Keywords: Metabolic engineering
Abstract: Norbert Wiener’s 1948 Cybernetics presented a vision unifying the study of control and communication in the animal and the machine. Predating the discovery of the structure of DNA and the ensuing molecular biology revolution, applications in the life sciences at the time were limited. More than 60 years later, the confluence of modern genetic manipulation techniques, powerful measurement technologies, and advanced analysis methods is enabling a new area of research in which systems and control notions are used for regulating cellular processes at the gene level. We refer to this promising nascent field as Cybergenetics. This presentation describes novel analytical and experimental work that demonstrates how de novo control systems implemented with stochastic components can be interfaced with living cells and used to control their dynamic behavior. The feedback systems can either be realized on a computer (in-silico control) or through genetically encoded parts (in-vivo control). The two approaches will be compared and contrasted, and applications in biotechnology and therapeutics will be described.

Biography: Mustafa Khammash is the Professor of Control Theory and Systems Biology at the Department of Biosystems Science and Engineering (D-BSSE) at the Swiss Federal Institute of Technology in Zurich (ETH). He received his PhD in control theory at Rice University, Houston in 1990. From 1990 till 2001, he was on the faculty of Electrical Engineering at Iowa State University, Ames, Iowa. In 2001 he joined the Mechanical Engineering Department at the University of California at Santa Barbara (UCSB) where he served as the Director of the Center for Control, Dynamical systems, and Computations (CCDC) from 2006 till he joined ETH in 2011.

Working at the interface of systems biology, synthetic biology, and control theory, Khammash develops novel computational methods for the modeling, simulation, analysis, and control of biological networks. In the area of systems biology, he utilizes these methods for reverse engineering biological complexity, with particular interest in understanding the role of dynamics, feedback, and randomness in endogenous biological circuits. In the area of synthetic biology, his research focuses on creating the mathematical foundation and necessary tools for the robust control of living cells.

Khammash is a Fellow of the IEEE, IFAC, and the Japan Society for the Promotion of Science (JSPS). He is the recipient of the National Science Foundation Young Investigator Award, the Iowa State University Foundation Early Achievement in Research and Scholarship Award, the ISU College of Engineering Young Faculty Research Award, and the Ralph Budd Best Engineering PhD Thesis Award.

Keywords: Constraint and security monitoring and control
Abstract: This paper introduces a measurement based fuzzy logic controller for stabilizing large interconnected power systems af-ter the occurrence of a severe disturbance. This proposed fuzzy controller uses Wide Area Phasor Measurements as inputs and is triggered by a transient instability prediction scheme developed earlier. The effectiveness of the proposed algorithm has been demonstrated on the Venezuela power system. Simulation results demonstrate that appropriate control actions can be determined as early as 100 ms after a major fault leaving enough time to stabilize the power system.

Keywords: Power systems stability, Smart grids
Abstract: This paper presents a quantitative framework for assessment of voltage stability in smart power networks. First, new stability indices similar to gain and phase margins in linear time invariant control systems are introduced. Then, a novel risk assessment framework incorporating the new stability indices is developed to methodologically quantify the voltage stability risks a power system faces at any given operating condition. In contrast to existing local stability indices and qualitative risk approaches, the indices and framework introduced provide analytical and quantitative evaluation of voltage stability and associated risks. The results are illustrated with a numerical example.

Keywords: Power systems stability, Analysis and control in deregulated power systems
Abstract: Within this paper two techniques for online estimation of frequency and damping ratio of inter-area oscillations in a power system are compared. The techniques are the Kalman filtering technique and the wavelet transformation in combination with the random decrement technique. First, the two methods are compared regarding the accuracy of frequency estimation and the accuracy of damping estimation by use of a test system with known parameters. Second the capability of detecting changes in the frequency or in the damping ratio is investigated. Subsequently a combined approach consisting of the Kalman filtering technique as well as the wavelet transformation is developed to overcome the drawbacks of both methods. The usability of the developed method is finally shown on basis of measurement data of the European power system

Keywords: Power systems stability, Modeling and simulation of power systems, Smart grids
Abstract: In this paper a two terminal VSC-HVDC system embedded in a strong grid AC-environment is considered, emphasizing modeling, controllers design and small-signal stability analysis. Traditionally, DC cables are most often modeled by Pi-sections, and when using them for higher frequencies or in case of transmission over long distances, approximation accuracy aspects must be considered. Here, a distributed parameter cable model, based on the damped wave equation, is used to overcome this limitation. It is shown that the VSC-HVDC system can be described by a forward transfer function cascaded with a feedback loop. The first transfer function will be different, due to which input and output variables that are considered but is in all realistic cases stable. The feedback loop, where the forward path is a rational function and the return path is a dissipative infinite dimensional system, remains the same in all cases. The stability is then analyzed, using the Nyquist criterion, in a straight forward manner. Numerical examples are given by MATLAB.

Keywords: Power systems stability;
Abstract: This paper deals with the dynamic performance of the active power control of two or more generators electrically connected in parallel, the average power control is used to enable the generators to share the load equally with constant speed. The new system suffered from instability due to the interaction between the different loops. Base on the multi-variable linear equivalent machine system model, the system was analyzed and its stability limits were obtained using MATLAB. The system was simulated using Simulink. A phase-lag network was introduced in the load sharers, and had a major effect on the stability of the system which enables the increase of the gain of the controllers without breaking into instability.

Keywords: Intelligent control of power systems, Power systems stability
Abstract: Artificial Neural Networks (ANNs) are nonlinear maps that have the potential to make the realisation of practical nonlinear controllers possible. This paper is concerned with the development of a feedforward multilayer perceptron (MLP) Neural Network and its use as an Automatic Voltage Regulator (AVR) with Power System Stabiliser (PSS). The performance of the MLP excitation system is compared with that of a conventional AVR+PSS. The MLP shows good performance compared to that of the conventional excitation system.

Keywords: Asymptotic stabilization, Convex optimization, Sum-of-squares
Abstract: In this paper we introduce the concept of universal stabilizability: the condition that every solution of a nonlinear system can be globally stabilized. We give sufficient conditions in terms of the existence of a control contraction metric, which can be found by solving a pointwise linear matrix inequality. Extensions to approximate optimal control a straightforward. The conditions we give are necessary and sufficient for linear systems and certain classes of nonlinear systems, and have interesting connections to the theory of control Lyapunov functions.

Keywords: Asymptotic stabilization, Lyapunov methods
Abstract: We are motivated by the idea of finding feedback laws for rotations that only require transmission of scalar signals. In particular, we develop an extremum seeking control law for systems living on the special orthogonal group. In addition, we equip our system with the capability of obstacle avoidance using the idea of navigation functions, along the lines of Koditschek and Rimon [1990].

Keywords: Stability of nonlinear systems, Systems biology
Abstract: The problem of scalability of trajectories in homogeneous and locally homogeneous systems is considered. It is shown that the homogeneous systems have scalability property, and locally homogeneous systems possess this property approximately. The issue of closeness of trajectories for the system and its local homogeneous approximation is investigated. The notions of scale invariance and fold change detection are also studied. The results are illustrated by a cascade system analysis.

Keywords: Lyapunov methods, Application of nonlinear analysis and design, Stability of nonlinear systems
Abstract: For a 3-link planar robot moving in the vertical plane with a single actuator, this paper studies the effect of its actuator configuration from the perspective of the energy-based control. For the control objective of simultaneous stabilization of the actuated variable and of the desired level of the total mechanical energy of the robot corresponding to those of the upright equilibrium point, where all three links of the robot are in the upright position, this paper presents an energy-based controller for three configurations of the single actuator in a unified way. Moreover, this paper carries out a global motion analysis of two configurations of the robot under the presented controller. Specifically, this paper shows that the control objective is achievable for the robot with the actuator at joint 1 for almost all initial conditions of angular displacements and velocities without any condition on the mechanical parameters of the robot, and the control objective is achievable for the robot with the actuator at joint 2 for almost all initial conditions provided that another condition on the mechanical parameters of the robot is satisfied. The numerical simulation shows that the presented control can be applied to the swing-up and stabilizing control for a physical 3-link robot with a single actuator at joint 1 or 2.

Keywords: Stability of nonlinear systems, Lyapunov methods, Switching stability and control
Abstract: A substantial literature exists on the stability and control of 2D systems, including repetitive processes where iterative learning control algorithms designed in this setting have been experimentally verified. Most of this development has assumed that the dynamics can be adequately represented by linear state-space models, but applications exist where this assumption does not hold. This paper contributes to the development of a stability theory for nonlinear discrete-time 2D systems, where main results are on the use of vector Lyapunov functions to characterize exponential stability. The analysis includes systems where random failures modeled by a Markov chain with a finite set of states can arise in an iterative learning control application. An illustrative example is also given.

Keywords: Asymptotic stabilization, Nonlinear observers and filter design, Stability of nonlinear systems
Abstract: Discovery of undesirable hidden oscillations, which cannot be found by the standard simulation, in phase-locked loop (PLL) showed the importance of consideration of nonlinear models and development of rigorous analytical methods for their analysis. In this paper for various signal waveforms, analytical computation of multiplier/mixer phase-detector characteristics is demonstrated, and nonlinear dynamical model of classical analog PLL is derived. Approaches to the rigorous nonlinear analysis of classical analog PLL are discussed.

Keywords: Fault detection and diagnosis, Time series modelling, Recursive identification
Abstract: The object of this paper is to address data-driven fault detection design for systems with unsteady trend, which shows cyclicity, monotonicity and non-zero mean. Firstly, mean theorem and covariance theorem are proposed and proved. The former is the mean property of projection matrix, and the latter is the recursive formula for covariance matrix of regression residual. Secondly, an improved fault detection statistic, called Least Square T2 (LST2), is proposed. It can partly solve the detection problem for systems with unsteady trend. The improvement can also partly cope with the limitations of the traditional multivariate detection methods, such as Principal Component Analysis (PCA). Thirdly, based on the two theorems, a recursive algorithm and a moving window algorithm of LST2 are given, thus both time and space complexity are greatly reduced for online detection. The effectiveness of the presented detection statistic is evaluated with an application of monitoring satellite attitude control system. The case study result shows that the false alarm rate of LST2 is much lower than that of T2 based on PCA, while LST2 is more sensitive to fault.

Keywords: Fault detection and diagnosis
Abstract: Based on a consistent interface between a data-driven and a model-driven approach within an interval framework, the paper deals with the detection of two important electrical flight control system failure cases of aircraft control surfaces, namely runaway and jamming. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and for achieving sustainability goals. The motivation behind this work is the development of an original set-membership methodology for fault detection where a data-driven characterization of random noise variability (which is not usual in a bounded error context) is combined with a model-driven approach based on interval prediction in order to improve the accuracy of the overall detection scheme. The efficiency of the proposed methodology is illustrated through simulation results using data sets recorded on a highly representative aircraft benchmark.

Keywords: Fault detection and diagnosis, Bayesian methods, Error quantification
Abstract: In order to sustain safety and high product quality, the data-driven fault detection tools are increasingly used in industrial processes. The quality variables are the key index of the final product. Obtaining them in high frequency is time-consuming in the laboratory because they require the efforts of experienced operators. Meanwhile, process variables such as the temperature, the pressure, and the flow rate can be readily sampled in high frequency; hence the sample size between the process and the quality data is quite unequal. To effectively integrate two different observation sources, the high-rate process measurements and low-rate quality measurements, a semi-supervised regression model with probabilistic latent variables is proposed in this article to enhance the performance monitoring of the variations of the process and the quality variables. The corresponding statistics are also systematically developed and a TE benchmark problem is presented to illustrate the effectiveness of the proposed method.

Keywords: Fault detection and diagnosis, Nonlinear system identification
Abstract: In this paper, a new fault diagnosis methodology based on nonlinear system modelling and frequency analysis is developed. The method is derived via resolving several fundamental issues associated with conducting system fault diagnosis using the NARMAX (Nonlinear Auto-Regressive Moving Average with eXougenous inputs) modelling and NOFRFs (Nonlinear Output Frequency Response Functions) based frequency analysis, which was recently proposed and extends the well-known linear system modelling and FRF (Frequency Response Function) based frequency analysis to the nonlinear case. Simulation studies verify the effectiveness of the new method and demonstrate the performance of the method when applied to address a mechanical structural system fault diagnosis problem. The new methodology has the potential to resolve a wide range of input output data based engineering system fault diagnosis problems

Keywords: Fault detection and diagnosis, Nonparametric methods, Estimation and filtering
Abstract: This paper proposes off-line and on-line data-driven approaches to anomaly detection based on generalized likelihood ratio tests for a bias change. The procedure is divided into two steps. Assuming availability of a nominal dataset, a nonparametric density estimate is obtained in the first step, prior to the test. Second, the unknown bias change is estimated from test data. Based on the expectation maximization (EM) algorithm, batch and sequential maximum likelihood estimators of the bias change are derived for the case where the density estimate is given by a Gaussian mixture. Approximate asymptotic expressions for the probabilities of error are suggested based on available results. Real world experiments illustrate the approach.

Keywords: Fault detection and diagnosis, Identification for control, Filtering and smoothing
Abstract: The paper proposes a new method for a kind of parametric fault online diagnosis with state estimation jointly. The considered fault affects not only the deterministic part of the system, but also the random circumstance. The proposed method first applies Kalman Filter (KF) and Maximum Likelihood (ML) technique to identify the fault parameter, and then employs the obtained result to make fault diagnosis decision based on a predefined threshold. Then these estimated parameters are substituted into the parameterized state estimation of KF to obtain the state estimation. Finally, a robot case study with two different fault scenarios shows the proposed method can lead to a good performance in terms of fast and accurate fault diagnosis and state estimation.

Keywords: Multi-agent systems, Cooperative systems
Abstract: This paper studies the delay robustness of a class of periodically edge-event driven synchronous consensus protocols in time-invariant networks. These protocols have the benefits of improved performance at reduced communication and computation costs. Under the assumption that all information links share a common time-varying transmission delay, we give non-conservative estimates of the maximum allowable time-delay and event-detecting period for solving the average consensus problem in terms of the algebraic structure of interaction topologies. Furthermore, rigorous stability analysis shows that the proposed technique is also applicable to the asynchronous consensus with multiple time-delays.

Keywords: Multi-agent systems, Distributed control and estimation, Sensor networks
Abstract: This paper studies the multi-agent consensus problem of complex networks consisting of general homogeneous continuous linear time-invariant systems with controls under a time-invariant directional communication topology. A new approach based on relative-input-output is proposed to solve the problem. In contrast to a popular method where each agent is equipped with an observer, and hence involving heavy observer dynamics, the proposed approach is static and utilizes relative-input and relative-output eliminating the observers aforementioned, therefore is practically much simpler and easier to implement significantly reducing computational and communicational burdens and costs. Extension of the framework is then made to tackle a dual problem: consensus of distributed filtering without controls. Two numerical examples are provided to validate the proposed approach.

Keywords: Multi-agent systems, Coordination of multiple vehicle systems
Abstract: This paper considers the problem of attitude synchronization and formation keeping of two spacecrafts, with a leader. Attitudes of spacecrafts are not measured directly, instead control torques are determined from the line-of-sight (LOS) vectors between two spacecrafts, and another set of LOS vectors from a common leader. We provide distributed position and attitude control laws. Attitude control law is proposed to ensure that the two follower spacecrafts reach attitude consensus. A formation keeping position control law is also proposed so that desired distance between two follower spacecrafts and each spacecraft to the leader is achieved. Proposed position control law uses relative velocities and LOS vectors in the respective body frames of the two spacecrafts. The state feedback laws proposed in this work guarantee almost semi-global asymptotic stability of the desired closed-loop equilibrium.

Keywords: Multi-agent systems
Abstract: This work considers the convergence rate of multi-agent systems with discrete-time single-integrator dynamics and undirected interaction topologies. In recent work it has been proven that in case of lattice interaction topologies the convergence rate can be bounded away from zero, independent of the network size, using asymmetric weightings that give the interaction graph a preferred communication direction. Approximation methods for more general graphs, based on relative angles between agents, are presented, which suggest that the convergence rate is bounded away from zero as well. This work proposes alternative approximation methods, that improve the convergence rate compared to previous approximation methods. Furthermore it is shown that the improvement of the approximation methods degrade in comparison to other weighting approaches, the more the considered topology differs from a lattice graph. Therefore an iterative algorithm is proposed, that extends the idea of a preferred communication direction to general graphs, which are not similar to lattices or where the relative angles are not known.

Keywords: Multi-agent systems, Cooperative systems
Abstract: Motivated by practical applications in satellite formations and antenna arrays, the problem of targeting a linear array of point sources at one common point of interest is formulated and then solved using a novel distributed control strategy. The point sources are located collinearly and the two at the two ends orient readily to the target point. The other point sources have to rely on sensing the changes of the relative orientation angles of their nearest neighbors to adjust their own orientations; we rigourously prove that under our control law using only local information, their orientation lines will intersect at the same point of concurrency as the two point sources at the two ends. The crucial idea behind our designed control law is the intuitive argument from plane geometry that reducing the differences between the distances to the baseline of the pairwise intersection points of the orientation lines will help realizing concurrent targeting. This idea is further utilized to construct a key argument in our analysis about the boundedness and the exponential convergence speed of the orientation angles. Numerical simulations are used to validate the effectiveness of our control strategy.

Keywords: Multi-agent systems, Control over networks
Abstract: This brief studies the causal consensus problem of singular (descriptor) multi-agent systems with networked communication delays and agents described by general singular systems. For the studied systems, only the information of outputs are available through the network. An observer-based networked predictive control scheme is employed to compensate for the communication delays actively. Furthermore, based on the output feedback, observer and the networked predictive control scheme, a novel protocol is proposed in this paper. By using the tools of graph, algebra and singular system theory, the necessary and sufficient conditions are given for the existence of the proposed protocol to solve the considered consensus problem. The given conditions depend on not only the topologies of singular multi-agent systems but also the structure properties of each agent dynamics. Moreover, a consensus algorithm is proposed to design the novel observer-based predictive protocol. A numerical example demonstrates the effectiveness of compensation for networked delays using the provided consensus algorithm.

Keywords: Process control, Chemical engineering, Industrial applications of optimal control
Abstract: Model-based optimization (MBO) has been widely applied for quality control of batch processes, however, it is not easy to obtain globally effective and accurate quality model with affordable effort. Instead of building a quality model, model-free optimization (MFO) uses process data directly, which is more efficient and economic for quality control of batch process. Considering the complex nonlinearity and dynamics in batch process, a quality control scheme using natural gradient based dynamic optimization is proposed in this paper. Optimization algorithm is developed from the aspect of manifold in non-Euclidean space. An approximation method is derived for the calculation of the natural gradient, and a multivariate iterative sensitivity matrix based on Riemannian geodesic distance is proposed to obtain a novel adaptive stepping strategy. The proposed quality control scheme has been verified in injection molding process. A set of comparison tests are presented to demonstrate the feasibility and effectiveness of the proposed method.

Keywords: Linear systems, Process control, Control of constrained systems
Abstract: Basically conventional controllers operate on a crisp set point, even if the control task does not require an exact value of the controlled variable. In fact the requirements for the controlled variable can often be described as intervals of acceptable or ideal ranges. In this paper we present an approach of a predictive functional controller combined with fuzzy decision making, which leads to a controller that operates on complete fuzzy goals. This approach is demonstrated for simple processes with dead time and the performance is analyzed by comparison with conventional controllers.

Keywords: Process control, Model following control, Robust control (linear case)
Abstract: This paper makes contribution in the area of model based predictive control (MPC) and in particular examines to what extent structured optimisation dynamics can improve regions of attraction, performance and computational burden in the robust case. This paper focuses on the use of structured prediction dynamics within a robust triple mode MPC context and demonstrates that these can simplify the design significantly while retaining good robust feasibility regions and performance. The improvements, with respect to existing algorithms are demonstrated by numerical examples using statistical analysis.

Keywords: Linear systems, Robust control, Process control
Abstract: This paper presents the results of an engine air-path Hinf state feedback switched linear controller. The main advantage of this approach is that controller gains are calculated offline, with the robust specifications allowing fast calculation and is cost-effective in calculation and resources.

The air-path system dynamics are governed by the Saint-Venant equations, difficult to handle in control synthesis due to its nonlinear properties (states - control inputs coupling, nonlinear functions). To employ advanced control techniques, a switched linear state space representation is proposed. The model is simplified considering that slow dynamics are quite constant.

The proposed approach provides a robust state feedback control toward perturbations (model error, unmodeled dynamics for example) by considering these disturbances as unknown inputs.

Keywords: Process control
Abstract: This paper treats the trade-off between robustness, load disturbance attenuation, and measurement noise injection for PI and PID control using Lambda, SIMC, and AMIGO tuning. The effects of measurement noise are characterized by SDU, which is a measure of noise activity analogous to the IAE commonly used to characterize performance of load disturbance response. Simple design rules to choose the filter time constant for PI and PID controllers are also given for the three tuning methods.

Keywords: Identification and control methods, Mechatronic systems, Human centred automation
Abstract: This paper focuses on the control of the mechanical impedance of an aircraft yoke. The control architecture involves an inner position servo-loop and an outer loop feedbacking the torque/force measurement to the position reference input through the required admitance model. Two different approaches are presented for the inner position servo-loop design: a classical Proportional-Derivative control and a structured H1 control. These two approaches are compared from the performance/robustness trade off point of view. The performance and robustness indexes are respectively the maximal variation on the required admittance model and the maximal pilot own impedance supported by the closed loop system before to become unstable. These indexes are computed using real µ-analysis. Both approaches are implemented on an experimental test-bed. Analysis and exprimental results with a pilot in the loop confirm that the structured Hinfinity controller is the best solution.

Keywords: Identification and control methods, Motion Control Systems, Mechatronic systems
Abstract: An H-infinity synthesis method for control of a flexible joint, with non-linear spring characteristic, is proposed. The first step of the synthesis method is to extend the joint model with an uncertainty description of the stiffness parameter. In the second step, a non-linear optimisation problem, based on nominal performance and robust stability requirements, has to be solved. Using the Lyapunov shaping paradigm and a change of variables, the non-linear optimisation problem can be rewritten as a convex, yet conservative, LMI problem. The method is motivated by the assumption that the joint operates in a specific stiffness region of the non-linear spring most of the time, hence the performance requirements are only valid in that region. However, the controller must stabilise the system in all stiffness regions. The method is validated in simulations on a non-linear flexible joint model originating from an industrial robot.

Keywords: Identification and control methods, Motion Control Systems, Mechatronic systems
Abstract: This paper developes a discrete-time variable structure control method for servo drives. The objective is to achieve tungless control in industrial servo systems. The developed control method is experimentally evaluated for load variations and parameter uncertainties. Experiments are performed on typical automation applications: a 400 W servo motor with a belt-drive connection and a 400 W servo motor with a ball-screw connection. Various inertia loads are tested. The performance of the developed method is compared with the conventional servo drive controller for all cases. The experimental results show that the developed discrete-time variable structure control control provides better performance characteristics.

Keywords: Identification and control methods, Motion Control Systems, Robotics technology
Abstract: In this paper we present the Model Predictive Control (MPC) with dynamic constraints for generating dynamic walking for the compliant humanoid COMAN. The dynamics of the robot are modeled using the cart-table model which allows the generation of a dynamically balanced gait given a planned walking pattern based on the Zero Moment Point (ZMP). Our simulation study of the MPC’s implementation on bipedal walking finds out that a large receding and control horizons are needed to track a predefined walking pattern, leading to numerical instability. Therefore, the Extended Prediction Self-Adaptive Control (EPSAC) approach for MPC has been used and a method based on the analysis of the Singular Value Decomposition (SVD) is presented as new contribution to guarantee feasibility, robustness and stability of the MPC formulation. Study on an inverted pendulum and the COMAN humanoid prove that the proposed strategy improves the robustness and stability of the original EPSAC controller, in both well or ill conditioned systems. The simulation results finally demonstrate that the proposed methodology is well suited to smoothly track a dynamic walking pattern.

Keywords: Identification and control methods, Robots manipulators, Information and sensor fusion
Abstract: Off-line robot dynamic identification methods use the Inverse Dynamic Identification Model (IDIM), which calculates the motor torques that are linear in relation to the dynamic parameters of both links and drive chains, and use linear least squares technique (IDIM-LS technique). For most robots, the only available data are the motor position and the motor torques which are calculated as the product of the known current reference signal by the joint drive gains. Then the accuracy of links parameters may be limited by noise and error modeling in the drive chains. The Kuka LWR robot (industrial version IIWA: Intelligent Industrial Work Assistant) gives the possibility for an industrial robot to investigate this problem using the joint torque sensors data, measured at the output of the harmonic drive geared drive chains, to identify only the links inertial parameters without the errors coming from the drive chains. This paper focuses on the comparison of the accuracy of the identification of the dynamic parameters of the rigid model of the LWR4+ version, which is very popular in robotics research, using measures of the motor positions and the motor currents, or the torque sensors measurements or both side data. This paper is giving a first complete and reliable identified rigid dynamic model of the LWR4+, publicly available for the robotics community. Moreover, this work shows for the first time the strong result that motor torques calculated from motor currents can identify the links inertial parameters with the same accuracy than using joint torque sensors at the output of the joint drive chains.

Keywords: Identification and control methods, Vibration control, Motion Control Systems
Abstract: This paper presents a method for the on-line identifcation of the payload carried by a single-link fexible manipulator whose movement is constrained to the vertical plane. The vertical movement of the robot makes the effect of gravity not negligible, and determines therefore, the non-linear behaviour of the analysed system. The proposed estimator is based on the algebraic derivative approach in the frequency domain. The short period of time it takes for the algorithm to make an estimate, and its low computational burden make it suitable for real-time and adaptive control applications. The proposed estimator is experimentally tested in several situations carefully chosen to reproduce routine operations of the fexible manipulator.

Keywords: Clinical trial, Control of voluntary movements, respiration, Healthcare management, disease control, critical care
Abstract: Current practice in determining Mechanical Ventilation (MV) settings is highly variable with little consensus, forcing clinicians to rely on general approaches and clinical intuition. The Clinical Utilisation of Respiratory Elastance (CURE) system was developed to aid clinical determination of important MV settings by providing real-time patient-specific lung condition information at the patient bedside. The pilot clinical trials to investigate the performance and efficacy of this system are currently being carried out in the Christchurch Hospital ICU, New Zealand. This paper presents the CURE clinical trial protocol and its initial findings from the two patients recruited to date. In particular, this paper focuses on CURE’s ability to determine patient-specific responses in real time to PEEP changes and recruitment manoeuvres (RM). The results from this study demonstrate the potential for CURE Soft to improve the reliability and ease with which clinicians make decisions about MV settings in the ICU.

Keywords: Physiological Model, Model formulation, experiment design, Developments in measurement, signal processing
Abstract: This work presents the development of a systemic mock circulation loop designed for testing of an axial flow, pneumatically operated left ventricular assist device (LVAD). A simulation of the heart proximal systemic circulation has been implemented that resembles the environment in which the LVAD will be operating, from where a hydraulic system, with several reconsiderations, has been constructed. An alternative method for measurement of the pulsatile aortic flow is suggested. A significant match between the simulated and measured system responses is demonstrated which substantially simplifies the search for parameter sets that determine the hemodynamic behavior of the system. Due to a high degree of flexibility and a wide range of hemodynamic states that can be simulated, the presented mock circulation is suitable for in-vitro experiments with the LVAD.

Keywords: Bio-signals analysis and interpretation, Identification and validation, Decision support and control
Abstract: This work addresses the research and implementation of methodologies for time series prediction, mainly to support the early detection of critical events. To this aim, a wavelet decomposition based scheme is proposed to estimate the evolution trend of physiological signals. The scheme does not involve the explicit development of a model and is essentially supported on the hypothesis that future evolution of a biosignal can be estimated from similar historic patterns. The strategy considers an à-trous wavelet decomposition, where the most representative trends are extracted from the historic similar patterns. Then, a set of distance-based measures able to assess the prediction likelihood of each representative trend is introduced. From these measures and through an optimization process, a subset of these trends is selected and aggregated to derive the required time series evolution trend. The performance of the scheme was, in a first phase, compared with other typical prediction strategies, namely the autoregressive integral moving average model and the generalized regression neural network. Then, in a second phase, the effectiveness of the methodology was tested in the assessment of arrhythmic episodes using heart rate measurements computed from ECG signals collected in the context of TEN-HMS telemonitoring study.

Keywords: Pharmacokinetics and drug delivery, Control of physiological and clinical variables, Physiological Model
Abstract: Amiodarone is an antiarrhythmic drug that exhibits highly complex and non-exponential dynamics whose controlled administration has important implications for its clinical use especially for long-term therapies. Its pharmacokinetics has been accurately modelled using a fractional-order compartmental model. In this paper we design a fractional-order PID controller and we evaluate its dynamical characteristics in terms of the stability margins of the closed loop and the ability of the controlled system to attenuate various sources of noise and uncertainty.

Keywords: Pharmacokinetics and drug delivery, Decision support and control, Intensive and chronic care or treatment
Abstract: Bivalirudin is a direct thrombin inhibitor used in the cardiac intensive care unit in patients who develop an allergic reaction to heparin. Since it is not a commonly used drug, clinical experience with its dosing is sparse. In earlier work we developed a dynamic system model that accurately predicts the effect of bivalirudin when given dosage over time and patient physiological characteristics. This paper develops adaptive dosage controllers that regulate its effect to desired levels. To that end, and in the case that bivalirudin model parameters are available, we develop a Model Reference Control law. In the case that model parameters are unknown, an indirect Model Reference Adaptive Control scheme is applied to estimate model parameters first and then adapt the controller. Our algorithms are validated using actual data from a large hospital in the Boston area.

Keywords: Physiological Model, Decision support and control, Intensive and chronic care or treatment
Abstract: Mathematical model simulations may assist in the selection of mechanical ventilator settings. Previously, simulations have been limited to control ventilator modes, as these models lacked representation of respiratory control. This paper presents integration of a chemoreflex respiratory control model with models describing: ventilation and pulmonary gas exchange; oxygenation and acid-base status of blood; circulation; interstitial fluid and tissue buffering; and metabolism. A sensitivity analysis showed that typical response to changing ventilator settings can be described by base excess (BE), production of CO₂ (VCO₂), and model parameters describing central chemoreceptor behavior. Since BE and VCO₂, can be routinely measured, changes in ventilator support may therefore be used to identify patient-specific chemoreceptor drive, enabling patient-specific predictions of the response to changes in mechanical ventilation.

Keywords: Vehicle dynamic systems, Automotive system identification and modelling
Abstract: The vehicle mass and its distribution on the vehicle is an important information for several applications, from safety-oriented control systems to fleet management in public transport, courier services, garbage collection and so on. Specifically, an online estimation of such quantities must be provided and the results should be available relying on a minimum set of sensors, to reduce costs and possibly avoid an ad-hoc instrumentation. This paper proposes a frequency-based data processing scheme to address this issue, which relies on vertical acceleration and speed angular velocity measurements only. The validity of the approach is assessed both on simulations in a multibody environment and on experimental data.

Keywords: Adaptive and robust control of automotive systems, Vehicle dynamic systems
Abstract: Road profile is considered as an essential input that affects the vehicle dynamics. An accurate information of this data is fundamental for a better understanding of the vehicle behavior, and vehicle control systems design. The present paper presents a novel algorithm (observer) suitable for real-time estimation of vertical road profile. The developed approach is based on a quarter-car model, and on elementary measurements delivered by potentially integrable sensors. The road elevation is modeled as a sinusoidal disturbance signal affecting the vehicle system. Since this signal has unknown and time-varying characteristics, the proposed estimation method implements an adaptive control scheme based on the internal model principle and on the use of the Youla-Kucera parametrization technique (also known as Q-parametrization). For performances assessment, estimations are comparatively evaluated with respect to measurements issued from the LPA (Longitudinal Profile Analyzer) profiler during experimental trials. Further, this new method is compared to the approach provided in (Doumiati et al. [2011]), where a Kalman filter is applied assuming a linear road model. Results show the validity and efficiency of the present observer scheme.

Keywords: Automotive sensors and actuators, Sensors and actuators, Vehicle dynamic systems
Abstract: This paper proposes one new type of speed measurement sensor using difference structure to solve the problem of measurement errors resulted by the vibration of low-speed urban maglev train. By the design of the detection coils and the signal detection circuits, the sensor could fulfills the requirements of rapid and accurate detection. The experiment results show that the speed measurement sensor could effectively overcome the affect of the detection distance changes. The aim of reducing the locating errors caused by the vibration of the train body is achieved. At the same time, the measurement resolution is also increased.

Keywords: Automotive sensors and actuators, Autonomous Vehicles, Automatic control, optimization, real-time operations in transportation
Abstract: The automotive industry is increasingly using electrical control systems in order to assist the driver of a vehicle or to automate certain maneuvers. This paper describes the conversion of a Formula SAE car into a vehicle with autonomous drive capability. The setup of the low-level controller and actuators are discussed. The systems used to convert the vehicle to drive-by-wire acceleration, braking and steering are documented along with details of the low-level controller and safety systems.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Automotive sensors and actuators, Engine modelling and control
Abstract: This paper presents a novel approach of Particulate Material (soot) measurement in a Diesel particulate filter using Electrical Capacitance Tomography. Modern Diesel Engines are equipped with Diesel Particulate Filters (DPF’s), as well as on-board technologies to evaluate the status of DPF because complete knowledge of DPF soot loading is very critical for robust efficient operation of the engine exhaust after treatment system. In course of time, soot will be deposited inside the DPFs which tend to clog the filter and hence generate a back pressure in the exhaust system, negatively impacting the fuel efficiency. To remove the soot build-up, regeneration (active or passive) of the DPF must be done as an engine exhaust after treatment process at pre-determined time intervals. Since the regeneration process consume fuel, a robust and efficient operation based on accurate knowledge of the particulate matter deposit (or soot load) becomes essential in order to keep the fuel consumption at a minimum. In this paper, we propose a sensing method for a DPF that can accurately measure in-situ soot load using Electrical Capacitance Tomography (ECT). Simulation results show that the proposed method offers an effective way to accurately estimate the soot load in DPF. A hardware-in-loop bench has been built to further develop this sensing concept and experimentally verify the associated measurement technology. Preliminary experimental data is very promising. The proposed method is expected to have a profound impact in improving overall PM filtering efficiency (and thereby fuel efficiency), and durability of a Diesel Particulate Filter (DPF) through appropriate closed loop regeneration operation.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Automotive system identification and modelling, Automotive sensors and actuators
Abstract: In modern cars, the hydraulic power steering (HPS) is continuously substituted by the electric power steering (EPS), active front steering (AFS) or steer-by-wire (SbW). In general, the main task of the EPS/HPS is the support of the driver in controlling the lateral position of the car and compensating the tyre aligning torques. These torques result in a steering rack force, which reliable long-term measurement induces disadvantages e.g. high sensor costs. Since the knowledge of the steering rack force is useful to improve various automotive control applications, the estimation of the steering rack force with real-time capable algorithms is in the focus of this research. First, a non-linear dynamic 4-mass model is given and validated by a prototype EPS. Second, an algorithm for steering rack force estimation is introduced using a non-linear friction compensation module and a linear disturbance observer. Finally, the estimation algorithm is analysed by means of validated numerical EPS model, a steering test bench and a real prototype car. The results state the excellent performance of the estimation algorithm, even under real operation conditions.

Keywords: Robots manipulators, Intelligent robotics, Motion Control Systems
Abstract: In this paper, a new Behavioral-based Particle Swarm Optimization algorithm is proposed in order to solve the inverse kinematics problem for a manipulator operating in an environment cluttered with obstacles. The introduced variant of the Particle Swarm Optimization relies on the idea of dividing the population of the particles in subgroups, each of which with a specific task, achieving in this manner a faster convergence to the final result. The proposed algorithm is exploited in order to solve the inverse kinematics problem for a generic serial manipulator both in its dexterous and reachable workspace.

Keywords: Robots manipulators, Embedded robotics, Networks of sensors and actuators
Abstract: In this paper, a networked embedded control of modular robot manipulators without using joint torque sensing is presented. The proposed solution uses an effective control and communication mechanism based on the virtual decomposition control (VDC) approach with embedded FPGA (Field Programmable Gate Array) implementation without using joint torque sensing. A hierarchical master-slaves control structure is used, supported by a high speed communication data bus. The master computer handles only kinematics computation and the dynamics-based computations are all performed by individual embedded FPGA module controllers. The virtual stability of each module is ensured, resulting in the L2/L∞ stability of the entire robot. Experimental results achieved on a three-module robot manipulator using harmonic drives are presented.

Keywords: Robotics technology, Mechatronic systems, Robots manipulators
Abstract: Variable Stiffness Actuators (VSA) are currently being regarded as promising actuator types in robotics research especially due to their capability to store potential energy in their elastic elements and to control this energy by altering the elastic properties of these elements. The controllable potential energy enables these actuators to outperform their rigid counterparts especially when realizing fast explosive motions. Most of joint designs with VSA’s, however, are described by nonlinear deflection-torque relations and consequently a thorough analysis regarding their maximum attainable performance is difficult. In this work, we tackle this problem by using Pontryagin’s Minimum Principle and develop a general method to solve the optimal control problem of minimizing any given terminal cost for these joints. In other words, we show the optimal control strategies to alter VSA’s elastic properties for various tasks such as maximization of the final link velocity or time-optimal tracking, which are all found to depend on the change of the system’s potential and kinetic energy relative to its total energy. The application of the method is illustrated for VSA’s with adjustable linear and cubic springs, where the potential energy stored in the springs is maximized at a given final time.

Keywords: Robots manipulators, Robotics technology, Identification and control methods
Abstract: In this work, we propose a solution to the inverse kinematics problem based on the differential kinematics and on a recently proposed algorithm which estimates the inverse of the Jacobian matrix dynamically. The output of the algorithm can be interpreted as a filtered inverse (FI) of the Jacobian matrix. An interesting property of the FI algorithm is its ability to cope with kinematic singularities. The update law of the estimator is driven by error signals that consider both the left and the right inverse matrices, thus enabling trajectory tracking and minimization of the control effort simultaneously. This paper shows that the FI algorithm can be applied to a Jacobian matrix augmented with additional constraints, which allows for setting the priority or weight to different control objectives, such as obstacle avoidance. Simulation results are presented to illustrate the performance and feasibility of the proposed solution.

Keywords: Intelligent robotics, Mobile robots, Robots manipulators
Abstract: This article presents a multi-agent approach for controlling autonomous mobile manipulators. The proposed approach assigns a hybrid agent (Mobile base agent) for the control of the mobile base, a reactive agent (Joint agent) to each degree-of-freedom (dof) of the manipulator, and a Supervisory agent to assure coordination and to synchronize the work of the whole agents of the system. The initial simulation results, obtained via different positioning tasks on RobuTER/ULM with and without considering breakdowns, show that the main advantage of such an approach is that it pledges a fault-tolerant response to various types of breakdowns without adding any specific dysfunction treatment.

Keywords: Robots manipulators, Application of mechatronic principles, Modeling
Abstract: This paper presents a novel control architecture for operational space control when the end-effector or the robotic chain is kinematically constrained. Particularly, we address kinematic control of robots operating in the presence of obstacles with complex geometry, such as point, line, or barrier constraints imposed on a point on the manipulator. The main advantage of the proposed approach is that we are able to control the end-effector motion in the normal way using conventional operational space control schemes, but by re-writing the Jacobian matrix we also guarantee that the constraints are satisfied. The most challenging problem of obstacle avoidance of robotic manipulators is the extremely complex structure that arises when the obstacles are mapped from the operational space to joint space. We solve this by first finding a new set of velocity variables for a point on the robot in the vicinity of the obstacle, and on these new variables we impose a certain structure which guarantees that the robot does not hit the obstacle. We then find a mapping denoted the Constrained Jacobian Matrix from the joint variables to these new velocity variables and use this mapping to find a trajectory in velocity joint space for which the constraints are not violated. We present for the first time the Constrained Jacobian Matrix which imposes a kinematic constraint on the manipulator chain and show the efficiency of the approach on our experiments on a real robot.

Keywords: Process control applications, Expert systems in process industry, Control system design
Abstract: Fused magnesium furnaces (FMF) are widely used in China to extract fused magnesia through a smelting process that has to be well controlled to assure high product output and low power consumption. With manual control, which is still widely used in practice, it is difficult to adjust the setpoints for control loop involved, thereby leading to compromised product output and increased power consumption. In this paper, an operational feedback control method is proposed to control the technical indices within their desired range. This is realized by on-line adjusting the setpoints of control loops for optimal operation of the FMF in response to changes in operating points. The techniques used consist of case based-reasoning (CBR), rule based-reasoning (RBR), neural network (NN), iterative learning control (ILC), in addition to classic PI and PID control. The proposed method and the developed control system have been applied to a real FMF. Industrial applications show the usefulness and effectiveness of the proposed operational feedback control method and its promises in other industrial processes with similar features.

Keywords: Process control applications, Nonlinear process control, Advanced control technology
Abstract: Slugging flow in the offshore oil & gas production attracts lot of attention due to it's limitation of production rate, periodic overload on processing facilities, and even direct cause of emergency shutdown. This work aims at two correlated objectives: (i) Preventing slugging flow; and meanwhile, (ii) maximizing the production rate at the riser of an offshore production platform, by manipulating a topside choke valve through a learning switching model-free PID controller. The results show good steady-state performance, though a long settling time due to the unknown reference for no slugging flow.

Keywords: Industrial applications of process control, Modeling and simulation of power systems, Control system design
Abstract: The Distributed Control System (DCS) "Siemens Power and Process Automation" (SPPA) T3000 for process automation of power plants enables testing of PLC code in a virtual environment based on process models. However, for generating and parameterizing process models, there exist more specialized tools like Matlab/Simulink. This paper is about how to transfer models from Matlab/Simulink to SPPA-T3000. Based on a newly developed Automation Function Instance (AFI) SPPA-T3000 Simulink-Library, which maps the SPPA-T3000 modeling blocks to Simulink blocks with the same functionality, a process model can be designed, optimized and tested in Simulink. Thereafter, this Simulink process model can be converted automatically into SPPA-T3000 Function Plan (FUP) using the newly developed Simulink to SPPA-T3000 code generator.

Keywords: Process control applications, Industrial applications of process control, Applications in semiconductor manufacturing
Abstract: Microwave sintering has emerged in recent years as a non-conventional method for sintering materials with significant advantages against conventional procedures. By microwave sintering are achieved higher density (∼99%), hardness (18 GPa) and fracture toughness (6.3 MPam1/2) properties and homogeneous micro-structure compared to conventional heating. Reason why, the design of microwave systems, including adaptive control schemes, becomes a very important topic to be performed. The present work describes the microwave cavity control system design for sintering alumina-zirconia nanocomposite. The samples were sintered in a multimode microwave furnace (2.45 GHz) at different temperatures (1200-1400 ºC) in air. The main challenge from the control point of view is to guarantee an small tracking error, when a temperature profile is required, due to the nonlinear dynamical behaviour of nanocompistes.

Keywords: Control of large-scale systems, Model predictive and optimization-based control, Process modeling and identification
Abstract: This paper presents a decentralized adaptive model predictive control scheme for alumina trihydrate precipitation process that is a large-scale nonlinear complex system with strong disturbances and a large time constant. The overall precipitation system is decomposed into several small-scale systems expressed as several Hammerstein structures with an adaptive disturbance model. The local controller takes the upstream state into account as a measured disturbance. The proposed method preserves the small computation and communication properties of decentralized MPC while ensuring a good performance. A simulation based on real data shows the model is true and the control method is effect.

Keywords: Model predictive and optimization-based control, Control of large-scale systems, Process control applications
Abstract: Model predictive control (MPC) has attracted wide attention in process industries with its ability to handle constrained multivariable processes. Computational complexity can become a limiting factor when MPC is applied to large-scale systems with fast sampling times. In this paper, a control scheme known as multi-step robust MPC is presented for polytopic uncertain multi-input systems. Only one or several state feedback laws are optimized at each time interval to reduce computational complexity. A set invariance condition for polytopic uncertain systems is identified and the invariant set is determined by solving a linear matrix inequality (LMI) optimization problem. Based on the set invariance condition, a min-max multi-step robust MPC scheme is proposed. Numerical simulations show the effectiveness of the proposed scheme.

Keywords: Kalman filtering techniques in automotive control, Hybrid and alternative drive vehicles, Modeling, supervision, control and diagnosis of automotive systems
Abstract: In this paper, an adaptive fault diagnosis technique is used for fault detection in Lithium ion batteries. The monitoring setup consists of multiple models representing the different degree of parameter shift due to over-discharge in the Lithium ion battery. A recursive least square estimator along with equivalent circuit methodology is used to construct the non-linear battery models. Extended Kalman filters are used to generate the estimated terminal voltages for each system. The residuals are further evaluated using the conditional probability evaluation function, to generate probabilities that determine the presence of a particular operational condition. Using experimental data, it is shown that Li-ion battery performance shift due to over-discharge can be accurately detected in real time.

Keywords: Fault-tolerant, Linear multivariable systems, Robust control (linear case)
Abstract: Fault diagnosis has become a significant issue due to rising demands for reliability as well as higher system performance and product quality. There are various approaches for the goal of fault detection and isolation among which we focus on the unknown input observer since observer-based schemes have been successfully adopted in a variety of application fields. This paper presents a novel design of Unknown Input Observer (UIO) for fault detection and isolation which can be applied to non-minimum phase systems. Unlike many previous UIO methods developed in the literature, the proposed scheme can carry out real-time reconstruction of unknown input in the presence of unstable invariant zero (with respect to the relation between the output and the unknown input). We extend the system by augmenting low pass filter(s) in the input(s)-output(s) path to tackle the non-minimum phase problem and generate robust residuals. Finally, the effectiveness of the proposed design method is confirmed through demonstrating a simulation-base example.

Keywords: Continuous time system estimation, Nonlinear system identification, Fault detection and diagnosis
Abstract: An adaptive High-Gain observer (AHG) as well as an Extended (EKF) and Unscented Kalman filter (UKF) are implemented for joint state and parameter estimation of a novel multi-axial electromagnetically actuated punch. These observers are compared in terms of convergence and response time to erroneous parameter and state initialization, as well as parameter modifications during operation. The AHG is further analyzed proposing an adaptive gain, which reduces the observer’s high sensibility with respect to noise. Simulation results show that AHG is more suitable compared to state-of-the-art EKF and UKF.

Keywords: Fault detection and diagnosis
Abstract: This paper deals with a multi-objective fault detection observer design problem in time and frequency domain for time invariant systems. Firstly, to improve the abilities of designed observer, different design criteria are proposed, which are evaluated by some suitable performance indices in time and frequency domain. Details about the relationships among different criteria are analyzed for two cases when the fault appears and disappears. With selected residual evaluation function and threshold, a formula with an envelope in time domain to realize fast fault detection is proposed for some typical faults. The designed observer considers the trade-off between fast transients of the residual for specified faults and the traditional criterion H_{-}/H_{infty} for general faults and disturbances. Compared with H_{-}/H_{infty} frequency design method, the effectiveness of the proposed method is demonstrated by the numerical simulation with a vehicle lateral dynamics system.

Keywords: Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles, Automotive sensors and actuators
Abstract: The reliable detection of faults located in the control loop of safety critical systems is an important aspect in reducing potential hazards induced by possible faults. Any fault detection and diagnosis system for a safety critical system has to fulfill strong safety specifications which can be expressed in terms of different performance criteria as detection time performance, missed detection rate, or false alarm rate. To satisfy all design requirements in the presence of unknown external signals and parametric uncertainties a tuning of the free parameters in the fault detection and diagnosis system often becomes necessary after the design of residual filters. In this paper an advanced approach to tune these free parameters based on a multi objective parameter optimization setup is presented.

Keywords: Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles, Trajectory Tracking and Path Following, Decentralized Control and Systems
Abstract: The paper presents a fault tolerant control (FTC) strategy for maintaining the lateral stability of a 4WS4WD autonomous vehicle in presence of an unknown component fault. It is designed using the flatness theory and the backstepping technique, and consists in actively controlling actively the vehicle’s rear-wheel steering system. In a normal situation, only the front-wheel steering actuator is used since it is able to ensure the desired tracking performances on its own. However, for different faulty scenarios, it is needed to use actively the vehicle rear-wheel steering system in order to preserve the system’s lateral stability. We prove that, by using this strategy, faults on the traction and the steering systems can be tolerated. The main advantage of our fault tolerant scheme is that these faults can be compensated by online computing new references for the control loops without changing the controller, providing the necessary time for a diagnosis system to precisely isolate the faulty component. This method is tested and validated on a realistic vehicle dynamic model co-simulated using CarSim and Matlab-Simulink softwares.

Keywords: Smart grids, Intelligent control of power systems, Control system design
Abstract: As part of its efforts to progress and innovate in the field of Smart Grid technologies, Enexis developed and currently operates a Battery Energy Storage System (BESS) called the Smart Storage Unit (SSU), part of the Smart Storage Project (SSP). The SSU is a 400 kVA, 232 kWh storage system, equipped with internet-connected control hardware in order to add “smartness” into the equation. The objective of the project is to enable field-testing and research on advanced electricity storage solutions in the LV distribution grid. This paper mainly focusses on the development of an addition to the controller system for the purpose of minimizing grid losses and maximizing grid asset utilization. Its principles rely on using historic data for load prediction, in order to flatten the power demand at the transformer, through peak-reduction and valley-filling.

Keywords: Application of power electronics, Control of renewable energy resources
Abstract: The energy management issue of a hydrogen fuel cell (FC) with ultracapacitors (UCs) for applications with high instantaneous dynamic power is considered. Singular perturbation approach is employed for the control of two converters and converter losses. The resistance load is considered unknown variable and the proposed controller uses load current and voltage measurements. The convergence of the voltage controller under converter losses is analysed by using Lyapunov theory. According to a significant FC-UCs benchmark, experimental results highlight the interest of the proposed approach and also show the difficulty due to converter losses.

Keywords: Optimal operation and control of power systems, Smart grids, Intelligent control of power systems
Abstract: The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Keywords: Control of renewable energy resources, Optimal operation and control of power systems, Smart grids
Abstract: High penetration of renewable energy to the grid may cause a number of problems such as supply and demand mismatch, voltage uctuations and even network instability. Electric Vehicles (EVs) with on-board batteries are capable of supporting the grid with large integration of renewable energy sources by absorbing (charging) the excessive amount of energy and returning it back (discharging) to grid when needed. This paper proposes a new smart control algorithm using the idea of Certainty Equavilent and Adaptive Control and a "customer participating program" to coordinate both the charging and discharging of EVs to achieve the above objective. The advantages of the proposed algorithm come from its simplicity, robustness and hence a promising opportunity for real-life applications in future smart grid. The eectiveness of the proposed scheme is evident by the numerical simulations on a micro- grid system with high penetration level of wind energy

Keywords: Modeling and simulation of power systems, Monitoring of product quality and control performance, Monitoring and performance assessment
Abstract: This paper provides an advanced electrochemical impedance spectroscopy (EIS) technique for improving the state of charge and state of health estimation of Li-ion cells. This advanced technique is derived from various tests and experimental studies conducted in the CAPSE labs at the University of South Wales. The results indicate that the technique is very accurate compared to other methods such as coulomb counting and OCV prediction method.

Keywords: Input-to-State Stability, Lyapunov methods, Stability of nonlinear systems
Abstract: This paper considers global asymptotic stability and robustness of cascaded nonlinear stochastic systems, and presents Lyapunov-based criteria. Constituent systems are characterized in terms of dissipation inequalities almost in the form of input-to-state stability (ISS) which is popular for both stochastic and deterministic systems. As an important unique feature of stochastic systems, this paper first demonstrates that assuming ISS systems having unbounded decay rate is restrictive, which contrasts sharply with the deterministic case. This motivates the second part of the main results focusing on ISS systems with bounded decay rate as well as systems which are not ISS. With these developments, this paper proposes a framework based on integral input-to-state stability (iISS) and demonstrates its usefulness.

Keywords: Input-to-State Stability, Lyapunov methods, Stability of nonlinear systems
Abstract: This paper considers the problem of inherent robustness analysis for nonlinear discrete-time systems using the concept of a finite-time Lyapunov function. The main contribution is to prove that, for sufficiently continuous dynamics and finite-time Lyapunov functions, inherent global input-to-state stability to general disturbances can be established for nominally stable nonlinear systems. Moreover, under mere continuity of the finite-time Lyapunov function and of the dynamics, inherent input-to-state stability on a compact set is obtained.

Keywords: Lyapunov methods, Discontinuous control, Control of constrained systems
Abstract: Recently, locally semiconcave control Lyapunov functions (LS-CLFs) play important roles in nonlinear control theory. Many LS-CLF based stabilizing controllers are proposed. In this paper, we consider the locally asymptotic stabilization problem of the input affine nonlinear systems with convex input constraints. To design a stabilizing state feedback under the input constraints, we employ the LS-CLF and convex optimization theory. Due to nonsmoothness of LS-CLF, the proposed state feedback is discontinuous on the state space. Therefore, we consider sample and hold solutions and guarantee the asymptotic stability of the closed loop system in the sense of sample stability. The effectiveness of the proposed method is confirmed by the numerical example.

Keywords: Lyapunov methods, Discontinuous control, Sliding mode control
Abstract: We propose a generalized homogeneous controller for a class of single-input uncertain dynamical system. The controllers are designed by means of Homogeneous Control Lyapunov Functions (HCLF's). The proposed controllers in feedback with the system make the close-loop system homogeneous of some degree. Depending on the selection of the parameters in the control law, the state trajectories reach the origin of the system asymptotically or in finite time. A class of homogeneous discontinuous controllers is also recovered. Furthermore, different discontinuous controllers with improved convergence rate can be synthesized.

Keywords: Lyapunov methods, Stability of nonlinear systems
Abstract: In this paper, stability analysis of discrete-time homogeneous nonlinear dynamics is considered. In particular, necessary and sufficient conditions for semiglobal KL-stability are established. These conditions lead to equivalence between semiglobal KL-stability and semiglobal exponential stability of general homogeneous dynamics. Furthermore, systematic stability analysis methods based on solving optimization problems are developed. These methods are tractable even for homogeneous systems in state spaces of high dimensions.

Keywords: Lyapunov methods, Stability of nonlinear systems, Tracking
Abstract: This paper considers a boundary feedback control problem for a MIMO counter-propagating Raman amplifier. The system is modeled as a set of coupled semilinear hyperbolic partial differential equations with Lotka-Volterra type nonlinearity. The system is linearized about the steady-state solution, and a boundary controller is designed based on a Lyapunov functional whose time derivative is made strictly negative by an appropriate choice of boundary conditions. As a result, exponential convergence to the steady-state solution is shown in the L^2-norm. The results are extended to the nonlinear system under a key assumption.

Keywords: Particle filtering/Monte Carlo methods, Nonlinear system identification, Bayesian methods
Abstract: We propose an improved proposal distribution in the Particle Metropolis-Hastings (PMH) algorithm for Bayesian parameter inference in nonlinear state space models. This proposal incorporates second-order information about the parameter posterior distribution, which can be extracted from the particle filter already used within the PMH algorithm. The added information makes the proposal scale-invariant, simpler to tune and can possibly also shorten the burn-in phase. The proposed algorithm has a computational cost which is proportional to the number of particles, i.e. the same as the original marginal PMH algorithm. Finally, we provide two numerical examples that illustrates some of the possible benefits of adding the second-order information.

Keywords: Particle filtering/Monte Carlo methods, Multi-agent systems, Distributed control and estimation
Abstract: Distributed consensus in the Wasserstein metric space of probability measures is introduced for the first time in this work. It is shown that convergence of the individual agents' measures to a common measure value is guaranteed so long as a weak network connectivity condition is satisfied asymptotically. The common measure achieved asymptotically at each agent is the one closest simultaneously to all initial agent measures in the sense that it minimises a weighted sum of Wasserstein distances between it and all the initial measures. This algorithm has applicability in the field of distributed estimation.

Keywords: Particle filtering/Monte Carlo methods, Software for system identification, Adaptive control -applications
Abstract: Rao-Blackwellied particle filter is a stochastic filter combining Kalman filters with particle filters. It is suitable for models that could be decomposed into linear and nonlinear part. Since the conditionally linear part can be solved by the Kalman filter, the sequential Monte Carlo is run only on the non-linear subspace. The resulting algorithm is a parallel evaluation of multiple Kalman filters with resampling. The parallel nature of this algorithm allows for very efficient implementation in parallel hardware. In this contribution, we present implementation of the algorithm in the Field Programmable Gate Array (FPGA). Due to the used model and optimized implementation, the execution time of the filter is in units of microseconds and scales very favorably with the number of particles. This is demonstrated experimentally on a laboratory prototype of sensorless drive with permanent magnet synchronous machine (PMSM) of rated power of 10.7kW.

Keywords: Particle filtering/Monte Carlo methods, Bayesian methods, Nonlinear system identification
Abstract: We propose a novel method for maximum-likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using these log-likelihood estimates, a surrogate objective function is created by utilizing a Gaussian process model. Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model. The method is profiled on two state space models with good performance both considering accuracy and computational cost.

Keywords: Particle filtering/Monte Carlo methods, Multi-agent systems, Bayesian methods
Abstract: We present a Monte Carlo solution to the distributed data fusion problem and apply it to distributed particle filtering. The consensus-based fusion algorithm is iterative and it involves the exchange and fusion of posterior densities between neighbouring agents. As the fusion method is Monte Carlo based it is naturally applicable to distributed particle filtering. Furthermore, the fusion method is applicable to a large class of networks including networks with cycles and dynamic topologies. We demonstrate both distributed fusion and distributed particle filtering by simulating the algorithms on randomly generated graphs.

Keywords: Grey box modelling, Software for system identification, Nonlinear system identification
Abstract: Confronting with complex and troublesome optimization problems, Particle Swarm Optimization (PSO) algorithm maybe easy to be led to local optimum and suffer the unacceptable phenomenon as premature convergence. This paper proposes a new rapid PSO algorithm (RPSO), utilizing all particles’ individual best positions found so far to update its velocity; providing a distinguishing weight according to the particles’ different positions; and an adaptive learning factors turning strategy based on particle’s fitness value. Then Jury Criterion is adopted to make convergence analysis of our proposed algorithm. Ultimately, the optimization performance of RPSO is simulated by searching the optimums of four common benchmark functions and training a RBF network for approximating a nonlinear system. The simulation results reveal the satisfactory efficacy of our proposed algorithm.

Keywords: Stochastic control and game theory, Estimation and filtering, Synthesis of stochastic systems
Abstract: A control problem for a partially observed linear stochastic system with an exponential quadratic cost functional is formulated and explicitly solved. It is assumed given that the estimation of the state is described by the solution of the information filter which is known. This solution is a sufficient statistic for the unknown state based on the observations. In this paper an optimal control is determined explicitly in a simple, direct manner from this sufficient statistic. This approach does not use either the solution of a Hamilton-Jacobi-Bellman equation or a stochastic maximum principle with backward stochastic differential equations. This control problem is often called a linear partially observed risk sensitive control problem.

Keywords: Stochastic control and game theory
Abstract: Design conditions for the existence of the Hinf state feedback control for discrete-time stochastic systems with state-multiplicative noise, stabilizing the closed-loop in such a way that the state variables satisfy equality constraints in the mean are presented in the paper. Using an enhanced form of the bounded real lemma for discrete-time stochastic systems with state-multiplicative noise, the LMI-based procedure is provided for computation of the gain matrix of state control law and the influence of equality constraints is explained. The approach is illustrated on an example demonstrating the validity of the proposed method.

Keywords: Stochastic control and game theory, Estimation and filtering
Abstract: In work by Huang (2010) and Nguyen and Huang (2012) the linear quadratic mean field systems and control problem is solved in the case where there is a major agent (i.e. non-asymptotically vanishing as the population size goes to infinity) together with a population of minor agents (i.e. individually asymptotically negligible). The new feature in this case is that the mean field becomes stochastic and by minor agent state extension Huang and Nguyen (2012) establish the existence of e-Nash equilibria together with the individual agents' control laws that yield the equilibria. This paper presents results initially announced in (Caines and Kizilkale, 2013; Caines, 2013) where it is shown that if the major agent's state is partially observed by the minor agents, and if the major agent completely observes its own state, all agents can recursively generate estimates (in general individually distinct) of the major agent's state and the mean field, and thence generate feedback controls yielding e-Nash equilibria.

Keywords: Stochastic control and game theory, Multi-agent systems, Distributed control and estimation
Abstract: This paper presents a modified Q-learning algorithm and provides conditions for convergence to a pure Nash equilibrium in potential games. In general Q-learning schemes, convergence to a Nash equilibrium may require decreasing step-sizes and long learning time. In this paper, we consider a modified Q-learning algorithm based on constant step-sizes, inspired by JSFP. When compared to JSFP, the Q-learning with constant step-sizes requires less information aggregation, but only reaches an approximation of a Nash equilibrium. We show that by appropriately choosing frequency dependent step-sizes, sufficient exploration of all actions is ensured and the estimated equilibrium approaches a Nash equilibrium.

Keywords: Stochastic control and game theory, Stochastic system identification, Simulation of stochastic systems
Abstract: The probability distribution for profits and losses associated with a feedback-based stock-trading strategy can be highly skewed. Accordingly, when this random variable has a large expected value, it may be a rather unreliable indicator of performance. That is, a large profit may be exceedingly improbable even though its expected value is high. In addition, the lack of confidence in the underlying stock price model contributes to lack of reliability in the expected value for profits and losses. Motivated by these issues, in this paper, we propose a new measure, called the Conservative Expected Value (CEV), which discounts the "ordinary" expected value. Once the CEV is defined, it is calculated for some classical probability distributions and a few of its important properties are established.

Keywords: Stochastic control and game theory, Stochastic hybrid systems
Abstract: This paper investigates the problem of local stabilization of Markov jump nonlinear quadratic systems. A method is presented for the synthesis of a static nonlinear quadratic state feedback control law that ensures the local exponential mean square stability of the zero equilibrium point of the closed-loop system in some polytopic region of the state-space with a guaranteed region of stability inside this polytope. The proposed control design is tailored in terms of linear matrix inequalities together with convex optimization to achieve an enlarged stability region. A numerical example is presented to illustrate the application of the stabilization method.

Keywords: Efficient strategies for large scale complex systems, Methodologies and tools for analysis of complexity
Abstract: We look at understanding the main mechanisms behind the renewal of complex systems. These systems, such as trains or airplanes, have a long operation or service duration. They should be renewed and their renewal projects are often partial for economic reasons. A renewal act is performed through integration of engineering changes. Any functional or structural change could have cascading effects on consecutive subsystems. The system architecture can be used as the main driver of renewal acts definition and planning. We position the renewal problem and define some key concepts usable for complex systems renewal. They allow to define the renewal possibilities as scenarios that should be qualified for ultimate selection. This paper explores these mechanisms and suggests a first set of propagation mechanisms. Throughout the paper, we illustrate our purposes by using examples from a train renewal project.

Keywords: Optimization and control of large-scale network systems
Abstract: Synchronization is a ubiquitous phenomenon in nature. Over the last two decades, the synchronization of complex networks receives an increasing attention. This paper aims at developing a new pinning synchronization approach for complex networks by using the heterogeneous information of different nodes cooperatively. Heterogeneous information means the different observation of the reference signal. In detail, different from the traditional pinning control idea, this paper utilizes different feedback gains to cope with the corresponding personalized observations of a specific static reference signal for each node to realize network synchronization. Based on our proposed method, this paper obtains three fundamental pinning synchronization theorems with necessary and sufficient conditions for undirected connected networks, strongly connected networks, and networks with a spanning tree, respectively. Following the above theorems, this paper also designs the feedback gains and coupling strength for networks to achieve synchronization. Finally, the numerical simulations are given to validate the proposed approach.

Keywords: Water supply and distribution systems, Integrated monitoring, control and security for critical infrastructure systems, Optimization and control of large-scale network systems
Abstract: Water is becoming a scarce resource for many developing countries and its management has become an important issue. In this work, we derive channel models for regulation in irrigation canals using system identication techniques. We take inspiration from existing research on control of large-scale irrigation networks and perform experimental identification of distributory irrigation channels in Pakistan. System identication models are computationally inexpensive and accurate for simulating water proles; thus qualifying for addressing ecient control design problems. The channel models are estimated and validated on operational data from Khaira Distributory o Main BRB Canal, Lahore. Our results show that system identication techniques capture the ecacy of canal models appropriately and will play a vital role in building accurate models for controlled irrigation canals in Pakistan.

Keywords: Optimization and control of large-scale network systems, Modelling and control of hybrid and discrete event systems, Intelligent system techniques and applications
Abstract: A feedback-based scheme for energy conservative cooperative content distribution for mobile systems is presented together with analysis of its behaviour in simulated operation. The mechanism described is designed to adress problems of co-operation in transient groups, where group members are assumed to act selfishly if possible to preserve their limited resources. Properties of the resulting barter-trade like economy are discussed as well as design rules when designing exchange systems of this type.

Keywords: Konwledge discover (data mining), Modeling of manufacturing operations
Abstract: Support Vector Machines (SVMs) is a popular machine learning technique, which has proven to be very effective in solving many classical problems with balanced data sets in various application areas. However, this technique is also said to perform poorly when it is applied to the problem of learning from heavily imbalanced data sets where the majority classes significantly outnumber the minority classes. In this paper, we tackle the problem of learning from severely imbalanced Rail dataset via a new iterative support vector machine algorithm with bootstrapping-based over-sampling and under-sampling. We combine the good generalization ability of SVMs with the class distribution advantages of resampling techniques. Under-sampling and Over-sampling are commonly used methods for overcoming the class imbalance problem. In this work, we also address the influence of under-sampling and over-sampling techniques on rail data and show that achieving an optimal sampling rate yields a better SVM generalization capability. Experimental results show that the under-sampling outperforms over-sampling. The iterative SVM technique also shows a competitive generalization performance on the under-sampled rail data set, and that under-sampling can decrease the computational complexity of SVM algorithm.

Keywords: Modeling and simulation of transportation systems, Information processing and decision support, Automatic control, optimization, real-time operations in transportation
Abstract: In this paper, we consider a railway traffic scheduling problem. We aim to find a schedule for a train scheduling networks where time duration uncertainties are considered. This problem was intensively studied with mixed linear models where trains moving duration are deterministic. In this paper, we formulate the problem as a classical one with scenario-based stochastic programming taking expected values as objective functions. Then, a new criterion is proposed to quantify scheduling robustness in the face of uncertainty. Besides, additional constraints were introduced to make the model feasible when unexpected event occurs during scheduling execution.

Keywords: Modeling and simulation of transportation systems, Simulation, Intelligent transportation systems
Abstract: Dealing with traffic management for complex crossroads is a challenging problem for traffic control planners. As a contribution to solve this problem, the present paper develops a mesoscopic simulation model for detecting the most suited fire plan for a complex road intersection, using a discrete event simulation tool and an evolutionary algorithm optimization. The modeling goal is to eliminate congestion by choosing an appropriate fire plan which will be adapted to the actual configuration of the intersection, as well as to a future reconfiguration meant to accept a higher inflow of vehicles. The proposed model is applied to a down-town crossroads from Nancy, France. Four different configurations of the input data flow were studied under the proposed simulation-optimization approach, and an optimal fire plan is proposed.

Keywords: Modeling and simulation of transportation systems, Automatic control, optimization, real-time operations in transportation
Abstract: While the problem of optimizing traffic light phases dates back to the fifties, in the daily practice the optimal solutions are often still determined by means of deterministic approaches that assume to know exactly the incoming traffic flows and the other intersection parameters. In this framework, the problem of parameter uncertainty is normally neglected, and the flow variability is only taken into account by the responsive traffic light plans. In this paper, a hybrid model of traffic light dynamics, and a global sensitivity analysis approach are proposed with the aim of providing a methodology for evaluating the performance robustness of different traffic light optimization approaches with respect to parameters uncertainty and variability. To this end, two well known solution approaches are compared and discussed by means of a real world case study.

Keywords: Modeling and simulation of transportation systems, Automatic control, optimization, real-time operations in transportation
Abstract: Mutli-class traffic flow models account for the heterogeneous characteristics of traffic networks. This leads to higher accuracy when applying them for on-line model-based control. We propose a new multi-class METANET model. The proposed model is an extension of the single-class macroscopic traffic flow model METANET. In the new model, each vehicle class is subject to its own single-class fundamental diagram, and is limited within an assigned space. In this paper, model predictive control is used for on-line traffic control based on the newly proposed model. A case study is implemented for illustrating the efficiency of the new multi-class model. More specifically, the simulation results show that the new multi-class METANET model leads to a better performance than single-class METANET model.

Keywords: Modeling and simulation of transportation systems, Automatic control, optimization, real-time operations in transportation, Modeling, supervision, control and diagnosis of automotive systems
Abstract: In the automotive industry driving cycles have been used to evaluate vehicles in different perspectives. If a vehicle manufacturer focuses only on a fixed driving cycle there is a risk that controllers of the vehicle are optimized for a certain driving cycle and hence are sub-optimal solutions to real-world driving. To deal with this issue, it is beneficial to have a method for generating more driving cycles that in some sense are equivalent but not identical. The idea here is that these generated driving cycles have the same vehicle excitation in the mean tractive force, MTF. Using the individual force components of the MTF in the generation of driving cycles with Markov chains makes it possible to generate equivalent driving cycles that have the same vehicle excitation from real-world driving data. This is motivated since the fuel consumption estimation is more accurate when the MTF components are considered. The result is a new method that combines the generation of driving cycles using real-world driving cycles with the concept of equivalent driving cycles, and the results are promising.

Keywords: Modeling and simulation of transportation systems, Vehicle dynamic systems, Modeling, supervision, control and diagnosis of automotive systems
Abstract: In this paper, a multi-objective control of a three-degrees of-freedom cabin model for a commercial truck excited by random road disturbances is studied. The multi-objective control problem is formulated as a non-convex and non-smooth optimization problem with controller order restricted to be less than the vehicle model order. For a range of control orders, controllers are synthesized by using the HIFOO toolbox. The simulation results show that the cabin vibrations are effectively suppressed by this methodology.

Keywords: Nonlinear and optimal automotive control, Marine system navigation, guidance and control, Autonomous underwater vehicles
Abstract: Compared to the traditional 2D path following case, 3D path following is more complicated since the coupling effect between horizontal and vertical plane has to be taken into account. In this paper, a sliding mode prediction controller is presented, which drives an underactuated AUV to follow a desired 3D path under time-varying current disturbances. A sliding mode technique combined with the predictive control strategy is developed to compensate for the impact of the hydrodynamic damping coupling via the feedback correction and receding horizon optimization techniques. In addition, Different from the traditional reference orientation method for path following control, here a methodology named 3D virtual guidance orientation (VGO) is built to deal with the vehicle dynamics. The geometric characteristic of tracking curve is contained in the control design as an extra degree of freedom to drive the vehicle to track a virtual target along desired path. Hence, the proposed method can avoid saturation problem of driving devices (stern plane and rudder) of AUV. Numerical simulations illustrate the excellent path following performance of the AUV in 3D under the proposed control scheme.

Keywords: Autonomous underwater vehicles
Abstract: In systems and control literature, underwater vehicles are studied extensively. The translational equations of motion coupled with rotational equations, which encompass the evolution of linear velocity vector in combination with angular velocity vector, describe the equation of motion of underwater vehicles. As a result of this, the underwater vehicle dynamics assumes the structure of a six-dimensional ordinary differential equation. Ocean current circulations influence stochastically the motion of underwater vehicles. In this paper, first we account the underwater vehicle-ocean current interaction in the stochastic framework that leads to a multi-dimensional Stochastic Differential Equation (SDE). Secondly, we accomplish the noise analysis of underwater vehicles using the Fokker-Planck technique. This paper will be of interest to mechanists, control theorists, naval scientists looking for research in autonomous systems under noise influence. Further research on stochastically perturbed underwater vehicles will refine existing results available in literature.

Keywords: Marine system navigation, guidance and control, Autonomous surface vehicles, Nonlinear and optimal marine system control
Abstract: Autonomous surface vehicles are with increasing popularity being seen in various applications where automatic control plays an important role. In this paper the problem of two-dimensional trajectory tracking for autonomous marine surface vehicles is addressed using Model Predictive Control (MPC). At each time step, the reference trajectories of a vessel are assumed to be known over a finite time horizon; the MPC controller computes the optimal forces and moment the vessel needs in order to track the trajectory in an optimal way. Based on a horizontal 3 degrees of freedom nonlinear scaled vessel model, we present both nonlinear MPC (NMPC), which solves a constrained multi-variable nonlinear programming problem, and linearized MPC (LMPC), which solves a constrained quadratic programming problem through on-line iterative optimization. In the latter case, the model used in LMPC for prediction is obtained from a successive linearization of the nonlinear vessel model. Comparisons on performance and computational complexity of the two approaches are presented. The effectiveness of the MPC formulations in vessel trajectory tracking, especially the ability of explicitly handling constraints, is demonstrated via simulations.

Keywords: Marine system navigation, guidance and control, Sensors and actuators
Abstract: This paper deals with vertical motion compensation, or motion damping, on a free-floating Surface Effect Ship (SES) at zero speed. The Motion Compensation System (MCS) works by varying the air cushion pressure of a Surface Effect Ship (SES) to minimize vertical motion due to sea waves. We present a control system which guarantees Global Exponential Stability for the closed-loop state space system and ultimately boundedness for the perturbed system. A study of the performance of the control system is demonstrated through model-test results of a 3 meter long SES.

Keywords: Dynamic positioning, Sensors and actuators
Abstract: Various industries adopt different techniques for isolating or reducing vibration. This paper investigates flatness control for fitted equipment on marine vessels which are subject to random sea wave. These disturbances significantly affect the operational quality and durability of marine systems, thereby decreasing overall safety. In hostile environments where safety considerations are important, vibration isolators are often used to suppress the effects of vibrations. The system described here is based on a four degree-of-freedom active marine suspension model, which allows for effective disturbance rejection. Sliding Mode Control (SMC) is then used to reduce the disturbance simulated as random sea waves. It has been shown that this method offers the advantages of easy implementation, reduced maintenance requirements, increased system efficiency and reduced vibration.

Keywords: Flight dynamics identification, formation flying, Autonomous systems, Avionics and on-board equipments
Abstract: The preliminary platform design for a twin-helicopter slung load transportation system is presented. The framework is intended for testing robust multivariable control system design methods and nonlinear state-estimation algorithms. An avionics unit is designed for on-board sensing and control. A low-cost visual tracking system is designed for estimating the helicopter pose within a flying volume. Using flight data logged by the avionics and visual tracking system, an uncertain set of parameters are estimated which completely define the plant dynamics for hover flight condition.

Keywords: Flight dynamics identification, formation flying, Autonomous systems, Guidance, navigation and control of vehicles
Abstract: The total amount of UAVs civil applications is getting bigger and bigger. The cost and the risks of the development phase of this systems has to be decreased in order to make them affordable. It is required to minimize the number of hours of real flight, making use of simulation tools and taking full advantage of the acquired data. Thus, obtaining a dynamic model that tightly adjusts to the real flight behaviour of the aircraft gains in importance, in the way that it will lead to precise simulation results and, therefore, to correctly designed control algorithms. A model identification technique based on experimental data and Multi-Objective optimization evolution algorithm, is presented here. This methodology makes profit of the possibility given by this type of algorithm of facing different objetives at the same time, to take full advantage of the experimental data and to get better adjusted models.

Keywords: Autonomous systems, Guidance, navigation and control of vehicles, Fault Detection, Diagnosis, Identification, Isolation and Tolerance for Autonomous Vehicles
Abstract: Tail rotor failure is one of the major failures occurring frequently and causing accidents. In this paper we investigate Tail rotor Control Failure(TRCF) based on helicopter dynamic models. There is no notable work is reported in literature on control of a small helicopter with TRCF. In contrast to hover mode, forward flight improves yaw damping naturally. The yaw damping is investigated at hover mode and in forward flight. Multi-loop PID controller, Fuzzy PID controller and full state feedback controller based on LQR are designed as per ADS-33PRF requirements to control the helicopter when the tail rotor control failure occurs in forward flight. Robustness in terms of stabilizing ability of developed controllers is also analyzed. All developed control schemes are tested in Matlab/Simulink environment including linear as well as non-linear simulations.

Keywords: Guidance, navigation and control of vehicles, Trajectory Tracking and Path Following, Localization
Abstract: This paper addresses the trajectory tracking problem for a quadrotor affected by external perturbations and inaccurate measures. A control strategy is proposed using time scale separation of the translational and rotational dynamics. A second order sliding mode controller is developed for the translational dynamic in order to deal with external perturbations while avoiding the undesired chattering effect. The rotational dynamics are controlled by a linear PD control. A data fusion algorithm using the Extended Kalman Filter (EKF) is proposed to estimate the position and velocity of the quadrotor, taking into account information from multiple commonly used sensors such as an Inertial Measurement Unit (IMU), optic flow and Global Positioning Systems (GPS). Simulations are carried out to test the proposed observer-controller scheme while introducing perturbations and inaccurate measures common in non expensive sensors.

Keywords: Control of systems in vehicles, Guidance, navigation and control of vehicles
Abstract: Active control techniques aimed at reducing helicopter vibrations have been extensively studied in the last few decades. The most studied control law is the so-called T-matrix algorithm; its implementation requires knowledge of the frequency response relating the control input to the output measurements at the disturbance frequency, which is very hard to characterise analytically. Adaptive schemes have been employed in literature to handle this problem. Surprisingly, however, very little effort has been devoted to the analysis of the T-matrix algorithm and in particular to the trade-off between robustness and adaptation in its deployment. In this paper an H_infty approach to the design of a robust T-matrix algorithm is proposed, with the aim of developing a systematic approach to the design of active vibration control laws for helicopters.

Keywords: Flight dynamics identification, formation flying, Guidance, navigation and control of vehicles
Abstract: The availability of accurate models for helicopter aeromechanics is becoming more and more important, as rotorcraft flight control systems have to meet progressively more stringent performance requirements: as the required control bandwidth increases, model accuracy becomes a vital part of the design problem. In this paper, the results of a joint industry-academia research project aimed at developing a novel time/frequency domain approach to rotorcraft model identification will be presented and discussed. The proposed approach will be illustrated by means of a case study based on data collected during piloted simulations of a state-of-the-art flight dynamics code.

Keywords: Fuzzy and neural systems relevant to control and identification, Reinforcement learning control, Robust neural and fuzzy control
Abstract: This milestone report addresses first the position of the areas of computers, computational intelligence and communications within IFAC. Subsequently, it addresses the role of computational intelligence in control. It focuses on four topics within the Computational intelligence area: neural network control, fuzzy control, reinforcement learning and brain machine interfaces. Within these topics the challenges and the relevant theoretical contributions are highlighted, as well as expected future directions are pointed out.

Keywords: Statistical methods/signal analysis for FDI, Industrial applications of process control, Advanced control technology
Abstract: Multivariate data analysis (MDA) is a well-established technique for abnormal situation management and early event detection (EED). This paper presents the development and on-line deployment of a Principle Component Analysis (PCA) model based EED system for an industrial-scale slurry stripper processing a solid state particle suspension. The developed solution was designed to detect plugging or blockage of the stripping column trays earlier than it is possible using traditional monitoring techniques and to avoid process disruption and production losses. The paper describes the project steps from data selection and preparation to the online implementation and utilization by operators and plant personnel. It was developed within a close collaboration between university and industry.

Keywords: Statistical methods/signal analysis for FDI, Analysis of reliability and safety
Abstract: This paper deals with acoustic detection of sodium boiling in a Liquid Metal Fast Breeder Reactor (LMFBR) cooled by liquid sodium. As sodium boiling induces acoustic emission, the method consists in real time analysis of acoustic signals measured through wave guides. AutoRegressive (AR) models are estimated on sliding windows and are classified in boiling or non-boiling models using Support Vector Machines (SVM). One of the difficulties to cope with is disturbances due to the influence of some environment noises like the liquid coolant cavitation, vortex flow, shaft vibration and mechanical pump noise. These disturbances can generate false alarms or mask the boiling. The proposed method is designed to be robust toward these disturbances. Furthermore, the SVM are designed to be robust toward the operating mode changing. The application for online monitoring is made on data obtained from French nuclear power plant Phenix and boiling sound signals generated from Laboratory experiments. Different acoustic boiling sound levels are used and the effectiveness of the method is shown by the good detection rate and its low false alarm rate even for low acoustic boiling sound level.

Keywords: Statistical methods/signal analysis for FDI, Control of renewable energy resources
Abstract: Reliability and sustainability of wind turbines increase in importance as wind turbines contribute with increasing power generation to the world's power grids. One possible way to achieve this is by using advanced fault detection and isolation methods in wind turbines based on the measurements provided to the control system. In this paper a Karhunen-Loeve basis approach is designed for detecting changes in frequency response from rotating parts like a gearbox. The potential of this method is shown by applying it to an established Wind Turbine FDI and FTC Benchmark model. These faults are industrial wind turbines detected using auxiliary condition monitoring systems, which uses their own sensors etc. Usage of the existing control system including sensors will thereby provide a potential cost reduction of the wind turbine.

Keywords: Statistical methods/signal analysis for FDI, Fault diagnosis and fault tolerant control, Estimation and fault detection
Abstract: The process faults of shaft furnace roasting processes, e.g. fire-emitting, flame-out, under-reduction, and over-reduction are undesirable for stable operation of the processes. The processes share multiple complexities such as multi-variate and strong correlations, which make it difficult to diagnose the faults using model-based or knowledge-based methods. In this paper, a data-driven fault diagnosis method for shaft furnace roasting processes is presented based on reconstruction and reconstruction-based contribution. The proposed method exploits historical faulty data to derive fault directions to identify ongoing faults with the help of additional explanation from contribution plots. A case study on a simulation system of shaft furnace roasting processes illustrates the effectiveness of the proposed method.

Keywords: Statistical methods/signal analysis for FDI, Estimation and fault detection, Monitoring and performance assessment
Abstract: In this paper a fault diagnosis framework based on detection with feature extraction methods and identification based on data-driven process topology methods was investigated. A simulation of a simple system consisting of two tanks with heat exchangers was used to generate data for normal operating conditions and a number of faults. Fault detection methods included principal component analysis and kernel principal component analysis feature extraction with Shewhart, cumulative sum and exponentially weighted moving average monitoring charts. Process topology information was extracted with linear cross-correlation, partial cross-correlation and transfer entropy. Connectivity maps were constructed to identify possible fault propagation paths to aid root cause analysis and changes in connectivity structure due to faults were exploited for fault identification. Kernel principal component analysis with a CUSUM chart gave the best detection performance, while connectivity graphs based on partial correlation gave an accurate representation of the system and assisted fault identification.

Keywords: Statistical methods/signal analysis for FDI, Process performance monitoring/statistical process control, Monitoring and performance assessment
Abstract: This paper addresses the problem of fault isolation in processes which are working in different operating points. Due to nonlinearities and set-point changes, the statistical model which is obtained from data is different from one operating point to another. Therefore the classical multivariate statistical process monitoring approaches may not be suitable for monitoring and diagnosis purposes. For that, a data-driven fault isolation method is proposed which splits the process into several local models. Based on the local models, a probabilistic approach is proposed to determine the contribution of each variable to the fault detection index and find the risky variables which are responsible for the fault. Finally, the proposed method is demonstrated through its application on a laboratory setup of continuous stirred tank heater.

Keywords: Production planning and control, Identification and model reduction, Manufacturing plant control
Abstract: The paper presents a problem statement of guaranteed control of production output by use of a probabilistic criterion that requires presence of the product output within specified limits with a given probability. Within the problem statement, the existence of the controls is considered in the form of suffi-cient mutual conditions which the initial problem characteristics are to meet to. Deriving the conditions is based on applying corresponding probabilistic inequalities enabling one to establish a connection between the limits for the production output, the amount of the mean square deviation of the production output, and the required probability of presence of the production output within the specified limits. The probabilistic conditions obtained are also shown to be applicable within a distribution free input/output system identification problem statement with a probabilistic criterion.

Keywords: Production planning and control, Intelligent decision support systems in manufacturing
Abstract: The quality and obtained quantity of Virgin Olive Oil is bounded by the characteristics of the olives to be processed, and further determined by the influence of the process variables during the actual elaboration. Since the quality of the olives evolves during the harvesting season, it is relevant to consider when to harvest the olives in order to maximize the profit over the whole season. This work proposes a method to determine an optimal production plan for the whole harvesting season and presents the results obtained in its application to four different scenarios.

Keywords: Production planning and control, Logistics in manufacturing, Optimization and control of large-scale network systems
Abstract: Demand for final products have grown significantly worldwide, particularly, due to the increase of emerging countries on the consumer market. The big problem is that consume of energy and use of raw material increase in parallel. As a consequence, an unsustainable environmental situation will be observed on the next future as arguing some researchers. In order to mitigate such a situation, the reuse of resources can be an interesting initial solution. However, reuse products or materials require a somewhat treatment or remanufacturing process that together with collection process can be highly expensive. The paper tries to identify a return rate that leads to a minimum lower cost for running a reverse logistics. A Linear Quadratic Gaussian (LQG) problem with constraints is used to provide both an optimal return rate and an optimal production policy for returnable products.

Keywords: Production planning and control, Logistics in manufacturing, Enterprise networks design and implementation
Abstract: Today’s supply networks have become more and more efficient but also complex and prone to strong disturbances that may generate blocking and starving effects. This study provides a risk analysis of supply networks based on a stochastic model of product flows subject to strong disturbances and threshold constraints. The main objective is to construct vulnerability indices from analytical simulation of the multistage system, both in nominal conditions when an ARIMA model describes the system dynamics and in disturbed running conditions when one or several state variables are subject to saturation effects.

Keywords: Production planning and control, Procedures for process planning, Logistics in manufacturing
Abstract: The paper introduces the concept of a fractal-like multimodal transportation network (FMTN) in which several isomorphic subnetworks interact each other via distinguished subsets of common shared workstations as to provide a variety of demand-responsive work-piece transportation/handling services. The set of transportation modes supporting production flows within the FMTN environment is considered. In that context, a fractal-like layout of FMS equipped with AGVS where work-piece flows are treated as multimodal processes can be seen as a real-life example of this model. In opposite to the traditional approach we assume that the given network of local cyclic acting AGV services, i.e. corresponding to distinguished isomorphic subnetworks of FMS layout. So, in the case considered the work-pieces pass their origin-destination routes among workstations using local transportation means. The goal is to provide a declarative model enabling to state a constraint satisfaction problem aimed at multimodal transportation processes scheduling encompassing production flows. Then, the main objective is to provide conditions guaranteeing solvability of the cyclic processes scheduling, i.e. guaranteeing the right match-up of local cyclic acting AGV schedules to a given work-pieces machining schedules

Keywords: Job and activity scheduling, Production planning and control, Modeling of manufacturing operations
Abstract: This paper gives a new viewpoint for steady-state analysis of classical job-shop cyclic scheduling (CJCS). After refining the constraints in the analysis of steady-state, improved models are achieved. By using the improved models, two kinds of problems can be solved, which are minimizing work-in-progress (WIP) with optimal cycle time and minimizing the cycle time with limited WIP. Moreover, the mathematical models are programmed in CPLEX 12.5 and good experimental results are presented.

Keywords: Autonomous Vehicles, Autonomous Mobile Robots, Guidance, navigation and control of vehicles
Abstract: This paper considers design of the control system for prototype of stratospheric airship, that is distinctive for its hybrid shape, leading to essential aerodynamic moments in flight. Mathematical model of the airship is presented. Control system implements remotely controlled by pilot flight and autonomous flight. Algorithm of automatic distribution of controlling forces and moments in actuators is presented. Adaptation of control system is provided with robust estimator of disturbances as indirect robust control. Control system is experimentally tested with hardware and software complex for HIL-simulation and pilots training.

Keywords: Localization, Positioning Systems, Navigation, Guidance and Control
Abstract: Extended Kalman Filtering (EKF) based GPS-INS has poor performance when there are strong nonlinearities and the measurements are corrupted by noise or outliers. This paper proposes a Contemplative Realtime (CRT) framework for tightly coupled Differential GPS aided inertial navigation. This method guarantees low latency for real-time application and meanwhile enhances the accuracy and reliability of navigation. To realize these improvements, a Bayesian optimization based smoothing is combined with conventional filtering in an efficient way. For demonstration purpose, this CRT framework is implemented on an automotive vehicle with differential pseudorange and IMU measurements. The implementation result shows that compared to standard EKF method, the proposed approach could provide accurate vehicle navigation with robustness.

Keywords: Navigation, Guidance and Control, Positioning Systems, Localization
Abstract: Existing Visible Light Communication (VLC) methods can recover each LED's on-off status only after its projection location is identified on each image. Identifying the LED projection is challenging because: 1) Clutter and noise corrupt the measurements; 2) The LED status will be ``off'' in some frames; 3) The predicted projection location sequence depends on the estimated vehicle state trajectory, which is uncertain. This article presents a new method determining the q most probable data and LED position sequences simultaneously, using Bayesian multiple hypothesis tracking (MHT) techniques by maximizing posterior probabilities. This article focuses on the VLC data and LED position sequence extraction, that includes vehicle state estimation. The MHT based algorithm is demonstrated by the simulation results.

Keywords: Motion control, Navigation, Guidance and Control, Autonomous Vehicles
Abstract: The depth control of an Autonomous Underwater Vehicle (AUV) is addressed. The vehicle is equipped with two separate actuation systems for the heave axis: a ballast tank system and a set of four jet motors generating vertical forces in the bow and stern of the vehicle. These two actuation systems have different time constants: the ballast tank system is rather slow, while the jet actuators have a much faster dynamics. The proposed control systems is inspired by the theory of complementary filtering in estimation where the output of slow sensors (low pass filters) is combined with the output of faster sensors (or the very plant model, i.e. high pass filters) to generate an estimate. In the given setting a heave controller is designed generating a desired force command. This signal is partitioned in a lower and higher frequency component: the former is sent to the ballast tank system and the latter to the jet thruster actuators. As a result, the depth force command is seemingly executed concurrently by the two actuation systems each working in its most natural frequency domain. The control design methodology is outlined and simulation results are reported illustrating the overall performance.

Keywords: Trajectory and Path Planning, Navigation, Guidance and Control
Abstract: This paper explores the potential for applying newly available numerical methods in optimal control to solve motion planning problems created by the search for targets with motion uncertainty characterized by constant but unknown parameters. These recent developments enable the efficient computation of numerical solutions for search problems with multiple searchers, nonlinear dynamics, and a broad class of objectives. We demonstrate the efficacy of these methods through implementing a multi-agent optimal search problem. We then derive an expansion of the optimal search modeling framework which facilitates the consideration of multi-agent searching problems with more general strategic objectives and utilize this expanded framework to implement an example combat defense scenario.

Keywords: Complexity modelling, Policy, Economic models
Abstract: Even the simple run of a medium size system dynamics model can be a cumbersome process, since the uncertainty of the parameters forces the modeller to consider many runs before being confident of how the model behaves. System dynamics simulation packages include some analysis tools, but there is a lack of some commonly used analysis tool such fuzzy clustering. In this paper we explore the possibilities of a programming language, Matlab, and its simulation tool, Simulink, for those possibilities mentioned. These languages enable the development of customized analysis tools at a very low programming cost. World3 model is a widely known example of a large model whose analysis becomes a difficult task. We have programmed this model in Simulink and afterwards, some tools, such as fuzzy clustering and pathway participation metric (PPM), have been applied to show their potential capacity to facilitate model simulation analyses.

Keywords: Dynamic games, Control in economics, Game theories
Abstract: Mean-field games have been studied under the assumption of very large number of players. For such large systems, the basic idea consists to approximate large games by a stylized game model with a continuum of players. The approach has been shown to be useful in some applications. However, the stylized game model with continuum of decision-makers is rarely observed in practice and the approximation proposed in the asymptotic regime is meaningless for networked systems with few entities. In this paper we propose a mean-field framework that is suitable not only for large systems but also for a small world with few number of entities. The applicability of the proposed framework is illustrated through a dynamic auction with asymmetric valuation distributions.

Keywords: Dynamic games, Control in economics, Financial dynamics
Abstract: In the standard formulation of a game, a player's payoff function depends on the states and actions of all the players. Yet, real world applications suggest to consider also a functional of the probability measure of states and actions of all the players. In this paper, we consider cooperative mean-field type games in which the state dynamics and the payoffs depend not only on the state and actions but also on their probability measure. We establish stochastic maximum principle and provide a time-dependent payoff allocation procedure for coalitions. The allocated payoff considers not only fairness property but also the cost of making the coalition. Finally, time consistency and subgame perfectness solution concept equations are established.

Keywords: Dynamic games, Game theories, Equilibrium models
Abstract: We provide a method for designing a quadratic pricing scheme to induce a desired local Nash equilibrium in an open-loop differential game with nonlinear dynamics. In addition, we present conditions when the induced equilibrium is a global equilibrium. The results are applied to the problem of inducing network managers to invest in security in a multi-network with an epidemic model of the spread of malware.

Keywords: Stochastic optimal control problems, Optimal control theory
Abstract: While rich empirical evidence always shows certain degree of dependency among different time periods for the returns of risky assets, the current literature on dynamic portfolio selection has been dominated by the results under an assumption of independency, although in various forms.We consider in this paper a multiperiod mean-variance (MV) portfolio selection problem for a market with multiple risky assets whose returns are statistically correlated among time periods. Instead of assuming some particular stochastic processes to model the correlation, we adopt a formulation with a general form of correlation, which enables us to better matching our model with real markets. Recognizing the fact that, under this general setting, parameters in the portfolio policy become path-dependent adaptive processes themselves, we solve the problem analytically with an explicit form of the optimal portfolio policy, which remains as a linear affine function of the current wealth.

Keywords: Control education using laboratory equipment, Low cost technologies, Systems Theory
Abstract: This paper explains motivation and gives a brief development history of a laboratory model of thermo-optical plant. Then it continues with a short description of linear and nonlinear identification and control experiments that may be carried out with its recently innovated model directly in a classroom from the Matlab/Simulink environment, or remotely via Internet.

Keywords: Control education using laboratory equipment, Teaching curricula developments for control and other engineers
Abstract: This paper discusses and illustrates core aspects in control education (prerequisites, working conditions, course structure) necessary to empower students of general engineering disciplines to understand and apply advanced optimal and robust control concepts. The results of a master thesis on the robust and optimal control of a flexible beam laboratory setup are presented and related to the skills formed during the undergraduate studies. Optimal and robust control design methods are utilized for active damping of bending vibrations of a simply supported thin structural aluminium beam. For this purpose a multi-input multi-output (MIMO) control system setup is used, comprising four collocated piezo patch actuators and sensors acting in longitudinal direction. An electrodynamic shaker introduces a disturbance force in the direction of oscillation. The design plant is obtained by a measurement-data-driven system identification. Also, finite element based modelling is carried out. The suitability of Linear-Quadratic Gaussian (LQG) control with modal weighting, mixed-sensitivity H-infinity control and D(G)K-synthesized control for improving disturbance rejection of the experimental control system setup is investigated and compared.

Keywords: Control education using laboratory equipment, Teaching curricula developments for control and other engineers
Abstract: Model-based fault detection and isolation is nowadays a well established and mature field. It starts to become part of the curricula of graduate or post-graduate students. However, the lack of good teaching materials makes dicult the teaching/learning process to students and professors. This paper shows how project-based learning methodology has been used to organise the labs of the fault diagnosis course using a real set-up based on a three-tank system. Observer based methods for fault detection and structured residuals for fault isolation will be introduced to the students from a practical point of view by means of a set of exercises that intend students achieve a set of learning objectives.

Keywords: Control education using laboratory equipment, Balance issues of theoretical-versus-practical training
Abstract: This paper describes a magnetic levitation system for use in graduate controls education. We explain how to use this system to show the nonlinear system modeling, and how to used advanced control techniques for this interesting and visually impressive equipment. Several open problems in areas of electrical and control engineering are offered. Also, the paper presents some initial outcomes in creating a laboratory environment for remote monitoring of the magnetic levitation equipment.

Keywords: Control education using laboratory equipment, Balance issues of theoretical-versus-practical training
Abstract: The PID controller is the most common controller and it is taught in most introductory automatic control courses. To develop an intuitive understanding of the basic concepts of the PID controller and PID parameter tuning, interactive and freely available tools are important. A PID module for educational purposes has been implemented in JGrafchart, a freely available development environment for the graphical programming language Grafchart. JGrafchart includes interactive graphical elements such as live plots and it is possibile to create animated graphics, for example of a simulated process. JGrafchart's variables, for example controller parameters and modes, can be changed interactively while executing. The PID module will be included in future releases of JGrafchart with sample applications which can be used for example to demonstrate a PID controller live in lectures or to let students interactively change controller parameters and modes to develop an intuitive understanding of the PID controller and PID parameter tuning. The sample applications are designed for users without any knowledge about JGrafchart and can be used to control both simulated and physical processes.

Keywords: Control education using laboratory equipment, Teaching curricula developments for control and other engineers
Abstract: Automatic control, as well as the introduction to programming, are subjects increasingly being taught to young engineering students. For this reason the availability of a suitable platform for the laboratory work becomes a critical decision, in order to promote students' motivation to experience the theoretical concepts studied in the classroom. Until recently, the only option to perform laboratory work in this kind of subjects was to use closed platforms commercially available but today, thanks to advances in technology, many open hardware options supporting standard and intuitive programming languages are available. In order to provide a multidisciplinary low cost open platform for the automatic control introductory courses, an example of open-source educational platform easily programmable composed by a differential mobile robot with a robotic arm manipulator is presented in this paper.

Keywords: Control education using laboratory equipment, Virtual and remote labs, Internet based teaching of control engineering
Abstract: In this work a remote lab developed to explore the properties of event-based controllers is presented. The architecture is based on the decomposition of the system in three tiers. The server layer is directly connected to the plant, the client side implements the controller and the graphical interface, and the middle-tier acts as interface between client and server. The implementation of the remote lab allows the control of a Coupled Tank plant through a generic network. The platform is used to evaluate the effectiveness of an algorithm that allows the user to find numerically the limit cycles in control schemes where the feedback is done through a level crossing sampling, and which can be applied to LTI systems with delay.

Keywords: Virtual and remote labs, Control education using laboratory equipment, Internet based teaching of control engineering
Abstract: In this paper, we present a cost effective approach to remote experimentation. We propose ArPi Lab - remote laboratory for education in area of process control. This lab is built on very cheap hardware components, including single-board computers Raspberry Pi and open prototyping platforms Arduino based on 8-bit micro-controllers. This approach combines several different software technologies. These are HTML 5 and JavaScript for client-side application, PHP and MySQL for laboratory server implementation, JSON as structure for data transfer, and C language for experiment server and micro-controller programming. ArPi Lab provides three different types of educational physical systems. Three thermal plants, one magnetic levitation, and one hydraulic tank system are available for remote laboratory experiments. Each part of ArPi Lab's hardware architecture can be controlled in the meaning of power supply. For this purpose we propose an efficient power management model, designed to solve occasional hardware and communication failures in such kind of laboratories, where physical absence of supervising person can result in serious malfunctions or security issues.

Keywords: Teaching curricula developments for control and other engineers, Control education using laboratory equipment, Balance issues of theoretical-versus-practical training
Abstract: This article presents practical course content of a control theory subject used in the Control Systems and Informatics department of the ITMO University. There are many dierent examples of the empirical development and model based control algorithms. Course begins with the simple line tracer robot and ends with the inverted pendulum on a cart. During the course, students are oered to learn the problems related with identication, modelling and programming, based on the Lego NXT platform.

Keywords: Power systems stability, Smart grids, Modeling and simulation of power systems
Abstract: The new technique of Frobenius norm calculation of gramians, subgramians and transfer functions of electric power systems (EPS) is suggested. It is based on spectral expansion of gramian by combinational modes of dynamic matrix of EPS predicting model. It is more effective comparing to modal analysis as it allows to prescribe the criterion of EPS stability loss risk, and also to evaluate the interaction of ill-stable system modes. The suggested approach has a transparent physical interpretation, i.e., stability loss risk corresponds to the energy of ill-stable modes group. The predicting model is formed basing on associative search technique for nonlinear dynamic plants.

Keywords: Power systems stability, Dynamic interaction of power plants, Modeling and simulation of power systems
Abstract: Voltage and angle stability of power systems with renewable power supply and the corresponding region of attraction are analyzed jointly. First, the power system model is derived that consists of algebraic power network equations and differential power generator equations. The renewable power generators like photovoltaic inverters or wind turbines are modeled with first order dynamics. Second, power system stability is analyzed for two kinds of power networks: high-voltage networks with purely inductive power lines and medium-/low-voltage networks with homogeneous resistive-inductive power lines. For both cases, decoupling droop controllers are presented and the stability of this power system including these controllers is analyzed. The analysis is based on contraction arguments that have been used before to prove consensus in multi-agent system. However, due to the particular structure of the power system model, conventional contraction arguments have to be adapted for this analysis.

Keywords: Power systems stability, Smart grids, Real time simulation and dispatching
Abstract: Real-time vulnerability assessment (VA) is one of the essential tasks of the so called Smart Grid, since it has the function of detecting the necessity of performing global control actions. In view of this, the present paper will introduce a novel data-mining-based approach to map post-contingency Dynamic Vulnerability Regions (DVRs), taking into account three short-term instability phenomena. Based on probabilistic models of relevant inputs (e.g. nodal loads and occurrence of contingencies), the approach applies Monte Carlo (MC) simulation to recreate a wide variety of possible post-contingency dynamic data of some electric variables, which could be directly available from PMUs in a real system (e.g. voltage phasors or frequencies). From this information, a pattern decomposition method, based on empirical orthogonal functions (EOF), is used to approximately pinpoint the DVR spatial locations. The identified DVRs are then used to ascertain the actual dynamic state relative position with respect to their boundaries, which is accomplished by using a support vector classifier (SVC). The proposal is tested on the IEEE New England 39-bus test system. Results show the feasibility of the approach in finding hidden patterns in dynamic electric signals as well as in numerically mapping power system DVRs.

Keywords: Power systems stability, Design of fault tolerant/reliable systems, Constraint and security monitoring and control
Abstract: We propose a new method for the small-signal stability analysis of power systems based on the spectral decomposition of a square H2 norm of the transfer function. Compared with the dynamics of H2 and H-Infinity norms of the transfer functions, the analysis of the behavior of individual eigen-components allows earlier identification of the pre-fault condition occurrence. Since each eigen-component is associated with a particular eigenvector, the potential sources of instability can easily be localized and tracked in real time. An important class of systems operating under the pre-fault conditions near the boundary of stability is considered. We demonstrate that in such cases several ill-stable modes can increase the system energy up to a critical level much earlier due to their synergetic effect. In particular, an ill-stable low-frequency mode can act as a catalyst increasing the energy in the system. An illustrative test for the stability analysis of a real small power grid at Russky Island is provided.

Keywords: Power systems stability, Active approaches to fault tolerant control
Abstract: Stabiliser faults in multi-machine power systems are examined in this paper where fault-masking and system reconfiguration of the nonlinear system are obtained using a virtual actuator approach. Phasor Measurement Units, which can be integrated in wide-area transmission grids to improve the performance of power system stabilisers, are utilised when reconfiguring remaining stabilisers after a local failure has made one inoperable. A stability-preserving reconfiguration is designed using absolute stability results for Lure type systems. The calculation of a virtual actuator that relies on the solution of a linear matrix inequality (LMI) is detailed in the paper. Simulation results of a benchmark transmission system show the ability of the fault-tolerant reconfiguration strategy to maintain wide-area stability of a power system despite failure in one of the stabilisers.

Keywords: Modeling and simulation of power systems, Power systems stability, Application of power electronics
Abstract: This paper presents a simplified modeling approach for a voltage source converter based high voltage DC transmission line (VSC-HVDC) for use in power system dynamic studies. In the AC grid sending-end and receiving-end converters (SEC and REC) of the HVDC system are represented by controlled Thévenin sources. The controllers act on the two voltage sources to provide the prescribed terminal conditions by adjusting the magnitude and frequency of the source voltage. The DC link model is incorporated into the controller description and not explicitly represented. Based on this assumption and the typical control objectives in VSC-HVDC the simplified control structures have been systematically derived. The models then were implemented on DIGSILENT software. The simulation results using an open source data of an HVDC installation have demonstrated the feasibility of the approach and the validity of the models developed.

Keywords: Stability of nonlinear systems, Passivity-based control, Application of nonlinear analysis and design
Abstract: This paper studies the input-output properties of a class of control affine systems where the drift dynamics is generated by a metriplectic structure. Those systems, related to generalized (or dissipative) Hamiltonian systems, are generated by a conserved component and a dissipative component and appear, for example, in non-equilibrium thermodynamics. In non-equilibrium thermodynamics, the two potentials generating the dynamics are interpreted as generalized energy and generalized entropy, respectively. In this note, passivity and passive feedback stabilization of this class of systems are studied, with the output function taken as the gradient of the conserved component of the dynamics, and the proposed storage function is computed using the dissipative (metric) component of the dynamics.

Keywords: Passivity-based control, Lagrangian and Hamiltonian systems, Chemical engineering
Abstract: Irreversible port Hamiltonian systems are a class of pseudo Hamiltonian systems that expresses both the conservation of energy and the irreversible entropy production as a structural property. These systems encompass a large class of irreversible themordynamic systems, such as heat exchangers and chemical reactors, and also multi-energy systems such as coupled mechanic-thermodynamic systems. In recent work the irreversible port-Hamiltonian formulation has been used to derive a closed-loop stability condition using an energy based availability function, generated by the internal energy, as Lyapunov function. This paper presents an important extension of the previous results: the system theoretic interpretation of the stability condition in terms of conjugated inputs and outputs and the formulation of the control as an interconnection and damping assignment - passivity based control problem. A constructive method to derive the stabilizing control law is proposed and the formalism is illustrated on a general CSTR example.

Keywords: Process control, Lyapunov methods, Chemical engineering
Abstract: This paper proposes a Lyapunov-based approach for the control of reaction rate involved in chemical reactors through the use of irreversible thermodynamics. More precisely, the reaction rate is structurally derived as a nonlinear function of the reaction force in order to ensure the inherent non-negative definiteness property of the irreversible entropy production due to the reaction. On this basis, it allows to cover a large class of reaction rates described by the mass-action-law. As a consequence, the control of the reaction rate consists in controlling the reaction force through the support of an affinity-related storage function to operate the entire system at a desired operating point. Besides, the convergence condition is proposed.

Keywords: Process control, Model reduction, Lyapunov methods
Abstract: Motivated by considering structurally dynamical properties of reaction systems, we develop an approach to deal with the nonlinear control of an unstable CSTR. More precisely, this paper shows that the CSTR dynamics can be split into reaction variant and reaction invariant dynamics modes through the use of linear combinations of system variables (called inventories). Since the reaction invariant dynamics mode is exponentially stable, the control design for an exponential stabilization of the reactor is simply solved together with (partial) inventory control on the basis of controlling the reaction variant dynamics mode. An extension of the approach to multiple chemical reaction systems is briefly outlined. Simulation results for a first order chemical reaction with multiplicity behavior are given to illustrate the theoretical development.

Keywords: Parameter-varying systems, Adaptive control, Control problems under conflict and/or uncertainties
Abstract: In this paper, the minimization of an unknown and measurable cost function using extremum-seeking control is considered. The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying estimation problem. The concepts of invariant manifold and adaptive parameter update law are used for adaptive estimation of the time-varying gradient of the unknown cost function. The proposed approach is shown to avoid the need for averaging analysis which minimizes the sensitivity of the closed-loop performance to the choice of dither signal. The effectiveness of the proposed method is illustrated with a simulation example.

Keywords: Constrained control, Control of partial differential equations, control of heat and mass transfer systems
Abstract: In this contribution, an approach is presented for the optimal control of the boundary-controlled heat equation, which is subject to state- and input-constraits. Thereby, suitably chosen asymptotic saturation functions are used to reformulate the original infinite-dimensional system in new coordinates. The new unconstrained optimal control problem can then be solved with methods of unconstrained optimization. The method is demonstrated for a heat-up problem where both state and input constraints become active.

Keywords: Filtering and smoothing
Abstract: This paper presents a derivative free state estimation algorithm for nonlinear systems called the state dependent Riccati equation information filter (SDREIF). The SDREIF is developed by fusing an SDRE filter with an information filter. Similar to the extended Kalman filter (EKF) or the extended information filter (EIF), the proposed SDREIF consists of two stages, prediction and update. However, unlike the EKF or EIF, the SDREIF does not use Jacobians and hence the filter is suitable for highly nonlinear systems. Furthermore, the framework is extended to handle state estimation for multi-sensor measurements. The efficacy of the proposed SDREIF is demonstrated by a simulation example of a permanent magnet synchronous motor.

Keywords: Filtering and smoothing, Continuous time system estimation, Identification for control
Abstract: This work proposes a frequency domain interpretation of the Algebraic Differentiators introduced in “M. Mboup, C. Join and M. Fliess, Numerical Differentiation with annihilators in noisy environment, Numerical Algorithms, vol. 50, pp. 439-457, 2009”. This interpretation complements the least squares interpretation in the above reference. In particular, it allows one to explain 1) the behaviour of different types of algebraic differentiators and 2) why it can be preferable to apply e.g. two successive first order differentiations in place of one second order differentiation. The frequency domain interpretation also enables the use of the Fourier theory (continuous and discrete transforms) in order to tune and discretize the Algebraic Differentiators.

Keywords: Filtering and smoothing
Abstract: This paper investigates the use of algebraic estimators for numerical filtering and derivation applications. After giving some explanations for the choice of the estimator order, we focus on the order one. The frequency and time responses are compared with standard filtering and derivation methods, including the Kalman filter. These estimators are finally implemented and tested with real sensor signals. Results show that for quite equivalent performances, Kalman filter is less time consuming, while the first order algebraic filter is easier to implement without a priori knowledge on the signal.

Keywords: Filtering and smoothing
Abstract: This paper deals with the left inversion problem for a class of nonlinear systems. It is well-known that such problem is a challenging one because of the presence of non-smooth unknown inputs and non-involutivity of the forced fields' distribution. To overcome these difficulties, we first propose a novel left invertible form. Then, using this form, we design an observer in order to reconstruct the state and the control inputs. Numerical results are given in order to validate the proposed approach.

Keywords: Filtering and smoothing, Statistical data analysis
Abstract: We develop a method of constructing trivariate optimal smoothing splines using normalized uniform B-spline as the basis functions. Such splines are useful particularly for modeling dynamic shape of 3-dimensional deformable object by using two variables for 3D shape and one for time evolution. The trivariate splines are constructed as a tensor product of three B-splines, and an optimal smoothing spline problem is solved together with typical examples of constraints as periodicity. The problem is formulated as convex quadratic programming (QP) problem in such a way that 3D array of control points is vectorized and a MATLAB QP solver is readily applicable for numerical solutions. We demonstrate usefulness of the method by dynamic shape modeling of red blood cell, where we will see that relatively small number of observation data yield satisfactory results.

Keywords: Coordination of multiple vehicle systems, Control under communication constraints, Distributed control and estimation
Abstract: Poor scalability arises in many vehicle platoon problems. Bidirectional strings appear to show some promise for mitigating these problems. In some cases these solutions have the undesirable side effect of non-scalable response to measurement errors. In this paper, we examine this problem and show how information exchange between vehicles may eliminate scalability difficulties due to measurement errors.

Keywords: Coordination of multiple vehicle systems, Cooperative systems, Multi-agent systems
Abstract: This paper presents some results regarding location of transfer function zeros in general formations with dynamics of arbitrary order. The numerator polynomial of the transfer function is derived as a function of single agent dynamics and a Laplacian matrix. The results already known in literature are extended from single integrator and bidirectional formations to general dynamics and general formation structures. The location of zeros is related to poles of a slightly modified formation. Therefore, in some cases the zeros must follow the same root-locus-like rules as the poles do and they interlace.

Keywords: Coordination of multiple vehicle systems, Distributed control and estimation, Multi-agent systems
Abstract: Most of the existing results on distributed distance-based rigid formation control establish asymptotic and often exponentially asymptotic convergence. To further improve the convergence rate, we explain in this paper how to modify existing controllers to obtain finite time stability. For point agents modeled by single integrators, the controllers proposed in this paper drive the whole formation to converge to a desired shape with finite settling time. For agents modeled by double integrators, the proposed controllers allow all agents to both achieve the same velocity and reach a desired shape in finite time. All controllers are totally distributed. Simulations are also provided to validate the proposed control.

Keywords: Coordination of multiple vehicle systems, Multi-agent systems, Distributed control and estimation
Abstract: In this paper, we combine ideas from multi-agent cooperative coverage control, with problem formulations from the resource allocation field, to create a distributed convergent approach to the ambulance positioning problem. Inspired by coverage control we use the graph version of so-called Voronoi regions, making the solution distributed and reactive, thereby freeing computational resources. The solution is distributed in the sense that each vehicle only needs to know the positions of its neighbors, and the computations of each vehicle only depend on the size of its Voronoi region/set. This implies that considering a problem of twice the size, using twice the number of vehicles will leave the computational load per vehicle unchanged. The freed resources are used to capture the allocation problem in more detail: maximizing an estimate of the victim survival probability instead of more coarse measures of ambulance availability. Using real city street map data from OpenStreetMap (OSM), we provide simulation results illustrating the applicability of our approach. Finally, we prove that the proposed distributed algorithm is convergent in the sense that it finds a local optimum in finite time.

Keywords: Multi-agent systems, Coordination of multiple vehicle systems, Networked robotic systems
Abstract: In this paper synchronization of both the orientation and velocity for a group of unicycle robots is studied. It is assumed that a robot can only detect and obtain information from those robots that lie in the proximity (within certain distance). A minimum dwell time is imposed on the updating of neighborhood relation in the controller in order to avoid introducing chattering in the closed-loop system that may be caused by abrupt changes of the relation, which as a consequence implies that the neighbor information will be updated only at discrete time instants in the control. In the paper a distributed feedback control law is designed for each agent, and a sufficient condition for uniformly and independently distributed initial states is provided for reaching the synchronization, which depends on the neighborhood radius, the maximum initial translational velocity and the dwell time.

Keywords: Coordination of multiple vehicle systems, Multi-agent systems
Abstract: The attitude coordinated tracking problem of multiple spacecraft with control input saturation is studied in this paper. We rst construct a decentralized attitude coordinated tracking algorithm with input saturation, such that spacecraft can track a time-varying desired trajectory and align their attitudes. We then introduce a novel auxiliary dynamical systems to present an attitude coordinated tracking algorithm with input saturation and without angular velocity measurements. Throughout this paper, the information ow between spacecraft is assumed to be undirected. Finally, design examples are given to show the eectiveness of the proposed attitude coordinated tracking algorithms.

Keywords: Hardware-in-the-loop simulation, Modeling, field robotics
Abstract: The compact rescue robot (CRR), a quadruped pneumatically-actuated walking robot, seeks to use the benefits garnered from pneumatic power: force & power density, lightweight, inexpensive actuators, and inherent compliance. A simulation has been developed that models the dynamics of the robot and its interaction with the environment. However, development of an entirely new dynamic simulation specific to the system is not practical. Instead, the simulation combines a MATLAB/Simulink actuator simulation with a readily available C++ dynamics engine. The actuator simulation places particular emphasis on accuracy near the neutral position (the region most active during closed loop control), modeling this regime with a higher level of detail than observed in past literature. Because the multi-platform approach results in additional incurred challenges due to the transfer of data between the platforms, it is valuable to analyze the necessity of detail in the actuator model. The goal of this analysis is to find a balance of realistic behavior, model integrity, and practicality.

Keywords: Modeling, Application of mechatronic principles, Identification and control methods
Abstract: This contribution presents a new methodology for the online identification, which allows the modeling of the stationary and dynamic behavior of nonlinear combustion engines with many input and output variables in short time with an appropriate model structure. The used models are local polynomial model trees. The necessary enhancements and adaptations of the identification algorithm for local polynomial model trees for the online methodology are presented. To enable an online identification, not only the parameters but also the structure of the models has to be adapted to the ongoing measuring procedure at a test bench. This structure adaptation methods use iteratively recorded measured data for the determination of an optimized model partition and regressor selection regarding the quality and complexity of the model. This maximizes the iterative improvement of the mathematical model, leading to a reduced test bench time. The applicability of the developed methodology is shown for the identification of a model from both, an artificial test function as well as a real diesel engine.

Keywords: Modeling, Autonomous robotic systems, Mechatronic systems
Abstract: The new idea presented in the paper is to reverse of an assembly line in order to allow complete disassembly. A generalised Synchronised Hybrid Petri Nets (SHPN) model based control of an assembly/disassembly mechatronics line (A/DML) served by a wheeled mobile robot (WMR) equipped with robotic manipulator (RM) is presented. The approach is a hybrid one, the assembly/disassembly line being the discrete part whilst the WMR with RM is the continuous part. The mobile platform is used only in disassembly operations, in order to transport the components from the disassembly locations to the storage locations. Therefore, SHPN approach is used to model and control. This hybrid system takes into consideration the distribution of the necessary tasks to perform hybrid disassembly, using robot synchronisation with A/DML. Using SHPN model and a LabView platform, a real-time control structure has been designed and implemented, allowing automated assembly and disassembly where the last one is assisted by a mobile platform equipped with a manipulator.

Keywords: Modeling, Mechatronic systems, Application of mechatronic principles
Abstract: Simulation models are becoming efficient tools for real plant testing and control system fine-tuning. They are necessary for advanced process control. Since a design phase of simulation models is time-consuming and error-prone, this paper proposes a method for the systematic and semi-automated design of simulation models. It is intended mainly for signal-oriented simulators such as MATLAB-Simulink. The method presumes that large-scale industrial systems consist of subsystems, whose behavior is known. The task for the proposed approach is to create a simulation model from atomic simulation blocks approximating system components. The proposed solution utilizes the bond graph theory in a non-traditional way. Compared to the classical use of bond graphs, the proposed method does not create simulations from scratch, but it re-uses existing simulation components. It extends bond graphs with a concept of component implementations and annotations of their interfaces. Thus, models are assembled from atomic component implementations, whose selection is done by the proposed method for each node of the system topology.

Keywords: Modeling, Mechatronic systems, Networks of sensors and actuators
Abstract: The European X-ray Free-Electron Laser will generate high energy light pulses in the femto-second range. A clock signal, which synchronizes all components, is distributed via laser pulses. The accuracy of these laser pulses is crucial to achieve precise measurements which are needed to control the Free-Electron Laser. In order to reach the maximum performance, advanced model-based control approaches are used to compensate external influences. This paper shows the modeling of the Master Laser Oscillator by fractional order systems, which improve known approaches to system modeling by providing a higher accuracy.

Keywords: Modeling, Motion Control Systems, Identification and control methods
Abstract: This paper presents a new method of analysing the error of a sampled-data velocity stabilising system with a wide range of pulse width modulation. The analysis is based on multi-channel model obtained as a result of approximation of pulse-modulated signal at the output of a PWM converter. Approximation of piecewise-linear modulation characteristics of each channel has been obtained as a series expansion of Hermite polynomials where the expansion comprises two polynomials of the first and third orders. The transfer function of every channel and a closed-loop system has been obtained using multidimensional Z-transform. The analytical expression of an error under impact of a step input has been derived using a transfer function of the closed-loop system. A dc electric drive has been used as an example of high accuracy sample-data stabilising system to verify and demonstrate the proposed method.

Keywords: Artificial pancreas or organs, Kinetic modeling and control of biological systems, Control of physiological and clinical variables
Abstract: This paper investigates the capabilities of a sophisticated robust nonlinear controller designed directly for a widely known and used high-order nonlinear type 1 diabetes (T1DM) model to lessen the dependency from patient compliance and to answer practical requirements such as avoiding hypoglycaemia. The resulting controller can perform adequately in nominal conditions, but expected to keep this performance even in extreme situations, e.g. high carbohydrate intake, rejecting hypoglycaemic episodes.

Keywords: Artificial pancreas or organs, Control of physiological and clinical variables
Abstract: This work provides a time-varying controller to improve the glycemic regulation in Type 1 Diabetes Mellitus (T1DM) patients. To this end, a Linear Parameter-Varying (LPV) control is designed in order to minimize the risk of hypoglycemia (glucose concentrations < 60 mg/dl) and hyperglycemia (glucose concentrations > 180 mg/dl). The controllers have been tested in the 10 in silico adults from the distribution version of the UVA/Padova metabolic simulator (30 patients). All Continuous Glucose Monitoring (CGM) and Continuous Subcutaneous Insulin Infusion (CSII) pump constraints are considered during the simulations. Different meal scenarios have been tested showing very promising results.

Keywords: Identification and validation, Pharmacokinetics and drug delivery, Clinical validation
Abstract: The recursive identification of a parsimonious nonlinear Wiener model for the neuromuscular blockade in closed-loop anesthesia is considered. The performance of two popular nonlinear estimation techniques, namely the extended Kalman filter (EKF) and the particle filter (PF), is evaluated on synthetic and clinical data. The parameter estimates obtained with the PF, that does not rely on model linearization, exhibit less bias and shorter settling time than the ones produced by the EKF. This behavior persists when the parameter tracking capabilities of both estimation algorithms are assessed for the model in hand. Taking advantage of the model parameters that were recursively estimated from clinical data, it is demonstrated that the main source of intra-patient variability lies in the nonlinear pharmacodynamic part of the model. The distance to a bifurcation phenomenon leading to nonlinear oscillations of the Wiener model under PID feedback is also evaluated.

Keywords: Kinetic modeling and control of biological systems, Biomedical system modeling, simulation and visualization, Physiological Model
Abstract: This paper derives an elementary mathematical model for the movement of the shank around the knee joint. The resulting equation of motion is a nonlinear differential equation. The control purpose is to regulate the angle of the shank by manipulating the torque applied to the knee joint. This model has applications in dynamical knee rigs and active orthoses. As the system is nonlinear with a rather larger range of motion, a nonlinear predictive controller is designed. The implemented control strategy is NEPSAC (Nonlinear Extended Prediction Self-Adaptive Control). The effect of the filter parameter and the prediction horizon in the NEPSAC algorithm are investigated using MATLAB/SIMULINK. The results show that for both the filter parameter and the prediction horizon an optimal value can be chosen as a trade-off between robustness and fastness of the response.

Keywords: Control of physiological and clinical variables, Biomedical system modeling, simulation and visualization, Kinetic modeling and control of biological systems
Abstract: Cancer treatment is one of the most important research fields of modern medicine. In the last decades, targeted molecular therapies showed prosperous results. These treatments achieve tumor regression with limited side-effects. Mathematical models were posed which describe the dynamics of tumor regression under the applied control. The current paper investigates antiangiogenic therapy, which inhibits the tumor to grow own endothelial capillaries, thus inhibits tumor to grow over a certain size. Many different control approaches were elaborated and published since the model formulation was posed. The aim of this paper is to give an overview of these methods and results, and to review the work carried out by the authors.

Keywords: Biomedical system modeling, simulation and visualization, Pharmacokinetics and drug delivery
Abstract: This paper presents the mathematical roots in fractional calculus that are present in the modeling of diffusion in the human body. Respiratory diffusion, as well as drug diffusion, are discussed and presented with their common mathematical framework. The links and discussion presented in this paper suggests that emerging tools from fractional calculus are natural solutions for modeling complex phenomena in biological tissue.

Keywords: Robust control (linear case), Optimal control theory, Uncertainty descriptions
Abstract: This paper describes the design of attitude-hold controllers and their subsequent stability and performance analysis for directional drilling tools as typically used in the oil industry. Based on an input transformation developed in earlier work that partially linearizes and decouples the plant dynamics of the drilling tool, the resulting plant model is used as the basis for both a pole placement (as detailed in previous work) and optimal H∞ controller designs. A structured uncertainty stability and performance analysis is then performed on each of the two controller designs. Results for a transient simulation of the proposed controller are also presented.

Keywords: Robust control (linear case), Optimal control theory, Robust control applications
Abstract: In previous papers by the use of the architecture generating property of the optimal linear quadratic tracking theory those problems that give the PI, PD and PID controllers had been presented, thus avoiding heuristics and giving a systematic approach to explanation for their excellent performance. This approach has been used also in derivation of the family of generalized PImDn-1controllers. In this paper by the combined use of the optimal linear quadratic tracking and system sensitivity theories a problem is formulated and solutions are shown that give a family of robust PID controllers. Examples of the control architectures and structures for first and second order systems are presented.

Keywords: Robust control (linear case), Control of interconnected systems, Polynomial methods
Abstract: Motivated by the difficulty of designing low-order controllers for large-scale plants consisting of numerous interconnected subsystems, this paper addresses the issue of quantifying the nu-gap metric between the plant and a lower-order identified model, using only plant frequency response data. The main result of this paper is the construction of a bound on the nu-gap metric between plant and model that exploits the convergence properties of Chebyshev polynomial interpolants of point-wise in frequency system graph symbols. This bound subsequently informs the design of low-order robust controllers synthesised from the identified model. The techniques developed in this paper are demonstrated upon a semi-discretised 1D heat equation.

Keywords: Robust control (linear case), Systems with time-delays, Process control
Abstract: Two decades ago, a predictive PI controller was introduced for regulation of dead time dominated linear time-invariant first-order processes. The introduction involved a simple tuning rule with consideration on easy implementation and good performance. Later, the predictive PI controller has been improved by having an additional low-order filter in the controller structure against high-order modelling uncertanties. Also, several publications have been released with a detailed analysis on performance and robustness in particular with comparison of predictive and conventional PI(D) controllers. However, there have not been tuning rules with a design parameter for fine-tuning performance and dealing with robustness. In this paper, stability and robustness of a predictive PI without an additional filter for any time-invariant system is considered resulting in tuning rules for an optimal performance with a targeted robustness against model mismatch. Consequently, simple controller tuning rules are given for a first-order plus dead time and integrating plus dead time systems.

Keywords: Robust control (linear case), Linear systems, Output feedback control (linear case)
Abstract: This paper provides new insights into the currently available extended linear matrix inequality (LMI) conditions for control of discrete-time linear systems, motivating the use of two intrinsically different extended LMI characterizations for H_2 performance. While these conditions are equivalent for H_2 analysis and many H_2 control problems related to precisely known linear time-invariant (LTI) systems, they generally yield different results when employed for multi-objective H_2 / H_infinity control and H_2 analysis and control of uncertain linear systems. The advantage of considering both the H_2 LMIs for robust H_2 state feedback and multi-objective H_2 / H_infinity control design is demonstrated by means of exhaustive numerical comparisons.

Keywords: Robust control (linear case), Control problems under conflict and/or uncertainties, Decoupling problems
Abstract: In the study, a model inversion based two degree-of-freedom (DOF) controller is proposed to achieve optimal robust performance for multi-input-multi-output (MIMO) system with existence of both model and signal uncertainties. The original plant dynamics are modified by a pre-compensator and a post-compensator to obtain a diagonally dominant matrix, where the selected dynamics of the plant are contained within the diagonal entries. The inversion of the diagonal matrix, as a result, reflects the inversion of the plant dynamics within a specified frequency range. The feedback controller is designed using a standard H2 mixed sensitivity approach. The feedforward controller consists of plant inversion and a feedforward filter, which is optimized to balance the trade-off between nominal performance and robust performance under model uncertainty and signal uncertainty. A numerical example is used to demonstrate the improvement of the robust performance when using the optimal feedforward control.

Keywords: Robotics technology, Motion Control Systems, Design methodologies
Abstract: A novel anti-windup PID controller design method under holonomic constraints is proposed for nonlinear Euler-Lagrange systems with actuator saturation. The controller design is based on passivity, quasi-natural potential and saturated-position feedback. According to four saturation cases, switching of four integrating functions in the control law is utilized and four Lyapunov functions, such as hybrid control, are derived. Global asymptotic stability is ensured by energy dissipation between the four Lyapunov functions. The control performance is verified by numerical simulations using a two-link robot arm.

Keywords: Robots manipulators, Robotics technology
Abstract: We consider an iterative learning control (ILC) approach to machining with industrial robots. The robot and the milling process are modeled using system identification methods with a data-driven approach. Two different model-based ILC algorithms are proposed and subsequently experimentally verified in a milling scenario. The difference between the two approaches is the required sensors for acquiring relevant input data for the algorithms. The results from the experiments indicate that the proposed methods have the potential of significantly decreasing the position errors in robotic machining, up to 85% in the considered milling scenario.

Keywords: Robots manipulators, Robotics technology, Autonomous robotic systems
Abstract: This paper proposes a method that finds a locally optimal grasp of an articulated 2D object with n links considering frictionless contacts. The surface of each link of the object is represented by a finite set of points, thus it may have any shape. The proposed approach finds, first, an initial force-closure grasp and from it starts an iterative search of a local optimum grasp. The quality measure considered in this work is the largest perturbation wrench that a grasp can resist with independence of the direction of the perturbation. The approach has been implemented and some illustrative examples are included in the article.

Keywords: Telerobotics, Motion Control Systems, Robots manipulators
Abstract: It is well known that in most bilateral teleoperation systems, variable time-delays arise in the communications. Motivated by this fact, the present work reports the extension to the (asymmetric) variable time-delays case of the adaptive controller for bilateral teleoperators with uncertain parameters and constant time-delays, reported in Nu~no et al (2010) [Nuño, E., Ortega, R. and Basañez, L. (2010). An adaptive controller for nonlinear teleoperators. Automatica, 46(1), 155-159]. Compared to the previous work, the stability analysis does not rely on the cascade interconnection structure of the local and remote nonlinear dynamics and the linear interconnection map. Instead, the paper employs a different Lyapunov candidate function that incorporates a strictly positive term, the local and remote position error. With the only assumption that a bound of the time-delays is known, the paper also presents a sufficient condition that ensures the asymptotic convergence of position errors and velocities to zero. Some simulations, in free space and interacting with a stiff wall, illustrate the performance of the proposed control scheme in the presence of variable time-delays.

Keywords: Robots manipulators, Decision making and cognitive processes
Abstract: This paper presents a new approach to self-calibration of the robotics hand-eye relations based on active vision systems. At the same time, the calibration of the line structured light sensor can be accomplished. According to the special designed motion of the camera, the whole calibration process can be divided into two steps, including two groups of four linearly independent translations and one pure rotation. This method requires no reference object and only needs two characteristic points in the scene. Besides, it can accomplish the self-calibration of the camera intrinsic parameters and the rotational matrix as a whole, which reduce the computation errors and cost. After verification by our experiments, the method is proved to be convenient and rapid with the precision required by the industrial fields with much fewer steps of calibration.

Keywords: Robots manipulators, Modeling, Identification and control methods
Abstract: This paper addresses the forward kinematic model of a class of hyper-redundant continuum robot, namely Compact Bionic Handling Assistant (CBHA). Inspired from the elephant trunk, it can reproduce some biological behaviors of trunks, tentacles, or snakes. Such systems, like the CBHA are subjected to a set of nonlinearities (flexibility, elasticity, redundancy,...) and uncertainties (parameters and modeling), making difficult to build an accurate analytical model, which can be used to develop control strategies. Hence, learning method becomes a suitable approach for such scenarios in order to capture un-modeled nonlinear behaviors of this continuum arm. The proposed approach makes use of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Neural Networks for the approximation of forward kinematic model (FKM) of CBHA trunk. The experiments have been conducted on the CBHA in order to validate the forward kinematic model where the arm trajectories are generated using a physical coupling with a rigid manipulator. A comparison of both qualitative approaches with a quantitative geometric approach, according to the model accuracy is given at the end of the experiment.

Keywords: Estimation and fault detection, Process modeling and identification, Identification and modelling
Abstract: In many processing plants decanter systems are used for separation of heterogeneous mixtures, and even though they account for a large fraction of the energy consumption, most decanters just runs at a fixed set-point. Here, multi model estimation is applied to a waste water treatment plant, and it is shown using real production data, that it is possible online to distinguish between different operating modes, which can be used in the control strategy for energy optimization.

Keywords: Process modeling and identification
Abstract: This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinear industrial process, the multiple-model LPV model in which the global model is constructed by smoothly weighted combination of multiple local models is considered here. The problem of missing output variables data is commonly encountered in practice. In order to handle the multiple-model identification problems of LPV systems with incomplete data, the local model is taken to have a finite impulse response (FIR) model structure and the generalized expectation-maximization (EM) algorithm is adopted to estimate the unknown parameters of the global LPV model. To avoid the problems of illconditioned matrices and high sensitivity of parameters to noise, the prior information on the coefficients of each local FIR model is employed to construct the prior probability of unknown parameters. Then the maximum a posteriori (MAP) estimates of the global model parameters are derived via the generalized EM algorithm. The numerical example is presented to demonstrate the effectiveness of the proposed method.

Keywords: Process modeling and identification
Abstract: Palladium membranes can be effectively employed inside reactors for Water-Gas Shift (WGS, namely CO + H_2O ⇄ CO_2 + H_2), to separate hydrogen from the products, thus increasing the reaction conversion at the same time. Driven by its own partial pressure difference on the two sides of palladium, hydrogen permeates across the membrane up to the reactor lumen, from where it is pushed towards the outlet thanks to a sweep gas. A control-oriented dynamic model has been set up, accounting for the permeation characteristics of palladium and of the underlying porous support layer and for the presence of the sweep gas. The main uncertain parameters in the model have been identified from laboratory experiments, carried out on two membranes and on a WGS reactor equipped with one of them.

Keywords: Estimation and fault detection
Abstract: The aim of this work is to propose a method for the simultaneous state and parameter estimation of the individual columns in the Simulated Moving Bed chromatographic process based on concentration measurements only in the switched product outlets. The method exploits the fact that the measurements switch from the inlets of the columns to their outlets sequentially. The estimation problem is cast as a dynamic optimization problem where the prediction error of the output profile is minimized. The initial concentrations in the individual columns and the parameters of the columns are treated as degrees of freedom of the optimization problem. As at each outlet the concentrations of one component are very small and therefore are subject to large relative measurement errors for given expected absolute errors of the concentration measurements, measurements for two different points in time, when the extract port and when the raffinate port is at the outlet of the column under consideration are taken into account simultaneously. The effectiveness of the method is demonstrated for the separation of an isopropanolol racemic mixture.

Keywords: Process modeling and identification, Advanced control technology, Control of large-scale systems
Abstract: The objective of this work is to generalize input excitation designs suggested for system identification of ill-conditioned 2x2 systems to cases with more than two inputs and outputs. The methods are evaluated using a four inputs-four outputs distillation column/stripper system as a case study. The performance of the various procedures is evaluated through the model fit and different plant-friendly indices. The obtained models, and thus the quality of the associated input excitations, are also evaluated through cross validations.

Keywords: Process modeling and identification
Abstract: This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectation maximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.

Keywords: Fault-tolerant, Linear systems, Diagnosis
Abstract: In this paper we introduce a new approach to the design of fault-tolerant systems. Unlike traditional methods which rely on a mathematical model of the system, the proposed method is based on measurements only and a model is not required. The key elements of this approach are the determination of the design element values to preserve acceptable system performance under predetermined failures of fault prone elements. It is shown that such designs can be carried out without the necessity of a model and based only on strategic processing of measurements. An illustrative example of an electrical circuit that continues to maintain acceptable operation despite component failures is included.

Keywords: Decentralized control, Large scale optimization problems, Linear systems
Abstract: Graph colorability theory is shown here to be instrumental for the improvement of scalability and performance of recently introduced distributed non-cooperative sequential Command Governor (CG) strategies. Such properties were limited in earlier distributed CG schemes because the structure of constraints was not taken into account in their implementation. Here, by exploiting the idea that agents that are not jointly involved in any coupling constraint can simultaneously update their control actions, this is done by grouping all agents in the network into particular subsets (Turns) and allowing all agents belonging to a single turn to update simultaneously their commands while agents in other turns are instructed to keep applying their current commands. Graph colorability concepts and results are used to determine the minimum number of independent turns and distribute exclusively agents into them. A Turn-Based distributed CG strategy is therefore proposed and its main properties analyzed. A final example is also presented to illustrate the effectiveness of the proposed strategy by comparisons.

Keywords: Decentralized control, Linear systems
Abstract: A novel distributed Command Governor (CG) supervision strategy relying on cooperative game theoretical concepts is presented for multi-agent networked systems subject to pointwise-in-time coordination constraints. Unlike non-cooperative distributed CG schemes, here all agents contribute individually to the minimization of a global performance index. As a result, these methods are able to achieve Pareto-optimal solutions, not only in steady-state conditions as the non-cooperative ones, but also during transients and are not hampered by the presence of undesirable non-Pareto Nash-equilibria or deadlock situations. Other noticeable difference with respect to non-cooperative methods is that all agents need to exchange data among them several times within a decision step in order to coordinate their local optimization procedures and arrive to the optimal solution. The properties of the algorithm are fully investigated. A final example is presented where the proposed distributed solution is contrasted with both centralized CG solutions and distributed CG methods based on non-cooperative game theoretical concepts.

Keywords: Fault-tolerant, Optimal control theory, Control problems under conflict and/or uncertainties
Abstract: This paper studies use of scenario based model predictive control (MPC) to control a plant into a fault-tolerant state set. For some systems it is not always sufficient that the controller is reconfigured after a fault, it is also necessary that the system is in a given state set when the fault occurs in order to guarantee that the constraints are not violated. This is often achieved by using static safety constraints. However, it may be hard to find such constraints, and they may be conservative. This article presents a method for implementing dynamic safety constraints based on fault scenarios with an economic MPC. The controller is tested using closed-loop simulations.

Keywords: Decentralized control, Systems with time-delays, Output feedback control (linear case)
Abstract: Decentralized controller design problem for linear time-invariant retarded commensurate-time-delay systems is considered. The continuous pole placement algorithm, which has recently been introduced for static state feedback controller design for retarded time-delay systems, is first extended to design dynamic output feedback controllers. This algorithm is then used in the proposed decentralized controller design algorithm. An example is also presented to demonstrate the proposed approach.

Keywords: Control of renewable energy resources, Application of power electronics, Control system design
Abstract: In this paper a novel optimal control strategy for energy management of an off-grid hybrid Stirling engine/Supercapacitor power plant is presented based on preliminary results proposed in [1]. An implementation of the control strategy is performed on a real time target that drives a dedicated testbed with a DC machine reproducing the behaviour of the thermodynamic Stirling Engine. The DC machine is linked to a Permanent Magnet Synchronous Generator (PMSG) connected to an AC load through an appropriate controlled Power electronics and an energy storage device which is a supercapacitor. The performances of the developed control Strategy are then assessed through experimental results on the testbed.

Keywords: Model predictive and optimization-based control, Modeling and simulation of power systems, Control system design
Abstract: Polymer electrolyte membrane (PEM) fuel cells are highly efficient energy converters and provide electrical energy, cathode exhaust gas with low oxygen concentration and water. They are investigated as replacement for auxiliary power units (APU) that are currently used for electrical power generation on aircraft. For generation of oxygen depleted cathode exhaust air (ODA) oxygen concentration must be 10-11%. A challenging task is controlling the fuel cell system for this product and simultaneously keeping fuel cell stack degradation, voltage losses and stack damage as low as possible as well as keeping the system within operational limitations such as bounds and gradients on control parameters. This constrained control task for PEM fuel cell systems is attacked by a nonlinear model predictive control (NMPC) strategy. Simulation and experimental results are shown.

Keywords: Control system design, Control of renewable energy resources
Abstract: In order to address the output current imbalanced problem of the parallel chargers for energy storage type light rail vehicles, a current-sharing scheme is proposed based on the distributed cooperative control of multi-agent systems. If each charger is considered to be an agent, the current-sharing control design would resemble a consensus-tracking problem of multi-agent systems with bounded input constraints. The communication network among the chargers can be taken as an undirected graph. Each charger exchanges current information with its neighbors through the communication network. Due to the fact that the charging system has non-identical feature and the control input is bounded, input-output feedback linearization is adopted to transform the current-sharing control of the charging system to a first-order integrator consensus-tracking problem. A saturation function is introduced to design a bounded cooperative current-sharing control law based on the nearest neighborhood rule. The cooperative stability of closed-loop system under the fixed topology is rigorously proved by Lyapunov function integrating LaSalle invariant principle. The output current of parallel chargers can be balanced by adopting the proposed control methodology, which is verified by simulating a multi-charging test system.

Keywords: Smart grids, Control of renewable energy resources, Distribution automation
Abstract: Cloud-induced intermittency of photovoltaic (PV) generation forces equipment on the electrical grid to cycle excessively and prevents PV from being considered as a reliable or dispatchable source of power. Energy storage units (ESU) are proposed as a solution to turn PV power dispatchable. In order to use an ESU most effectively, it must be controlled appropriately. To this end, the cloud structure is modeled as a random process inferred from clouding data. The proposed model is valid for centralized PV installations and serves to develop not only a control methodology to coordinate an ESU with existing grid equipment but also as a sizing criterion for an ESU. The above methodology is demonstrated on both clouding data collected from a pyranometer and a rooftop PV installation.

Keywords: Control of renewable energy resources, Instrumentation and control systems, Control system design
Abstract: In a renewable energy system, lead-acid batteries are required to last as long as possible whilst providing deep discharges and are usually charged in an intermittent manner. This type of operation is severely detrimental to the electrodes and optimal performance cannot be guaranteed. Correct operation can be achieved if a charge controller receives accurate information about the state of the battery and its cells. Accurate approximation of the available active surface area is essential to optimal performance since it determines the overall capacity and reaction rates of various secondary processes. This paper improves on the current understanding of the active surface area in a lead-acid cell during discharge by comparing three approximation methods. Two methods use the state-of-charge based on porosity whilst the other method uses the charge per unit volume to determine the remaining capacity. These three methods are used in a comprehensive macro-homogeneous electrochemical model which accounts for initial temperature differences. Reported experimental behaviour is used to evaluate each method and recommendations are made regarding the selection of associated parameter values. The approximation method by Cugnet et al. (2009) is the most suitable because it always results in a concave morphology. The current work can aid in the development of a health-conscious battery management system.

Keywords: Smart grids, Modeling and simulation of power systems, Optimal operation and control of power systems
Abstract: The integration of renewable energy sources into modern electrical grids contributes to satisfying the continuously increasing energy demand. This can be done in a sustainable way since renewable sources are both inexhaustible and non-polluting. Different renewable energy devices, such as wind power, hydro power, and photovoltaic generators are available nowadays. The main issue with the integration of such devices is their irregular generation capacity (in particular for wind and solar energy). Therefore energy storage units are used to mitigate the fluctuations during generation and supply. In this paper we formulate a model for the Alternate Current Optimal Power Flow (ACOPF) problem consisting of simple dynamics for energy storage systems cast as a finite-horizon optimal control problem. The effect of energy storage is examined by solving a Norwegian demo network. The simulation results illustrate that the addition of energy storage, along with demand based cost functions, significantly reduces the generation costs and flattens the generation profiles.