coal, mostly lignite (brown), in the heat generation the coal has an even larger share of 90%. In South Africa, it is produced 85% of its electricity from coal (heat is insignificant due to climatic conditions). From this perspective, the Czech Republic and South Africa dealt with similar problems, e.g. using of coal mills, combustion chambers and equipment, whole powdered steam boilers and their optimal intelligent Smart Control. Therefore paper firstly shows the models of the coal mill and steam boiler. Power and Heating Plants model was also including the steam common pipeline, condensing turbines, turbines with controlled extraction steam pressure, and back pressure turbines. At the steam output of the machine room is placed the Main Heat Exchanger (MHE), which already belongs to the District Heating Networks. The power and heat source is controlled according to the Daily Consumption Diagrams, which are corrected by the current and predicted weather conditions (air temperature, solar irradiation, wind, rain and frost). For predicting consumption are used methods of classical and artificial intelligence control. Control of electricity and heat is carried out using advanced methods of optimal control, including the different configurations of machine room equipments and MHE (involvement of the basic heaters and top heaters). The paper secondly describes the model and simulation experiments for control of central Power and Heating Plant Komorany for regional District Heating Networks MO-CHO-LI in northern Bohemia. The developed Information and Control System serves as an on-line support system for the operators and dispatchers and is part of the operating procedures in the implementation of Smart Grid.

Keywords: Smart grids, Distribution automation, Robotics
Abstract: This paper describes path planning and control of an autonomous power distribution system. The aim is to study the use of the autonomous mobile power-grid systems after disasters to accelerate search, rescue, and recovery efforts. The concept is demonstrated through an autonomous electrical cabling and connection mission between a power source and a power load in a cluttered environment using lab-size platforms. The developed system will be scalable to real-size. The ultimate goal of this work is developing intelligent power electronics and a distributed autonomous mobile microgrid. It will be capable of regulating power flow at a desired voltage and frequency level, meeting load demands and adaptable to changes in situation, power demands, or generations.

Keywords: Smart grids, Test and documentation, Control of renewable energy resources
Abstract: In Denmark it will be a challenge in near future to balance the electrical grid due to a large increase in the renewable energy production mainly from wind turbines. Smart grid solutions which exploit all storage capacities are essential to meet this challenge. In this work single family houses with heat pumps and floor heating are investigated for storage capability. The aim is to shift energy consumption a few hours in time to mitigate the effect of fluctuating production from wind and other renewable energy sources on the grid. Based on measurements in six inhabited houses for approximately a year prediction models are analysed. The main topic of this work is to investigate how behaviour of inhabitants affect the quality of predictions. Unfortunately the output of the models for single inhabited single family houses seems to give large standard deviations of the predictions, and aggregated models for use in a coordinated control scheme could improve the possibility to use thermal capacity in houses as dynamical storages.

Keywords: Modeling and simulation of transportation systems, Automatic control, optimization, real-time operations in transportation
Abstract: Energy efficiency is firstly considered into the control of overhead cranes. Based on the model of crane system, energy consumption as well as operational safety is formulated in an optimal control problem. The optimal control is used to search optimal trajectories of velocity and acceleration for minimizing energy consumption. Existing related work mainly focused on reducing transportation time and swing, but trajectory in this paper focuses on increasing energy efficiency of transportation while satisfying practical and physical constraints. Model predictive control (MPC) is then proposed to track optimal trajectories in real-time. As a result, the actual trajectories can match the reference trajectories with small errors when external disturbances exist. In the simulation, it can be shown that the proposed control approach can improve energy efficiency of overhead cranes robustly.

Keywords: Modeling and simulation of power systems, Smart grids, Control system design
Abstract: The paper deals with the power network model for investigation of Smart Grid technologies at Russky Island that is the end part of the Russian Far East at the Pacific coast. Possible configurations of a Virtual Power Plant, which could be created at this Island, is discussed. Several possible combinations of different energy sources as well as energy storage units is examined. Dynamics of this smart grid is examined too. As an example of the control under loss of the link with the main grid, the use of Fuzzy Controller is shown. The paper describe the direction of the future investigations on the base of this model.

Keywords: Instrumentation and control systems, Real time simulation and dispatching
Abstract: The Smart Grid environment allows the integration of resources of small and medium players through the use of Demand Response programs. Despite the clear advantages for the grid, the integration of the consumers must be carefully done. This paper proposes a system which simulates small and medium players. This system is essential to produce tests and studies about the active participation of the small and medium players on the Smart Grid environment. The advantages of this system, compared to similar ones, are the fact that the proposed system can deal with three types of loads (virtual, contextual and real); it can possess several modules of loads optimization and it can run in real time. The use of modules and the dynamic configuration of the player results in a system which can represent different players in an easy and independent way. This paper will describe the system and all its capabilities.

Keywords: Nonlinear observers and filter design
Abstract: A framework to extend Luenberger observer to nonlinear systems is proposed. The theory of invariant manifolds plays a central role in the framework. It is shown that the invariant manifolds for observer design are often non-standard ones and this makes their computations challenging. The proposed theory successfully removes the condition imposed on the system to be observed in the previous research in nonlinear Luenberger observer. Numerical examples show that the design methods proposed produce more effective observers compared with linear observers.

Keywords: Nonlinear observers and filter design, Analytic design
Abstract: The goal of this paper is to apply some concepts and techniques from algebraic geometry to study the observability of nonlinear continuous-time polynomial systems. After deriving some new results for observability and embeddings, it is shown how to use such concepts to easily design high-gain observers. The proposed technique is illustrated by an application to the well known Rossler oscillator.

Keywords: Nonlinear observers and filter design, Application of nonlinear analysis and design
Abstract: In this paper, various type of differentiators are studied. In the context of velocity and acceleration estimations of a pneumatic system, a comparison is made between numerical methods, based on classical or algebraic approaches, and a high order sliding mode differentiator

Keywords: Nonlinear observers and filter design, Energy systems, Output feedback control
Abstract: This paper presents the design of a nonlinear observer and a nonlinear feedback controller for sensorless operation of a continuously rotating energy harvester. A mathematical model of the harvester with its power electronic interface is discussed. This model is used to design an observer that estimates the mechanical quantities from the measured electrical quantities. The gains of the observer depend on the solution of a modified Riccati equation. The estimated mechanical quantities are used in a control law that sustains power generation across a range of source rotation speeds. The proposed scheme is assessed through simulations and experiments.

Keywords: Nonlinear observers and filter design, Descriptor systems
Abstract: We consider a novel method to design a H∞ filter for a class of nonlinear systems subject to unknown inputs. First, we rewrite the system dynamics as a descriptor system. Then, we design a robust H∞ reduced-order filter to estimate both state variables and unknown inputs at the same time. Based on a Lyapunov functional, we derive a sufficient condition for existence of the designed filter which requires solving a nonlinear matrix inequality. The achieved condition is further formulated in terms of a linear matrix inequality (LMI) that is straightforward to solve by popular methods. Finally, the proposed filter is illustrated with an example.

Keywords: Nonlinear observers and filter design, Convex optimization, Lyapunov methods
Abstract: This paper presents a technique for designing rational nonlinear observers for rational nonlinear systems with guaranteed cost performance. The approach is based on a Lyapunov function that is quadratic in the estimation error and rational in the system states. The design conditions are formulated as Linear Matrix Inequalities (LMIs). If the conditions are satisfied, then the estimation error is guaranteed to asymptotically converge to zero for initial conditions on an estimated region of attraction. An optimization procedure for enlarging the region of attraction is also provided. An example is used to illustrate the results.

Keywords: Nonlinear system identification, Estimation and filtering, Fault detection and diagnosis
Abstract: The purpose of this paper is to provide a novel model to characterize the nonlinear restoring force of a marine vertical riser by using position-and-velocity-dependent polynomials. This model permits the obtaining of a specific state space representation—via the Liénard transformation—for the design of state observers that identify the structural parameters of vertical risers. The main results presented here are: (i) an approximation of the nonlinear restoring force by means of polynomials and its incorporation into a distributed parameter (DP) model, (ii) the transformation of the DP model into a Liénard system and (iii) an analysis of its observability and identifiability properties.

Keywords: Filtering and smoothing, Statistical data analysis
Abstract: In inertial human motion capture, a multitude of body segments are equipped with inertial measurement units, consisting of 3D accelerometers, 3D gyroscopes and 3D magnetometers. Relative position and orientation estimates can be obtained using the inertial data together with a biomechanical model. In this work we present an optimization-based solution to magnetometer-free inertial motion capture. It allows for natural inclusion of biomechanical constraints, for handling of nonlinearities and for using all data in obtaining an estimate. As a proof-of-concept we apply our algorithm to a lower body configuration, illustrating that the estimates are drift-free and match the joint angles from an optical reference system.

Keywords: Continuous time system estimation, Event-based control, Reachability analysis, verification and abstraction of hybrid systems
Abstract: In this work, the problem of state estimation for nonlinear continuous-time systems from discrete data is tackled in a bounded error context. One assumes that all poorly-known system variables belong to a bounded set with known bounds. Then, a self-triggered algorithm is proposed to improve the performance of the classical set-membership state estimator based on the prediction-correction procedures. In order to cope with pessimism propagation linked to the bounding methods, this algorithm triggers the correction step whenever the size of a part of the estimated state enclosure becomes greater than a time-converging threshold a priori defined by the user. The effectiveness of the proposed self-triggered algorithm is illustrated through numerical simulations.

Keywords: Grey box modelling, Nonlinear system identification
Abstract: Magnetometers and inertial sensors (accelerometers and gyroscopes) are widely used to estimate 3D orientation. For the orientation estimates to be accurate, the sensor axes need to be aligned and the magnetometer needs to be calibrated for sensor errors and for the presence of magnetic disturbances. In this work we use a grey-box system identification approach to compute maximum likelihood estimates of the calibration parameters. An experiment where the magnetometer data is highly disturbed shows that the algorithm works well on real data, providing good calibration results and improved heading estimates. We also provide an identifiability analysis to understand how much rotation is needed to be able to solve the calibration problem.

Keywords: Hybrid and distributed system identification, Nonlinear system identification, Hybrid and switched systems modeling
Abstract: In this paper, we address the problem of identifying a semi-batch olive oil esterification reactor. In fact, this reactor can be considered as a PieceWise AutoRegressive eXogenous (PWARX) system. The Chiu’s clustering procedure for the identification of PWARX systems is then applied. It consists in estimating both the parameter vector of each submodel and the coefficients of each partition. The results of the experimental validation illustrate the effectiveness of the proposed method. A comparative study with three existing approaches is also considered in this paper which shows that the proposed approach gives the best results in terms of precision.

Keywords: Software for system identification, Frequency domain identification, Filtering and smoothing
Abstract: This paper describes a software application for traffic sign recognition (TSR). The application works in four stages. First, an image preprocessing step and the detection of regions of interest (ROIs), which involves a series of steps that include transforming the image to grayscale and applying edge detection by the Laplacian of Gaussian (LOG) filter. Secondly, the potential traffic signs detection, where the ROIs are compared with each shape pattern. Thirdly, a recognition stage using a cross-correlation algorithm, where each potential traffic sign, if validated, is classified according to the data-base of traffic signs. Finally, the previous stages can be managed and controlled by a graphical user interface, which has been specially designed for this purpose. The results obtained show a good performance of the developed application, taking into account acceptable conditions of size and contrast of the input image.

Keywords: Control and estimation with data loss, Control under communication constraints, Stochastic control and game theory
Abstract: This paper considers a minimax control problem over multiple packet dropping channels. The channel losses are assumed to be Bernoulli processes, and operate under the transmission control protocol (TCP); hence acknowledgments of control and measurement drops are available at each time. Under this setting, we obtain an output feedback minimax controller, which are implicitly dependent on rates of control and measurement losses. For the infinite-horizon case, we first characterize achievable H^infty disturbance attenuation levels, and then show that the underlying condition is a function of packet loss rates. We also address the converse part by showing that the condition of the minimum attainable loss rates for closed-loop system stability is a function of H^infty disturbance attenuation parameter. Hence, those conditions are coupled with each other. Finally, we show the limiting behavior of the minimax controller under the disturbance attenuation parameter.

Keywords: Control and estimation with data loss, Distributed control and estimation, Sensor networks
Abstract: We study a networked state estimation problem for a linear system with multiple sensors, each of which transmits its measurements to a central estimator via a lossy communication network for computing the minimum mean-square-error (MMSE) state estimate. Under a general Markov packet loss process, we establish necessary and sucient conditions for the stability of the estimator for any diagonalizable system in the sense that the mean of the state estimation error covariance matrix is uniformly bounded. For the second-order systems under an i.i.d. packet loss model, the stability condition is expressed as a simple inequality in terms of open-loop poles and the packet loss rate.

Keywords: Control and estimation with data loss, Sensor networks, Estimation and filtering
Abstract: We study remote estimation in a wireless sensor network. Instead of using a conventional battery-powered sensor, a sensor equipped with an energy harvester which can obtain energy from the external environment is utilized. We formulate this problem into an infinite time-horizon Markov decision process and provide the optimal sensor transmission power control strategy. In addition, a sub-optimal strategy which is easier to implement and requires less computation is presented. A numerical example is provided to illustrate the implementation of the sub-optimal policy and evaluation of its estimation performance.

Keywords: Control and estimation with data loss, Sensor networks, Networked embedded control systems
Abstract: Security issue has become a new hotspot in cyber-physical systems (CPS) research field in recent years due to the vulnerability of CPS to security threats. This paper focuses on stealthy deception attack in remote state estimation, which is one typical attack in CPS. From the standpoint of deception attacker, we investigates how to design proper deception attack strategy to degrade the state estimation quality with communication rate constraint. We design an online attack strategy and prove that the proposed attack strategy can degrade the estimation quality. To study the effectiveness of the proposed strategy, we analyze the cost deviation, which depicts the difference between the estimation quality with and without the proposed attack strategy. Our results show that the cost deviation will be maximum when the communication rate is 0.75. A numerical example is presented to demonstrate the main results.

Keywords: Control over networks, Control under communication constraints, Control and estimation with data loss
Abstract: We investigate resilient control strategies for linear systems under Denial-of-Service (DoS) attacks. By DoS attacks we mean interruptions of communication on measurement (sensor-to-controller) and control (controller-to-actuator) channels carried out by an intelligent adversary. We characterize the duration of these interruptions under which stability of the closed-loop system is preserved. The resilient nature of the control descends from its ability to adapt the sampling rate to the occurrence of the DoS.

Keywords: Control and estimation with data loss, Stability and stabilization of hybrid systems, Hybrid and switched systems modeling
Abstract: This paper deals with the estimation of the state of linear time invariant systems for which measurements of the output are available sporadically. An observer with jumps triggered by the arrival of such measurements is proposed and studied in a hybrid systems framework. The resulting system is written in estimation error coordinates and augmented with a timer variable that triggers the event of new measurements arriving. The design of the observer is performed to achieve uniform global asymptotic stability (UGAS) of a closed set including the points for which the state of the plant and its estimate coincide. Furthermore, a computationally tractable design procedure for the proposed observer is presented and illustrated in an example.

Keywords: Disturbance rejection (linear case)
Abstract: This paper presents a model based disturbance feedback control scheme. Industrial process systems have been traditionally controlled by using relay and PID controller. However these controllers are affected by disturbances and model errors and these effects degrade control performance. The authors propose a new control method that can decrease the negative impact of disturbance and model errors. The control method is motivated by industrial practice by Fuji Electric. Simulation tests are examined with a conventional PID controller and the disturbance feedback control. The simulation results demonstrate the effectiveness of the proposed method comparing with the conventional PID controller.

Keywords: Disturbance rejection (linear case), Output feedback control (linear case), Linear systems
Abstract: We are at the dawn of a huge data explosion therefore companies have fast growing amounts of data to process. For this purpose Google developed MapReduce, a parallel programming paradigm which is slowly becoming the de facto tool for Big Data analytics. Although to some extent its use is already wide-spread in the industry, ensuring performance constraints for such a complex system poses great challenges and its management requires a high level of expertise. This paper answers these challenges by providing the first autonomous controller that ensures service time constraints of a concurrent MapReduce workload. We develop the first dynamic model of a MapReduce cluster. Furthermore, PI feedback control is developed and implemented to ensure service time constraints. A feedforward controller is added to improve control response in the presence of disturbances, namely changes in the number of clients. The approach is validated online on a real 40 node MapReduce cluster, running a data intensive Business Intelligence workload. Our experiments demonstrate that the designed control is successful in assuring service time constraints.

Keywords: Disturbance rejection (linear case), Tracking, Industrial applications of optimal control
Abstract: This paper addresses iterative learning control (ILC) for periodic systems using model predictive and optimization methods to redesign trajectories and reject periodic disturbances. Stability and optimality of these optimization methods is analysed and illustrated on simulations. The additional prospects of the optimization formulation (e.g. including energy costs, system identication) referred to the trajectory planning are accentuated. To reduce the calculation eort of the optimization algorithm a variable and adaptive sampling period is introduced. The advantages compared to classical ILC methods especially in consideration of constraints are presented.

Keywords: Switching stability and control, Decoupling problems, Structural properties
Abstract: This work deals with state feedback compensation of disturbance inputs in continuous-time switched linear systems, with the requirement that the closed-loop systems be exponentially stable under switching signals with a sufficiently large dwell-time. Constructive conditions for the problem to be solvable are shown, on the assumption that the given switched linear system has zero initial state. The effects of nonzero initial states are inspected. The theoretical background consists of both classic and novel ideas of the geometric approach, enhanced with notions specifically oriented to switched linear systems.

Keywords: Process control, Disturbance rejection (linear case), Linear systems
Abstract: This paper addresses the design of feedforward compensators for integrating processes. Initially, the disturbance rejection problem for a classic two degrees-of-freedom control scheme with feedfoward is analyzed to highlight the problem caused by integrating dynamics. Afterwards, two simple tuning rules are derived to obtain undershoot-free responses based only a desired settling time or by satisfying a tradeoff between maximum peak and settling time specifications. Finally, some simulations are shown to prove the advantages of the proposed controller.

Keywords: Disturbance rejection (linear case), Output feedback control (linear case), Controller constraints and structure
Abstract: Preview control using a fedforward imperfect forecast measurement of a disturbance signal is investigated in the context of discrete-time linear quadratic Gaussian (LQG) control. A new approach for incorporating such forecast measurements is built directly on established preview control models and results. The calculation of the optimal control gain, for which an efficient computation has already been derived, is found to be independent of the stochastic forecast measurements, implying that the optimal state estimator is where performance improvements in this problem set up occur. Most significantly, the forecast data model is shown to equip the problem with a nested information structure whereby any forecast feedforward control problem of a fixed horizon length is always equivalent to a problem with a longer horizon and infinitely unreliable forecast measurements beyond the smaller horizon length. A numerical example illustrates the effect of forecast horizon length and data quality on the closed-loop system performance.

Keywords: Motion Control Systems, Intelligent controllers, Design methodologies
Abstract: The realization of advanced control concepts for parallel kinematic machines (PKM) requires the knowledge of system states. Despite the fact that the direct kinematic problem (DKP) is not explicitly solvable for PKMs they need not to be measured directly. This can be achieved by the usage of state estimation techniques, which can also be extended for disturbance estimation and hence allow for disturbance compensation - a possibility that direct measurement devices or iterative algorithms to solve the DKP cannot provide for. Subsequently, two possibilities for the realization of adequate estimation are presented: a new approach for the state and disturbance estimation for PKMs via sliding mode techniques and a more classical approach based on continuous Kalman filtering. Their suitability for the usage within an advanced state control scheme is validated simulatively with models of different complexity of a hydraulically actuated hexapod intended to be used as a motion simulator for automotive testing purposes.

Keywords: Motion Control Systems, Decision making and cognitive processes
Abstract: In this paper, multiobjective optimization is applied to an optimal control problem for a grab-shift unloader crane. The crane is modeled as a cart-pendulum system with varying rope length and the trajectory of the grab is limited by the ship, the quay, and the crane structure. The objectives to minimize are chosen as time, energy and maximal instantaneous power. The optimal control problem is solved using a direct simultaneous optimal control method. The study shows that MOO can be an efficient tool when choosing a good compromise between conflicting objectives such as time and energy. Furthermore, navigation among the Pareto optimal solutions has proven to be very useful when a user wants to learn how the control variables interact with the process.

Keywords: Motion Control Systems, Design methodologies, Vibration control
Abstract: The paper brings experimental results and evaluation to modular disturbance observer based filtered PD and PID controller design based on theoretical results of papers published at IECON 2013. The approach is focused on achieving the possibly best filtering properties by keeping nearly constant dynamics of the setpoint responses. The developed controller applied to a positional DC motor control is evaluated for different values of a signle tuning parameter and different filter orders by using time and shape related performance measures.

Keywords: Motion Control Systems, Identification and control methods, Design methodologies
Abstract: The development of high performance controllers capable of dealing with both tracking control and disturbance rejection has attracted considerable attention. This paper investigates the two-degree-of-freedom control structures with a disturbance observer and command feedforward control, respectively, and presents a double-loop control structure. The proposed control structure allows intuitive and independent tuning of disturbance rejection as well as tracking performance. Experimental results obtained from an illustrated servo control system are given to demonstrate the effectiveness and feasibility of the double-loop control structure and design methodology.

Keywords: Motion Control Systems, Identification and control methods, Mechatronic systems
Abstract: Control of a flexible joint of an industrial manipulator using H-infinity design methods is presented. The considered design methods are i) mixed H-infinity design, and ii) H-infinity loop shaping design. Two different controller configurations are examined: one uses only the actuator position, while the other uses the actuator position and the acceleration of end-effector. The four resulting controllers are compared to a standard PID controller where only the actuator position is measured. The choices of the weighting functions are discussed in details. For the loop shaping design method, the acceleration measurement is required to improve the performance compared to the PID controller. For the mixed H-infinity method it is enough to have only the actuator position to get an improved performance. Model order reduction of the controllers is briefly discussed, which is important for implementation of the controllers in the robot control system.

Keywords: Identification and control methods, Modeling, Motion Control Systems
Abstract: The Hand Arm System of DLR is an complex mechatronic system built to approach the human in terms of dynamics and dexterity. Its fingers are antagonistically actuated by flexible tendons, resulting in a nonlinearly flexible joint. The advantage of such a design are the high dynamics but more importantly the enhanced robustness. Nonlinear control methods have been developed in the community that are, at least in principle, applicable to such systems. However, because of the complexity of some methods, most works are focusing on simulations and do not consider the practical issues arising with hardware. Such issues are, for example, the need for derivatives or the use of large matrix inversions. In this paper, we adapt the backstepping method to the specific case of an antagonistic actuation. The modeling of the mechanism is followed by the design of the controller and the work is concluded by a set of experimental results on the hand of the Hand Arm System, the Awiwi hand.

Keywords: Dynamics and control, Parameter and state estimation, Artificial pancreas or organs
Abstract: A novel state estimation scheme is proposed for use in Model Predictive Control (MPC) of an artificial pancreas based on Continuous Glucose Monitor (CGM) feedback to treat type 1 diabetes mellitus. The performance of state-feedback MPC strategies heavily depends on the initial condition of the predictions, typically characterized by a state estimator. Commonly employed Luenberger-observers and Kalman-filters are effective much of the time, but suffer limitations. Three particular limitations are tackled by the proposed approach. First, CGM recalibrations, step changes that cause highly dynamic responses in recursive state estimators, are accommodated in a graceful manner. Second, the proposed strategy is not affected by CGM measurements that are asynchronous, i.e., neither of fixed sample-period, nor of a sample-period that is equal to the controller's. Third, the proposal suffers no offsets due to plant-model mismatches. The proposed approach is based on moving-horizon optimization.

Keywords: Artificial pancreas or organs, Control of physiological and clinical variables, Decision support and control
Abstract: This paper addresses overnight blood glucose stabilization in people with type 1 diabetes using a Model Predictive Controller (MPC). We use a control strategy based on an adaptive ARMAX model in which we use a Recursive Extended Least Squares (RELS) method to estimate parameters of the stochastic part. We compare this model structure with an autoregressive integrated moving average with exogenous input (ARIMAX) structure, and with an autoregressive moving average with exogenous input (ARMAX) model, i.e. without an integrator. Additionally, safety layers improve the controller robustness and reduce the risk of hypoglycemia. We test our control strategies on a virtual clinic of 100 randomly generated patients with a representative inter-subject variability. This virtual clinic is based on the Hovorka model. We consider the case where only half of the meal bolus is administered at meal time and the case where the insulin sensitivity varies during the night. The simulation results demonstrate that the adaptive control strategy can reduce the risks of hypoglycemia and hyperglycemia during the night.

Keywords: Artificial pancreas or organs, Healthcare management, disease control, critical care, Control of physiological and clinical variables
Abstract: The clinical significance of glycemic variability in Type 1 Diabetes is asymmetric: a 40 mg/dl deviation below a nominal 110 mg/dl would represent a significant risk of hypoglycemia, while the same deviation above would not cause major concern. The Blood Glucose (BG) risk function of Kovatchev et al. [1997], which is widely used in retrospective analysis of BG data, reflects this asymmetry as a disutility function that is quadratic in the logarithm of BG. Interestingly, the prospective use of the same risk function in model-predictive control can be complicated by the requirement for on-line numerical methods in computing insulin doses that minimize risk over a given prediction horizon. In this work we propose an empirical linear model that expresses the dynamic relationship between plasma glucose and remote-compartment insulin in logarithmic coordinates, a model that (i) provides a natural representation of the multiplicative effect of insulin action on glucose clearance and (ii) is such that linear-quadratic methods applied to the model naturally reflect the BG risk function with closed-form solutions. We demonstrate the potential of this approach through the design of a Semi-Automated Insulin Advisor that uses continuous glucose monitoring to continuously estimate the patient’s metabolic state, informing both episodic correction advice prompted by the patient (for the treatment of hyperglycemia) and automated basal insulin attenuation (for prevention of hypoglycemia). In silico pre-clinical trials show favorable performance with respect to idealized “optimal” open-loop treatment, even in scenarios involving miscalibrated carbohydrate ratios and misestimated carbohydrate content in meals.

Keywords: Biomedical system modeling, simulation and visualization, Artificial pancreas or organs, Control of physiological and clinical variables
Abstract: The primary goal of a low glucose suspend system is to reduce the risk of overnight hypoglycemia (low blood glucose) in individuals with type 1 diabetes by reducing/suspending insulin infusion. We have developed a Kalman filter-based algorithm, combined with a number of safety rules, to implement a predictive low glucose suspend system that shuts off an insulin pump based on a prediction of hypoglycemia 30-70 minutes in the future. This system has been studied in over 2,000 nights in an outpatient-home environment. In this paper, based on an analysis of this data, we isolate the effects of the individual rules in part by simulating their removal from the existing data. Specifically, we decompose the basal insulin into small boluses and, using a model of insulin pharmacodynamic action (the time effect of insulin on blood glucose), alter the real data corresponding to the addition or removal of basal insulin via simulation. Our results show that limiting the total suspension to 180 minutes per night prevents excessive suspension in cases where the average calibration is an excessive 58 mg/dl, above the mean of 18 mg/dl. Further, we also show that a simple threshold algorithm that suspends below 100 mg/dl if the glucose level is flat or falling, is comparable in performance. Lastly, we show that the Kalman filter at the heart of this algorithm reduces the time spent below 70 mg/dl by 50% at the expense of a mean rise of 12 mg/dl in morning glucose levels.

Keywords: Artificial pancreas or organs, Chronic care and/or diabetes, Modeling and identification
Abstract: An artificial pancreas (AP) system with a hypoglycemia early alarm system and adaptive control system based on multivariable recursive time series models is developed. The inputs of the model include glucose concentration (GC) and physiological signals that provide information about the physical activities and stress of the patient. The stability of the recursive time-series models is guaranteed by a constrained optimization method. Generalized predictive control (GPC) is used to regulate GC. Experiments in clinical a setting illustrate the performance of the AP and compare it to open-loop regulation by the patient. Results show that the AP can regulate GC successfully and prevent hypoglycemia in spite of exercise.

Keywords: Artificial pancreas or organs, Clinical trial, Control of physiological and clinical variables
Abstract: The Artificial Pancreas (AP) is a device for closed-loop modulation of insulin infusion, aiming to maintain patient glycemia in a nearly normal range. In the last decade AP prototypes using subcutaneous glucose sensing and subcutaneous insulin delivery have been extensively studied in clinical trials involving hospitalized patients. To ensure the highest level of patient safety, these studies usually employed very structured protocols and subcutaneous glucose measurements were accompanied by frequent and accurate blood glucose measurement via intravenous sampling. Therefore, in-patient studies were usually short and patients were often unable to move freely. The next step in the AP development is testing safety and efficacy of AP in a real-life scenario, outside the hospital environment and free of strict protocol prescriptions. This paper offers a review of some technological and algorithmic challenges posed by the in-to out-patient transition and reports the authors' experience in making this transition possible. Issues related to devices, telemedicine and control algorithms are discussed and out-patient clinical results are presented in support.

Keywords: Multi-agent systems, Cooperative systems
Abstract: This paper studies the global leader-following consensus problem for a group of discrete-time linear systems with bounded controls. For each follower agent, we construct a bounded nonlinear feedback control law which uses the information of other agents obtained through multi-hop paths in the communication network. The number of hops each agent uses to obtain its information about other agents is no bigger than the largest algebraic multiplicity of the eigenvalues on the unit circle of the system matrix. We show that these control laws achieve global leader-following consensus when the communication topology is a strongly connected and detailed balanced directed graph and the leader is a neighbor of at least one follower agent.

Keywords: Continuous time system estimation, Hybrid and distributed system identification, Control over networks
Abstract: We design observers for nonlinear systems with sampled and delayed output measurements. The observers are of continuous and hybrid in nature. Based on an auxiliary integral technique, the exponential stability of the estimation errors is achieved, and the maximum allowable sampling period and the maximum delay are also given. Finally, numerical examples are provided to illustrate the design methods.

Keywords: Stochastic control and game theory, Control of networks, Multi-agent systems
Abstract: This paper presents a comprehensive modeling technique for networked evolutionary games (NEG). Three kinds of network graphs are considered, which are (i) undirected graph for symmetric games; (ii) directed graph for asymmetric games, and (iii) d-directed graph for symmetric games with partial neighborhood information. Three kinds of fundamental evolutionary games (FEGs) are discussed, which are (i) two strategies and symmetric (S-2); (ii) two strategies and asymmetric (A-2); and (iii) three strategies and symmetric (S-3). Three strategy updating rules (SUR) are introduced, which are (i) Unconditional Imitation (UI); (ii) Fermi Rule (FR); (iii) Myopic Best Response Adjustment Rule (MBRA). Then we review the fundamental evolutionary equation (FEE), and give the detailed formulation for different models. Finally, the network profile dynamics (NPD) of NEGs are investigated via their FEE.

Keywords: Stochastic control and game theory, Control of networks, Multi-agent systems
Abstract: As Part 2 of the paper "on networked evolutionary games", this paper uses the framework presented in Part 1 (Qi et al., 2014) to explore further issues about networked evolutionary games (NEGs). First, the strategy profile dynamics (SPD) is constructed from the fundamental evolutionary equations (FEEs). Using SPD, the control of NEGs are investigated. Detailed mathematical models are obtained for both deterministic and dynamic cases respectively. Then certain more complicated NEGs are explored. They are: (i) NEG with strategies of different length information, which allows some players use longer history information such as the information at t and t-1 or so; (ii) NEG with Multi-Species, which allows an NEG with various kinds of players, they play several difierent fundamental network games according to their identities. (iii) NEG with time-varying payoffs. Since payoffs determine the evolution, the network profile dynamics will be a time-varying one. These more complicated NEGs can cover more general evolutions and they generalized the method proposed in Cheng et al. (Preprint2013).

Keywords: Multi-agent systems, Distributed control and estimation, Networked robotic systems
Abstract: This paper investigates the distributed tracking control problem for multiple Lagrangian systems under a general directed graph where only a portion of the agents have access to the desired time-varying trajectory. To overcome the problem that only positions are measured, a observer is designed to estimate the velocity for each follower. By employing the estimated states, the distributed observer-based controller is proposed using only position measurements. Furthermore, the condition for the distributed tracking problem on the directed graph is derived, such that the tracking errors and observer errors semi-globally converge to zero. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

Keywords: Multi-agent systems, Distributed control and estimation, Nonlinear adaptive control
Abstract: In this paper, we consider the leader-following consensus problem for multiple uncertain rigid spacecraft systems with the attitude being represented by unit quaternion. Existing results on this problem rely on the assumption that all parameters of the rigid spacecraft system are known exactly. By employing a nonlinear distributed observer for the leader system, we first convert the leader-following consensus problem into a global adaptive stabilization problem of a well defined error system. Then, under the standard assumption that the state of the leader can reach every follower through a path, we further show that this stabilization problem is solvable by a distributed adaptive control law.

Keywords: Robots manipulators, Autonomous robotic systems, Intelligent robotics
Abstract: In this paper, the problem of in-hand dexterous manipulation has been addressed on the base of postural synergies analysis. The computation of the synergies subspace able to represent grasp and manipulation tasks as trajectories connecting suitable configuration sets is based on the observation of the human hand behavior. Five subjects are required to reproduce the most natural grasping configuration belonging to the considered grasping taxonomy and the boundary configurations for those grasps that admit internal manipulation. The measurements on the human hand and the reconstruction of the human grasp configurations are obtained using a vision-based mapping method that assume the kinematics of the robotic hand, used for the experiments, as a simplified model of the human hand. The analysis to determine the most suitable set of synergies able to reproduce the selected grasps and the relative allowed internal manipulation has been carried out. The grasping and in-hand manipulation tasks have been reproduced by means of linear interpolation of the boundary configurations in the selected synergies subspace and the results have been experimentally tested on the UB Hand IV.

Keywords: Robots manipulators, Motion Control Systems, Robotics technology
Abstract: Optimal path following for robots considers the problem of moving along a predetermined Cartesian geometric end eector path (which is transformed into a predetermined geometric joint path), while some objective is minimized: e.g. motion time or energy loss. In practice it is often not required to follow a path exactly but only within a certain tolerance. By deviating from the path, within the allowable tolerance, one could gain in optimality. In this paper, we dene the allowable deviation from the path as a tube around the given geometric path. We then search for the optimal motion inside the tube. This transforms the path following problem to a tube following problem. In contrast to the (time or energy) optimal path following problem, the tube following problem is not convex. However, we propose a problem formulation that can still be solved eciently, as will be illustrated by some numerical examples.

Keywords: Robots manipulators, Motion Control Systems, Intelligent robotics
Abstract: In this paper, we present a technique for online generation of dual arm trajectories using constraint based programming based on bound margins. Using this formulation, we take both equality and inequality constraints into account, in a way that incorporates both feedback and feedforward terms, enabling e.g. tracking of timed trajectories in a new way. The technique is applied to a dual arm manipulator performing a bi-manual task. We present experimental validation of the approach, including comparisons between simulations and real experiments of a complex bimanual tracking task. We also show how to add force feedback to the framework, to account for modeling errors in the systems. We compare the results with and without feedback, and show how the resulting trajectory is modified to achieve the prescribed interaction forces.

Keywords: Robots manipulators, Identification and control methods, Vibration control
Abstract: In contrast to the common understanding of robot link elasticity as a detrimental effect the paper presents a novel approach to beneficially exploit the intrinsic link compliance for the detection and reaction to unpredicted collisions between the robot and its environment. The paper employs an inner loop controller to rapidly attenuate structural oscillations. Next, a linear relationship between the actuator joint torques as well as the damped link surface strains is derived to accurately model the residual dynamics. The model is identified for an experimental multi-elastic link robot arm under the influence of gravity. Experimental results are provided using a generalized momentum based technique for the detection and reaction to collisions with fragile objects placed on a force sensor as well as interactions with a human.

Keywords: Telerobotics, Mobile robots, Robots manipulators
Abstract: The focus of this research is to compare the mental and theoretical evaluations of remotely controlled mobile manipulators. Evaluating the performance of control methods for mobile manipulation is challenging because both the user experience and the actual performance of the completed task need to be taken into account. How the user experiences the control law is of course very subjective and in general hard do quantify numerically. Theoretical evaluations of the performance are easier to find, but do not tell us anything about stress, frustration, and mental demand. In this paper we evaluate the mental and theoretical performance of three relatively simple approaches for controlling a mobile manipulator with a haptic device. We find that even though the performance in terms of execution time, number of fails, and robot mobility can be used to distinguish the approaches quite clearly, this difference is difficult to identify using for example the NASA-TLX test.

Keywords: Robots manipulators, Motion Control Systems, Mechatronic systems
Abstract: In human-robot interaction passive compliance is fundamental for safety, however stiffness is necessary for performance. These two factors motivate intrinsic compliance modulation in robots interacting with humans. Variable Stiffness Actuators (VSAs) allow for simultaneous position and stiffness control of a joint, therefore they have been implemented in the realization of intrinsically compliant and high performance robot arms. Most applications employ VSAs in a monoarticular structure, that is one actuator produces torque about one joint. In the biological world however, bi-articular muscles (muscles spanning two joints) play a fundamental role in motion control for humans, not only reducing link inertia, but also increasing isotropy of end effector force and compliance. In this work, a robot arm with VSAs and two interchangeable actuation structures (the traditional monoarticular and human-like biarticular) is built.The end effector intrinsic compliance in both the actuation structures is measured. Experimental results suggest that the biarticular VSA structure is more suitable for compliant robot arms.

Keywords: Industrial applications of process control, Advanced process control, Expert systems in process industry
Abstract: The mixed separation thickening process (MST) of hematite beneficiation is a strong nonlinear cascade process where the input is the underflow slurry pump speed and the outputs are the inner loop underflow slurry flow-rate (USF) and the outer loop underflow slurry concentration (USC),. During its operation, some large random disturbances generated from the flotation middling would cause the underflow slurry flow-rate (USF) to fluctuate frequently, making it outside the technologically specified range. This would decrease the flotation time and cause the unexpected level fluctuations of flotation machines, leading to the deterioration of the concentration grade. In this paper, a cascade control structure based upon the USC and USF intervals is proposed for such an un-modellable nonlinear cascade process, where the fuzzy control, rule-based reasoning, switching control and cascade control are combined together. A novel intelligent switching control method is established that includes a USF presetting unit via system static model, a fuzzy reasoning based USF set-point compensator, a retainer of USF set-point and a switching mechanism using rule based reasoning. The successful application to a real hematite concentration plant has shown the effectiveness of the proposed method. In particular, the real application has shown that the proposed method can ensure the USC, the USF and its rate fluctuation within their target range when the process is subjected to the flotation middling random disturbance. As a result, the concentration grade has been much improved.

Keywords: Industrial applications of process control, Process control applications, Control system design
Abstract: A direct procedure for deriving the component transfer functions in plants with recycle when mechanistic procedure is used in the mathematical modeling is demonstrated. Multi-loop feedback controllers are then designed for the plants with recycle incorporating recycle compensator and without recycle compensator using internal model control (IMC) parameterization. In general, it is found that controllers parameterized using the recycle compensated plant model result in closed-loop systems with better nominal and robust performance characteristics when implemented on the uncompensated plant model than when its controller is parameterized using the uncompensated plant model. Furthermore, recycle compensated plants result in overall best closed-loop systems when their feedback controllers are parameterized using the compensated plant model.

Keywords: Industrial applications of process control, Process control applications, Advanced control technology
Abstract: In the metals and mining industry flotation plays a vital role in selectively concentrating and separating valuable minerals from gangue minerals in a process slurry. The control of this has proved difficult in the past with large variations in concentrate grade and the recovery of the valuable minerals as has been experienced at FQML’s Kansanshi site. The introduction of image analysis with online grade measurements paired with the advanced process control by means of fuzzy logic has enabled fast, more precise control of the flotation process. This has allowed experienced engineers, metallurgists and operators to build ‘best practice control solutions’ and deploy this with fuzzy logic, resulting in increased grade and recoveries.

Keywords: Industrial applications of process control, Model predictive and optimization-based control, Advanced process control
Abstract: This paper describes the economical and operational benefits achieved with the use of advanced process control techniques and dynamic simulation applied to a Naphtha splitter column that is part of the Diesel blending system of Henrique Lage Refinery (REVAP) located in the state of S~ao Paulo, Brazil. The control strategy was designed to maximize production rate, respecting the operational constraints. The results include an increase in the Naphtha flow stream of the Diesel blending system and improvement of the operational stability, leading to valuable economic gains. The project is also a step forward in the use of Dynamic simulation for modelling and identification, where the simulation models have shown to be representative for the inferential variables integration, adding values to the final result.

Keywords: Industrial applications of process control, Model predictive and optimization-based control, Advanced process control
Abstract: This article presents the results of the use of advanced process control algorithm to optimize the H_2 production of Henrique Lage Refinery (REVAP) located in the state of S~ao Paulo, Brazil. The control methodology is applied to the second Hydrogen Generation Unit (HGU) of the Refinery and consists of optimizing its production in order to guarantee the hydrogen supply for the refinery's header without production loss. The designed controller had the support of dynamic simulation for disturbances modelling and identification which contributed for the improvement of the control strategy. The results in this paper represents the application of the control methodology in the real plant.

Keywords: Control system design, Industrial applications of process control, Process modeling and identification
Abstract: The need for processes to be operated under tighter performance specifications and satisfy constraints have motivated the increasing applications of nonlinear model predictive control (MPC) by the process industry. Nonlinear MPC conveniently meets the higher product quality, productivity and safety demands of complex processes by taking into account the nonlinearities and constraints in the processes. This paper examines the application of a nonlinear MPC to a multi-variable coagulation chemical dosing unit for water treatment plants. A nonlinear model of the dosing unit based on mechanistic modelling and identified by nonlinear autoregressive with external input (NLARX) estimator was developed. The simulation of the MPC based control system showed very good performance for set-point tracking and disturbance rejection. The closed loop performance of the nonlinear MPC (NMPC) compares favourably with the unconstrained and linearised nonlinear MPC (LTIMPC). The results of this study shows the suitability of nonlinear MPC for process control in the water treatment industry.

Keywords: Adaptive control, Static optimization problems, Robust estimation
Abstract: This paper proposes an alternative extremum seeking control design technique for the solution of real-time optimization control problems. The technique considers a proportional-integral approach to avoid the need for a time-scale separation in the formulation of the ESC. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The dynamics are assumed to be asymptotically stable and relative order one with respect to the objective function. The extremum-seeking problem is solved using a time-varying parameter estimation technique. Two simulation examples are used to illustrate the effectiveness of the proposed technique.

Keywords: Adaptive control, Disturbance rejection, Tracking
Abstract: This paper presents an extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for changes in the extremum of a selected performance function are available for measurement. Based on these additional measurements, an adaptive extremum seeking disturbance feedforward is designed that approximates the unknown, static mapping between the disturbances and the optimal inputs. For this purpose, orthogonal, multivariate Tchebyshev polynomials are used. The feedforward enables the extremum seeking to be conducted in the proximity of the extremum thus yielding improvements both in terms of accuracy and increased convergence speed compared to the traditional scheme. Simulation results given for a turbine driven electrical generator system demonstrate the benefits of the presented design.

Keywords: Adaptive control, Regulation (linear case), Structural properties
Abstract: An adaptive regulator is proposed for parameter dependent families of linear systems subject to changes in the zero structure. Adaptation is required for the parameter dependent family of plants but continuous adaptive regulation is limited by relative degree and right half plane zeros. A form of adaptive regulation is presented that accommodates parameter induced changes in the zero structure. The conditions for regulation divide the parameter space into disjoint sets, defining subfamilies of plants. These plant subfamilies guide controller design. Controller stability is guaranteed by Linear Matrix Inequalities (LMI) and a switch logic based on Lyapunov functions.

Keywords: Adaptive control, Time-invariant systems, Industrial applications of optimal control
Abstract: This paper develops an iterative learning control algorithm starting from some recent results in the area of predictive repetitive control. The algorithm uses receding horizon control and Laguerre functions to parameterize the future control trajectory, where the Laguerre functions reduce the number of parameters requiring optimization on-line. Stability of the predictive iterative learning control system is analyzed and conditions on error convergence are established. Supporting experimental results from application to a robot arm are also given.

Keywords: Adaptive control, Robust control, Input-to-State Stability
Abstract: We present in this paper a preliminary result on extremum seeking (ES)-based adaptive trajectory tracking control for nonlinear systems. We propose, for the class of nonlinear systems with parametric uncertainties which can be rendered integral Input-to-State stable (iISS) w.r.t. the parameter estimation errors input, that it is possible to merge together the integral Input-to-State stabilizing feedback controller and a model-free extremum seeking algorithm to realize a learning-based indirect adaptive controller. We show the efficiency of this approach on a mechatronic example.

Keywords: Optimal operation and control of power systems, Control system design, Modeling and simulation of power systems
Abstract: Forced draft evaporative cooling towers are often used for condenser cooling in small steam power plants. Conventional control strategies keep the cooling pump operating at full speed all the time, and control the cooling water temperature by modulating the tower fan speed. In this work, an energy-efficient control strategy is proposed, that minimizes the combined consumption of the tower fans and circulation pump, while also avoiding the sustained operation of the fans within some forbidden velocity range, to avoid resonance effects. The proposed strategy can be easily implemented within standard industrial control system, and is demonstrated to operate satisfactorily by a simulation study.

Keywords: Modeling and simulation of power systems, Control system design, Dynamic interaction of power plants
Abstract: Direct-energy-balance (DEB) based coordinated control strategy is critical in achieving good load following and steam pressure stability in modern power plant. Dealing with the adverse impact of perturbation in coal quality, however, is still a challenge to be resolved. In this paper, a 300MW drum-boiler unit model is first established and verified by experimental data, for the purpose of DEB and calorific value flexibility study. Unlike conventional measurement correction methods, the heat value variation is considered here as a part of the internal disturbance, which is estimated and compensated in an improved active disturbance rejection control (ADRC) structure. The proposed control strategy brings a novel design concept for a critical problem of heat variation in any coal-fired power plant. The simulation results demonstrate that the dynamic performance of main steam pressure can be significantly improved.

Keywords: Modeling and simulation of power systems, Control system design, Control of renewable energy resources
Abstract: We present a control scheme for a parabolic trough power plant that is equipped with a molten salt thermal energy storage system. We show that the multivariable control problem for the solar field can be decoupled by controlling the three way valve that splits the heat transfer fluid between the storage system and the steam generator. The steam generation cycle is regulated with inlet pressure control. The subsystems of the plant are modeled based on first principles, where we focus on the steam generation cycle. A multi-group extraction turbine with pre- and reheater is considered, which is suitable for solar power plants. We demonstrate the benefits of the proposed control scheme with a day-to-day simulation.

Keywords: Modeling and simulation of power systems
Abstract: Wide and complex process models set challenges to the modelling work. Especially the determination and tuning of the parameters of complex models are often laborious and time-consuming. The strong cross-interconnection of modelled variables also makes tuning work more difficult. Efficient tuning tools can be used to accelerate tuning work. In this paper it is proposed how a nonlinear multivariable optimisation method can be adapted for the tuning of the parameters of a dynamic circulating fluidized bed (CFB) drum boiler model, which based on the first principle laws of mass and energy. In the fine-tuning example, the tuneable variables are the gas side heat transfer coefficients and the water side local resistance coefficients of the heat exchanger model blocks into the flue gas duct.

Keywords: Modeling and simulation of power systems, Optimal operation and control of power systems, Control system design
Abstract: This paper presents dynamic models of a compressor train for an air separation unit. The model is derived from physical equations. A block library and complete models are implemented in Matlab/Simulink. Model accuracy is validated by comparing simulation results with realistic data gained from an existing industrial plant. The goal of modeling is to obtain a better understanding of the dynamic behavior in different operating modes and to get a base for design of new control strategies, textit{e.g.} a supervisory controller for minimizing power consumption of the total compressor train.

Keywords: Modeling and simulation of power systems
Abstract: Abstract: The system under scrutiny in this paper is a thermal power plant in Savannah Sugar Company Numan (SSCN) factory, Nigeria. The final power output of the plant is affected by random events such as equipment failures. Whenever such random events occur, the power plant’s output is unstable due to the electrical power demand by the factory instruments and estate. Unavailability of plant means shorter production periods and hence profit loss, these effects should be minimized, which is not a trivial matter because the plant is a highly complex system. This paper presents the principal dynamic phenomena that determine the model of boiler-turbine-generator system. The formation of the model is based on fundamental physical and thermodynamic laws. The nonlinear nature of the model is made up of differentials and algebraic equations, steam tables and the use of algebraic polynomial formulae provided the means of obtaining required data for the modelling. Raw data was taken from this power plant for period of two years. The derived model is realized in the MATLAB/SIMULINK 7.10 environment using the SSCN power plant raw input data. Validation result shows that the plant’s outputs (Steam Pressure and Electrical Power) are within acceptable range of the manufacturer’s recommended values. This model can be said to be the true representation of SSCN power plant. Keywords: model, dynamic phenomena, raw data, simulation, steam pressure, electrical power, algebraic equation, differential equations.

Keywords: Aerospace, Nonlinear predictive control, Stability of nonlinear systems
Abstract: This paper provides analytical solutions to the closed loop system kinematics for a class of almost globally asymptotically stable feedback laws on SO(n). The resulting closed loop kinematics are solved for the respective matrices as functions of time, the initial conditions and the gain parameters of the control laws. The analytical solutions provide insight into the transient dynamics of the system and can be used to prove almost global attractiveness of the identity matrix. We consider an application of these results towards model predictive control where the transient phase of the system is utilized to attempt to complete a task of secondary importance by choosing the gain parameters as functions of time and the initial conditions.

Keywords: Aerospace, Parametric optimization, Large scale optimization problems
Abstract: A novel optimisation framework using an adjoint cost sensitivity calculation, and integrating computer simulations of fluid dynamics, rigid body dynamics and control is proposed. A generic tail-fin steered missile under closed-loop control is used to show that the framework is able to generate a detailed geometrical tail-fin design and tune control performance parameters that are directly related to the range and manoeuvrability of the missile. It is shown that this new methodology is able to reduce the aerodynamic drag by 2% and the tracking error by about 3% relative to the original design.

Keywords: Control of constrained systems, Aerospace, Industrial applications of optimal control
Abstract: Abstract: In this presentation we will present the result from a implementation study performed at Saab AB, where a state-of-art Model Predictive Controller has been implemented in the simulator for the Swedish fighter aircraft, JAS 39 Gripen. The objective of this study has been to investigate a new methodology to incorporate complex state and control constraints into the design of the flight control system. Focus in this study has been put on practical aspects of robustness, safety and real time implementation.

Keywords: Nonlinear predictive control, Stability of nonlinear systems, Aerospace
Abstract: In this paper, an MPC scheme for a missile pitch axis autopilot is proposed. The scheme uses a nonlinear prediction model to give it an ability to push the controlled missile very close to its operating limits, and is stabilised through the use of an ellipsoidal terminal constraint. Tracking performance and computational load of the scheme are compared to that with a linear prediction model and other types of terminal constraint. Specifically, the choice of ellipsoidal, polytopic, or no terminal constraint is discussed. The terminally constrained nonlinear MPC scheme achieves comparable solution times to that with a linear prediction model, whilst being more aggressive to give a superior tracking performance.

Keywords: Lyapunov methods, Application of nonlinear analysis and design, Aerospace
Abstract: This paper aims to present a robust attitude control strategy with guaranteed transient performance. Firstly, a Lyapunov-based control law is designed to achieve high-performance attitude control in the absence of disturbance and parameter variation. The proposed control law uses small feedback gains to suppress the control torque at large attitude error, and increases those gains with the convergence of attitude error to accelerate the system response. The overshooting phenomenon is also avoided by imposing a restriction on the parameter selection. Then, the integral sliding mode control technique is employed to improve the robustness, where the Lyapunov-based control law is used as the equivalent control part. Theoretical analysis and simulation results verify the effectiveness of the proposed strategy.

Keywords: Aerospace, Stability of nonlinear systems, Lyapunov methods
Abstract: A new guidance law utilizing variable structure control with finite time sliding sector is proposed. First, a finite time sliding sector is defined. The finite time sliding sector is a subset of state space in which the Lyapunov function candidate satisfies the finite time stability condition, in contrast to the commonly used notion of asymptotic stability in conventional sliding sector. Then, based on the finite time sliding sector, a sliding sector control law is designed to move the system state in to the sector in finite time. The target acceleration is considered as an uncertainty. The proposed sliding sector guidance law is derived by supposing the target acceleration upper bound can be estimated as a priori. Simulation results show that the new guidance law is highly effective.

Keywords: Nonlinear system identification, Frequency domain identification, Adaptive control -applications
Abstract: Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. Presently, the linear subsystem may be parametric or not, continuous- or discrete-time. The input nonlinearity is allowed to be a memory operator of backlash type bordered by straight lines. The output nonlinearity may be noninvertible and is only supposed to be well approximated, within any subinterval belonging to the working interval, with a polynomial of unknown order and parameters. An optimal strategy is presented to identify the system nonlinearities and an identification approach is developed that provides estimates of the linear subsystem. The method involves easily generated excitation signals. Finally, all the suggested estimators are shown consistent.

Keywords: Nonlinear system identification
Abstract: Block-oriented models are often used to model nonlinear systems. They consist of linear dynamic (L) and nonlinear static (N) sub-blocks. This paper proposes a method to generate initial values for a Wiener-Hammerstein model (LNL cascade). The method starts from the best linear approximation (BLA) of the system, which provides an estimate of the product of the transfer functions of the two linear dynamic sub-blocks. Next, the poles of the BLA are assigned to both linear dynamic sub-blocks. The linear dynamics are then parameterized in terms of rational orthonormal basis functions, while the nonlinear sub-block is parameterized by a polynomial. This allows to reformulate the model to the cascade of a parallel Wiener (with parallel LN structure) and a linear dynamic system, which is bilinear in its parameters. After a bilinear optimization, the parallel Wiener part is projected to a single-branch Wiener model. The approach is illustrated on a simulation example.

Keywords: Nonlinear system identification, Estimation and filtering
Abstract: In this work, a non-iterative identication approach is presented for estimating a single-input single-output Wiener model, comprising an innite impulse response discrete transfer function followed by static non-linearity. Global orthogonal basis functions and orthogonal Hermite polynomials are used as expansion bases for the linear subsystem and the non-linearity, respectively. A multi-index based method is used to transform the non-convex optimization over the parameter values into an over-parametrized linear regression. A singular value decomposition based method is then used to project the result of the over-parametrized linear regression onto the class of Wiener models, each comprising a linear element followed by a memoryless non-linearity. The advantages obtained by using orthogonal polynomials are illustrated using a series of simulation examples.

Keywords: Nonlinear system identification, Closed loop identification, Time series modelling
Abstract: Many practical applications, such as the fuel control of a gas turbine engine, can be modeled by a feedback connection of a linear controller in series with a Hammerstein system, where the nonlinearity provides a representation of the control element or actuator. An iterative gradient-based method is proposed to simultaneously identify the nonlinear fuel valve characteristic and a low-order linear plant model in gas turbine applications that leverages a priori knowledge of both the nonlinearity and engine dynamics. The identification is a nonlinear prediction error minimization method in a closed-loop Hammerstein model framework. It is applied to data from a high-fidelity simulation of a 5 megawatt Taurus™ 60 industrial gas turbine.

Keywords: Nonlinear system identification, Frequency domain identification, Nonparametric methods
Abstract: Block oriented nonlinear models capture the dynamics of a nonlinear system with linear dynamic sub-systems (L), the nonlinear behavior is modelled using static nonlinear sub-blocks (N). In this paper we study the generation of initial estimates for the linear dynamic blocks of a Wiener-Hammerstein system that has a cascaded LNL structure. While it is very easy to identify the product of the transfer functions of the first and last dynamic block using linear system identification methods, it turns out to be very difficult to split the global dynamics over these individual blocks. In this paper a method is proposed that allows the poles of the best linear approximation to be assigned to the first or second linear block. Once this split is made, it is shown in the literature that the remaining initialization problem can be solved much easier than the original one. The first step of the method is the design of a special random phase multisine excitation, using pair-wise coupled random phases. Next, a modfied best linear approximation will be estimated on a shifted frequency grid. It will be shown that this procedure shifts the poles and zeros of the first linear sub-block with a known frequency offset, while those of the second sub-block are not changed. The shifted poles and zeros result in a transfer function with complex coefficients that can be identified using a modified frequency domain estimation method. This results in a simple initialization method, based on a linear system identification step.

Keywords: Nonlinear system identification
Abstract: Block-oriented models are often used to model a nonlinear system. This paper presents an identification method for parallel Wiener-Hammerstein systems, where the obtained model has a decoupled static nonlinear block. This decoupled nature makes the interpretation of the obtained model more easy. First a coupled parallel Wiener-Hammerstein model is estimated. Next, the static nonlinearity is decoupled using a tensor decomposition approach. Finally, the method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.

Keywords: Optimal operation and control of power systems, Control system design, Modeling and simulation of power systems
Abstract: Power electronics plays an important role in the control and conversion of modern electric power systems. In particular, to integrate various renewable energies using DC transmissions and to provide more flexible power control in AC systems, significant efforts have been made in the modulation and control of power electronics devices. Pulse width modulation (PWM) is a well developed technology in the conversion between AC and DC power sources, especially for the purpose of harmonics reduction and energy optimization. As a fundamental decoupled control method, vector control with PI controllers has been widely used in power systems. However, significant power loss occurs during the operation of these devices, and the loss is often dissipated in the form of heat, leading to significant maintenance effort. Though much work has been done to improve the power electronics design, little has focused so far on the investigation of the controller design to reduce the controller energy consumption (leading to power loss in power electronics) while maintaining acceptable system performance. This paper aims to bridge the gap and investigates their correlations. It is shown a more thoughtful controller design can achieve better balance between energy consumption in power electronics control and system performance, which potentially leads to significant energy saving for integration of renewable power sources.

Keywords: Optimal operation and control of power systems, Control system design, Modeling and simulation of power systems
Abstract: This paper proposes resonant finite control set (FCS)-predictive control of a two level power converter. In the stationary reference frame, firstly the traditional FCS-predictive control system is shown to be closed-loop feedback proportional control system. In the presence of constraints, the objective function is a weighted error function between the desired optimal and candidate voltage control signals. The weighting coefficient in the objective function is the ratio between the sampling interval and the inductance. Secondly, it is proposed in this paper to design a discrete-time resonant controller for eliminating the sinusoidal error using a cascaded control system structure. The proposed method is simple in design and implementation with an analytical solution for the resonant controller gain. Experimental results are used to demonstrate the efficacy of the proposed approach and much improved closed-loop control performance.

Keywords: Control system design, Application of power electronics, Control of renewable energy resources
Abstract: This paper presents the modeling and control of a multilevel DC/DC bidirectional converter suitable for medium voltage and power applications, with a special interest in renewable applications. The multilevel converter is based on Dual Active Bridge (DAB) and an average dynamic model of it. Two different controls are addressed in this paper in oder to achieve the overall control of the DC/DC converter, a bilinear control based on quadratic feedback control, and nonlinear control based in Lyapunov theory. System's controllability and stability are studied, in particular by the analysis of zero dynamics. The performance of both controllers are illustrated by computer simulations, and concluding remarks analyze theirs characteristics and then comparison.

Keywords: Application of power electronics, Control of renewable energy resources, Control system design
Abstract: Three phase inverters are commonly used to transfer energy from a source to the power grid. The quality of the power delivered to the grid, can be ensured via the use of an output LC filter. However inserting an output filter to an inverter circuitry would introduce new challenges to the controller design due to the additional parametric uncertainties imposed. In this study we present a new model based robust controller for a three phase inverter with output LC filter under the constraint that the output filter parameters are not exactly known. Specifically, textit{d-q} reference frame model of an inverter with output LC filter is used to develop a nonlinear robust controller that ensures the 3-phase output voltage with desired amplitude and frequency and with lowest harmonic distortion. Stability of the proposed method and the boundedness of the closed--loop system, is established via Lyapunov based tools in conjunction with a robust backstepping procedure. Simulation results are given in order to demonstrate performance and effectiveness of the proposed robust controller.

Keywords: Smart grids, Application of power electronics, Power systems stability
Abstract: The purpose of this paper is to apply the Immersion & Invariance and Passivity-based Control to a dc-dc power converter driving a nonlinear load. Experimental results are shown in order to evaluate the performance of the proposed control techniques. Another goal is to find a model for a dc microgrid (load sharing problem) and to show some stability issues.

Keywords: Control system design, Power systems stability, Modeling and simulation of power systems
Abstract: This paper presents a new control scheme to regulate the DC voltage of a VSC terminal. It significantly simplifies the control design process itself and in general also results in an uncomplicated and efficient control architecture. Firstly, an equivalent state space model established in a synchronous dq reference frame is presented. Next we split the overall system into two interconnected subsystems and suppose that different dynamics can be imposed on them under their own sub-controllers. During the design process, we use a reduced model obtained by singular perturbation techniques instead of the original system. The developed control law is actually based on this reduced model and an explicit division of time scales. Simulation results clearly demonstrate that the controller based on the reduced model can regulate the DC voltage with good performances and in the meantime, the real DC voltage can be well approximated by its reduced model.

Keywords: Nonlinear adaptive control, Continuous time system estimation, Adaptive control -applications
Abstract: In this paper we propose a new finite-time state observer for a nonlinear space launch vehicle with flexible dynamics and uncertain parameters. Indeed, flexible states are required to ensure nonlinear control objectives of both reference path tracking and bending mode damping. Our main contribution is to show that it is enough to observe a sublinear uncertain system to ensure a finite-time convergence of the whole state. For that purpose, a Luenberger observer is mixed with a parameter and initial state estimator, based on algebraic estimation tools. Closed loop simulations show the effectiveness of the observer in combination with a backstepping control design (extended to flexible systems).

Keywords: Nonlinear adaptive control, Autotuning, Continuous time system estimation
Abstract: This paper investigates a self adaptation mechanism regarding the rate with which new measurements have to be incorporated in Moving-Horizon state estimation algorithms. This investigation can be viewed as the dual of the one proposed by the author in the context of real-time model predictive control. An illustrative example is provided in order to assess the relevance of the proposed updating rule

Keywords: Nonlinear adaptive control, Nonlinear system identification
Abstract: In this paper the problem of adaptive observer design in the presence of disturbances is studied, and an augmented adaptive observer is proposed. First, the H_{infty} gain of a conventional adaptive observer is estimated, which characterizes the effect of disturbances on output errors. Next, it is shown that if the disturbance is ``matched'' in the plant equations, then it is possible to introduce additional sliding-mode feedback, dependent on the plant output, improving the accuracy of observation. Simulation results confirm the improvement.

Keywords: Switching control, Continuous time system estimation, Identification for control
Abstract: This paper addresses the problem of state and parameter estimation for the class of affine systems in the state space representation. The method does not require a specific state representation of the system and consists of designing a switched observer that, under certain conditions given in the paper, allows for the state and parameter estimation errors to converge to zero. Assuming that the parameters to be estimated belong to a given polytope, the idea of the method is to recast the parameter estimation problem as a switching rule design for an auxiliary switched system whose matrices at the equilibrium correspond to the matrices of the system to be estimated. A guaranteed cost is used in the design and the switching rule is based on a max composition of a set of quadratic functions of the observation error. The method is simple and has low computational cost. The main disadvantage regards the amount of information that is needed to have both state and parameters estimated simultaneously. The case when there is no parameter to estimate the method reduces to a standard Luenberger observer with guaranteed cost.

Keywords: Adaptive system and control, Nonlinear adaptive control
Abstract: The problem of state observation, based on sampled output measurements, is addressed for a class of nonlinear state-affine systems. The difficulty lies in the fact that the state equation involve the undisturbed output with unknown parameters. A hybrid adaptive observer is designed and formally analysed. Sufficient conditions are established for the observer to be exponentially convergent in the absence of output disturbance. The sufficient conditions include a usual persistent excitation condition as well as an explicit upper bound on the admissible sampling time. In presence of nonzero disturbance, the -gain between the disturbance input and the (state and parameter) estimation errors is explicitly established in function of the design parameters.

Keywords: Nonlinear adaptive control, Adaptive control -applications
Abstract: This study deals with the problem of controlling DC-to-DC switched power converter of boost type associated with a fuel cell generator. The system is modelled using the PWM technique. The aim is to tightly regulate the output voltage of the converter to a desired reference. The voltage-current characteristic is subject to parameter uncertainty. The system load is in turn time-varying. A nonlinear adaptive controller is designed using the Lyapunov approach, based on the nonlinear model of the system. The controller is formally shown to meet its control objectives.

Keywords: Control under computation constraints
Abstract: We show how to synthesize simple, yet well-performing feedback strategies that mimic the behavior of optimization-based controllers, such as those based on model predictive control (MPC). The approach is based on employing regression trees to derive dependence of real-valued control inputs on measurements. Quality of classical regression policies is improved by finding, simultaneously, optimal affine splits and optimal local affine regressors. We furthermore illustrate how to refine the local regressors such that the overal feedback strategy guarantees satisfaction of input constraints. The main advantage of the proposed regression-based control strategy stems from its fast implementation even on very simple hardware. The approach is demonstrated on a case study that assumes control of temperature in a one-zone building. Here, the data used in the learning process are generated by MPC. We show that the simple feedback law attains almost the same level of performance as the complex MPC controller.

Keywords: Adaptive system and control
Abstract: In the building climate control area, the linear model predictive control (LMPC) - nowadays considered a mature technique - benefits from the fact that the resulting optimization task is convex (thus easily and quickly solvable). On the other hand, while nonlinear model predictive control (NMPC) using a more detailed nonlinear model of a building takes advantage of its more accurate predictions and the fact that it attacks the optimization task more directly, it requires more involved ways of solving the non-convex optimization problem. In this paper, the gap between LMPC and NMPC is bridged by introducing several variants of linear time-varying model predictive controller (LTVMPC). Making use of linear time-varying model of the controlled building, LTVMPC obtains predictions which are closer to reality than those of linear time invariant model while still keeping the optimization task convex and less computationally demanding than in the case of NMPC. The concept of LTVMPC is verified on a set of numerical experiments performed using a high fidelity model created in a building simulation environment and compared to the previously mentioned alternatives (LMPC and NMPC) looking at both the control performance and the computational requirements.

Keywords: Identification for control, Frequency domain identification
Abstract: Efficient and accurate modeling techniques have become increasingly important in the context of model predictive control (MPC) for building automation. For modeling single-input single-output systems such as a ventilated room (with either constant air flow or constant supply temperature), system identification methods are promising and provide insight into the physical nature of these systems. In collaboration with the company SAUTER an office type test room was instrumented for experiments. Three models for the room were derived: i) an empirical transfer function estimate (ETFE) derived from a pseudo-random binary sequence input signal; ii) an ETFE derived from a relay feedback approach; iii) a physics based resistance-capacitance (RC) model.

Using additional validation data, the different models and approaches were compared in terms of accuracy and efficiency. The effect of air mixing dynamics was demonstrated in an additional experiment to be one of the main differences between the experimentally identified and the RC model. An additional pole can be added to the RC model in order to compensate for the differences.

Keywords: Complex system management, Randomized methods, Control over networks
Abstract: Heating Cooling and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.

Keywords: Autotuning, Fault detection and diagnosis, Adaptive control -applications
Abstract: The path of an innovative technology from the research stage to the validation in real test beds and subsequent commercialization and wider deployment may not always be straightforward. Multiple domain-specific constraints need to be considered and properly addressed. In case of the advanced control solutions for building’s Heating Ventilation and Air Conditioning (HVAC) systems, one needs to keep in mind limitations given by the legacy control hardware, typical instrumentation levels, and overall cost-to-benefit ratio. The paper is written from the corporate R&D perspective and discusses methodological and practical aspects of design, validation and implementation of advanced control solutions in the application domain of commercial buildings. All issues are illustrated based on experience from the development of two different technologies: an embedded solution for control performance monitoring and a cloud-based supervisory control for HVAC systems.

Keywords: Modeling for control optimization, model predictive control for distributed parameter systems, Time-invariant systems
Abstract: Computational fluid dynamics (CFD) is used to generate dynamic response data for the thermal and moisture distributions in a restaurant. We present a procedure for developing a reduced order model (ROM) by formulating a linear time-invariant (LTI) model that approximates the perturbations recorded in the CFD responses. An overall system model would include the ROM model of the indoor-air environment along with models for the dynamics of the building envelope and for the mechanical equipment. Here we include a simple model for the building envelope and demonstrate an optimal control problem.

Keywords: Control problems under conflict and/or uncertainties, Modeling for control optimization, Uncertainty descriptions
Abstract: This paper presents a method to address the pursuit-evasion problem which incorporates the behaviors of the opponent, in which a continuous-time Markov decision process (CTMDP) model is introduced, where the significant difference from Markov decision process (MDP) is that the influence of the transition time between the states is taken into account. By introducing the concept of situation, the probabilities addressing average behaviors are obtained. Furthermore, these probabilities are introduced to construct the transition matrix in the CTMDP. A policy iteration method for solving the CTMDP is also given. To demonstrate the CTMDP method for pursuit-evasion, examples in a grid environment are computed. The CTMDP-based method presented in this paper offers a new approach to pursuit-evasion modeling and may be extended to similar problems in the sequential decision process.

Keywords: Control problems under conflict and/or uncertainties, Adaptive control, Real-time control
Abstract: Self-optimization enables technical systems to adapt their behavior to varying environmental conditions and changing system settings. Objective functions serve as evaluation criteria for the system behavior. In this paper we propose a hierarchical control approach of an objective-based Pareto controller. We separate the processes of optimization and control. First, we use multiobjective optimization to compute a Pareto set of optimal system configurations offline. This set serves as a data base for the Pareto controller in an upper control loop, which is designed secondly. The goal of the Pareto controller is to drive the system toward a desired relative weighting of the objective values, despite unknown and varying environmental disturbances. Furthermore, the Pareto controller has to cope with limits of the objective values. For that, we propose a calculation of a reference value, which is based on an approximated Pareto front of the current situation. The Pareto controller selects suitable configurations out of the Pareto set and applies them to a lower control loop. A test rig of an active suspension system affected by unknown track excitations serves as application example. Finally, we give some results with the test rig that validate our approach and point out the advantages.

Keywords: Control problems under conflict and/or uncertainties, Differential or dynamic games, Optimal control theory
Abstract: In this paper a linear dynamical system controlled under the conditions of disturbances and with control delays is considered. The Euclidean norm of a set of the system motion deviations at given instants of time from the origin is minimized. A typical case of the location of the indicated instants of time is studied. Within the game-theoretical approach the problem of calculating the value of the optimal guaranteed result and constructing a positional (closed-loop) control law that ensures this result is posed. For this problem, an effective solution procedure based on the recurrent construction of upper convex hulls of auxiliary program functions is elaborated. Results of numerical simulations are given.

Keywords: Control problems under conflict and/or uncertainties, Nonlinear predictive control, Asymptotic stabilization
Abstract: We propose a dual-objective MPC formulation in which the dual objective is the convex combination of an economic- and regulatory stage cost, using a specially formulated state- and input-dependent dynamic weight function. The purpose of the dynamic weight function is to promote increased economic performance while ensuring asymptotic stability for the economically optimal steady-state setpoint. First, sufficient conditions are derived for which the dual-objective MPC value function is a Lyapunov candidate function. Next, we propose a weight function which satisfies these conditions. We implement the combined economic and regulatory MPC, with proposed weight function, in an isothermal CSTR numerical case study, which illustrates how economics are emphasized during process transients, while retaining stability by emphasizing the regulatory cost close to the setpoint.

Keywords: controllability and observability of distributed parameter systems, motion planning for distributed parameter systems
Abstract: We derive in a straightforward way the exact controllability of the 1-D Schrodinger equation with a Dirichlet boundary control. We use the so-called flatness approach, which consists in parameterizing the solution and the control by the derivatives of a "flat output". This provides an explicit control input achieving the exact controllability in the energy space. As an application, we derive an explicit pair of control inputs achieving the exact steering to zero for a simply-supported beam.

Keywords: port Hamiltonian distributed parameter systems, Tracking, control of hyperbolic systems and conservation laws
Abstract: The boundary feedforward control problem for a class of distributed-parameter port-Hamiltonian systems in one spatial dimension is addressed. The considered hyperbolic systems of two conservation laws (with dissipation) are discretized in the spatial coordinate using an energy-based, structure preserving discretization scheme. The resulting finite-dimensional approximate state representation has a feedthrough term which allows to directly express the differential equation for the inverse dynamics. The inverse system needs to be solved in order to determine the control inputs for given desired output trajectories. For non-collocated pairs of boundary in- and outputs the magnitude of dissipation determines whether the inverse discretized models are stable or not. In the unstable case, the problem at hand can be attacked with classical approaches for the dynamic inversion of non-minimum phase systems.

Keywords: motion planning for distributed parameter systems, infinite-dimensional multi-agent systems and networks, UAVs
Abstract: The differential flatness of the one-dimensional heat equation controlled at each boundary is used to propose, through the Hopf-Cole transform, a finite-time motion planning for multi-agent systems. The proposed paths, which are solutions of Burgers' viscous equation, are smooth while non analytical in initial and final equilibria. The paths may respect various geometrical constraints allowing them to be used for different purposes.

Keywords: motion planning for distributed parameter systems, thermal and process control applications of distributed parameter systems, control of heat and mass transfer systems
Abstract: Model-based trajectory planning is addressed for the realization of desired temperature trajectories for a deep drawing tool. Based on the distributed-parameter system description of the tool a finite element approximation is deduced which serves as basis for a systematic flatness-based design of a feedforward control. Trajectory assignment is considered by taking into account the minimization of a process-related objective function. Simulation and experimental data obtained from a fully equipped forming tool are presented for pure feedforward control and a two-degree-of-freedom control concept amending the feedforward part by an output error feedback controller. These confirm the applicability of the design technique and the tracking performance.

Keywords: motion planning for distributed parameter systems, Distributed nonlinear control, Energy systems
Abstract: Extensive research has been conducted to find operating scenarios that optimize the plasma performance in nuclear fusion tokamak devices with the goal of enabling the success of the ITER project. The development, or planning, of these advanced scenarios is traditionally investigated experimentally by modifying the tokamak’s actuator trajectories, such as the auxiliary heating/current-drive (H&CD) scheme, and analyzing the resulting plasma evolution. In this work, a numerical optimization algorithm is developed to complement the experimental effort of advanced scenario planning in the DIII-D tokamak. Two properties related to the plasma stability and performance are the safety factor profile (q-profile) and the normalized plasma beta (beta_N). The optimization algorithm goal is to design actuator trajectories that steer the plasma to a target q-profile and plasma beta_N, such that the achieved state is stationary in time, subject to the plasma dynamics (described by a physics-based, nonlinear, control-oriented partial differential equation model) and practical plasma state and actuator constraints, such as the maximum available amount of H&CD power. This defines a nonlinear, constrained optimization problem that we solve by employing sequential quadratic programming. The optimized trajectories are then tested through simulation with the physics-based model and experimentally in DIII-D.

Keywords: Stability of nonlinear systems, stability of distributed parameter systems, Time-varying systems
Abstract: The asymptotic behaviour is studied for a class of non-linear distributed parameter time-varying dissipative systems. This is achieved by using time-varying infinite-dimensional Banach state space description. Stability criteria are established, which are based on the dissipativity of the system in addition to another technical condition. The general development is applied to semi-linear systems with time varying nonlinearity. Stability criteria are extracted from the previous conditions. These theoretical results are applied to a class of transport-reaction processes. Different types of nonlinearity are studied by adapting the criteria given in the early portions of the paper

Keywords: Embedded computer architectures, Logical design, physical design, and implementation of embedded computer systems, Knowledge-based control
Abstract: The construction of augmented autonomy controllers is hampered by the growing complexity of their implementation. This complexity is a source of faults that eventually derive in controller malfunction. A strategy for improving resilience is the use of metacontrollers to monitor and manage the control system operation. Instead of following the usual strategy of instantiating an specific architectural design, the controller design strategy described in this paper addresses the synthesis of custom controller architectures by means of the application of composable design patterns. The patterns presented in this paper are inspired in the cognitive performance of resident engineers, constituting the core resources of a pattern language for a new class of adaptive and robust control architectures for autonomous systems. An implementation is demonstrated in the controller of an autonomous mobile robot.

Keywords: Embedded computer control systems and applications, Real-time algorithms, scheduling, and programming, Embedded computer architectures
Abstract: Cloud computing is an application hosting model providing the illusion of infinite computing power. However, even the largest datacenters have finite computing capacity, thus cloud infrastructures have experienced overload due to overbooking or transient failures.

The topic of this paper is the comparison of different control strategies to mitigate overload for datacenters, that assume that the running cloud applications are cooperative and help the infrastructure in recovering from critical events. Specifically, the paper investigates the behavior of different controllers when they have to keep the average response time of a cloud application below a certain threshold by acting on the probability of serving requests with optional computations disabled, where the pressure exerted by each request on the infrastructure is diminished, at the expense of user experience.

Keywords: Embedded computer control systems and applications, Programmable logic controllers, Embedded computer architectures
Abstract: This paper presents the redundancy architecture of the Programmable Logic Controller called the Safety PLC(SPLC) for the safety functions such as reactor protection in a nuclear power plant. The architecture of the SPLC is designed to switch flexibly redundancy model between the Dual Modular Redundancy(DMR) and Triple Modular Redundancy(TMR). Using this flexible redundancy architecture, the controller can be optimally congured to the application area, and the reliability and availability of overall system can be increased because redundancy model varies as failures occur. The operating system of the SPLC is also specially designed to guarantee the strict real-time operation using the non-preemptive state-based scheduler and the supervisory task that manages timing violation of each task. The data communication of the SPLC uses the deterministic state-based protocol based on Guaranteed Time Slot(GTS) protocol. The reliability analysis results show that MTTF of SPLC is 41,630 hours, which is about 15% and 50% more reliable than the TMR or DMR architecture, respectively.

Keywords: Real-time algorithms, scheduling, and programming, Embedded computer architectures, Fuzzy and neural systems relevant to control and identification
Abstract: Spiking Neural Networks (SNN) are a popular field of study. For a proper development of SNN algorithms and applications, special encoding methods are required. Signal encoding is the first step since signals need to be converted into spike trains as the primary input to an SNN. We present an efficient frequency encoding system using receptive fields. The proposed encoding is versatile and it can provide simple image transforms like edge detection, spot detection or removal, or Gabor-like filtering. The proposed encoding can be used in many application areas as image processing and signal processing for detection and classification.

Keywords: Embedded computer control systems and applications, Embedded computer architectures
Abstract: Many Wireless Sensor Network (WSN) services rely on time synchronisation, and since WSN nodes frequently operate in varying ambient conditions, adequate compensation for thermally-induced clock drift is needed. Also, WSN software -- including both applications and operating systems -- has to run on heterogeneous hardware, which requires synchronisation solutions that can be deployed with no or minimal tuning in the field. In a previous paper we proposed a joint clock synchronisation and skew/drift compensation scheme entirely realised as a decentralised discrete-time LTI control system, achieving high precision and low-power operation, and compensating thermal drift without temperature measurements. Here, conversely, we concentrate on generality with respect to hardware, and tuning-free deployment. We show that such requirements practically rule out feedforward thermal compensation (which further backs up our previous proposal), devise a methodology to tackle the problem, and present an application demonstrated by experimental tests.

Keywords: Real-time algorithms, scheduling, and programming, Embedded computer architectures, Embedded computer control systems and applications
Abstract: Despite the substantial interest in UAVs, little attention has been paid to developing realistic simulators which represents a complete mathematical model of the UAV and the external variables, that is the reason why we present a Multi-Aerial Vehicle 3D Simulator (MAV3DSim) that will help in the development of new controllers. It is multi-aerial because it has the possibility to simulate a xed-wing or a quadrotor UAV. The multiple display options of the MAV3DSim provides a big help in the development of the controllers. Finally we present two previous implemtation of dierent controllers, for the xed-wing a path following strategy is implemented while for the quadrotor an attitude and altitude controller, both strategies are implemented on the same simulation enviroment.

Keywords: Robust control (linear case), Robust controller synthesis
Abstract: The intention of the paper is to demonstrate the beauty of geometric interpretations in robust control. We emphasize Klein's approach, i.e., the view in which geometry should be defined as the study of transformations and of the objects that transformations leave unchanged, or invariant. We demonstrate through the examples of the basic control tasks that a natural framework to formulate different control problems is the world that contains as points the equivalence classes determined by the stabilizable plants and whose natural motions are the Mobius transforms. Transformations of certain hyperbolic spaces put light into the relations of the different approaches, provide a common background of robust control design techniques and suggest a unified strategy for problem solutions. Besides the educative value a merit of the presentation for control engineers might be a unified view on the robust control problems that reveals the main structure of the problem at hand and give a skeleton for the algorithmic development.

Keywords: Robustness analysis, Descriptor systems, Convex optimization
Abstract: Robust stability analysis is investigated for discrete-time linear systems with rational dependency with respect to polytopic type uncertainties. Two type of uncertainties are considered: constant parametric uncertainties and time-varying switching uncertainties. Results are in LMI formalism and proofs involve parameter-dependent, quadratic in the state, Lyapunov functions. The new proposed conditions are shown to extend and merge two important existing results. Conservatism reduction is tackled via a model augmentation technique. Numerical complexity is contained by exploiting the structure of the models with respect to the uncertainties.

Keywords: Robust controller synthesis, stability of distributed parameter systems
Abstract: The paper deals with the robust output feedback discrete control of continuous-time linear plants with arbitrary relative degree under parametric uncertainties and external bounded disturbances with quantized output signal. The parallel reference model (auxiliary loop) to the plant is used for obtaining the uncertainties acting on the plant. The proposed algorithm guarantees that the output of the plant tracks the reference output with the required accuracy.

Keywords: Robust controller synthesis, Convex optimization, Parametric optimization
Abstract: In this paper a novel approach for H ∞ controller design for linear parameterizable controllers is presented. The approach uses the generalized Nyquist stability criterion to find the parameters of linear parameterized controllers for Multi-Input Multi-Output (MIMO) systems. The main advantage of the proposed approach is that the generalized plant does not have to be diagonally dominant and that there is no need for a desired open-loop response function. By constraining the Nyquist curve from certain parts in the frequency domain, controller parameters that guarantee stability and performance of the closed-loop system can be found. The method is successfully applied to two cases involving a double-mass-spring-damper system. In the first case only controller parameters are optimized and in the second case both structural and controller parameters are optimized.

Keywords: Robust linear matrix inequalities, Robustness analysis, Robust controller synthesis
Abstract: Variants of the S-procedure provides an important tool in robust stability and robust performance design, mainly in the state space framework based on linear matrix inequalities. The aim of this paper is to formulate three classical results, the basic S-lemma, the Finsler's lemma and a variant of the full-block S-procedure, in the input output framework, i.e., in the infinite dimensional setting. While the presented results widen the scope of the applicability of these fundamental tools, they also provide a link to the theory of indefinite spaces, as an efficient framework to handle robust control problems.

Keywords: Process supervision, Discrete event systems in manufacturing, Modeling of manufacturing operations
Abstract: There are many situations where manual intervention in automated systems, such as flexible manufacturing systems, is inevitable. Commonly the control of a manufacturing system is implemented in terms of operations, and sequences thereof, and these operations can typically be run manually. However, running the operations in arbitrary order may lead to situations such as blocking or collisions, from which it its hard or even impossible to recover or to resume automatic execution. This paper describes an implementation of an operator interface for robust manual control of a manufacturing system, where the operator is aided not to manually drive the system into a state that breaches the system requirements. Hence, blocking and collisions are avoided, and automatic mode can always be resumed. From a model of the manufacturing system based on self-contained operations, each of which is identified in terms of events with preconditions, a supervisor is calculated by use of the Supervisory Control Theory framework. From this supervisor additional preconditions are extracted for each operation. The operations with the extended preconditions are then ported to an operator interface which allows manual control of the production cell by dynamically guiding the operator to only those operations for which the extended preconditions are satisfied.

Keywords: Production & logistics over manufacturing networking, e-Manufacturing technologies and facilities, Intelligent manufacturing systems
Abstract: Augmented Reality (AR) applications are becoming mature technologies for the use in manufacturing systems. Their very innovative character together with the variety of devices are now forcing production managers and researchers to analyse their application from technological to organizational point of view. The aim of the paper is to propose a multi-criteria model which integrates technical and organizational metrics to provide reliable decision support system for analysing the application of AR technologies in manufacturing. The proposed model applies the AHP (Analytic Hierarchy Process) method for integrating effectively technological and organizational factors which will contribute to analyse how an AR system could be effectively applied in the manufacturing sector.

Keywords: Modeling of assembly units, Enterprise modelling and BPM, Modeling of manufacturing operations
Abstract: This research work deals with assembly line design and its performance measurement at early stage of a project. The objective is to support decision process on strategical choices regarding the future system configuration. In this perspective, simulation and digital factory concept represent tools and related methodologies to support manufacturing design process, taking into account the whole system lifecycle. However, these model based approaches usually focus on detailed design and less on conceptual design where key decisions are made based on preliminary analysis and usually few available information. In this paper, conceptual models of an assembly line are presented to support the designer’s choices and solution analysis. Business process modeling and simulation are explored to address this issue. Several scenarios and preliminary ideas on process layout and structure are evaluated.

Keywords: Modeling of manufacturing operations, Process supervision, Quality assurance and maintenance
Abstract: In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

Keywords: Logistics in manufacturing, Modeling of manufacturing operations
Abstract: In hotly competitive international industrial and economic environments, supply chain coordination (SCC) is one of active research topics in production and operation management. In this research work, we present a new consensus-based fuzzy TOPSIS approach for supply chain coordination problem. It is formulated as a multi-criteria group decision making (MCGDM) problem and solved by combining consensus-based possibility measure with TOPSIS method in a fuzzy environment. To demonstrate the applicability of the proposed approach, a simple example of robot selection problem is presented and the numerical results analyzed. Moreover, using the Levenshtein distance, the deviation between individual solutions and group solution is analyzed.

Keywords: Modeling of assembly units, Production planning and control, Assembly and disassembly
Abstract: The aim of this paper is to propose models to adapt and parameterize the Material Requirement Planning (MRP) approach under lead time uncertainty. Multi-level assembly systems with one type of finished products and several types of components aer studied. Each component has a fixed unit inventory cost and the finished product has a backlogging cost per unit of time. The lead times of components are discrete random variables, and the costumer’s demand of the finished product is known. A general mathematical model for supply planning of multi-level assembly systems is presented. A Genetic Algorithm (GA) method is proposed to minimize the sum of the average inventory holding cost for components and the average backlogging and inventory holding costs for the finished product.

Keywords: Biomedical system modeling, simulation and visualization
Abstract: HIV disease is well-controlled by the use of combination antiviral therapy (cART), but lifelong adherence to the prescribed drug regimens is necessary to prevent viral rebound and treatment failure. Populations of quiescently infected cells form a “latent pool” which causes rapid recurrence of viremia whenever antiviral treatment is interrupted. A “cure” for HIV will require a method by which this latent pool may be eradicated. Current efforts are focused on the development of drugs that force the quiescent cells to become active. Previous research has shown that cell-fate decisions leading to latency are heavily influenced by the concentration of the viral protein Tat. While Tat does not cause quiescent cells to become active, in high concentrations it prevents a newly infected cell from becoming quiescent. In this paper, we introduce a model of the effects of two drugs on the latent pool in a patient on background suppressive therapy. The first drug is a quiescent pool stimulator, which acts by causing quiescent cells to become active. The second is a Tat analog, which acts by preventing the creation of new quiescently infected cells. We apply optimal control techniques to explore which combination therapies are optimal for different parameter values of the model.

Keywords: Physiological Model, Kinetic modeling and control of biological systems, Control of physiological and clinical variables
Abstract: The objective of this work is to develop a comprehensive computational model that will enable quantitative understanding of plasma calcium regulation under normal and pathological conditions. Ca regulation is represented as an engineering control system where physiological sub-processes are mapped onto corresponding block components, and underlying mechanisms represented by differential equations. The resulting model is validated with clinical observations of induced hypo- or hypercalcemia in healthy subjects, and its applicability demonstrated by comparing model predictions of Ca-related pathologies to corresponding clinical data. Our model accurately predicts clinical responses to induced hypo- and hypercalcemia in healthy subjects within a framework that facilitates the representation of Ca-related pathologies in terms of control system component defects. The model also enables a deeper understanding of the emergence of pathologies and the testing of hypotheses about related features of Ca regulation, for example, why Primary Hyperparathyroidism (PHPT) and Hypoparathyroidism (HoPT) arise from “controller defects”. The control engineering framework provides an efficient means of organizing the sub-processes constituting Ca regulation, thereby facilitating a fundamental understanding of this complex process. The resulting validated model’s predictions are consistent with clinically observed short- and long-term dynamic characteristics of the Ca regulatory system in both healthy and diseased patients. The model also enables simulation of currently infeasible clinical tests, and generates predictions of physiological variables that are currently not measurable.

Keywords: Identification and validation, Model formulation, experiment design, Bio-signals analysis and interpretation
Abstract: Many biomolecular networks are still widely unknown. With high structural uncertainties, bottom-up identification based on model discrimination tends to fail because of combinatorially many possible model structures. Top-down approaches, in contrast, describe general signal processing properties, rather than specific mechanisms. Most of these approaches originated in engineering disciplines where linearity is commonly (approximately) given, but naturally evolved networks exhibit a rather high degree of nonlinearity. Here, we present a top-down structural identification method that is not only applicable to nonlinear biomolecular networks, but indeed requires the network under study to be nonlinear. Similar to traditional frequency domain analysis, we apply an oscillatory input signal to a biomolecular network. However, instead of varying the frequency, we vary the mean value of the oscillatory signal to detect, discriminate, and partly characterize single feedback and feedforward loops in nonlinear networks.

Keywords: Modeling and identification, Microbial technology, Metabolic engineering
Abstract: The planktonic-biofilm transition is reported to facilitate the survival of disease-causing pathogens in a hostile environment, as the ability of pathogens to resist antibiotic treatment is 10 to 1000 times higher in a biofilm than in the planktonic mode. The availability of nutrient components such as amino acids in the surrounding environment plays an important role in the initiation of the biofilm formation. This work proposes a metabolic modeling framework to study the influence of availability of amino acids on the biofilm formation capability of Pseudomonas aeruginosa, one of the major pathogens causing nosocomial infections. Specifically, the genes that are up-regulated during the biofilm initiation were used to determine the metabolic reactions that are positively associated with P. aeruginosa biofilm formation. A criterion was then defined from the change of fluxes of these biofilm-associated reactions and the biomass growth rates to quantify the chance of biofilm formation upon the change of amino acid up-take rates. It was found that adding one of the following eleven amino acids, including Arg, Tyr, Phe, His, Iso, Orn, Pro, Glu, Leu, Val, and Asp, into the minimal medium may trigger P. aeruginosa biofilm formation. These results are perfectly consistent with the existing experimental data. The developed modeling framework was further used as an in silico platform to investigate the impact of the availability of two amino acids on the biofilm formation. It was found that the availability of additional amino acids can enhance biofilm formation and that there may be synergistic mechanisms for multiple amino acids to promote the biofilm formation.

Keywords: Modeling and identification, Data mining tools, Bioinformatics
Abstract: Advances in high throughput screening experiments have significantly improved our ability to discover and predict biomarkers for complex diseases. Systems biology approaches have played a critical role in realizing these improvements by providing computational tools for modeling such diseases at the network level. Within these tools, statistical scores such as the two sample t-statistic (t-score) are commonly used to rank genes/features for downstream analyses. In this paper, we propose a new alternative to the t-score textemdash the ensemble sensitivity (ES) metric textemdash which is a multivariate strategy to obtain feature rankings. To validate our method, we employ the COre Module Biomarker Identification with Network Exploration (COMBINER) tool on publicly available breast cancer gene expression data sets. Top candidates obtained by both the t-score and ES method serve as an input to COMBINER, which identifies the candidate biomarkers. Our results, as quantified by the COMBINER-generated area under the ROC curve (AUC), suggest that the ES approach improves the average AUC and identifies biomarkers with sim 93% overlap with known cancer-related genes. In addition, the overlap of genes known to be associated with cancer that are identified using the two methods is small. This suggests that our proposed approach captures signals missed by methods relying on the t-score.

Keywords: Parameter and state estimation, Kinetic modeling and control of biological systems, Model formulation, experiment design
Abstract: It is common that only a subset of the parameters of dynamic models can be accurately estimated. One approach for identifying a subset of parameters for estimation is to perform clustering of the parameters into groups based upon their sensitivity vectors. However, this approach has the drawback that uncertainty cannot be directly incorporated as the sensitivity vectors are based upon the nominal values of the parameters. One technique to address this deficiency is to define sensitivity cones, where a sensitivity cone includes all possible sensitivity vectors of one parameter for different values resulting from the uncertainty. Parameter clustering can then be performed based upon the sensitivity cones, instead of the sensitivity vectors. This paper applies this new approach to a signal transduction pathway model with a large number of uncertain parameters.

Keywords: Cyber-Physical Systems, Knowledge modelling and knowledge based systems
Abstract: The paper aims to introduce a new vision for the enterprise of the future, based on the most recent paradigms in manufacturing control and on the newest concepts in technology, as Cyber-Physical Systems, Internet of Things and Internet of Services. The concept of and Intelligent Cyber-Enterprise, based on a dynamic and holistic Knowledge Management System, allowing efficient cooperation and informational exchange between humans and machines is presented.

Keywords: Cyber-Physical Systems, Auto-configuration, Networks of sensors and actuators
Abstract: Although the concept of Cyber Intelligent Enteprise is largely based on Cyber Physical Systems (CPS), research in adjacent fields, like Internet of Things (IoT), is also important as it provides a valuable set of system architectures and integration techniques. The current socio-economic context provides the perfect opportunity for the development of a new breed of enterprise system architecture – Cyber Intelligent Enterprise. In order to design a generic architecture for Cyber Intelligent Enterprise system we introduce the concept of Intelligent Entity. Based on the generic architecture presented in the previous section, we propose an implementation for the “Integration Layer”.

Keywords: Task allocation-sharing and job design, Modeling of human performance, Design methodology for HMS
Abstract: Production system types evolved lately to multiple range production systems (MPRS) in the context of more complex and more interconnected economical functions and more restrictive time-efficiency constraints. In MRPSs, interactions between components are various and numerous. Concurrency, resource sharing and synchronization occur. Uncertainty, multiple state and control variables and various nonlinear relations characterize MRPSs. To properly handle these aspects, the modern production systems tend to be automated and consequently two tasks are required: (1) to describe as exact as possible the system, both structurally and behaviorally (by a proper model) and (2) to develop an adequate control strategy for the system. In the paper a hierarchical model for MRPSs and a genetic_algorithm-based control strategy are proposed.

Keywords: Knowledge modelling and knowledge based systems, Decision making and cognitive processes
Abstract: The Sensing Enterprise paradigm relies upon the collection and processing of several types of data, from a wide variety of sources, including sensor networks. If the data collected is to be useful in the support and decentralization of its decision-making capabilities, the data gathered must be transformed into actionable information and knowledge. Knowledge management principles can and should be applied as they can assist in the fulfillment of the Sensing Enterprise vision. It states that enterprises with sensing capabilities will be able to anticipate future decisions by the understanding of the information handled, which enables specific context situations awareness.

Keywords: Cyber-Physical Systems, Mobile robots, Networks of sensors and actuators
Abstract: Stability and convergence properties of large-scale integrated systems are essential aspects of the development of truly useful and well-designed cyber-physical systems (CPS). Because of the desire for flexible, adaptive and reactive cyber-physical systems, i.e., global operation, nonlinear analyses and tools are especially important in CPS. Hence, Lyapunov methods are at the core of many critical control methodologies for such systems. This paper considers Lyapunov stability for approximately symmetric systems. Many robotic systems, such as swarms and fleets of mobile robots, distributed sensor networks, highly integrated cyber-physical systems, etc., are comprised many identical interacting agents, and our prior work has developed computationally efficient stability analyses of the control and dynamics of such symmetric systems. This paper extends those results to the important case where all the agents are not identical, which is important for real-world applications where it is not possible to have exactly identical agents. Importantly, these results do not require the components to have small differences. However, the bounds on the nature of the solutions will obviously depend on how different the agents are.

Keywords: Cyber-Physical Systems, Modeling of HMS, Smart Sensors and Actuators
Abstract: Most of the current studies and solutions developed for building temperature control have been designed independent of the occupant feedback. An acceptable temperature range for the occupancy level is estimated, and control input is designed to maintain temperature within that range during occupancy hours. In this work we have incorporated active user feedback so as to minimize the aggregate user discomfort taking into account the total energy cost to design the optimal control input. The focus is on a multi-zone building, with lumped heat transfer model based on thermal resistance and capacitance for system analysis. We provide a stability analysis and establish convergence of the proposed solution to a desired temperature that minimizes the sum of energy cost and aggregate user discomfort. Simulation results on a four-room example are presented to demonstrate the performance of the proposed approach and validate the model.

Keywords: Marine system identification and modelling
Abstract: Riblets have been used as a passive method of reducing the drag of a fluid flowing over an airfoil. Riblets are structures that run parallel to one another that are positioned longitudinally to the flow. It has been shown experimentally that the drag coefficients over the surface can be reduced by up to 10% when the shape, spacing and height of the riblets are optimized. However, the mechanism of drag reduction is not fully understood. This paper investigates the effects of riblets on energy amplification in streamwise constant channel flow. The linearized Navier-Stokes equations are described by the two-dimensional/three-component model. The irregular domain is converted into a uniform one through a change of coordinates. Spectral methods are used to discretize these equations, leading to a finite dimensional state space model. The transient growth and H_2 norm of the flow system are calculated, which shows that the presence of riblets can reduce the transient growth and H_2 norm of channel flow.

Keywords: Marine system identification and modelling, Nonlinear and optimal marine system control
Abstract: Due to the efficiency and their classification as fast vessels, wing in ground crafts have the potential to become favored transport vehicles for mean distances in coastal regions. However, up to now, this potential could not be realized, because the safe operation of WIG crafts fails in view of the challenging control problems. The nonlinear character of the aerodynamics close to the ground, the constraints for actuators and states, as well as the need for high performance call for advanced control concepts. The paper will contribute to the solution of the control problem by providing appropriate mathematical models. On basis of a theoretical analysis, the paper describes the applied methods and results for the experimental process identification at a scaled 5:1 WIG craft. Apart from the effect of the actuators, the influence of wind and surface disturbances is investigated. The achieved nonlinear MIMO models have been verified at different experiments, and have already proven to be very useful for the design of first basic controllers.

Keywords: Guidance, navigation and control of vehicles, Control of systems in vehicles, High accuracy pointing
Abstract: Since a linear dynamic system parameter uncertainties and un-modeled dynamics in Airbreathing hypersonic vehicle (ABHV), extended it to a nonlinear dynamics system including the linear parameter uncertain term in control matrix. An adaptive tuning function is used to compensate uncertainty impact for system and the robust term is designed to solve the issue of approximation error. The projection operator function is introduced to avoid possible controller singularity problem. In adaptive inverse design progress, a dynamic surface method is used and the first order filter is introduced. The Lyapunov stability theorem guaranteed error is uniformly bounded. The simulation shows the effectiveness of the algorithm.

Keywords: Guidance, navigation and control of vehicles, Autonomous systems, Control of systems in vehicles
Abstract: This paper presents an aircraft Automatic Landing System (ALS) that uses Chaotic Artificial Bee Colony (CABC) optimization to improve the performance. Analysis of aircraft landing process and wind disturbance are provided. The couple behavior between the thrust and the elevator during automatic landing is investigated. A vertical rate referenced guidance system and an angle of attack referenced Approach Power Compensation System (APCS) are developed and evaluated for use in automatic landing. CABC optimization techniques are used to determine a set of optimal filter and control gains for the entire closed-loop control system. Series of comparative simulation results verified the feasibility and effectiveness of our proposed approach.

Keywords: Control of systems in vehicles, Adaptive and robust control of automotive systems, Trajectory Tracking and Path Following
Abstract: The design of a novel non-certainty equivalence adaptive control system for hypersonic vehicles is presented, based on the immersion and invariance (I&I) theory. The interest here is to achieve a robust trajectory tracking of the reference commands in spite of large parameter uncertainties. The architecture of the whole control system is constructed by decomposing the vehicle model into the velocity subsystem and the flight path angle (FPA) subsystem. A backstepping design procedure is applied to the cascaded FPA subsystem. Three non-certainty equivalence controllers, each consisting of a control module and an I&I based parameter estimator, are designed respectively for the velocity subsystem and the two steps of the FPA subsystem, which suffer from parameter uncertainties. First-order filters are implemented in all the design of the three controllers to avoid solving certain partial differential inequality in I&I adaptive control. Stability analysis is presented using Lyapunov theory and asymptotical convergence of the tracking errors to zero is accomplished. Simulation results with rapid and accurate command tracking show the effectiveness and robustness of the proposed control system.

Keywords: Control of systems in vehicles, Decision making and autonomy, sensor data fusion, Mission control and operations
Abstract: This paper extends the market-based task assignment algorithm to handle the networks with limited connectivity. The original algorithm was proposed to allocate tasks which cannot be executed by a single agent, but fully connected network is assumed. After analyzing the existing approaches for task allocation in limited networks, a new communications protocol is proposed which preserve key features of the original algorithm. The data required for task allocation algorithm is relayed via bridge agents. By adopting relaying operation, the proposed strategy allocates tasks regardless of the network topology. Convergence property is also preserved. Numerical simulation is performed to verify the feasibility of the proposed strategy considering the communication load.

Keywords: Guidance, navigation and control of vehicles
Abstract: The H∞ based decoupling tracking control is studied in this paper. A virtual system constituted by the controlled system and the no coupling reference model is firstly set up. The controlled system is driven to follow the reference model to realize the decoupling. And the tracking error can be formulated by the H∞ norm of the virtual system. Then the controller is derived by minimizing the H∞ norm, which can be described by Linear Matrix Inequalities (LMIs). The necessary and sufficient condition of existence of controller is derived based on the LMIs above. A flight control example is given to illustrate the effectiveness of the proposed method. The simulation results show that the proposed method is of better control performance than Linear Quadratic (LQ) tracking controller.

Keywords: Control of systems in vehicles, Guidance, navigation and control of vehicles, Flight dynamics identification, formation flying
Abstract: The general problem of Wing-in-Ground effect’s active usage in WIG-craft or ekranoplanes by minimization of average geometrical altitude of flight above sea waves is deeply considered in this paper. Two concepts of stabilisation of vehicle motion in longitudinal plane are discussed and compared. The necessary analytical formulae are developed for the first time. They are rather simple and may be used for approximate estimations only, however allow selecting the best motion stabilization concept. The main advantage of the used general approach consists in making important conclusions regarding the limited effectiveness of WIG-craft flight above the disturbed sea surface. Only large WIG-craft could provide the essential fuel saving in controlled flying close to the disturbed surface. For small WIG-craft, fuel saving is not great and significant in comparison with the plane mode of flight, and other advantages of WIG-craft have to be the decisive ones in competition with planes. The modern means of automatic control permit to realize any perfect dynamic features of vehicle, and it is important to recognize the best concept of flight control before developing the control laws for the certain vehicle. This task is solved in the paper and it is useful for estimating the best ways of perfection of automatically controlled WIG-craft.

Keywords: Control of systems in vehicles, Automotive sensors and actuators, Intelligent driver aids
Abstract: The objective of this paper is to propose a control strategy of air pressure inside an Inflatable Textile Pocket (ITP) in order to design an adjustable seat backrest for drivers comfort. For the said purpose, firstly a brief model of an Electro-pneumatic Pressure Converter (EPC) is presented along with the pressure dynamics inside an IFP. This nonlinear model is then linearized around an operating point and a control synthesis is performed using the classical linear control theory. The obtained PID controller is implemented on a developed experimental test bench. Furthermore, a comparative analysis, between the feedback reposes obtained with the help of the proposed control and an industrial pressure regulator, is also performed to show the effectiveness of the proposed controller.

Keywords: Intelligent transportation systems, Human factors in vehicular system, Man-machine interface in transportation
Abstract: This work addresses the design of a heart rate (HR) control system for a bicycle equipped with a continuously varying transmission. The control system helps the cyclist maintain a constant physical effort throughout the trip. The bicycle longitudinal dynamics and a control-oriented model of the cyclist's HR dynamics are the basis for the development of a controller. The HR dynamics is subject to considerable uncertainties due to inter subject differences, nonlinearities and other factors that are not directly influenced by the cycling physical effort. In the control system design phase, these are addressed by designing a second order sliding mode controller. The controller is experimentally validated on several subjects comparing the HR with the without the CVT control, showing that the HR is maintained within 10 BPM from the desired one.

Keywords: Intelligent transportation systems, Automatic control, optimization, real-time operations in transportation, Transportation logistics
Abstract: The widespread diffusion of Electric Vehicles (EVs) gives a concrete answer to the growing environmental problems linked to the mobility in urban areas. This paper deals with a particular management problem related to the EVs charging operations: the integration of the EVs with the power distribution system. Possible electrical grid disruptions due to uncoordinated charging operations and the need of guaranteeing to drivers a certain level of confidence while travelling with an EV explain the efforts in the identification of a smart approach for the emph{EVs charging management problem}. In this work, a hierarchical mathematical programming approach is considered and a system made up of two interdependent optimization models is introduced in order to identify the optimum spatial and temporal scheduling of EVs charging operations in an urban area served by several charging stations. Moreover, a Mixed Integer Linear Programming (MILP) formulation for the emph{Vehicle-to-Charging Station Assignment Problem} is proposed and a preliminary example of application is presented.

Keywords: Information processing and decision support, Modeling and simulation of transportation systems
Abstract: Dynamic Traffic Assignment (DTA) models are widely used for online and offline applications for efficient deployment of traffic control strategies and the evaluation of traffic management schemes and policies. A valid statistical methodology is proposed for assessing the successful implementation of traffic control strategies with regard to their inherit risk at the network efficiency and therefore providing a useful and easily applicable tool for practitioners in selecting the less risky alternative. In this paper we have considered a common method, Value-at-Risk assessment of financial portfolios, for the assessment of traffic control strategies using Dynamic Assignment Tools in the City Centre of Gaborone in Botswana and the results are presented here.

Keywords: Information processing and decision support
Abstract: Under foggy viewing conditions, image contrast is often significantly degraded by atmospheric aerosols, which makes it difficult to quickly detect and track moving objects in intelligent transportation systems (ITS). A foggy image visibility enhancing algorithm based on an imaging model and wavelet transform technique is proposed in this paper. An optical imaging model in foggy weather conditions is established to determine the image degradation factors and compensation strategies. Based on this, the original image is firstly transferred into YUV color space of a luminance and two chrominance components. Then the luminance component is decomposed through wavelet transform into low- and high-frequency subbands. In the low-frequency subband, the medium scattered light component is estimated using Gaussian blur and removed from the image. Nonlinear transform for enhancement of foggy images is applied to high-frequency subbands. In the end, a new image is recovered by combining the chrominance components and the corrected luminance component altogether. Experimental results demonstrate that this algorithm can handle the problem of image blurring caused by atmospheric scattering effectively, and has a better real-time performance compared with a standard model-based procedure.

Keywords: Intelligent transportation systems, Modeling and simulation of transportation systems, Automatic control, optimization, real-time operations in transportation
Abstract: In the paper a traffic control strategy based on ramp metering is proposed in order to reduce traffic emissions in freeway stretches. Such strategy is devised to take into account that two vehicle classes are present in the freeway (cars and trucks) and that they are separately controlled. The simulation analysis developed in the paper shows, first of all, that the proposed control strategy is able not only to reduce traffic emissions in the freeway but also to reduce the congestion, by decreasing the total time spent by vehicles in the system. Moreover, the effectiveness of the two-class ramp metering control is shown on different traffic scenarios, that are deeply analysed and discussed in the paper.

Keywords: Automatic control, optimization, real-time operations in transportation, Modeling and simulation of transportation systems, Simulation
Abstract: The potential fuel savings of hybrid vehicles strongly depend on the energy management strategy, which distributes the required power between the prime mover and the additional power sources. Here, it is necessary to take into account the efficiencies of the whole propulsion system. In this paper, a novel operating strategy is developed that considers the efficiencies of all major components of the propulsion system. Fuel-optimal load points are calculated off-line by using a numerical sensitivity analysis which takes the fuel consumption as well as the variable state of charge of the energy storage system (ESS) into consideration. Using these fuel-optimal load points, the optimal operating strategy is determined by means of an optimization approach which depends on the duty cycle itself.

Keywords: Intelligent transportation systems, Automatic control, optimization, real-time operations in transportation, Modeling and simulation of transportation systems
Abstract: The problem of optimally routing a class of guided vehicles on an urban traffic network is considered in this paper. The guided vehicles give their requests in terms of origin, destination, and time window, and the control system returns to each of them the start time and the path to be followed, as provided by the solution of an original individual route guidance (IRG) problem. The control system integrates the IRG problem with a macroscopic traffic model in order to take into account the evolution of the traffic (in particular, of the congestions) on the network links. The traffic model and the IRG problem are mutually dependent one from the other, and for this reason the ultimate solution of the route guidance problem is iteratively sought. The control system has the objective of minimizing both the impact of the whole traffic and the individual costs of the guided vehicles.

Keywords: Networks of robots and intelligent sensors, Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles, Multi-vehicle systems
Abstract: This paper is concerned with the consensus problem of a multi-agent system where some nodes inject unexpected values as a consequence of a fault. A fault compensation algorithm is proposed and its properties are thoroughly analyzed. Afterwards, closeness of system trajectories to the consensus value in the presence of compensated persistent faults is studied. Finally, an example on two different applications of the proposed strategy is shown and simulation results are reported to validate theoretical results developed in the paper.

Keywords: Smart grids, Power systems stability, Control system design
Abstract: Intermittent generations such as large-scale renewable energy increases the risk of instability in a power grid. In this paper, we introduce the concept of observability and its computational algorithms for power networks equipped with synchrophasors. The goal is to estimate the angles and its derivatives around unstable trajectories, the information that is critical for the detection of power network instabilities. The algorithm is developed to determine the number and the siting of synchrophasors in a power network so that the state of the system can be accurately estimated in the presence of instability. An unscented Kalman filter (UKF) is adopted as a tool to estimate the states that are not directly measured by synchrophasors. The theory and its computational algorithms are illustrated by using a 9-bus model with three generators.

Keywords: Smart grids, Optimal operation and control of power systems, Control of renewable energy resources
Abstract: This paper presents the concept of autonomous control, This paper presents the concept of autonomous control, operation, and protection for a future distribution system named FREEDM system. The system architecture is presented first. Targeted as a grid suitable for large scale integration of distributed resources, the FREEDM System uses solid state transformers to interface the medium voltage distribution system to residential AC and DC microgrids. Innovative autonomous control, operation and protection are proposed and discussed in this paper. Simulation and experimental results are presented to verify the main concept.

Keywords: Model predictive and optimization-based control, Control of multi-scale systems, Smart grids
Abstract: A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive control design. It is however shown that taking into account the knowledge of different time scales in the dynamical subsystems makes possible a linear formulation of a centralized predictive controller. A realistic scenario of regulatory power services in the smart grid is considered and formulated in the same objective as of cost optimization one. A simulation benchmark validated against real data and including significant dynamics of the system are employed to show the effectiveness of the proposed control scheme.

Keywords: Control system design, Power systems stability, Smart grids
Abstract: Model predictive control (MPC) is investigated as a control method for frequency control of power systems which are exposed to increasing wind power penetration. For such power systems, the unpredicted power imbalance can be assumed to be dominated by the fluctuations in produced wind power. An MPC is designed for controlling the frequency of wind-penetrated power systems, which uses the knowledge of the estimated worst-case power imbalance to make the MPC more robust. This is done by considering three different disturbances in the MPC: one towards the positive worst-case, one towards the negative worst-case, and one neutral in the middle. The robustified MPC is designed so that it finds an input which makes sure that the constraints of the system are fulfilled in case of all three disturbances. Through simulations on a network with concentrated wind power, it is shown that in certain cases where the state-of-the-art frequency control (PI control) and nominal MPC violate the system constraints, the robustified MPC fulfills them due to the inclusion of the worst-case estimates of the power imbalance.

Keywords: Smart grids
Abstract: Energy is a major driver of developments in human society. Naturally, the energy efficiency of power grids qualities as an important topic. Half a century old, the world's power grids are due for a major overhaul. The envisioned energy-efficient, environment-friendly, distributed-generation-friendly, demand-response-friendly, self-healing power grid is called the smart grid. This article is intended as a primer on the control and communication techniques that form the basis of several smart grid subsystems, namely distributed generation, wide-area monitoring, distribution automation, and substation automation. The theme of this article is to show how these techniques contribute to the efficiency of the generation, transmission, and distribution segments of a grid. Potential research challenges and opportunities are indicated. To complement our technical discussion, noteworthy smart grid developments in Australia (one of the world's top investors in smart grids) are highlighted.

Keywords: Neural networks in process control, Intelligent control of power systems, Artificial intelligence
Abstract: A supercritical (SC) once-through boiler unit is a typical multivariable system with large inertia and non-linear, slow time-variant, and time-delay characteristics, which often makes the coordinated control quality deteriorate under wide-range loading conditions, and thus influences the unit load response speed and leads to heavy fluctuation of the main steam pressure. To improve the SC unit’s coordinated control quality with advanced intelligent control strategy, the neural-network based inverse system models of a 600MW supercritical boiler unit were investigated. A feedforward neural network with time-delayed inputs and time-delayed output feedbacks was adopted to establish the inverse models for the load and the main steam pressure characteristics. Based on the model, neural network inverse coordinated control scheme was designed and tested in a full-scope power plant simulator of the given SC power unit, which showed that the proposed coordinated control scheme can achieve better control results compared to the original PID coordinated control.

Keywords: Nonlinear predictive control
Abstract: This paper aims at reducing the computational load caused by the online optimization in nonlinear model predictive control (MPC) by introducing a new type of parametrizations for the predicted input trajectory. In order to determine offline the state dependent parametrizations, a tailored data mining algorithm is introduced. Refinements to achieve feasibility of the parametrized constrained optimization problem are presented. Theoretical guarantees on constraint satisfaction and feasibility employing the parametrizations are provided for Lipschitz continuous systems. In a numerical example the benefits of the new method are illustrated.

Keywords: Nonlinear predictive control, Advanced process control, Control system design
Abstract: This paper presents a SPC based in the filtered Smith predictor structure in order to improve the performance of SPC when applied to a stable or integrative dead-time processes. This technique combines the robustness of subspace identification algorithms, the ability of predictive controllers to deal with multi variable processes and operational constraints and robustness of filtered Smith predictor in presence of model uncertainties. The proposed controller is applied in two simulation cases. The first is a boiler temperature control and the second is the control of effluent concentration and temperature in a stirred tank reactor. The obtained results show that the proposed strategy gives good performance when controlling dead-time processes considering estimation errors in dynamics and dead time.

Keywords: Nonlinear predictive control, Control of constrained systems, Robust control
Abstract: This note studies the formulation of model predictive control exploiting passivity properties. The introduction of passive constraints in model predictive control schemes is particularly appealing since robustness against model uncertainty is inherently guaranteed. The potential of the discussed control scheme is shown on the regulation problem of a robot manipulator.

Keywords: Nonlinear predictive control, Optimal control theory, Adaptive control
Abstract: This paper considers the solution of a real-time optimization problem using adaptive extremum seeking control for a class of unknown discrete-time nonlinear systems. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying discrete-time estimation problem. The proposed approach is applied in the design of nonlinear model predictive control algorithms where the extremum-seeking controller is used to perform the real-time optimization of the MPC. A simulation study and an experimental study is presented that demonstrates the effectiveness of the proposed technique.

Keywords: Nonlinear predictive control, Decentralized control
Abstract: In this paper, we propose a distributed economic MPC algorithm for cooperative control of several self-interested interacting dynamical systems. Each system considers its own, local, performance criterion, and coordination between the systems is enforced via coupling constraints. The proposed control strategy consists of a distributed optimization algorithm, used to determine an overall optimal steady-state, whose current iterates at each time are then used by each system to compute a control input in an economic MPC framework. We analyze the properties of the proposed algorithm and prove convergence results for the resulting overall closed-loop system. Furthermore, we apply our results to the problem of synchonizing several agents with conflicting objective.

Keywords: Nonlinear predictive control, Quantized control, Parametric optimization
Abstract: We investigate how the performance of explicit MPC feedback laws is affected by rounding-based quantization of the control commands. Specifically, we address the problem of providing a rigorous certificate that a given quantized piecewise affine explicit MPC feedback is bounded from below and from above by specific functions. These functions are constructed as to reflect typical control requirements, such as recursive feasibility and closed-loop stability. We show how to obtain an analytical form of the quantized MPC feedback and how to provide the certificate by solving a set of linear programs.

Keywords: Bayesian methods, Nonlinear system identification, Error quantification
Abstract: The pipeline pressure of blast furnace gas (BFG) system in steel industry provides effective information for the energy scheduling operations. However, due to the complexity of the byproduct gas pipeline network and the large fluctuations of the gas flow, it is rather difficult to establish an accurate prediction model for the pipeline pressure. Additionally, the quantitative reliability of the prediction accuracy is the key concerns of energy scheduling workers since there are always a variety of uncertainties in industrial process. In this study, an echo state network (ESN) modelling with output feedback is proposed to predict the BFG pipeline pressure. Given the gas flows data and the pressures sampled from the sensors are generally accompanied with noise, a Bayesian framework for the prediction intervals (PIs) is designed, which can quantify the input and output noises. To verify the effectiveness of the proposed method, a number of prediction experiments coming from industrial data are conducted here. And, the experimental results indicate that the proposed approach has a satisfactory performance on PIs for the pipeline pressure.

Keywords: Bayesian methods, Nonparametric methods
Abstract: There are two key issues for the kernel-based regularization method: the kernel structure design and the hyper-parameter estimation. In this contribution, we introduce a new family of kernel structures based on state space models. It has more flexible and more general structure, and includes some of stable spline kernels and diagonal correlated kernels as special cases. We also tested a different method for the hyper-parameter estimation by maximizing a profile marginal likelihood and examined three methods dealing with the initialization. Monte Carlo simulations show that the tested kernels are on the average a bit better than the tuned correlated kernel and the profile marginal likelihood maximization and the pre-windowing method work well for hyper-parameter estimation and initialization.

Keywords: Bayesian methods, Distributed control and estimation, Particle filtering/Monte Carlo methods
Abstract: Sliding mode controllers are frequently employed to track reference signals in the presence of model errors provided a worst-case bound on such errors is known. The uncertainty bound is often chosen through intuition or heuristic observations, which can degrade control authority if the bound is too conservative. We describe a method for applying quantified parameter uncertainties obtained via Bayesian techniques to the design of sliding mode controllers. We illustrate the performance of this approach through numerical simulation in the context of ferroelectric actuator systems.

Keywords: Bayesian methods, Estimation and filtering, Nonlinear system identification
Abstract: The reconstruction of environmental events has gained increased interest in the recent years. In this paper, the focus is on estimating the location and strength of a gas release from distributed measurements. The estimation is formulated as Bayesian inverse problem, which utilizes a Gaussian plume forward model. A novel recursive estimation algorithm based on statistical linearization and Gaussian mixture densities with adaptive component number selection is used in order to allow accurate and computationally efficient source estimation at the same time. The proposed solution is compared against state-of-the-art methods via a simulations and a real-word experiment.

Keywords: Bayesian methods
Abstract: For complex industrial plants with multiphase and multimode characteristic, traditional multivariate statistical soft sensor methods are not applicable as Gaussian distribution assumption of data is not met. Thus Gaussian mixture model (GMM) is used to approximate data distribution. In the previous GMM-based soft sensor modeling researches, GMM is only used to identify operating mode, then other regression algorithms like PLS are used for quality prediction in different localized modes. In this article, a new method—Gaussian mixture regression (GMR) is introduced for soft sensor modeling, which has been already successfully applied in robot programming by demonstration (PbD). Different from past GMM-based soft sensors, GMR is directly used for regression. In GMR, data mode identification and regression are incorporated into one model, thus there is no need to switch prediction model when data mode has changed. Feasibility and efficiency of GMR based soft sensor are validated in the fermentation process and TE process.

Keywords: Bayesian methods, Particle filtering/Monte Carlo methods, Learning theory
Abstract: In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. To build robustness to outliers, we model the measurement noise as realizations of independent Laplacian random variables. The identification problem is cast in a Bayesian framework, and solved by a new Markov Chain Monte Carlo (MCMC) scheme. In particular, exploiting the representation of the Laplacian random variables as scale mixtures of Gaussians, we design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods.

Keywords: Diagnosis, Descriptor systems, Linear parameter-varying systems
Abstract: This paper design a method for fault detection and isolation based on observers for systems modelled as Descriptor-Linear Parameter Varying (D-LPV) with Unmeasurable Scheduling Functions (USF). The first contribution of the paper is deal with the USF problem by transforming the into an uncertain D-LPV system with an estimated scheduling parameter. As a second contribution, a robust LPV observer is designed with H_{infty} performance to supply a robust state estimation against uncertainties provided by USF. Sufficient conditions to guarantee the robustness and convergence are obtained via linear matrix inequalities (LMIs). Finally, an observer bank based on robust H_{infty} observers is used to generate residuals and perform sensor fault detection and isolation. A numerical example demonstrates the effectiveness of our methods.

Keywords: Polynomial methods, Diagnosis, Energy systems
Abstract: We present a symbolic algebraic electrical system analysis method . A single-phase model is applied to a renewable electrical power plant. Once the system relations established, algorithms are applied, calculating the Groëbner bases for determining the system observability. Parallel programming is used in order to manage the number of relations and to decrease the time computing. A cost sensor placement is finally proposed to improve the observability.

Keywords: Complex systems, Fault-tolerant, Model following control
Abstract: The inherent complexity of critical production systems, coupled with policies to preserve people´s safety and health, environmental management, and the facilities themselves, and stricter laws regarding the occurrence of accidents, are the motivation to the design of Safety Control Systems that leads the mitigation functionality. According to experts, the concept of Safety Instrumented Systems (SIS) is a solution to these types of issues. They strongly recommend layers of risk reduction based on hierarchical control systems in order to manage risks, preventing or mitigating faults, or to lead the process to a safe state. Additionally some of the safety standards such as IEC 61508, IEC 61511, among others, guide different activities related Safety Life Cycle design of SIS. The IEC 61508 suggests layers of critical fault prevention and critical fault mitigation. In the context of mitigation control system, the standard provides a recommendation of activities to mitigate critical faults, by proposing control levels of mitigation. This paper proposes a method to implement the mitigation layer based on the risk analysis of the plant and the consequences of faults of its critical components. The control architecture, based on distributed and hierarchical control systems in a collaborative way, will make use of the techniques of risk analysis raised and mitigation actions, based on the knowledge of an expert, implemented by fuzzy logic. The mitigation layer therefore seeks to reduce the inherent risk in a process, and besides proposing the mitigation layer, this work aims to a further reduction of process risk on proposing an anticipatory mitigation action through temporal analysis of the evolution of the parameter used to measure the effect of the occurrence of a critical fault.

Keywords: Diagnosis, Application of nonlinear analysis and design, Aerospace
Abstract: Since incipient faults often have small amplitudes and obscure symptoms, those observer-based or output residual-based fault indicators are ineffective for incipient faults, especially in the presence of system disturbances. The ToMFIR-based fault detection approaches use both controller and output residuals to construct a fault indicator, which make them sensitive to incipient faults. This paper extends the ToMFIR-based fault detection approaches to a class of closed-loop nonlinear systems under the consideration of system disturbances. The restriction on the fault type is removed compared with the existing approaches, and ToMFIR-based fault estimation algorithm has been derived. Verification results in a near space hypersonic vehicle (NSHV) simulation system can demonstrate the effectiveness of the proposed approach.

Keywords: Diagnosis, Linear systems, Randomized algorithms
Abstract: Interest in fault detection and diagnosis has increased in the last few decades. In an active approach, the detectability of potential faults is improved by injecting a suitable input into the system using an automated system. This input can be designed in either a stochastic or a deterministic framework. The stochastic framework aims to maximize the probability of a correct diagnosis at a certain time, whereas the deterministic framework aims to guarantee diagnosis within a certain time interval. Recently, a hybrid stochastic-deterministic approach has been developed in which all uncertainties are described by probability density functions (PDFs) with nite support on zonotopes. This method is able to provide a guaranteed diagnosis at a given time N, while approximately maximizing the probability of diagnosis at some earlier time M < N. However, this method is presently only applicable in the case of uniform PDFs. In this article, the hybrid stochastic-deterministic approach is extended to arbitrary PDFs using a sampling approach based on scenario optimization.

Keywords: Motion Control Systems, Flying robots, Hardware/software co-design
Abstract: In this paper a Control software Architecture for Cooperative multiple unmanned aerial VehIcle-manipulator Systems (CAVIS) is developed. The core of the architecture is a set of software components, communicating each other through a set of defined messages. To handle multiple control objectives simultaneously, a library of elementary behaviors is defined; then, multiple elementary behaviors are combined, in a given priority order, into tasks (compound behaviors); to this aim the Null-Space-based Behavioral (NSB) approach has been adopted. An application example, involving the cooperative transportation by two aerial vehicle-manipulator systems, is developed to assess the performance of the proposed software architecture.

Keywords: Motion Control Systems, Guidance navigation and control, field robotics
Abstract: This paper proposes a vector field guided auto-landing control of airship with wind disturbance, whose motion is described in the vertical plane. Whereas the hovering and vertical landing abilities are advantages of the airship, there is little research on the auto-landing mechanism. Unlike the previous airship control algorithms which compensate the disturbances using linear and nonlinear control laws such as backstepping control methods, the proposed auto-landing control algorithm is simpler than the backstepping controllers and covers the wind disturbances with unknown bound. To this end, the vector field based guidance law achieving the control objectives and adaptive-robust dynamic control law realizing the desired course are proposed. The stability analysis and simulation results of the proposed auto-landing control law are included to demonstrate the practical applicability of the proposed method.

Keywords: Regulation (linear case), Disturbance rejection (linear case), Digital implementation
Abstract: This paper presents a comparative analysis of two alternative control strategies based on the Internal Model Principle: the Repetitive Controller and the Multiple Resonant Controller. The proposed implementations are evaluated experimentally in a servo-vision ball and plate balancing system. The plant is composed by an orientable platform with a free rolling sphere on top, where the controlled variable is the ball position. The methodology considered to synthesize the associated state gains parameters is the Linear Quadratic Regulator approach. The experimental results compare characteristics such as steady-state error, transient response and input signal for each implemented control strategy.

Keywords: Motion Control Systems, Micro and Nano Mechatronic Systems, Vibration control
Abstract: In many applications of nanopositioning, such as scanning probe microscopy, tracking fast periodic reference trajectories with high accuracy is highly desirable. Repetitive control is a simple and effective control scheme to obtain good tracking of such reference trajectories. In order to implement repetitive control, a method for introducing time-delay is necessary. This can easily be implemented using a memory buffer with digital signal processing equipment. To achieve fast, high accuracy, and low noise performance, fast microcontrollers or field-programmable gate array hardware with fast high-resolution analog-to-digital and digital-to-analog converters are needed. As an inexpensive alternative to digital signal processing, the use of an analog bucket brigade device to implement the time-delay is investigated in this paper. Bucket brigade devices use switching to carry the input voltage over an array of capacitors, achieving a specified time-delay. Low-noise bucket brigade devices can achieve a signal-to-noise ratio around 80 dB, comparable to the actual performance when using 16-bit analog-to-digital converters. In this paper, the proposed control scheme utilizes a modified integral control law in conjunction with the repetitive control law. The overall control scheme ensures robustness towards plant uncertainty. Experimental results demonstrate the effectiveness of the overall control scheme and the analog implementation.

Keywords: Motion Control Systems, Microsystems: nano- and micro-technologies, Mechatronic systems
Abstract: This paper discusses a method for introducing frequency-domain robustness constraints in a data-driven control design. The method is based on adaptive weighting filters used in a time-domain cost function to be minimized. In an iterative way, violation of the frequency-domain robustness constraints is checked by model prediction and accounted for by adapting the weighting filters. The method is applied to the stage synchronization problem in wafer scanners, which involves synchronization of the outputs of two motion systems, and will be discussed in view of both simulation results and experimental results.

Keywords: Motion Control Systems, Microsystems: nano- and micro-technologies, Mechatronic systems
Abstract: This paper presents a framework for the nonlinear control of dual-stage actuators (DSA). Motivated by various nonlinear controllers that make use of sector bounded and L_infty nonlinearities for the control of saturated linear systems, a methodology for integrating such nonlinear functions in order to improve the performance of DSA is presented. The stability of the closed-loop system is assessed by casting the nonlinearities in a mixed sector-bounded plus quasi - Linear Parameter Varying (LPV) framework, leading to a set of linear matrix inequalities (LMIs) to be satisfied by the controller parameters. Taking advantage of the developed framework, a new L_infty function is proposed to avoid the saturation of the secondary actuator. Simulation results illustrate the validity of the proposed framework and its potential for the performance improvement of DSA.

Keywords: Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Modeling and identification, Kinetic modeling and control of biological systems
Abstract: After infecting a CD4 positive T cell, Human Immunodeficiency Virus (HIV) can either replicate and kill the cell or enter latency, a dormant state of the virus where viral gene-expression is turned OFF. This cell fate decision between viral replication and latency is controlled by the viral regulatory protein, Tat. This protein is known to activate its own production creating a positive feedback circuit. Our previous work has shown that a stochastic model of this feedback circuit exhibits bimodal distributions of Tat levels, even though this circuit lacks deterministic bistability. The modes of the distribution correspond to infected cells with high Tat levels (corresponding to viral replication) or no Tat at all (corresponding to HIV latency).

Experimental evidence points to an additional positive feedback loop mediated through a microRNA: a host microRNA targets Tat mRNA for degradation and Tat protein blocks synthesis of this microRNA. Here we investigate the interplay between Tat-mediated and microRNA-mediated positive feedback loops using deterministic and stochastic modeling. Our results show that these positive feedbacks together can exhibit deterministic bistability if the microRNA-mRNA interaction is sufficiently strong. Intriguingly, stochastic analysis reveals bimodal distributions for Tat even for parameter regimes where the coupled feedback system is not bistable. In summary, addition of the micro-mediated feedback loop can lead to bimodal Tat levels for a wide a range of parameter values, and suggests a role for microRNAs in the viral cell fate decision in vivo.

Keywords: Biomedical system modeling, simulation and visualization, Kinetic modeling and control of biological systems, Bioinformatics
Abstract: Under acute Ion Radiation (IR) perturbation from external environment, a normal cell can trigger a series of cellular self-defensive mechanisms. To model the complicated Cellular Response Genome Stress (CRGS, for short), a novel method is proposed based on combined Gene-Environment Network (GEN) and Kinetic Theory of Active Particle (KTAP) framework. In this method, the integrated CRGS model is divided into three subsystems, and each subsystem is composed of different particles with different discrete states at molecular level. Then the dynamic kinetics of integrated CRGS is dealt as outcomes of complicated mutual interactions among molecular particles from different subsystems and external environment cofactors at systematic level. The simulation results show that our method is efficient to illustrate a series of cellular activities, including stochastic Double Strand Break (DSB) generation and repair, switch-like Ataxia Telangiectasia Mutated (ATM) activation, oscillating P53-MDM2 feedback loop as well, and helpful to analyze the capability of cellular self-defensive mechanism, as well as genome stability in response to different IR perturbation circumstances.

Keywords: Bio-signals analysis and interpretation, Healthcare management, disease control, critical care, Control of physiological and clinical variables
Abstract: Predicting epileptic seizures would change the life of millions of people. This work presents the results of a large study involving 216 patients with long-term scalp (sEEG) and intracranial (iEEG)records. A high-dimensional features space is built using time series data of 6 channels and 22 features per channel Patient-specific predictors based on SVM are developed and evaluated in relation to sensitivity and false-prediction rate. A substantial number of seizures has been correctly predicted and a comparative study is made with relation to the choice of electrodes, localization lateralization and preictal time duration. For a set of patients the results may be considered of clinical relevance compared to an analytic random predictor.

Keywords: Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Identification and validation, Modeling and identification
Abstract: Large-scale networks of undamped coupled oscillators represent both modern engineered systems and systems from the biological world. The neural network of the brain is a particular example of motivating interest. The degree to which tracking, predicting and controlling dynamics in such networks will be successful is conditional on observability. Observability is considered using large-scale networked clocks, a linearisation of networked oscillators, through the structure of the observability matrix using Vandermonde matrices. Observability is found to be particularly poor for these undamped networked clocks. This raises interesting challenges to elucidate sufficient information from these networks for therapeutic benefit in medical bionics.

Keywords: Parameter and state estimation, Modeling and identification, Monitoring
Abstract: The monitoring and optimization of hybridoma cell fed-batch cultures depend on the availability of appropriate on-line sensors for the main culture components. A simple and efficient approach to maintain hybridoma cultures in the optimal operating conditions is to regulate the substrate concentrations at the critical values (G=Gcrit and/or Gn=Gncrit) such as to control the hybridoma cells at the critical metabolism state. However, reliable glucose and glutamine probes are currently rare and/or very expensive on the market and it is necessary to design software sensors which are at same time cheap and reliable and that can be used for online measurement. In this study, the overflow metabolism model is used to develop an extended Kalman filter for online estimation of glucose and glutamine in hybridoma cell fed-batch cultures based on the considered available measurements (biomasses (on-line), lactate and ammonia (on-line or off-line)). The observability conditions are examined, and the performances are analysed with simulations of hybridoma cell fed-batch cultures. Glutamine estimation sensitivity is enforced by minimizing a cost function combining a usual least-squares criterion with a state estimation sensitivity criterion.

Keywords: Physiological Model, Intensive and chronic care or treatment, Decision support and control
Abstract: Mechanical ventilation (MV) is a lifesaving therapy for patients with the acute respiratory distress syndrome. However, selecting the optimal MV settings is a difficult process as setting a high positive end-expiratory pressure (PEEP) value will improve oxygenation, but can produce ventilator induced lung injuries (VILI). To find a suitable value is patient specific and depends on different things like the underlying illness and the current state. In this study, a respiratory model that defined constant bronchial resistance and pressure-dependent variable elastance was fitted to pressure volume (PV) responses for 12 datasets of 10 acute respiratory distress syndrome (ARDS) patients which underwent a recruitment maneuver (RM) to open previous collapsed alveoli. We believe that the range of minimal elastance represents that range in which oxygenation can be improved by recruitment with reducing the risk of VILI. The first order model with a variable elastance (Edrs) described by Chiew et al. (2011) was modified with a factor α to express added end-expiratory volume due to an increased PEEP. Model parameters were identified using a nonlinear least square method that optimized Edrs agreement across PEEP-levels. The model yielded an increase in overlapping quality of pressure dependent Edrs-curves. A best pressure range for PEEP could be identified in 9 of 12 datasets. The model could potentially provide a simple method of decision support at the bedside for clinicians and could prospectively an automated extend in mechanical ventilation devices.

Keywords: Multi-agent systems, Sensor networks, Distributed control and estimation
Abstract: The security problem of consensus has been attracting increasing research attention for a large number of application scenarios. Many existing solutions in this aspect rely on the assumption that most of the neighboring nodes of each safe node are honest which may not be able to satisfy various practical situations. Motivated by a recent work dealing with the secure consensus for time synchronization in asynchronous networks, this paper aims to handle secure synchronous consensus under message manipulation attacks. Specifically, we first propose a secure synchronous consensus algorithm (SSCA), and prove that SSCA converges with an exponential rate through matrix analysis. Furthermore, we investigate how the exact behavior of message manipulation attack will affect the convergence of SSCA. Specifically, we show how such result can be employed to classify the attack behavior and help analyze the consensus performance. Examples and extensive simulations are provided to evaluate the effectiveness of proposed results.

Keywords: Cooperative systems, Control over networks, Multi-agent systems
Abstract: In this work, we introduce a new iterative design method for a coalitional control scheme for linear systems recently proposed. In this scheme, the links in the network infrastructure are enabled or disabled depending on their contribution to the overall system performance. As a consequence, the local controllers are divided dynamically into sets or coalitions that cooperate in order to attain their control tasks. The new design method allows the control system designer to include new constraints regarding the game theoretical tools of the control architecture, while optimizing the matrices that define the controller.

Keywords: Cooperative systems, Coordination of multiple vehicle systems, Multi-agent systems
Abstract: This paper presents a distributed decision making approach to the problem of control effort allocation to robotic team members. The objective is for a team of autonomous robots to coordinate their actions in order to efficiently complete a task. A novel controller design methodology is proposed which allows the robot team to work together based on a game-theoretic learning algorithms using fictitious play and extended Kalman filters. In particular each robot of the team predicts the other robots' planned actions while making decision to maximise its own expected reward that is dependent on the reward for joint successful completion of the task. After theoretical analysis the performance of the proposed algorithm is tested on a scenario of collaboration between material handling and patrolling robots in a warehouse.

Keywords: Multi-agent systems, Distributed control and estimation
Abstract: In dual decomposition, the dual to an optimization problem with a specific structure is solved in distributed fashion using (sub)gradient and recently also fast gradient methods. The traditional dual decomposition suffers from two main short-comings. The first is that the convergence is often slow, although fast gradient methods have significantly improved the situation. The second is that computation of the optimal step-size requires centralized computations, which hinders a fully distributed implementation of the algorithm. In this paper, the first issue is addressed by providing a tighter quadratic approximation of the dual function than what has previously been reported in the literature. Then a distributed algorithm is presented in which the provided dual function approximation is minimized in each step. Since the approximation is more accurate than the approximation used in standard and fast dual decomposition, the convergence properties are improved. For the second issue, we extend a recent result to allow for a fully distributed parameter selection in the algorithm. Further, we show how to apply the algorithm to optimization problems arising in distributed model predictive control (DMPC) and that the proposed algorithm enjoys distributed reconfiguration, i.e. plug-and-play, in the DMPC context.

Keywords: Cooperative systems, Control under communication constraints, Distributed control and estimation
Abstract: In this paper, we propose distributed optimization methods to solve systems of linear equations. We provide convergence analysis for both continuous and discrete time computation models based on linear systems theory. It is shown that the proposed computation approaches work for very general linear equations, scalable with data sets and can be implemented in distributed or parallel fashion. Furthermore, we show that the discrete time algorithm admits constant update step size in the presence of additive uncertainties. This robustness feature makes the approach computationally efficient and supplementary to the existing approaches to deal with uncertainties such as stochastic (sub-)gradient methods and sample averaging.

Keywords: Distributed control and estimation, Sensor networks, Control and estimation with data loss
Abstract: In a “censoring” or “send/no-send” approach to decentralized detection, sensors only transmit “informative” observations to the fusion center, which is able to significantly reduce energy consumption particularly when one hypothesis is more likely. The canonical “censoring” decentralized detection, however, assumes the communication channels between sensors and the fusion center are perfect, which is not quite realistic. We consider the problem of decentralized detection with censoring sensors over networks where packet dropout may occur. A sensor decides whether to use a high or low transmission power to communicate with the fusion center. With a general energy constraint, we prove that, to minimize the probability of error, the transmitting region of likelihood ratio associated with low power level is a single interval, and we derive necessary conditions for the lower and upper thresholds of this interval. For the special case that the available energy is sufficiently small, we show that the intervals have zero lower thresholds and can be determined independently for each sensor. A numerical example is provided to illustrate the main results.

Keywords: Flying robots, Perception and sensing, Guidance navigation and control
Abstract: This paper presents a review of the multidisciplinary approach to the design of a fleet of cooperative gliders capable of extended endurance operation. The flock of autonomous gliders is able to harvest energy from the environment, both through photo-voltaic energy generation and through exploitation of natural convective lift in the surrounding air, and act cooperatively to meet mission requirements and to share knowledge of the local environment. The paper begins with a brief overview of the total-energy approach required for such a feat, along with a short description of key system components and the principal technologies. This is followed by details of the evolution of a previously-developed architecture that supported autonomous thermaling, to an architecture that considers the total-energy budget in all flight segments, and utilizes the cooperative flight to maximize the cumulative energy capture while simultaneously meeting mission objectives.

Keywords: Flying robots, Autonomous robotic systems, Networked robotic system modeling and control
Abstract: A model predictive control based algorithm for maintenance of leader-follower formations of micro-scale aerial vehicles is proposed in this paper. The approach is designed for stabilization of teams of unmanned quadrotor helicopters via an onboard visual relative localization. The presented extension of the model predictive control with an additional planning horizon enables an integration of a novel obstacle avoidance function, which respects constraints imposed by the onboard localization. Deployment of the method in real-world scenarios without the necessity of using a pre-installed motion capture system is verified in various experiments. Requirements of practical utilization of the system are specified by theoretical analyses.

Keywords: Networked robotic system modeling and control, Networks of sensors and actuators, Flying robots
Abstract: In this paper, the impact of the main packet loss models on the Unmanned Aerial Vehicle (UAV) formation control has been evaluated. A simulation environment has been built to introduce a centralized architecture, usually employed in mobile robotics to pursue global tasks, in the presence of loss models affecting the communication through robots hops. Simulation results show that the control performance in terms of tracking, collision avoidance and loss of connectivity are affected by the specific characteristics of packet loss. This suggests that the design of UAV formation control over wireless network has to be carried out strongly taking into account the effects of specific packet loss characteristics (due to wireless protocol, disturbance, traffic) on the performance quality.

Keywords: Autonomous robotic systems
Abstract: Switching between the formation flight tactic and the dynamic encirclement tactic for a team of Unmanned Aerial Vehicles (UAVs) is done using a decentralized approach. A team formed from N UAVs, accomplishes a line-of-breast formation then dynamic encirclement around a desired target. A highlevel Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of the required formation tactic, while a combination of decentralized LMPC and Feedback Linearization (FL) is implemented on the UAV team to accomplish dynamic encirclement. During the simulations, Reynold’s rules of flocking are respected, where the UAVs avoid collisions with nearby flockmates, each UAV attempts to match the velocity of other team members and attempts to stay close to other flockmates. The linear plant, representing each UAV, is found through System Identification. The main contribution of this paper lies in the use of LMPC to implement multiple UAV tactics while ensuring stability and robustness of the system during tactic switching.

Keywords: Flying robots, Autonomous robotic systems, Guidance navigation and control
Abstract: UAVs are promising platforms for various missions such as remote sensing of agricultural products, forest fire surveillance, search-and-rescue and border monitoring. A relevant common challenge in the above missions is that of convergence to an objective circular orbit. To address this challenge for fixed-wing UAVs, intrinsic constraints such as nonholonomic nature of the vehicle, minimum and maximum forward velocity, maximum angular velocity and limited detection range must be considered. In this paper, a decentralized coordination strategy is developed so that a number of fixed-wing UAVs converge to an objective circle, respecting the mentioned constraints. Also, a priority-based collision avoidance scheme is proposed to avoid inter-UAV collision. Convergence of the system is proved by analysis of the finite state machine associated with the coordination algorithm. Simulation results are presented to verify the feasibility of the proposed approach.

Keywords: Flying robots, Cyber-Physical Systems, Networks of sensors and actuators
Abstract: In many future applications, such as pesticide spraying, unmanned aerial systems (UASs) are expected to operate cooperatively in a swarm configuration. A cyber-physical framework based on Centroidal Voronoi Tessellations (CVT) has been shown to provide optimal placement for sensors or actuators and can be used effectively for the control of a diffusion process such as a pathogen infection. However, previous work did not consider real-world constraints such as the effect of the UASs health on swarm utilization. While health degradation over time is inevitable, mismanaged swarms face expedited health degradation from over-utilization of one UAS over others. In this paper, a novel smart health balancing (SHB) system is implemented to extend the life of the most heavily taxed UASs and thus extending the life of the entire UAS swarm. The effectiveness of the proposed system is demonstrated with simulation results.