Keywords: Robust control
Abstract: Feedback is ubiquitous and is a core concept in control systems, where the main objective of using feedback is to deal with the influences of various uncertainties on the performance of the dynamical systems to be controlled. Although much progress has been made in control theory over the past 50 years, especially in such areas as adaptive control and robust control, the following fundamental problem remains less explored: How much uncertainty can be dealt with by the feedback mechanism? To answer this, we need not only to investigate what the feedback mechanism can do, but also need to understand, the more challenging and difficult issue, what the feedback mechanism cannot do. The feedback mechanism is defined as the class of all possible feedback laws (which are not restricted to a certain particular subclass), and the maximum capability of feedback is measured as the maximum size of uncertainties that can be dealt with by the feedback mechanism. In this lecture, we will mainly consider discrete-time (or sampled-data) nonlinear dynamical control systems with both structural and environmental uncertainties. We will present a series of “Critical Values” and “Impossibility Theorems” concerning the maximum capability of the feedback mechanism for several basic classes of uncertain nonlinear control systems, and will expand on their theoretical implications as well as their practical significances.

Dr. Guo is a Fellow of IEEE, Member of the Chinese Academy of Sciences, Fellow of the Academy of Sciences for the Developing World (TWAS), Foreign Member of the Royal Swedish Academy of Engineering Sciences, and Fellow of IFAC. He was a recipient of the National Natural Science Prizes of China, IFAC World Congress Young Author Prize, IFAC Outstanding Service Award, among others. He is currently an IEEE CSS Distinguished Lecturer (2012-2014). He has served as a Council Member of IFAC, Member of IEEE Control Systems Award Committee, Associate Editor of SIAM J. Control and Optimization and Systems and Control Letters, General Co-Chair of the 48th IEEE-CDC, and Vice-President of both Chinese Mathematical Society and Chinese Association of Automation. Currently, he serves as the President of the China Society for Industrial and Applied Mathematics (CSIAM), Congress Director of the 8th International Congress on Industrial and Applied Mathematics, and on the editorial boards of a number of journals in mathematics, systems and control. He has worked on problems in adaptive control, system identification, adaptive signal processing, and time series analysis. His current research interests include the capability of feedback, multi-agent systems, complex adaptive systems, and quantum control systems, among others.

Keywords: Distribution automation, Control system design, Dynamic interaction of power plants
Abstract: We study the problem of aggregator's mechanism design for controlling the amount of active, or reactive, power provided, or consumed, by a group of distributed energy resources (DERs). The aggregator interacts with the wholesale electricity market and through some market-clearing mechanism is incentivized to provide (or consume) a certain amount of active (or reactive) power over some period of time, for which it will be compensated. The objective is for the aggregator to design a pricing strategy for incentivizing DER's to modify their active (or reactive) power consumptions (or productions) so that they collectively provide the amount that the aggregator has agreed to provide. The aggregator and DERs' strategic decision-making process can be cast as a Stackelberg game, in which aggregator acts as the leader and the DERs are the followers. In previous work~[Gharesifard et al., 2013b,a], we have introduced a framework in which each DER uses the pricing information provided by the aggregator and some estimate of the average energy that neighboring DERs can provide to compute a Nash equilibrium solution in a distributed manner. Here, we focus on the interplay between the aggregator's decision-making process and the DERs' decision-making process. In particular, we propose a simple feedback-based privacy-preserving pricing control strategy that allows the aggregator to coordinate the DERs so that they collectively provide the amount of active (or reactive) power agreed upon, provided that there is enough capacity available among the DERs. We provide a formal analysis of the stability of the resulting close-loop system. We also discuss the shortcomings of the proposed pricing strategy, and propose some avenues of future work. We illustrate the proposed strategy via numerical simulations.

Keywords: Smart grids, Optimal operation and control of power systems, Power systems stability
Abstract: Frequency control is traditionally done on the generation side. Recently we have formulated an optimal load control (OLC) problem and derived a load-side primary frequency control as a primal-dual solution to OLC. The load-side control rebalances power and resynchronize frequencies after a disturbance but cannot restore the nominal frequency. In this paper we modify OLC and derive from it a frequency-preserving load-side control that rebalances power and restores the nominal frequency after a disturbance. Unlike the generation-side secondary frequency control that is centralized, our load-side control only requires each bus to communicate a Lagrange multiplier with its neighbors. We prove that such a distributed load-side control is globally asymptotically stable and illustrate its convergence with simulation.

Keywords: Modeling and simulation of power systems, Smart grids
Abstract: Direct load control can be achieved by varying the hysteresis band of switchable loads, thereby changing their on/off durations. Such hysteresis-based control methods may, however, display complex dynamics that must be thoroughly understood in order to design safe control mechanisms. This paper explores the dynamical behavior of a group of hysteresis-based PEV chargers. Of interest is the change in the total power demand of the group as the hysteresis-band limits are varied. A detailed state-space model is used to capture the dynamics of the load aggregation. This model suggests that for certain control inputs (e.g. periodic ramp signals), the system may display rich dynamical behavior. It is observed that structural stability of the system may be disrupted as certain characteristics of the input signal are varied. The paper explores these phenomena through a bifurcation analysis of the load population dynamics. The results identify performance limitations that govern the responsiveness of fast-acting demand control.

Keywords: Smart grids, Control system design
Abstract: A distributed control algorithm is proposed to manage the electrical power demand for the purpose of charging electric vehicles so that (a) the overall power demand remains within the limitations of the distribution network and (b) each vehicle obtains a sufficiently charged battery at the end of the charging cycle. The charging algorithm utilises only local measurements to determine the charge current. The control problem is modelled as a non-cooperative game with weakly coupled cost functions for each vehicle. The cost function for each vehicle consist of an individual cost term and a group cost term. The group cost term expresses the aggregated demand of all vehicles and serves the purpose of ensuring that the infrastructure capacity constraints are respected. It is shown that this term can be estimated from local voltage measurements. The individual cost term reflects the need to achieve a desired charge level in the battery. Sufficient conditions for the overall system to admit a unique Nash Equilibrium are identified. Convergence and stability properties for a particular greedy algorithm implementation are described. To illustrate the efficacy of the proposed charging methodology, the algorithm is simulated in the context of an Australian suburban low voltage electricity distribution network.

Keywords: Smart grids, Control of renewable energy resources, Optimal operation and control of power systems
Abstract: Stable and efficient operation of a smart grid requires coordination of the actions of many independent decision makers or agents that must cooperate in globally balancing the demand for power to the partly unpredictable and uncontrollable supply. This paper proposes the use of the correlation coefficient between the renewable supply and the (controllable) demand, measured in a given region over a specified time scale, as a performance measure that is imposed on lower level control agents by a higher level controller in order to achieve coordination between the actions of these individual agents. Using elementary calculations for simple distribution networks this paper illustrates that the expensive peak value of power imported from the grid, and the distribution losses, depend linearly on this correlation coefficient. Operational behavior is improved way by achieving a high correlation coefficient. The correlation coefficient they achieve can be used as a measure for determining the rewards control agents should get for their contribution to the ancillary services. Achieving a high correlation coefficient clearly will contribute to the stability of the smart grid. In this paper we briefly discuss some requirements for implementing a coordination strategy based on the correlation coefficients. Broadcasting of signals from a top level in the control hierarchy to the lower layer controllers only is needed.

Keywords: Smart grids, Control of renewable energy resources, Analysis and control in deregulated power systems
Abstract: This paper studies the problem to counter the fluctuation in renewable power supply in smart grid. A new fully distributed algorithm to control a network of thermostatically controlled loads (TCLs) is proposed to match, in real time, the aggregated power consumption of the TCLs and the forecast power supply. The algorithm is developed by converting the control problem into a consensus problem of individual utility functions.We then show that the problem is equivalent to a convex optimization problem and an algorithm based on distributed bisection method is presented to solve the problem. The proposed algorithm converges fast and is fully distributed, requiring only local information for each TCL and limited communication with neighboring TCLs, yet the available power supply is fairly dispatched among different TCLs. A numerical example is given to illustrate the effectiveness of the algorithm when applied to a network of variable-frequency air-conditioners.

Keywords: Stability of nonlinear systems, Asymptotic stabilization, Parameter-varying systems
Abstract: From a computational point of view it is natural to suggest the classification of attractors, based on the simplicity of finding basin of attraction in the phase space: an attractor is called a hidden attractor if its basin of attraction does not intersect with small neighborhoods of equilibria, otherwise it is called a self-excited attractor. Self-excited attractors can be localized numerically by the standard computational procedure, in which after a transient process a trajectory, started from a point of unstable manifold in a neighborhood of unstable equilibrium, is attracted to the state of oscillation and traces it. Thus one can determine unstable equilibria and check the existence of self-excited attractors. In contrast, for the numerical localization of hidden attractors it is necessary to develop special analytical-numerical procedures in which an initial point can be chosen from the basin of attraction analytically. For example, hidden attractors are attractors in the systems with no-equilibria or with the only stable equilibrium (a special case of multistability and coexistence of attractors); hidden attractors arise in the study of well-known fundamental problems such as 16th Hilbert problem, Aizerman and Kalman conjectures, and in applied research of Chua circuits, phase-locked loop based circuits, aircraft control systems, and others.

Keywords: Stability of nonlinear systems, Discontinuous control, Sliding mode control
Abstract: The paper presents mathematical tools required for finite-time stability analysis of discontinuous control systems using discontinuous Lyapunov functions. Elements of Filippov theory of differential equations with discontinuous right-hand sides and stability notions are briefly observed. Concepts of generalized derivatives and non-smooth Lyapunov functions are considered. The generalized Lyapunov theorems for stability analysis and convergence time estimation are presented and supported by examples.

Keywords: Stability of nonlinear systems, Input-to-State Stability, Lyapunov methods
Abstract: In this paper, we investigate integral input-to-state stability for interconnected discrete-time systems. The system under consideration contains two subsystems which are connected in a feedback structure. We construct a Lyapunov function for the whole system through the nonlinearly-weighted sum of Lyapunov functions of individual subsystems. We consider two cases in which we assume that one of subsystems is integral input-to-state stable and the other is either input-to-state stable or only integral input-to-state stable.

Keywords: Stability of nonlinear systems, Lyapunov methods, Switching stability and control
Abstract: Repetitive processes are a class of two-dimensional systems that arise in the modeling of physical examples and also the control systems theory developed for them has, in the case of linear dynamics, been applied to design iterative learning control laws with experimental verification. This paper gives new results on the stability of nonlinear differential repetitive processes for applications where a linearized model is either very limited or not applicable. The stability results are then applied to the design of iterative learning control laws in the presence of uncertain parameters and to the same problem when random failures occur that are modeled by a homogeneous Markov chain with a finite set of states. In both cases the computations required are expressed as a finite set of linear matrix inequalities.

Keywords: Stability of nonlinear systems, Output feedback control, Sliding mode control
Abstract: An output-feedback sliding mode based extremum seeking controller was recently introduced for linear uncertain systems by using periodic switching functions. Nonlinear systems were also considered but restricted to relative degree one plants as well as the former linear case. Here, generalization is achieved to include more general dynamics with arbitrary relative degree. Global properties of the closed-loop system with convergence to a controlled neighborhood of the desired maximum point are also rigorously proved. Simulation results illustrate the performance of the proposed extremum seeking control algorithm.

Keywords: Stability of nonlinear systems, Asymptotic stabilization, Input-to-State Stability
Abstract: A new algorithm is proposed for computing locally the linearizing output of single-input and multi-input nonlinear affine system. The algorithm modifies the extended Goursat normal form to iteratively obtain the successive integrations of one dimensional distributions of control system. The algorithm takes ideas from both vector field approach of feedback linearization and exterior differential system tools, hence the name textit{Blended Algorithm}. The proposed algorithm leads to a tower like structure depending upon the number of system inputs. Within individual tower, the coordinates are reduced one by one by finding the annihilators of vector fields at each step. The process is repeated till the single vector field is obtained for exact linearizable system. The scheme exhibits reduced computational complexity over the existing methods and can be extended to address feedback linearization of various class of control systems.

Keywords: Software for system identification, Estimation and filtering, Simulation of stochastic systems
Abstract: The paper presents the use of a MATLAB based software framework designed for nonlinear state estimation of discrete-time dynamic systems in optimal and adaptive control problems. The main focus of the framework is to facilitate implementation, testing and use of various nonlinear state estimation methods. Nevertheless, due to its versatility, the framework is also suitable for adaptive control purposes. The designer of the controller can utilize any of the large number of offered estimators which provide the necessary state and parameter estimates in the form of conditional probability density function. The paper presents the possibilities of the toolbox usage in optimal and adaptive control problems. It will be demonstrated how easily and naturally an estimation problem can be described by means provided by the framework and how easily it can be fitted into and controller.

Keywords: Estimation and filtering, Subspace methods, Sensor networks
Abstract: A model-based robust soft sensor is proposed here. The soft sensor is based on a Kalman filter (KF), which is designed to estimate the inaccessible variables using the output measurements in the face of the measurement noise, disturbances, and model perturbations. The performance of the soft sensor critically depends upon the reliability and accuracy of the identified system model it is based on. To overcome the degradation due to model mismatch, a reliable offline identification scheme, based on the powerful concept of emulator which significantly improved the accuracy of the proposed scheme, is proposed here. It involves performing a number of experiments using emulators, which are transfer function blocks connected to the system input or the output, and which are used to introduce model perturbations to mimic likely operating scenarios. It is shown that the KF residual is a function of the product of the model perturbation and the control input sensitivity function. The proposed new soft sensor is successfully evaluated on a simulated and laboratory-scale physical velocity control system

Keywords: Filtering and smoothing, Estimation and filtering, Nonlinear system identification
Abstract: This paper presents a method of designing a suboptimal Kalman filter (SKF) for nonlinear systems, including uncertainties on the system parameter dynamics. In general, governing dynamics behind real-world phenomena has significant associated uncertainties and all we can presume is the approximation to system models. We thus need a robust filter that is capable of rapidly following up the unknown system model parameters while adequately estimating the system states as well. The SKF is suboptimal in terms of standard square errors that the KF minimizes at each time step and this suboptimization results in the enhancement of filter’s parameter adaptation ability. The nonlinearity of system models is handled by using an unscented transforming statistical linearization without losing the Gaussianity of states to be estimated. We confirm the effectiveness of the proposed filter by numerical simulations.

Keywords: Filtering and smoothing, Estimation and filtering, Recursive identification
Abstract: The paper aims to propose an algorithm for PET (positron emission tomography) image reconstruction. Different from the algorithms designed to satisfy a single performance index in most cases, our algorithm inherently involves for two objects.Due to that the information upon noises is utilized fully, our algorithm provides a better reconstructed results than the one for the a single index when one obtains only some information on noises but does not know whether the statistics matches the noise level well in advance. The conclusion is also supported by the numerical experiments.

Keywords: Estimation and filtering, Event-based control, Sensor networks
Abstract: In this paper, the state estimation problem for continuous-time linear systems with two types of sampling is considered. First, the optimal state estimator under periodic sampling is presented. Then the event based state estimator with hybrid updates is designed, i.e., when an event occurs the estimator is updated linearly by using the measurement of output, while between the consecutive event times the estimator is updated by minimum meansquared error criteria. The average estimation errors under both sampling schemes are compared quantitatively for first and second order systems, respectively. A numerical example is given to compare the effectiveness of two state estimators.

Keywords: Identification for control, Filtering and smoothing, Nonparametric methods
Abstract: A method is proposed to detect if there is no coupling between an input and an output in systems operating in open-loop. The proposed technique is applicable to multiple input multiple output (MIMO) systems, i.e., the intent is to detect zeros in a transfer matrix. Traditional approaches to input/output (IO) selection are usually performed after the plant model is identified. The proposed approach is applied during the pre-identification stage and it is based on cross-correlation and fuzzy logic analysis. A study case involving identification of a 20 x 10 real system is discussed, as well as the advantages of detecting no-model IO pairs in the identification process.

Keywords: Distributed control and estimation, Sensor networks, Filtering and smoothing
Abstract: The paper addresses networked estimation of the state of a nonlinear dynamical system. It is shown how, exploiting a suitable consensus approach wherein prior and novel information are dealt with in a separate way along with the extended Kalman filter linearization paradigm, the resulting distributed nonlinear filter guarantees local stability under minimal requirements of network connectivity and system collective observability. A simulation case-study concerning target tracking with a network of nonlinear (angle and range) position sensors is worked out in order to show the effectiveness of the considered consensus filter.

Keywords: Distributed control and estimation, Cooperative systems, Multi-agent systems
Abstract: This paper presents a new method for estimating the eigenvalues of the Laplacian matrix associated with the graph describing the network topology of a multi-agent system. Given an approximate value of the average of the initial condition of the network state and some intermediate values of the network state when performing a Laplacian-based average consensus, the estimation of the Laplacian eigenvalues is obtained by solving the factorization of the averaging matrix. For this purpose, in contrast to the state of the art, we formulate a convex optimization problem that is solved in a distributed way by means of the Alternating Direction Method of Multipliers (ADMM). The main variables in the optimization problem are the coefficients of a polynomial whose roots are precisely the inverse of the distinct nonzero Laplacian eigenvalues. The performance of the proposed method is evaluated by means of simulation results.

Keywords: Control and estimation with data loss, Sensor networks, Distributed control and estimation
Abstract: This paper studies the problem of optimal network topology reconfiguration in sensor networks for state estimation. Multiple sensors make observations of a process, which are then transmitted, possibly via intermediate sensors, to a central gateway. Transmission over each link can experience packet drops. The time-varying wireless network environment is modelled by the notion of a network state as in Quevedo et al. (2013). For each network state, different network configurations can be used, which govern the network topology and routing of packets. Switching between different configurations incurs a cost, in that unwanted links will need to be removed before the establishment of new links, leading to a transient time in which some links may not be available. The problem is to determine the optimal network configurations to use in each network state, in order to minimize an expected error covariance measure that takes into account the cost of reconfiguration. A simpler sub-optimal method which minimizes the upper bound of the expected error is also proposed, which in numerical simulations gives essentially identical results to the optimal method.

Keywords: Control of networks, Multi-agent systems, Identification for control
Abstract: Recently, dynamical systems in science and engineering problems become increasingly larger and too complex. One of the ways to solve the difficulty is to model the systems with a hierarchically networked interconnection of subsystems. In this paper, we consider a hierarchical network system which is an interconnection of dissipative subsystems and identify both subsystems and network structure, called network system, preserving the dissipativity properties. We first estimate the state sequences of subsystems via the dissipation equalities instead of the matrix input-output equation in ordinary subspace identification methods. As a main result, we show an identification method in open-loop manner for subsystems and network systems based on the least-squares method. We also propose an identification method in a closed-loop manner.

Keywords: Networked embedded control systems
Abstract: Wireless sensor and actuator networks (WSANs) facilitate close interactions between human and the environment. Efficient coordination among the sensors and actuators plays a vital role in carrying out sensing and acting in WSANs. In this paper, we develop a collaborative estimation and actuation mechanism, which consists of a sensor-actuator coordination phase and an actuator-actuator coordination phase. The first phase is based on distributed Kalman filter in federated conguration, which is able to provide reliable and precise sensing data. On this basis, the second phase allocates proper tasks based on system requirements and coordinates actuators to accomplish the tasks. Particularly, the actuator-actuator coordination is formulated as an optimization problem and an effective method is proposed to search the solution based on sequential unconstrained minimization technique. Simulation results demonstrate the effectiveness of our proposed mechanism.

Keywords: Multi-agent systems, Estimation and filtering, Sensor networks
Abstract: This paper considers distributed estimation over heterogeneous sensor networks. We propose a distributed estimation strategy based on PageRank algorithm, where the link weight depends on the edge estimation covariances. We prove that the proposed estimator obtains better estimates than one typical estimator under identical initial conditions. Motivated by the advantage of the sensors with high accuracy locating at important positions, we propose an optimal sensor deployment strategy to arrange locations for sensors in a given physical network. Numerical examples are provided to demonstrate the effectiveness of the proposed estimator, showing a better performance under the optimal sensor deployment.

Keywords: Linear systems, Optimal control theory, Control design for hybrid systems
Abstract: This paper addresses and solves two optimal state-feedback control design problems for sampled-data systems. First, we reformulate the closed-loop system as a special linear hybrid system. Then, two theorems are developed to evaluate the H2 and the Hoo performances of hybrid systems using specific two-point boundary value problems. Both theorems are adapted to provide optimal control conditions, based on linear matrix inequalities (LMIs), for the state-feedback problems under consideration. These results are generalised to cope with non-uniform data-rates in the communication channel between the controller and the plant.

Keywords: Linear systems
Abstract: The poles/residues expression of the frequency-limited mathcal{H}_2-norm is used to derive two upper bounds on the mathcal{H}_{infty}-norm of a MIMO LTI dynamical system. These bounds can be efficiently computed when the eigenvalues and eigenvectors of the model are available. This specificity make them particularly well suited to watch the mathcal{H}_{infty}-norm of the approximation error in the context of the frequency-limited mathcal{H}_2 model approximation method suggested recently by the authors. The efficiency of these bounds, as well as their integration in the model approximation algorithm are illustrated through several numerical examples.

Keywords: Linear systems, Lyapunov methods
Abstract: This paper addresses the problem of determining the H-infinity norm of 2D mixed continuous-discrete-time systems. A novel approach is proposed based on the use of a class of complex Lyapunov functions with rational dependence on a parameter, which provides upper bounds on the sought norm via linear matrix inequalities (LMIs). It is also shown that the provided upper bounds are nonconservative by using rational functions in the chosen class with degree sufficiently large. Some numerical examples illustrate the proposed approach.

Keywords: Linear systems, Control of constrained systems, Asymptotic stabilization
Abstract: We present a new method for the approximation of the largest constraint admissible set (CAS) for linear continuous-time systems with state and input constraints. The CAS is the set of initial states for which the controlled system does not violate the input or state constraints. The presented approach is based on a suitable discretization of the continuous-time system. In fact, we will show that CAS in the continuous-time case can be computed analogously to the discrete-time case, given an appropriate sampling time was chosen. We stress that the computation of CAS for continuous-time systems is considerably more difficult than for discrete-time systems, since one has to guarantee that the system does not violate the constraints in between the sampling instances.

Keywords: Linear systems, Digital implementation
Abstract: The paper deals with the linearization problem of non controllable discrete time submersive systems. Following the approach recently introduced in the literature for continuous time systems in (Menini et al. (2012)), necessary and sufficient conditions are given for the equivalence of a discrete time (not necessarily controllable) single input system to a linear one.

Keywords: Linear systems, Analytic design, Time-invariant systems
Abstract: By the pole assignment theorem for linear controllable systems, there exists a linear feedback yielding the closed-loop system with any given set of eigenvalues. Systems with non-zero initial conditions are studied, and we prove that large deviations of trajectories arise for large eigenvalues as well as for small ones. Invariant ellipsoids techniques to minimize the maximal deviation are provided and its properties are examined.

Keywords: Mechatronic systems, Vibration control
Abstract: In this work we study the problem of rope sway dynamics control for elevator systems, with time-varying rope lengths. We formulate this problem as a nonlinear control problem and propose nonlinear controllers based on Lyapunov theory for time-varying systems. We study the stability of the proposed controllers, and test their performances on a numerical example.

Keywords: Motion Control Systems, Robots manipulators, Modeling
Abstract: This paper demonstrates the solution of digital observer implementation for industrial applications. A nonlinear high-gain discrete observer is proposed to compensate the tracking error due to the flexibility of robot manipulators. The proposed discrete observer is obtained by using Euler approximate discretization of the continuous observer. A series of experimental validations have been carried out on a 6 DOF industrial manipulator during a Friction Stir Welding process. The results showed good performance of discrete observer and the observer based compensation has succeed to correct the positioning error in real-time implementation.

Keywords: Modeling, Vibration control
Abstract: The increasing demand for light weight, fast and precise mechatronic systems while reducing the time to market dictates to use advanced simulation and analysis tools in the design process. This paper presents the on-going activities on the simulation and validation of combined 1D and 3D models for the design and analysis of complex mechatronic system. This includes the development of a flexible multi-body model, a lumped parameter driveline model and a control system. In order to demonstrate the potential of the virtual design and analysis process for modern mechatronic systems, an industrial machine tool is used as a case study. In order to predict the dynamic behavior of the machine, forecast the influence of specific design changes, and assess the impact of different control architectures with full confidence, the model needs to be validated. To this end, the simulations of the model are compared with the results obtained on a physical prototype.

Keywords: Vibration control, Design methodologies, Mechatronic systems
Abstract: A novel feedforward control strategy is presented to isolate precision machinery from broadband floor vibrations. The control strategy aims at limiting the low-frequency controller gain, to prevent drift and actuator saturation, while still obtaining optimal vibration isolation performance at higher frequencies. This is achieved by limiting the low-frequency control action such that almost no phase shift is introduced at higher frequencies. To minimize model uncertainties, the feedforward controller is implemented as a self-tuning IIR filter that estimates the parameters online. Only a few parameters have to be estimated, which makes the algorithm computationally efficient. An additional feedback controller is designed to make the self-tuning algorithm more robust. The effects of feedforward and feedback control add up. The control strategy is successfully validated on an experimental setup of a vibration isolator.

Keywords: Vibration control, Modeling
Abstract: Wind turbine blade vibration is a serious problem not only because it will reduce the life of blade but also can it pass some unexpected frequencies to the tower, which will cause tower to vibrate. The aim of this paper is to develop a model for wind turbine flapwise vibration and reduce the pitch angle caused vibrations in flapwise direction. Lagrange’s method is used to model the blade and input shaping method is used to reduce the residual vibrations caused by the change of pitch angle input. Effectiveness of designed input shaper is verified through comparison.

Keywords: Vibration control, Micro and Nano Mechatronic Systems, Identification and control methods
Abstract: This paper presents a SISO robust control of some lightly damped micro/nano-positioning system equipped with a piezoactuator. The adverse phenomenon of nonlinear hysteresis in the actuator is first compensated using an observer-based approach. Then an LQG/LTR method is proposed to additionally reduce vibrations affecting the system in a higher frequency range. Illustrative experimental results show a significant improvement of the closed-loop system behaviour with respect to the open-loop one.

Keywords: Biomedical system modeling, simulation and visualization, Quantification of physiological parameters for diagnosis and treatment assessment, Decision support and control
Abstract: Asynchronous events (AE) occur during mechanical ventilation (MV) therapy when the patient’s breathing is not synchronised with the ventilator support. Frequent AE indicates sub-optimal ventilation therapy and may lead to further complications. Asynchrony Index (AI) gives the percentage of AEs as a percentage of total breaths, but is only assessed via manual scrutiny. Thus, there is a need to automate AE detection in real-time. A model-based approach using time-varying elastance to detect AEs is developed and retrospectively assessed in MV patients. Data from 14 mechanically ventilated respiratory failure patients, enrolled in an observational study in Christchurch Hospital, New Zealand were used to investigate the performance of the method. Patient data is sorted according to the ventilation mode used, and AI is calculated for each episode separately. The model-based approach accurately identifies AEs, and shown not to give false positive readings when compared to manual detection (gold standard). None of the ventilation modes give significantly different AI levels (P > 0.05). AI decreases when ventilation mode changes and increases overall time indicate worsen patient-ventilator interaction. The model-based method is able to successfully and accurately calculate AI. Real time use of this metric will enable patients with sub-optimal ventilator settings to be automatically identified for the first time and the settings adjusted as necessary, improving the efficacy of mechanical ventilation therapy, and providing a quantified metric to help guide MV care.

Keywords: Identification and validation, Bio-signals analysis and interpretation, Biomedical system modeling, simulation and visualization
Abstract: Most parameter identification procedures find the parameter values that best fit some observed behavior according to the least squares criterion. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects and can thus yield poor results. Outlying data is often removed to avoid inaccurate outcomes. However, this process is complex and tedious. This research presents an adaptation of the Gauss-Newton parameter identification method that effectively ignores the contribution from outlying data. The adapted method was compared to the original Gauss-Newton method in two modeling exercises. The first exercise was a C-peptide pharmaco-kinetic model with noisy data that did not contain outliers. The second exercise is an insulin pharmacokinetic model with data that contained outliers and un-modelled behavior. The adapted method yielded similar results to the original methods for the C-peptide data with high correlations between identified parameters across approaches (R=0.90 and R=0.92). This was expected due to the relative lack of outliers in the C-peptide data. In contrast, the high rate of outliers and un-modelled behavior in the insulin data caused the significant differences between the parameter values identified by the approaches (R=0.47 and R=0.00). While the original method consistently found the least squares optima, the adapted method located the parameter set that fitted the majority of data points. The subtle difference between these approaches can yield large difference in identified parameter values. The adapted approach exhibits no erroneous effects in typical data, and should be considered more applicable in modeling exercises with outlier data or un-modelled effects.

Keywords: Quantification of physiological parameters for diagnosis and treatment assessment, Identification and validation, Healthcare management, disease control, critical care
Abstract: Total stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid resuscitation therapy, which is a treatment for cardiac failure. From an engineering point of view, this parameter dictates the cardiovascular system's dynamic behavior. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a method is developed to compute stressed blood volume from preload reduction experiments. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the most sensitive parameters; and (3) adjust only these sensitive parameters. Stressed blood volume was determined sensitive for all datasets, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal mean squared error of 11.77 %. Stressed blood volume has been computed to range between 450 and 963 ml, or 15 to 28 ml/kg, which matches previous independent experiments on pigs, dogs and humans. Consequently, the method proposed in this work provides a simple way to compute total stressed blood volume from usual hemodynamic data.

Keywords: Physiological Model, Identification and validation, Decision support and control
Abstract: Abstract: Accurate stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status. However, direct measurements are too invasive in clinical practice and current procedures for estimating SV require specialised devices. This study presents an analysis of the accuracy of SV estimation by combining pulse-wave and windkessel analyses. What makes this study different to existing pulse-contour analyses is that pressure contour variation due to altered arterial mechanical properties (resistance, characteristic impedance and compliance) were related to correct corresponding pressure zones, enabling this model to more accurately capture SV from aortic pressure measurements alone. Using data from three porcine experiments, the median difference between measured and estimated SV was 1.4 ml with a 90% range (5th-95th percentile) -11.3ml - 12.2ml. This result relies on an estimate of the average value of just one windkessel parameter. The presented method demonstrates that SV can be estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary from patient to patient.

Keywords: Model formulation, experiment design, Decision support and control, Quantification of physiological parameters for diagnosis and treatment assessment
Abstract: Bayesian networks can be used to build models of diseases for diagnosis, and, if complemented by decision theory and utility functions, can also suggest treatments. This paper presents a development framework for such a network that has been used to model sepsis and in particular focuses on incorporating knowledge from the literature and databases as well as expert opinion into the model. Two parameters are presented as examples of the methods used, and the model is validated for a cohort of patients suspected of infection.

Keywords: Physiological Model, Biomedical system modeling, simulation and visualization, Quantification of physiological parameters for diagnosis and treatment assessment
Abstract: Respiratory mechanics models can be used to optimise patient-specific mechanical ventilation (MV), but are limited to fully sedated MV patients who are not spontaneously breathing. This research presents a non-invasive model-based method to determine respiratory mechanics of spontaneously breathing MV patients. Patient-specific respiratory mechanics of 22 spontaneously breathing patients are described using a single compartment lung model with time-varying elastance (Edrs). The normalised Edrs trajectories and the area under the curves (AUCEdrs) are calculated using clinical data from 22 patients ventilated using pressure support (PS) and neurally adjusted ventilatory assist (NAVA). Edrs trajectories are also compared between ventilation modes. Edrs for PS ventilation were significantly higher compared to NAVA (p < 0.05). Edrs trajectories were more variable during NAVA than PS (p < 0.05). 20 of 22 patients had AUCEdrs > 25 cmH2Os/l. The AUCEdrs is a surrogate of elastance, and thus can be used as a respiratory failure severity indicator. This non-invasive model-based approach captures unique dynamic respiratory mechanics for spontaneously breathing patients during PS and NAVA. The model is fully general and is applicable to both fully controlled and partially assisted MV modes, with the resulting potential to standardise treatment for all MV patients.

Keywords: Adaptive and robust control of automotive systems, Nonlinear and optimal automotive control
Abstract: A prototype rotary valve engine has been built from a conventional single piston Honda generator. In this study, an engine control unit, ecu, is designed, tested and implemented for the purpose of carrying out the primary engine functions of sparking, fueling and valve events control. The rotary valve engine has no camshaft, therefore, only a crankshaft (optical) sensor is required. The controller has also the advantage of turning the engine in either direction, removing the need for a reverse gear. Full control over valve events, together with crank motion independent sparking, removes the need for a throttle valve and a mechanical starter. With the valve turning freely, no feedback sensing is necessary. The proposed ecu has been designed, successfully tested and implemented on ALTERA’s UP1 prototyping board. The FPGA programmable device used is the EPM7128SLC84-7.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Automatic control, optimization, real-time operations in transportation, Nonlinear and optimal automotive control
Abstract: This paper addresses the problem of maximizing the power produced by an organic rankine cycle (ORC) waste heat recovery (WHR) system along a given vehicle mission. A simplified model of the system allows the formulation of an optimal control problem that can be solved via dynamic programming (DP). To increase the smoothness and the accuracy of the solution obtained using the state-of-the-art implementation of DP known as level-set DP, an improved version is developed, making use of adaptive grids for discretization. The adaptive-grid algorithm is first validated on a simple problem with an analytical solution, then applied to the more complex ORC case study. It is shown that the required accuracy can be achieved with a lower computational effort than the original algorithm. The analysis of the resulting optimal trajectory for the ORC control problem provides useful insight for both control design and system design.

Keywords: Automotive sensors and actuators, Engine modelling and control, Modeling, supervision, control and diagnosis of automotive systems
Abstract: In this paper a modelling of a hybrid actuator is proposed. The hybrid actuator consists of a piezo, a mechanical and a hydraulic ratio displacement. This paper deals with a hybrid actuator composed by a piezo, and a hydraulic part controlled using a regulator for camless engine motor applications. The discrete control law is derived using the well-known Backward Euler method. An analysis of the Backward and Forward Euler method is also presented. Simulations with real data are shown.

Keywords: Engine modelling and control
Abstract: In this work, a Linear Parameter Varying (LPV) model of the Selective Catalytic Reduction (SCR) system, used for NOx reduction placed as aftertreatment systems in diesel engines is developed. The LPV model structure is motivated by some insights of the physical properties of the system. Yielding only 7 unknown parameters for a third order model structure of the SCR, which is a significantly lower compared with other nonlinear models for the same process. The states of the model however, do not possess any physical interpretation. The LPV model structure is validated using real measured data from cell tests at Scania AB with promising results. The proposed model is compared to previous global nonlinear models of the system, i.e. a nonlinear state space model and a Hammerstein-Wiener model of the system.

Keywords: Engine modelling and control, Control architectures in automotive control
Abstract: This paper newly proposes an on-line learning method for control maps by using Gaussian filters. In the present method, man-hours for calibration of control maps can be decreased, and complicated structures of control maps are learned without prior knowledge. Moreover, by the effect of Gaussian filtering, smoothed maps can be created even under noisy conditions or few measured points. We also introduce improvements of the algorithm to cope with engine deterioration due to aging. In this work, the proposed method is applied to minimum advance for best torque control on actual vehicles with just one driving data, and the accuracy of the learned map is verified through simulation and experiments.

Keywords: Nonlinear and optimal automotive control, Engine modelling and control
Abstract: This paper presents a model-based receding horizon optimal control algorithm for the engine speed tracking control. A mean-value model including the air intake dynamics and the rotational dynamics is exploited in the tracking controller design, which is calibrated based on the physical rules combined with curve fitting techniques. Based on this mean-value model, the dynamical model of the speed tracking error is derived for any given speed command. The design problem is reduced to the receding horizon control problem under the constraint of the tracking error dynamics.The online computational algorithm based on C/GMRES approach is adopted to solve this nonlinear receding optimal problem. Finally, simulation and experiments results demonstrate the satisfactory tracking performance by using the proposed controller.

Keywords: Mobile robots, Motion Control Systems, Autonomous robotic systems
Abstract: This paper deals with the problem of active target tracking with obstacle avoidance for multi-robot systems. A nonlinear model predictive formation control is presented which uses potential functions as terms of the cost function. These terms penalize the proximity with mates and obstacles, splitting the problem of obstacle avoidance into two repulse functions. Experimental results with real robots are presented to demonstrate the performance of the approach.

Keywords: Mobile robots, Networked robotic system modeling and control, Motion Control Systems
Abstract: In this paper the formation and trajectory tracking control problem for multi-agent systems is presented. Initially, a control strategy for a group of holonomic robots is proposed. The proposed control is extended to the nonholonomic case. The control scheme is based on potential functions which make possible the design of decentralized formation control scheme while avoiding agents collisions. The trajectory tracking is achieved defining leaders which attract the formation to a desired trajectory. Furthermore, if at least two leaders are defined, the formation orientation tends to a desired pose (for the planar case). Assuming that the communication graph is always connected, a stability analysis using Lyapunov theory ensures the minimization of the potential function and the trajectory tracking. The control strategies are verified by simulation.

Keywords: Mobile robots, Embedded robotics, Robotics technology
Abstract: This paper gives introspection to the concept of the Ethon – holonomic drive based mobile robot. Nowadays the mobile robots are widely spread at the industrial and the service sectors as well. It is a facing problem in the view of development cost and time that the mobile robots are redesigned for every different application, despite most of the mobile robots are move on wheels. Our mobile robot base development gives a solution for the movement of mobile robot applications whit its modularity, holonomic movement, high load and work time capacity. The robot base has 3 DoFs according to the holonomic wheels and it can move, handle or even drive different superstructures.

Keywords: Guidance navigation and control, Intelligent robotics, Autonomous robotic systems
Abstract: Visual homing which is behavior-based can make use of the natural landmarks to achieve homing in the unstructured environment. The local features, represented by the SIFT (Scale Invariant Feature Transform), have a variety of invariance and are quite suitable as the natural landmarks. Many visual homing methods can exhibit high precision only when the landmarks meet to isotropic distribution. However, most of the local feature extraction algorithms have not taken into account the uniformity of the feature distribution. Based on the initial SIFT, an improved feature extraction algorithm called UD-SIFT (Uniform Distribution-SIFT) is obtained which can improve the uniformity. The visual homing experiments are carried out indoors and in the corridor, using the ADV (Average Displacement Vector) and ALV (Average Landmark Vector) methods which are both based on the panoramic vision. The results turn out that the UD-SIFT algorithm has improved the feature distribution and the robot homing precision.

Keywords: Autonomous robotic systems, Guidance navigation and control, field robotics
Abstract: A nonlinear visual servoing approach is proposed for the stabilisation of fully-actuated autonomous underwater vehicles (AUVs) by exploiting the homography matrix between the two images of a planar scene. In a cascade manner, an outer-loop control defines a reference setpoint based on the homography matrix, and an inner-loop control ensures the stabilisation of the setpoint by assigning thrust and torque controls. In contrast with conventional kinematic solution, the proposed controller deals with the high nonlinearity and coupling of the system dynamics and ensures almost-global asymptotical stability. In addition, the interactions of the AUV with the surrounding fluid (e.g., added mass and drag effects) are often difficult to model precisely whereas they may significantly perturb its motion. The proposed controller -augmented with an effective integral action- allows for the compensation of model uncertainties and for robust performance against such perturbations. Simulation results illustrating these properties on a realistic AUV model subject to sea current are reported.

Keywords: Mobile robots, Modeling, Autonomous robotic systems
Abstract: his paper investigates the adaptive state-feedback stabilization of stochastic nonholonomic systems which have both uncertain parameters and time-varying coefficients. The state-scaling and backstepping techniques are exploited in the design of controllers. The adaptive state-feedback stabilizing controllers and switching control strategy are proposed so that the closed-loop system can be stabilized in probability. In the end, two simulation examples are provided to illustrate effectiveness of controllers.

Keywords: Monitoring and performance assessment
Abstract: This paper studies the design and performance assessment of setpoint feedforward controllers in univariate control loops. A design method for the setpoint feedforward controller is proposed, aiming at breaking the two fundamental tradeoffs among the setpoint tracking, the load disturbance rejection and the robustness against model uncertainties. The lower bound for the total variation (TV) is established. An IAE-TV-based performance index is devised to assess the performance of the setpoint feedforward controller. The effectiveness of the proposed feedforward controller design method and the performance assessment method are illustrated by simulation and experimental examples.

Keywords: Process control applications, Model predictive and optimization-based control, Advanced process control
Abstract: The paper deals with the determination of controller parameters for multivariable systems by means of parameter optimization. In order to reduce the computational burden, the starting point in the optimization is determined through information provided by the feedback controller parameterized using the multivariable internal model controller (IMC) computed from the plant’s p/q moment approximant and the original plant’s multi-loop controller parameter magnitudes. Thereafter, controller parameter optimization proceeds to completion using the MATLAB optimization toolbox which is widely available. A potency of this technique is that all performance specifications and stipulated constraints are easily accommodated in the problem formulation thus facilitating complete problem solution in one go. The technique has been found to be effective for all plants considered so far and generally produces closed loop systems with favorable characteristics when compared to systems designed by similar methods.

Keywords: Process control applications, Industrial applications of process control
Abstract: Anti-slug control of multiphase risers involves stabilizing an open-loop unstable operating point. PID control is the preferred choice in the industry, but appropriate tuning is required for robustness. In this paper, we define PIDF as a PID with a low-pass filter on its derivative action where the low-pass filter is crucial for the dynamics. We compared a new PIDF tuning based on Internal Model Control (IMC), together with two other tunings from the literature, with an optimal PIDF controller. The optimal PIDF tuning was found by minimizing a performance cost function while satisfying robustness requirements (input usage and complementary sensitivity peak). Next, we considered two types of robust mathcal{H}_infty controller (mixed-sensitivity and loop-shaping). We compared the controllers based on their pareto-optimality, and we tested the controllers experimentally. We found that the new IMC-PIDF controllers is the closest to the optimal PIDF controller, but the robustness can be further improved by mathcal{H}_infty loop-shaping.

Keywords: Process control applications, Process modeling and identification, Advanced control technology
Abstract: This paper proposes a two degrees of freedom modified Smith predictor scheme for controlling the outlet temperature uniformity of a crude oil preheating furnace. A reliable mathematical model for describing the nominal process dynamics has been obtained from an identification procedure using real-time field data. This procedure yields a second order model with a dominant time-delay term (significantly higher than the time constant values). Moreover, disturbances have been identified as step responses of first order processes. A PI controller embedded in a modified Smith predictor scheme is therefore designed. A disturbance rejection term is designed for this scheme. This term is designed using a new methodology which reduces the effect of unknown disturbances on the process output. Simulations show that our proposal significantly outperforms the one degree of freedom Smith predictor and a well-known two degrees of freedom Smith predictor.

Keywords: Process control applications, Advanced process control, Control system design
Abstract: This paper deals with an event-based PI control strategy for event occurrences reduction. The methodology is based on the send-on-delta technique where the measured value of the error signal is sent to the controller when it crosses predefined quantization levels. We propose to increase exponentially the distance between the thresholds in such a way that the number of events decreases by keeping a satisfactory transient performance and by fixing the maximum steady-state error independently. Simulation and experimental results show the effectiveness of the methodology and clarify the physical meaning of the design parameters of the controller.

Keywords: Advanced control technology, Process control applications, Control system design
Abstract: In order to improve the overall disturbance rejection response when both input and output disturbances are expected, we present a design method based on the disturbance observer (DOB) control structure. In this paper, optimal analytical DOB design is provided under the combined 2-norm objective. Results show that the proposed observer can optimally eliminate the input/output disturbance with the best combined performance criterion.

Keywords: Decentralized control, Complex systems, Optimal control theory
Abstract: This paper proposes a systematic method to design hierarchical, decentralized, stabilizing controllers for homogeneous hierarchical dynamical networks. Based on LQR approach with a properly chosen performance index including global and local objectives with control input penalty, an obtained optimal LQR feedback gain gives the closed-loop system a prescribed desirable hierarchical structure. In addition, the undesirable eigenvalues of the given homogeneous network can be selectively shifted by further selecting the weighting matrices based on the left eigenvectors associated with those eigenvalues. Finally, the proposed method is summarized into a systematic design procedure with an illustrative numerical example to show its effectiveness.

Keywords: Decentralized control, Control of interconnected systems, Control under communication constraints (nonliearity)
Abstract: We propose a decentralized and non cooperative algorithm for estimation and control in a multi-agent system of oscillators to achieve a balanced circular formation. Each agent gathers an uncertain measurement of its phase distance from other agents only when they are in its proximity. Based on this uncertain and intermittent data and on the a priori knowledge of the nominal (e.g. uncontrolled) agent's velocities, we employ an estimation method to reconstruct the relative angular positions. Then, we develop a bang-bang controller to achieve a balanced platoon formation. The novelty of the approach is that the balanced formation is achieved by using proximity sensors rather than distance transducers. Moreover, the bang-bang control strategy is designed so that the control goal is achieved even when the range of the sensors is lower than the desired spacing distance. The effectiveness of the approach is illustrated through extensive numerical simulations.

Keywords: Decentralized control, Fault-tolerant, Complex systems
Abstract: This paper studies a distributed consensus tracking control problem for a class of stochastic linear multi-agent systems subject to two types of attacks. The problem boils down to how to achieve robust consensus tracking of multi-agent systems with switching connected and disconnected directed topologies under attacks. The attacks on the edges instead of nodes lead to the loss of consensus tracking security. Based on a multi-step design procedure for designing a distributed secure algorithm, sufficient conditions on robust mean-square exponential consensus tracking are derived via the idea of average dwell time switching between some stable and unstable subsystems obtained from graph theory analysis. An application to a practical power system is considered. It is proved that each distributed generator (DG) modeled as an agent in a microgrid can successfully synchronize their terminal voltage amplitude to a prespecified reference value under these two types of attacks.

Keywords: Decentralized control, Systems with time-delays, stability of delay systems
Abstract: The inclusion principle for distributed-time-delay systems is defined. The properties of input-output map equivalence and preservation of stability between two systems, one of which includes the other, are presented. Controller and observer design, using the inclusion principle, are also discussed. Finally, overlapping decompositions of distributed-time-delay systems is illustrated. The use of overlapping decompositions in controller and observer design is also presented.

Keywords: decentralized control
Abstract: The paper aims to show the ability of the desired-dynamic-equations (DDE) based decentralized PID controller tuning method with its application to the ALSTOM gasifier benchmark control problem. The DDE-based PID controller tuning method was deduced from a kind of nonlinear adaptive controller with relative degree of two, which behaves good tracking performance and robustness by using an extended state observer and DDE to estimate and compensate the uncertainty and disturbance. The tunable parameters of DDE-based PID controller have explicit and distinctive physical meanings, thus can be tuned separately rather than iteratively as traditional PID controller does. The tuning method is firstly applied to the ALSTOM gasifier benchmark control problem with linear model, simulation shows that it exceeds output limits only twice (the least in literature) at 0% load with the step and sinusoidal disturbance; then the same controller is applied without any modification to nonlinear gasifier model, and the simulations show that it not only meets all control specifications, but also follows load change rapidly and shows good adaptability to the coal quality change.

Keywords: Decentralized control, Robust control (linear case), Control of interconnected systems
Abstract: In this paper, we present a decentralized variable gain robust controller for a class of large-scale serially connected systems with nonlinear perturbations via piecewise Lyapunov functions. The nonlinear perturbation means the interconnections between the subsystems and uncertainties, and the proposed decentralized variable gain robust controller is designed so as to reduce the e®ect of uncertainties and interconnections. In this paper, on the basis of the concept of piecewise Lyapunov functions, we show su±cient conditions for the existence of the proposed decentralized variable gain robust controller. Finally, numerical examples are presented.

Keywords: Optimal operation and control of power systems, Modeling and simulation of power systems, Control of renewable energy resources
Abstract: The Airborne Wind Energy paradigm proposes to generate energy by flying a tethered airfoil across the wind flow at a high velocity. While Airborne Wind Energy enables flight in higher-altitude, stronger wind layers, the extra drag generated by the tether motion imposes a significant limit to the overall system efficiency. To address this issue, two airfoils with a shared tether can reduce overall system drag. A study proposed in Zanon et al. (2013) confirms this claim by showing that, in the considered scenario, the dual-airfoil system is more advantageous than the single-airfoil one. The results computed in Zanon et al. (2013) however, do not model the interaction between the airfoils and the airmass. In this paper, the impact of the airfoil-airmass interaction on the extracted power is studied. As this phenomenon is complex to model, a blade element-momentum approach is proposed and the problem is solved by means of optimal control techniques.

Keywords: Control of renewable energy resources, Modeling and simulation of power systems, Optimal operation and control of power systems
Abstract: Current lidar technology is offering a promising opportunity to take a fresh look at wind turbine control. This work evaluates a flatness-based feedforward approach, that allows to calculate the control action based on trajectories of the rotor speed and tower motion using wind measurements. The trajectories are planned online considering actuator constrains to regulate the rotor speed and minimize tower movements. The feedforward signals of the collective pitch and generator torque update can be combined with conventional feedback controllers. This facilitates the application on commercial wind turbines. Simulations using a realistic lidar simulator and a full aero-elastic model show considerable reduction of tower and shaft loads.

Keywords: Control of renewable energy resources, Control system design
Abstract: Airborne wind energy systems are an emerging technology to harvest wind energy at higher altitude. Renewable energy is produced by exploiting the aerodynamic lift exerted by a wing tethered to the ground and controlled to fly crosswind paths. System realizations with either on-board or ground-based generation are possible. The focus of this paper is on ground-based generation systems where a two phase cycle is carried out. In the traction phase, power is produced by reeling-out the tether, which is coiled around drums connected to generators; in the retraction phase, the tether is reeled-in after the maximum length has been reached. A new flight controller for the retraction phase, which is straightforward to implement and tune, is proposed and a theoretical analysis and simulation results are presented.

Keywords: Control of renewable energy resources, Statistical methods/signal analysis for FDI, Smart grids
Abstract: In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind turbine fault detection and fault tolerant control benchmark model, in which one of the included faults results in a change in the gear box resonance frequency. This evaluation shows the potential of the proposed scheme to monitor the condition of wind turbine gear boxes in the existing control system.

Keywords: Modeling and simulation of power systems, Fault diagnosis and fault tolerant control
Abstract: This paper focuses on the modeling and H∞ fault tolerant control of wind turbine system with actuator faults. Stochastic piecewise affine (PWA) modeling tool is adopted to establish the multiple operating points models of wind turbine system with actuator faults under different wind speeds. Based on stochastic PWA model of wind turbine system with actuator faults, the theorem of H∞ fault tolerant control strategy is presented to solve the PWA state feedback control law of wind turbine system. The performance and the efficiency of the proposed approach are validated via simulations.

Keywords: Control of renewable energy resources, Control system design, Power systems stability
Abstract: The problem of frequency control and stability has become more prominent with the increasing use of converter-connected generators in many countries around the world. Especially in small autonomous systems, the replacement of conventional synchronous generation by variable-speed wind turbines may lead to large deviations of system frequency after severe transients and possibly even customer disconnection. For wind generators, a control scheme that is already being implemented, in order to mitigate this problem, is the addition of an inertia emulator. This paper proposes a design principle for wind turbine inertia emulators based on the characteristics of the autonomous system to which the wind turbines are connected.

Keywords: Application of nonlinear analysis and design, Analytic design
Abstract: Near-controllability is defined for those systems that are uncontrollable but have a large controllable region. It is a property of nonlinear control systems introduced recently, and it has been well demonstrated on discrete-time bilinear systems. The purpose of this paper is to propose a useful algorithm to compute the control inputs, which achieve the transition of a given pair of states, for a class of discrete-time bilinear systems that are nearly controllable. Accordingly, for such class of bilinear systems, not only near-controllability is proved, but also the computability of control inputs for near-controllability is shown. An example is provided to demonstrate the effectiveness of the proposed algorithm.

Keywords: Application of nonlinear analysis and design, control of fluid flows and fluids-structures interactions
Abstract: When a cylindrical probe vibrates laterally in a fluid at a Reynolds number of order 10, a circulatory pattern of flow is established in the fluid near the probe. In a plane transverse to the probe, a symmetric arrangement of four streaming cells is maintained by the probe’s vibration. A computational model has shown that inertial particles introduced to such a flow will be attracted to the centers of the streaming cells. We present preliminary experimental data supporting this prediction and outline a strategy whereby particles captured in this way can be transported from place to place as a result of periodic variations in the excitation of a fluid containing two independent probes. Specifically, we model the time-averaged flow around two probes by superposing two copies of a velocity field obtained analytically for the single-probe case and demonstrate that a cyclic change in the two-probe flow can engender acyclic variations in the positions and character of fixed points. A fixed point that’s initially attractive to inertial particles can be moved from the vicinity of one probe to the vicinity of another and then annihilated or altered via bifurcation so that it will surrender the particles it carries. If parametric variations in such a flow are slow relative to the dynamics of particles migrating to attracting points, then the net displacement of inertial particles from one trapping point to another as a result of a cyclic change in the flow exhibits certain features of a geometric phase.

Keywords: Application of nonlinear analysis and design, Optimal control theory, Hamiltonian trajectories in optimal control
Abstract: Perturbed inverse problems are under consideration for dynamical systems linear relative controls. It is assumed that sampling history and sampling error estimate are known. Auxiliary optimal control problems are introduced to minimize a regularized integral discrepancy functional. The trajectories of the system are constructed with the help of Optimal Synthesis in the domain of admissible motions. It is proven that realizations of Optimal Synthesis generating trajectories of the system and minimizing the discrepancy functional in the domain are solutions of the perturbed inverse problem of dynamics.

Keywords: Application of nonlinear analysis and design, Stability of nonlinear systems, Power systems
Abstract: This work is devoted to the investigation of oscillations in a drilling system using an induction motor with wound rotor as a drive. It is motivated by the problems of drilling rig failures in the oil and gas industry. The study is based on a modified version of the mathematical model of a drilling rig proposed by scientists from the Eindhoven University of Technology. The model of drilling rig developed in this work takes into account full description of the rotor geometry of induction motor. It is shown that such complex effects as hidden oscillations may appear in this kind of systems. To damper these oscillations a control strategy based on changing the external additional resistance in the rotor circuit is suggested.

Keywords: Application of nonlinear analysis and design, Tracking, Nonlinear observers and filter design
Abstract: This paper deals with modelling and control of an injection moulding process. During the packing phase the pressure in the mould must follow certain trajectories. First a mathematical model for the packing phase is presented. Therein a characteristic function is needed which depends mainly on the form of the mould. Since the characteristic function is hardly determinable it has to be estimated automatically. This is done with cubic basis splines. The mathematical model turns out to be input to state linearisable resp. differentially flat. So trajectory planning is easily feasible and a flattnes based controller is presented. Since not every state variable can be measured an observer is added.

Keywords: Application of nonlinear analysis and design, Tracking, Parameter-varying systems
Abstract: The shift control, especially clutch slip control in inertia phase, is considered as a key issue of dual clutch transmission for improving driving comfort. For the shift is a complex nonlinear dynamic process combining engine states and road information, and these are often variable, so the clutch control system can be described as a nonlinear parameter-varying model. Moreover, a novel nonlinear approach is proposed for clutch control of shift process, the control objective is to obtain smooth gearshift in the limited shift time by making clutch slipping speed difference tracking the planned reference. The proposed method can be formalized as a triple-step procedure and this procedure provides a concise design process so that the derivation of the control law is simplified and straightforward. Finally, the simulation results in the environment of AMESim with a complete vehicle model show the efficiency of the proposed approach.

Keywords: Micro and Nano Mechatronic Systems, Vibration control, Application of mechatronic principles
Abstract: Polynomial-based damping techniques such as Positive Position Feedback (PPF) and Positive Velocity and Position Feedback (PVPF) have been applied successfully to a number of lightly damped systems to overcome resonance-induced vibration issues. These control designs exhibit several advantages such as substantial damping performance, relative ease of design and adequate robustness in the presence of plant parameter uncertainties. Their formulation is based on the well-known pole-placement technique where damping is achieved by pushing the poles of the close-loop system arbitrarily away from the jω axis and in to the left-half plane. Current designs result in changing the real part of the poles while keeping the imaginary part unaltered; thus keeping the resonant frequency of the closed-loop, damped system unchanged, compared to the original undamped, open-loop system. In this work, we present a pole-placement technique which results not only in the substantial damping of the resonance but also in shifting the system resonance to a substantially higher frequency. This result is beneficial to a number of systems such as nanopositioners employed in Scanning Probe Microscopes, where maximizing the positioning bandwidth is a major goal and the achievable bandwidth is severely limited by the resonant frequency of the positioner. 

Keywords: Mechatronic systems, Vibration control, Motion Control Systems
Abstract: Ultra-precise positioning of the recording head over the data tracks is required to achieve the aggressive track density scaling envisioned for future tape storage systems. The track-follow control system is responsible for reducing the misalignment between the tape and the head created primarily by lateral motion of the flexible medium. The challenge of improving the track-following accuracy becomes even more difficult in the presence of external vibration disturbances. Typically, position error signal (PES) information is measured at the tape head using pre-formatted servo information and is used in a feedback controller during track-following to maintain the position of the read/write elements at the desired position during tape transport. In this paper, a track-follow scheme is introduced in which a high-bandwidth low-noise position sensor is used in conjunction with the position information read back by the head from preformatted servo patterns on the tape. A cascade control configuration is proposed, in which first an inner loop is designed based on the actuator position information provided by the sensor. The high-bandwidth and low-noise characteristics of the sensor enable a high closed-loop bandwidth of the inner system that effectively compensates for external vibration disturbances. Subsequently, using a cascade structure, a PES-based controller is designed that compensates for the lateral tape motion disturbances. Simulation and experimental results are presented that illustrate the performance of the proposed scheme compared to a conventional PES-based track-follow control system.

Keywords: Motion Control Systems, Identification and control methods, Mechatronic systems
Abstract: In this paper, we propose a new control scheme, position and velocity profile tracking control for new generation servo track writing (STW). While conventional servo track writers need controllers that perform fast positioning control with fast track seek and regulation, spiral servo track writers require accurate position and velocity profile tracking control for high quality servo patterns on the media disk. Since STW timing eventually renders geometrically accurate servo patterns, in constant velocity region both position and and velocity error signals should be regulated within small bounds. It is known that regulation via integral SMC provides a good tracking performance, but using a high switching gain is not appropriate for a system having resonance modes. In this paper, thus we apply sliding mode control with disturbance observer to STW. To verify utility of the proposed position/velocity profile track control, we conducted some experiments using a state-of-the-art STW system. Comparative study with the conventional control method was made. The performance of proposed method was experimentally validated.

Keywords: Micro and Nano Mechatronic Systems, Motion Control Systems
Abstract: In this paper, we present a novel non-raster scanning algorithm for high-speed imaging in Atomic Force Microscopy. In contrast to recent non-raster scanning algorithms for string-like samples, the proposed algorithm is developed for cells and other simple specimen samples. This algorithm collects data in the vicinity of the specimen to create sample contours at different heights to build the 3D topography of the target sample. During the scan process, the tip is steered based on a prediction of the contour curvature and contour tangent. The proposed scanning trajectory follows the contour of the sample and avoids crossing the specimen, while minimizing the possible excitation of resonances of the cantilever. For the prediction of the curvature and tangent of the contour, the current partially obtained contour and a previous contour scan are used: a prediction from both contours is suitably combined by a weighting algorithm derived from a reliability evaluation of both predictions. This permits the creation of topographical images of specific interest at a reduced scanning time in comparison to some prevalent non-raster scan algorithms and raster scans. Simulation results are provided.

Keywords: Modeling, Vibration control
Abstract: Lateral tape motion (LTM) is defined as the lateral deviation of the tape from its prescribed strain-free state path. The effects of tape, roller, guide or reel imperfections on the LTM are reasonably well understood. Yet, the effects of disturbances in the longitudinal (in-plane) direction, which can couple into LTM, have not been well described. Longitudinal tape vibrations can be due to, but not limited to a) tension dynamics due to servo control of the tape reels, and/or tension impulses due to the unwinding of tape layers that experience sticking. The problem is further complicated due to the uncertainty of the tape length on the downstream side, as a result of the “floating layers” in the take-up reel. In this work, the equations of motion of the longitudinal and lateral tape motion are derived from first principles. The coupling due to non-linear longitudinal strain is considered. The equations of motion are solved by using the finite element method, and an explicit time integration algorithm. The entire tape path is modeled directly, where the interaction of the tape with the recording head and the guides are represented as concentrated forces, and moments. The effects of disturbances, typical for a tape transport system, on the coupling or lack there off are investigated.

Keywords: Estimation and filtering, Mechanical and aerospace estimation, Adaptive system and control
Abstract: A common technique for improving the estimation performance of the Kalman filter and making the filter robust against any kind of faults is to adapt its process and measurement noise covariance matrices. Although there are numerous approaches for the adaptation such as full estimation or scaling, simultaneous adaptation of these two matrices is an ongoing discussion. In this paper, firstly, two common problems for the attitude estimation of a nanosatellite are solved by adapting the process and noise covariance matrices. Then these two adaptation methods are integrated with an easy to apply scheme and the matrices are simultaneously adapted. The newly proposed filtering algorithm, which is named Robust Adaptive Unscented Kalman Filter, considerably increases the estimation performance and is fault tolerant against the sensor malfunctions.

Keywords: Estimation and filtering, Statistical data analysis
Abstract: Dynamic weighing, i.e., weighing of objects in motion, without stopping them on the weighing platform, allows one to increase the rate of operation of automatic weighing systems used in industrial production processes without compromising their accuracy.The paper extends and compares two approaches to dynamic weighing, based on system identification and variable-bandwidth filtering, respectively. Experiments, carried on a conveyor belt type checkweigher, show that when appropriately tuned, both approaches yield satisfactory results that meet stringent metrological accuracy specifications.

Keywords: Estimation and filtering, Filtering and smoothing, Bayesian methods
Abstract: The paper deals with state estimation of nonlinear stochastic dynamic systems. Various unscented Kalman filter (UKF) algorithms are analyzed with the focus on computation and transformation of the sigma-points for the purpose of update of state estimate moments. An algorithm of the pure propagation UKF is developed transforming the initial sigma-point set forward in time without necessity of its re-computation as is usual in classical UKF algorithms. Such direct transformation of the sigma-points keeps higher order moments of the sigma-point set and leads consequently to an increased accuracy of the state estimate. The proposed pure propagation unscented Kalman filter is illustrated in a numerical example.

Keywords: Estimation and filtering, Fault detection and diagnosis, Filtering and smoothing
Abstract: In this paper we present a receding horizon estimation method for linear time invariant systems, subject to unknown inputs. The proposed approach is based on the idea of asymptotically decoupling the state estimation problem from the unknown input estimation problem. Consequently, the latter is formulated as a weighted least squares problem in a receding horizon manner. The proposed method does not assume a dynamic model for the unknown input, but it allows to incorporate prior knowledge about its abrupt nature by adding l1-regularization terms to the cost function of the weighted least squares problem. The receding horizon input estimation method and the necessary conditions for it to hold are outlined. The proposed method is illustrated in simulation for the case of abruptly changing piece-wise constant unknown inputs.

Keywords: Estimation and filtering
Abstract: For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality.

Keywords: Estimation and filtering, Filtering and smoothing, Bayesian methods
Abstract: The paper deals with analysis and illustration of the impact of the sigma-point set rotation on the approximation quality of the unscented transformation and the estimation performance of the unscented Kalman filter. It is shown that the covariance matrix factor, used in sigma-point computation, can be multiplied by an arbitrary rotation matrix which moves the sigma-points along the surface of a hyper-ellipsoid related to the covariance matrix. The rotation matrix can be thus considered as another user-defined parameter (in addition to the scaling parameter) and the unscented Kalman filter with adaptive selection of both user-defined parameters is proposed. The impact of fixed or adaptively selected parameters on the performance of the unscented Kalman filter is illustrated by a numerical study.

Keywords: Adaptive control -applications, Nonlinear adaptive control
Abstract: Stiction is one of the most adverse non-linearities that can affect a control valve. It impacts both valve longevity and product quality. Currently available stiction compensation methods either reduce the amplitude of oscillation at a cost of increased frequency or require knowledge about process and valve models. In this study a novel stiction compensation scheme is developed which reduces both oscillation amplitude and frequency, obtains good set point tracking and disturbance rejection, and yet requires minimal information about the process and is simple-to-implement online. The method has been successfully evaluated on a pilot plant interfaced to a commercial DCS system.

Keywords: Iterative learning control, Iterative modelling and control design, Quantized systems
Abstract: In this paper, we develop a control algorithm to generate entanglement in a quantum system with uncertainties. The system under consideration is an uncertain system of two twolevel atoms interacting with each other through a dipole-dipole interaction. The sampling-based learning control (SLC) strategy is employed to find a control law. An SLC strategy contains two steps of training and evaluation. In the training step, we obtain several samples to construct an augmented system by sampling the uncertainties according to a possible distribution of the uncertainty parameters and learn an optimal control law by maximizing the performance index. In the evaluation step, we apply the obtained control law from the training step to additional samples through randomly sampling the uncertainties. Numerical results are presented showing the success of the SLC method in control design for generating entanglement.

Keywords: Adaptive control by neural networks, Adaptive system and control, Nonlinear adaptive control
Abstract: The paper proposes an implicit type of dual control for a class of nonlinear stochastic systems subject to functional uncertainty. The unknown functions of the system are modelled by multi-layered perceptron neural networks where the unknown parameters are found in real-time. The control design is based on the Bellman optimisation recursion where the length of the recursion is shortened to two stages to reduce computational burdens and to ensure dual features between estimation and control aims. The inherent obstacle of determining the expectation is tackled by employing a technique based on the stochastic integration rule. The design is then accomplished using an iterative procedure, which is summarised by algorithms. Numerical simulations and a Monte Carlo analysis show that the proposed approach may compete with existing solutions based solely on the explicit type of dual control and removes their drawback of tuning additional design parameters.

Keywords: Model reference adaptive control, Adaptive system and control, Fault detection and diagnosis
Abstract: This work presents a strategy to estimate and to correct dynamics variations in nonlinear time variant systems. This correction is carried out by estimating the internal parameters of the process and determining the differences with an available nonlinear model of the system. The proposed approach has a double functionality; on the one hand, it allows a better performance of using nonlinear models for control purposes, like for nonlinear predictive controllers; and on the other hand, it can be used as a diagnosis mechanism since it provides relevant information about the current state of the system. Thus, in order to use this technique with nonlinear time variant systems, a nonlinear model predictive control strategy has been used. The estimator proposed within the framework of this work is similar to the Moving Horizon Estimation strategy. Experimental results on a real tank process are presented to show the main properties of the proposed architecture.

Keywords: Iterative learning control, Robust adaptive control
Abstract: This paper presents an experimental validation of a recently proposed robust norm-optimal iterative learning control (ILC). The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem. The proposed robust ILC design is experimentally validated on a lab scale overhead crane system, showing the advantages of the approach over classical ILC designs in monotonic convergence and tracking performance.

Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems
Abstract: This paper is concerned with the problems of stability for a class of impulsive positive systems. An impulsive positive system model is introduced for the first time and a necessary and sufficient condition guaranteeing the positivity of this kind of system is proposed. Several sufficient criteria of global exponential stability and global asymptotical stability for impulsive positive systems are established respectively by using a linear copositive Lyapunov function. Two numerical examples are given to illustrate the effectiveness and applicability of the proposed results.

Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems, Optimal control of hybrid systems
Abstract: We show that for any positive integer d, there are families of switched linear systems---in fixed dimension and defined by two matrices only---that are stable under arbitrary switching but do not admit (i) a polynomial Lyapunov function of degree leq d, or (ii) a polytopic Lyapunov function with leq d facets, or (iii) a piecewise quadratic Lyapunov function with leq d pieces. This implies that there cannot be an upper bound on the size of the linear and semidefinite programs that search for such stability certificates. Several constructive and non-constructive arguments are presented which connect our problem to known (and rather classical) results in the literature regarding the finiteness conjecture, undecidability, and non-algebraicity of the joint spectral radius. In particular, we show that existence of a sum of squares Lyapunov function implies the finiteness property of the optimal product. {bf Index terms:} {stability of switched systems, linear difference inclusions, the finiteness conjecture of the joint spectral radius, convex optimization for Lyapunov analysis.}

Keywords: Stability and stabilization of hybrid systems
Abstract: Lyapunov methods have been successfully applied to compute the Region of Attraction (ROA) of a system, i.e. the set of states that remain invariant for all time. A popular method for constructing Lyapunov functions is the Sum of Squares (SOS) approach. Current methods for determining stability of hybrid systems using SOS require either stability over the entire hybrid guard or knowledge of the switching times, limiting the types of systems that can be analyzed. In this paper, we introduce a new method to relax these constraints by only requiring decrescence properties over the `active' subsections of each guard. We introduce our new formulation, explore useful modeling techniques, then apply them to five examples.

Keywords: Stability and stabilization of hybrid systems, Reachability analysis, verification and abstraction of hybrid systems
Abstract: Four theorems consisting of novel necessary and sufficient conditions for absolute and weak exponential stability and instability are presented, for switched discrete-time linear systems. To reach these theoretical results, we exploit concepts from set theory and in specific the forward and inverse reachability set mappings. The theorems can be utilized to construct an iterative numerical method for stability analysis.

Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems
Abstract: This paper investigates the problem of finite-time stability for switched singular systems with state jumps. An effective kind of finite-time stability strategies is proposed under the average dwell time switchings. Based on the multiple Lyapunov functions method, sufficient conditions on switched singular systems to be finite-time stable are presented. A numerical example is given to illustrate the effectiveness of the results obtained.

Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems
Abstract: Hybrid systems with memory are dynamical systems exhibiting both hybrid and delay phenomena. We present a general modelling framework for such systems using hybrid functional inclusions, whose generalized solutions are defined on hybrid time domains and evolve in the phase space of hybrid memory arcs equipped with the graphical convergence topology. We prove a general existence result based on some basic conditions on the data of hybrid systems. We further establish sufficient conditions for the stability analysis of hybrid systems with memory using Lyapunov-Razumikhin (L-R) functions. We demonstrate the stability results on a general nonlinear system with switching dynamics and state jumps.

Keywords: Optimal control theory, Modeling for control optimization, Industrial applications of optimal control
Abstract: This paper presents a joint selection criterion for optimal trajectory planning using dynamic programming along a specified geometric path subject to torque and velocity constraints. A single non-stationary joint is normally considered for optimization purpose. In case of more than one non-stationary joint, the optimization by selecting any joint at random may end up with a non-optimal solution. This problem raises the importance of the proposed joint selection criterion. A novel joint selection criterion based on the geometric path information is presented for dealing with such cases having more than one non-stationary joint. In contrast of existing approaches to solve the optimization problem by considering each joint separately, the proposed criterion saves computational time, efforts and thus there is no need to solve the optimization problem multiple times with respect to each non-stationary joint to get the optimal solution. The proposed criterion can be used to optimize a given path with respect to travelling time, energy consumption or any other arbitrary form of cost function. The validity of the proposed selection criterion is tested on a parallel DELTA robot. The obtained results are compared with the optimal results obtained by other techniques which conrm the applicability of the proposed scheme.

Keywords: Optimal control theory, Time-invariant systems, Linear systems
Abstract: The optimal control problem of connecting any two trajectories in a behavior B with maximal persistence of that behavior is put forth and a compact solution is obtained for a general class of behaviors. The behavior B is understood in the context of Willems's theory and its representation is given by the kernel of some operator. In general the solution to the problem will not lie in the same behavior and so a maximally persistent solution is defined as one that will be as close as possible to the behavior. A vast number of behaviors can be treated in this framework such as stationary solutions, limit cycles etc. The problem is linked to the ideas of controllability presented by Willems. It draws its roots from quasi-static transitions in thermodynamics and bears connections to morphing theory. The problem has practical applications in finite time thermodynamics, deployment of tensigrity structures and biomimetic locomotion.

Keywords: Optimal control theory, Decentralized control
Abstract: One of the challenges of working with multi-agent systems is the limited energy of the agents, especially when the agents are flying vehicles with limited batteries to save weight. The main contribution of this paper is to formulate the energy-efficient coverage problem as an optimal control problem. The optimal control problem will be related to Lloyd’s algorithm. The solution to the optimal control problem is spatially distributed over Delaunay graphs and provides an energy-efficient local controller to maximize the coverage. As a second contribution, by imposing constraints on the parameters of the optimal control problem we guarantee that the agents maintain their energy during the coverage task. As expected, weighting the speed of the agents against the coverage objective will decrease energy consumption in the multi-agent system. Several examples demonstrate the performance of the energy-efficient approach.

Keywords: Optimal control theory, Linear systems
Abstract: A double generating functions based method for the discrete-time linear quadratic optimal control problem with fixed and free terminal state, i.e., hard constraint problem and soft constraint problem, is developed in this paper. By this method, the optimal state and input can be expressed by functions only in terms of the boundary conditions and pre-computed coefficients. Accordingly, the whole optimal computation can be divided into two parts in which the off-line part calculates the coefficients and the on-line part generates the optimal solutions by these proposed functions. In view of this, the developed method is useful in the on-line iterative computation for optimal control problem with a number of different boundary conditions. Examples demonstrate the effectiveness of the double generating functions method for the both hard and soft constraint problems with different boundary conditions.

Keywords: Optimal control theory, Sliding mode control
Abstract: This paper studies the problem of higher order singular linear quadratic optimization for Linear Time Variant Systems a¤ected by some sort of uncertainties. It is shown the natural connection between the order of singularity of the time varying quadratic cost and the order of Sliding Mode. An integral high order sliding mode is proposed to reach the corresponding higher order singular time varying optimal manifold in prescribed time. The transformation to the phase-variable form for the Linear Time Variant Systems becomes the key step solve the problem and the proposed solution provides insensitvity of trajectory w.r.t. matched bounded perturbation.

Keywords: Optimal control theory, Linear systems, Systems with time-delays
Abstract: The paper deals with a comparison of two possible approaches to the performance portrait method application for an optimal nominal tuning of an integral plus dead time (IPDT) plant. The first one relies on a more compact 2D approach used by Huba et al. [2009] inspired by the triple real dominant pole method [Víteček and Vítečková, 2008]. The second approach is a simpler but, at the same time, in some aspects a computationally more demanding 3D method. Pros and cons of both approaches are analyzed and discussed.

Keywords: stability of distributed parameter systems, Time-varying systems, Linear multivariable systems
Abstract: The framework of Lyapunov–Krasovskii functions is the most popular approach for studying the problem of stability analysis and synthesis for time-delay systems. In this framework, this paper addresses the exponential stabilization problem of a class of linear distributed parameter systems with time delays described by partial differential-difference equations (PDdEs). Both delay-independent and delay-dependent design methods are subsequently developed by constructing two integral types of Lyapunov–Krasovskii functions and introducing a new form of Jensen’s inequality, and presented in terms of a standard linear matrix inequality (LMI). Finally, the effectiveness and merit of the proposed design method are demonstrated by a numerical example.

Keywords: stability of delay systems, Time-varying systems, Linear systems
Abstract: The paper presents a method for constructing the characteristic equation of SISO linear periodic dynamical systems with distributed delay. The construction bases on a description in frequency domain, and a relation between the characteristic function of the periodic system and the Fredholm denominator of an associated Fredholm integral equation of the second kind. Using the Fredholm calculus, polynomial approximations of the characteristic function are derived. Together with estimation formulae, sufficient conditions for the location of the characteristic roots with respect to the unit circle are provided, i.e. ordinary criteria can be applied for stability investigations. The method is illustrated by an example.

Keywords: stability of delay systems, Polynomial methods, Process control
Abstract: In this study, a method for determining delay-independent stability zones of the general LTI dynamics with multiple delays against parametric uncertainties is presented. The method is utilized to design a delay-independent state-feedback controller and veried experimentally for a two-tank liquid level control system. The method is based on extended kronecker summation(EKS) to investigate controller parameter space for delay-independent stability(DIS) of the system. The main aim of the paper is recalling a new sucient condition for determination of controller parameter space for DIS and presenting the application of methodology for a physical experimental case study.

Keywords: fractional-order systems, Robust control (linear case), Robustness analysis
Abstract: This paper considers the robust stability for uncertain fractional-order (FO) descriptor nonlinear systems. A key analysis technique is enabled by proposed a fundamental boundedness lemma, for the first time. It is used for rigorous robust stability analysis of FO systems, especially for Mittag-Leffler stability analysis of FO nonlinear systems. More importantly, how to obtain a more accurate bound is analyzed to reduce conservative. An FO proportional-derivative controller is utilized to normalize the descriptor system. Furthermore, a criterion for stability of the normalized FO nonlinear system is provided by utilizing linear matrix inequality (LMI). Finally, two illustrative examples show the effectiveness of the proposed stability notion.

Keywords: fractional-order systems, Nonlinear observers and filter design, Stability of nonlinear systems
Abstract: This paper presents observer-based fuzzy control for nonlinear fractional-order systems with the fractional order alpha satisfying 1

Keywords: Programmable logic controllers, Real-time algorithms, scheduling, and programming, Embedded computer control systems and applications
Abstract: Industrial plant software is implemented in the programming languages of IEC~61131-3. As plant software needs to perform many tasks, it is often highly complex and typically characterized by a monolithic structure. Whereas for high-level programming languages, commonly occurring problems are solved using design patterns, such general reusable solution alternatives are not yet available for IEC 61131-3. Thus, an approach for statically analysing the plant software and visualizing the software units' complexity and interconnectedness is proposed in this paper. Furthermore, basic software design patterns are introduced and, subsequently, their appearance within plant software is evaluated using industrial code and interviews with experts. By that, a first step towards providing design patterns for IEC 61131-3 is made.

Keywords: Real-time algorithms, scheduling, and programming
Abstract: A correct design of controllers must necessarily account for the schedule of the controller task over the processor. Existing design techniques are based on the assumption that there is no delay, or the delay is a constant over time. However, in practice, almost all controllers have time-varying delay, hence invalidating this assumption. In this paper, we introduce a period design policy, in which the controller delay is modelled by the response time analysis distribution. We show, via simulation, that our method can reduce the control cost compared to the state-of-art methods.

Keywords: Real-time algorithms, scheduling, and programming
Abstract: The converter gas, especially Linz Donawitz converter gas (LDG), is one of the most significant secondary energy resources in a large scale steel plant. For such a thing, the accurate prediction for the gas tanks levels largely contributes to the energy optimization operations. Taking the LDG system of a steel plant in China into consideration, a regression model based on the Granular Computing (GrC) is proposed in this study to provide a long-term prediction for the LDG tanks levels, in

which the data segments are entirely considered for the prediction horizon extension rather than the generic data point-oriented modeling. For being more practical, this study specially granulates the initial data with regard to industrial semantic meaning. And, different from ordinary time series analysis, this method considers the factor related to the gas tank levels. Bearing this in mind, the fuzzy rules by adopting a fuzzy C-means based clustering is established. To verify the effectiveness of the proposed method, a series of practical experiments by using the industrial data coming from the energy data center of this plant are conducted, and the results demonstrate the practicability of the proposed approach.

Keywords: Real-time algorithms, scheduling, and programming, Fuzzy and neural systems relevant to control and identification, Smart energy grids
Abstract: Predictive maintenance has now become a possible avenue within the electricity distribution sector of Eskom. A recent roll-out of large-scale substation data acquisition projects have allowed this sector to use a predictive based maintenance scheduling plan instead of a previously used frequency based maintenance plan. This paper describes the design and implementation of a low-complexity anomaly detection algorithm, which is able to detect discrepancies, indicative of small electrical grid changes or substation equipment deterioration. The algorithm is based on projecting parametric multivariate Gaussian functions on to spatially distributed pre-selected substation data points. This method enables the utility to monitor critical variables, and their relationships, in an effort to foresee equipment or network distresses from high-value assets, particularly in the transmission and distribution sector. The results demonstrate an early positive detection of anomalous load behaviour from a live substation. The presented Semi-supervised learning methodology can form the underpinnings of an integrated approach, to aid with operational decision-making, and seems eminently suitable to reduce unscheduled asset downtime.

Keywords: Real-time algorithms, scheduling, and programming
Abstract: The multiprocessor revolution allows to have different processors accessing their local memory controller and the relative RAM portion, while the possibility for each processor to access the remote memory on another processor enables the so-called Non-Uniform-Memory-Access (NUMA) multiprocessor paradigm. This paper discusses a control-inspired recursive algorithm to allocate the memory requests by different applications in a NUMA systems, trying to maximize the RAM utilization under the locality requirement. The proposed method is tested in two case studies.

Keywords: Randomized algorithms, Parametric optimization
Abstract: Hit-and-Run is known to be one of the best versions of Markov Chain Monte Carlo sampler. Nevertheless, in practice the number of iterations required to achieve uniformly distributed samples is rather high. We propose new random walk algorithm based on billiard trajectories. Numerical experiments demonstrate much faster convergence to uniform distribution for Billiard Walk algorithm compared to Hit-and-Run. We discuss a class of global optimization problems that can be efficiently solved with Monte Carlo approach.

Keywords: Linear parameter-varying systems, Robust control applications, Robust controller synthesis
Abstract: A classical mixed sensitivity minimization approach and a model matching formulation are compared with the goal to design a linear parameter-varying augmented state feedback control law for a laboratory-scale control moment gyroscope. Dynamic weighting filters are used to impose integral action and roll-off on the controller. Consequently, measurement noise is effectively suppressed and steady state accuracy is guaranteed even in the presence of input disturbances. Both designs are validated in real-time experiments and compared to a previous design that uses static weights. With the new designs, control effort is reduced while transient performance is maintained and tracking accuracy, as well as disturbance attenuation, is improved.

Keywords: Linear parameter-varying systems, Robust controller synthesis, Robust linear matrix inequalities
Abstract: This paper presents an output feedback LPV gain-scheduling control design for a class of PSPWM full-bridge power converters. The design starts with an averaged model of the converter with adequate complexity. One main contribution of this work is to demonstrate how to reformulate the averaged model into an LPV one. A gain-scheduling controller can then be synthesized taking advantage of the design framework of LPV-based H-infinity control. Forming the open-loop LPV system and synthesizing the controller, a repetitive control module and an anti-windup scheme are incorporated into the design. The proposed design is further justified with a numerical simulation.

Keywords: Robustness analysis, Robust controller synthesis, Convex optimization
Abstract: This paper addresses the problem of H_∞ and H₂ performance analysis of continuous-time affine single parameter-dependent systems with polynomially parameter-dependent Lyapunov matrices. First, some necessary and sufficient conditions in terms of linear matrix inequalities (LMIs) are provided using (D,G)scaling approach. Then, the results are used to deal with the problem of fixed-order H_∞ and H₂ controller design via bilinear matrix inequalities (BMIs) in a non-conservative way. Simulation results demonstrate the effectiveness of the proposed approach.

Keywords: Linear parameter-varying systems, Uncertainty descriptions, Relaxations
Abstract: In standard linear fractional representation (LFR)-based linear parameter-varying (LPV) modeling the size of the (diagonal) scheduling block depends on the number of scheduling parameters and their repetitions, which in turn influences both the complexity of synthesis conditions and the computational load during online implementation of LPV controllers. A modeling framework motivated by, but not limited to, mechanical systems is proposed, where the size of the scheduling block depends on the system’s physical degrees-of-freedom. The scheduling block then turns out block-diagonal and can be parameterized in an affine or rational manner. This parameterization yields less complex LFRs when considering the example of a three degrees-of- freedom robotic manipulator, for which then full-block multipliers are tractable and also necessary in synthesis. Synthesis and both simulation and experimental implementation results indicate that the novel rational LPV controller provides improved performance at both reduced implementation and synthesis complexity as compared to an affine LPV controller.

Keywords: Robust estimation, Observers for linear systems, Convex optimization
Abstract: Robust Kalman lter (RKF) via l1 regression is a linear filter for non-Gaussian measurement noise, and it can be formulated as a l1 optimization problem. Generally, the optimization problem cannot be solved analytically, and some numerical iterative methods are needed. This paper proposes a closed form solution of RKF via l1 regression by an approximation of its optimal solution and it gives a fast algorithm. The approximated solution can be calculated by upper and lower bounds of the optimal solution. Moreover, a bound of an estimation error of the approximated solution can be analyzed. Some numerical simulations demonstrate the effectiveness of the proposed algorithm.

Keywords: Assembly and disassembly, Production planning and control
Abstract: Problems, approaches and analytical models on assembly line balancing that deal explicitly with cost and profit oriented objectives are analysed. This survey paper serves to identify and work on open problems that have wide practical applications. In addition, the conclusions derived might give insights in developing decision support systems DSS) in planning profitable or cost efficient assembly lines.

Keywords: Modeling of assembly units, Modeling of manufacturing operations, Production planning and control
Abstract: Process of assigning tasks to workstations arranged along a U-shaped assembly line is known in literature as U Assembly Line Balancing Problem (UALBP). Maximizing the line efficiency by way of minimizing number of workstations (m) for a given cycle time (c) leads to UALBP-I while targeting the same objective through minimization of ‘c’ for given ‘m’ is known as UALBP-II. Although, quite a good amount of research has been reported on UALBP-I since the first published work on U lines in 1994; very little work has been reported on type II. Type E (minimizing ‘c’ and ‘m’ together) and type F (finding feasible line balance for given ‘c’ and ‘m’) problems which represent other two types of U line balancing problems also have not received any attention. This paper reports the initial efforts towards application of metaheuristics for solving UALBP-II problem which is encountered when the line already exists.

Keywords: Job and activity scheduling, Modeling of assembly units, Production planning and control
Abstract: In this paper we introduce a new model and a computational approach for sequencing assembly lines with two types of constraints: (i) patterns described by regular expressions and (ii) linear bounds on the number of certain products that may occur in pre-specified intervals. If we restrict the problem to the second type of constraints only we obtain a generalization of the familiar car sequencing problem, whereas constraints of type (i) may be useful to add extra structure. Constraints of both types may have priorities and can be violated, and a Pareto optimal solution is sought minimizing the violation of constraints in the given priority order. We describe a computational method based on mathematical programming and genetic algorithms for finding suboptimal solutions.

Keywords: Assembly and disassembly, Modeling of manufacturing operations, Production planning and control
Abstract: This paper considers heuristics which can be helpful in reducing workstations’ number in assembly line balancing problem. In the last sixty years a large variety of heuristics and exact solutions procedures have been proposed to balance different structures of assembly lines. Author of this paper discussed some heuristics which almost lead to reduction of number of workstations (parallel single lines or u-line configuration) and productivity improvement. Special attention was given to the quality of final results. Some measures of solution quality: line efficiency (LE), line time (LT) and smoothness index (SI) are calculated and compared. At the end final remarks are given

Keywords: Job and activity scheduling, Production planning and control, Procedures for process planning
Abstract: Process scheduling is a classical problem in the field of production planning and control; in particular, effective job shop scheduling remains an essential component in today’s highly dynamic and agile production environment. This paper presents unified framework for solving generic job shop scheduling problems based on the formulation of a job shop into three main classes of problem, namely, static, semi-dynamic and dynamic scheduling problems. Algorithms based on artificial immune systems, an engineering analogy of the human immune system, are developed to solve the respective classes of job shop scheduling problems. A high level decision support model is presented for the effective deployment of the scheduling strategies whereby a unified approach to solving real job shop problems is achieved.

Keywords: Food engineering, Dynamics and control, Modeling and identification
Abstract: Yeasts play a central role in the wine making process. To study the yeasts in a stable environment and physiological state, a Multi-Stage Continuous Fermentor (MSCF) has been designed that mimics steps of the batch fermentation process. In this paper, the problem of the control of the sugar concentrations in each of the four reactors of the MSCF is considered. The cascade structure of the device leads to a constraint on the input flow rates (the control variables). A control strategy based on a linearizing control law coupled with a state observer and an anti windup component is proposed and finally implemented on the experimental process.

Keywords: Modeling and identification, Metabolic engineering, Parameter and state estimation
Abstract: In literature metabolic stoichiometric matrix reduction is based on convex analysis by choosing the greatest triangle. This paper proposes a new methodology for the reduction of metabolic networks based on the concept of convex hull by optimization methods. Different polygons are tested to conjointly minimize the squared error (convex hull - experimental data) and maximize the convex hull area in order to reduce the set of metabolic reactions involved in the model. The advantage of this method relies on its ability to select different geometries in a simple manner with the knowledge of the elementary modes. A cybernetic model implementing the proposed optimization method is tested with data for bioethanol production by Saccharomyces cerevisiae growing on four substrates. Parameter estimation and model validation allow comparing the performance of the chosen polygons for reduction of metabolic pathways.

Keywords: Modeling and identification, Dynamics and control, Bioenergy production
Abstract: In this work a kinetic model for the enzymatic transesterification of rapeseed oil using a solubilised lipase (Callera Trans L-Thermomyces lanuginosus) was developed from first principles. The model is based on a Ping-Pong Bi-Bi mechanism, with methanol inhibition, along with consideration of the differences in the interfacial and bulk concentrations of the enzyme. The model is then used to evaluate various feeding strategies to improve the enzymatic biodiesel production. The feeding strategies investigated, gave insight into how the methanol should be fed to potentially mitigate enzyme deactivation while improving the biodiesel yield. The best experimental results gave a yield of 703.76 g FAME/L and a reactor productivity of 28.12 g FAME/L/h. In comparison, to reach the same yield, the optimised two step feeding strategy took 6.25 hours less, which equates to an increase the reactor productivity of 36.9 %.

Keywords: Bioenergy production, Dynamics and control, Industrial biotechnology
Abstract: Oleaginous yeasts are microbial factories capable of converting carbohydrates and fat substrates into lipids. The transesterification of these lipids results in the production of biodiesel, a renewable energy source that has emerged as a promising and sustainable alternative to overcome the eminent depletion of fossil fuels and to reduce the environmental impacts of petroleum exploitation. To bring biodiesel production as a realistic technology to respond to the worldwide energy demand, process optimization is to be pursued. In this paper, we performed a model-based optimization of lipid productivity in continuous mode operation. It is considered that two input flow rates at different carbon/nitrogen ratio are available as manipulated variables. The optimal control problem was solved numerically by using the control vector parameterization approach. A simple parameterization with piecewise linear functions was found to be suitable for providing optimal performances. We showed that by driving the carbon/nitrogen ratio along the process adequately, optimal performances are attained. The control strategy here developed excels substantially an operation with a single input flow rate with fixed C/N ratio. This work sets up useful guidelines towards optimal bioprocesses for microbial lipid production.

Keywords: Bioenergy production, Dynamics and control, Industrial biotechnology
Abstract: Microalgae mass cultivation is a promising future source of biomass for energy and food production. In order to optimize productivity of large scale plants and to make them environmentally and economically sustainable, energy requirements have to be minimized. In particular, mixing of the growth medium is a major energy input, and its effect on overall productivity should be better understood. Several dynamic models have been developed to represent the effect of a rapidly time varying light on the photosynthesis process especially for the effect of photoinhibition on growth. In order to assess the mixing effects in a complex hydrodynamic regime, we propose to reconstruct the light profile received by a single cell. A multi-layer Saint-Venant approach is used to simulate the hydrodynamics of the system. It allows for the computation of Lagrangian trajectories, and finally, when knowing the light distribution, the light pattern perceived by a cell. This pattern is then used with the dynamical model for photosynthesis. In a last step, the growth rate of the whole system is estimated as the average over a set of trajectories.

Keywords: Industrial biotechnology, Microbial technology
Abstract: This work investigates the application of a Process Analytical Technology" (PAT) analyser to control the substrate concentration over traditional sequential batch control for an industrial scale fed-batch penicillin fermentation. A simulation that utilises the historical data from four batches, where a sequential batch control strategy was implemented, was used as the benchmark reference for this comparison. The simulation accurately predicts the main outputs variables of biomass and penicillin, given the inputs from the historical data set. The simulation includes a PAT analyser, used to build a calibration model with the available o-line substrate concentration from one of the batches. The prediction from this calibration model was used as the controlled variable within a proportional integral (PI) controller to manipulate the substrate feed rate for the three remaining batches. Performance of each control strategy was analysed by comparing the nal penicillin yield of each batch. An increase of 35, 20 and 9% was observed for the three batches controlled using the PI controller compared with the sequential batch control strategy.

Keywords: Model predictive control of water resources systems, Water prediction and control, Optimal operation of water resources systems
Abstract: This paper evaluates the application of command governor (CG) strategy for the operational control of drinking water networks (DWN) given their large-scale and complex nature, the permanent and relevant effect of the disturbances (water demands) and their marginal stability feature. Moreover, the performance improvement offered by CG is compared with the application of model predictive control for the same management purposes and in the same context. The paper also discusses the effectiveness of both strategies and highlights the advantages of each approach. The Barcelona DWN is considered as case study for the performed assessment analysis.

Keywords: Optimal operation of water resources systems, Environmental decision support systems
Abstract: This study presents a novel approach which combines direct policy search and multi-objective evolutionary algorithms to solve high-dimensional state and control space water resources problems involving multiple, conflicting, and non-commensurable objectives. In such a multi-objective context, the use of universal function approximators is generally suggested to provide flexibility to the shape of the control policy. In this paper, we comparative analyze Artificial Neural Networks (ANN) and Radial Basis Functions (RBF) under different sets of input to estimate their scalability to high-dimensional state space problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while the ANN solutions are often Pareto-dominated by the RBF ones.

Keywords: Model predictive control of water resources systems, Uncertainty in water resource system control/management, Environmental decision support systems
Abstract: Water systems are a challenging problem because of their size and exposure to uncertain influences such as the unknown demands or the meteorological phenomena. In this paper, two different stochastic programming approaches are assessed when controlling a drinking water network: chance-constrained model predictive control (CC-MPC) and tree-based model predictive control (TB-MPC). Under the former approach, the disturbances are modelled as stochastic variables with non-stationary uncertainty description, unbounded support and quasi-concave probabilistic distribution. A deterministic equivalent of the related stochastic problem is formulated using Boole’s inequality and a uniform allocation of risk. In the latter approach, water demand is modelled as a disturbance rooted tree where branches are formed by the most probable evolutions of the demand. In both approaches, a model predictive controller is used to optimise the expectation of the operational cost of the disturbed system.

Keywords: Model predictive control of water resources systems, Water prediction and control, Optimal operation of water resources systems
Abstract: This paper presents the combination of Linear Model Predictive Control (MPC) with Constraints Satisfaction Problem (CSP) for the operational control of drinking water networks. The methodology has been divided into two functional layers: First, a CSP algorithm is used to transfer nonlinear pressure equations of drinking water networks (DWNs) into linear constraints, which can enclose feasible solution of the hydraulic non-linear problem during the optimizing process. Then, a linear MPC with added linear constraints is solved. Finally, the proposed approach is simulated using Epanet to represent the real DWNs. PLIO, which is a generic operational tool for controlling water network that uses non-linear MPC, is used for validation. The classical Richmond water system is used as a case study

Keywords: Optimal operation of water resources systems, Environmental decision support systems, Negotiation support
Abstract: Water management problems generally involve conflicting and non-commensurable objectives. Assuming a centralized perspective at the system-level, the set of Pareto-optimal alternatives represents the ideal solution of most of the problems. Yet, in typical real-world applications, only a few primary objectives are explicitly considered, taking precedence over all other concerns. These remaining concerns are then internalized as static constraints within the problem's formulation. This approach yields to solutions that fail to explore the full set of objectives tradeoffs. In this paper, we propose a novel method, called direct policy conditioning (DPC), that combines direct policy search, multi-objective evolutionary algorithms, and input variable selection to design dynamic constraints that change according to the current system conditions. The method is demonstrated for the management problem of the Conowingo Dam, located within the Lower Susquehanna River, USA. The DPC method is used to identify environmental protection mechanisms and is contrasted with traditional static constraints defining minimum environmental flow requirements. Results show that the DPC method identifies a set of dynamically constrained control policies that overcome the current alternatives based on the minimum environmental flow constraint, in terms of environmental protection but also of the primary objectives.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Electric and solar vehicles, Hybrid and alternative drive vehicles
Abstract: In this paper, we investigate distributed thermal-electrochemical modeling of a Lithium-Ion battery cell to include the effect of temperature distribution across the thickness of the cell as a first step to study the module level temperature distribution. Most recent works have focused on lumped thermal models for a Li-Ion cell which ignore any temperature differential across cell thickness. However, even a small temperature differential across cell thickness at cell level can contribute to significant temperature differential in the thickness direction of stacked-up Li-Ion cells at module level. Such temperature differential can potentially impact the battery charging control system, especially at high charging rates. Here, we present a distributed thermal-electrochemical model and solve the system of partial differential and algebraic equations via a finite difference method. Simulation results show that the temperature differentials over the cell thickness at cell level are not insignificant, particularly at high charging rates.

Keywords: Hybrid and alternative drive vehicles, Modeling, supervision, control and diagnosis of automotive systems
Abstract: This paper investigates the performance of a continuously variable transmission (CVT) using an intuitive control in a hybrid electric vehicle (HEV) of a series architecture under varying driving conditions. The detailed dynamic vehicle simulation model employed in this study enables an in-depth evaluation of the CVT as compared to a fixed transmission (FT) that utilises a fixed final drive ratio between the motor and wheels. The investigation uses three distinct driving cycles to show that the CVT offers reduction in motor energy consumption of up to 9.38%. Also, the particular impacts of the transmission systems are understood by analysing the energy flows from the Permanent Magnet Synchronous Motor (PMSM).

Keywords: Automatic control, optimization, real-time operations in transportation, Intelligent driver aids, Trajectory and Path Planning
Abstract: This paper focuses on obtaining an optimal speed trajectory for a train regarding its energy consumption as well as its travelling time simultaneously. Dynamic model of a train on a predefined track including slopes and tunnels is developed. Considering complexity of analytical optimization method for antithetic objectives, NSGA-II and MOPSO evolutionary algorithms are employed to solve the multi objective optimization problem. A novel control index is defined instantaneously so that the important issue of passenger comfort is taken into account specifically. As a case study a Voyager train parameters are used to simulate the whole system via MATLAB software. Simulation results show the efficiency of the proposed methods to find the desired optimal speed trajectory as well as providing suitable passenger comfort.

Keywords: Automotive system identification and modelling, Man-machine interfaces, Intelligent driver aids
Abstract: A shared lane keeping assistance system supports the driver in the steering task. In contrast to an autonomous system, the shared system works in parallel with the driver by applying an additional torque on the steering wheel. This means the system and the driver perform the steering task in cooperation. As the driver is still part of the control loop a driver model is required to predict the steering behavior of the actual driver. In this paper we introduce a new lateral steering model which is suited to characterize individual drivers. This model describes, in contrast to other models, the neuromuscular system, limbs and its control for a specific driver by using a set of dynamic primitives (so called movemes). These movemes build a gray-box model for the neuromuscular system. The steering wheel angle is the predicted output of the moveme model. In order to generate a steering angle trajectory suited for the desired maneuver, the steering model switches between these movemes. Therefore, the central component of the driver model is a framework which determines the optimal switching sequence of the movemes. For this task an optimal control strategy is introduced. The approach is validated using a simulation of an ISO-double lane change with movemes which were identified from a set of real driver trajectories. The results show that the steering trajectories of the driver model highly correspond with the recorded driver trajectories.

Keywords: Vehicle dynamic systems, Nonlinear and optimal automotive control, Control architectures in automotive control
Abstract: For the horizontal motion of a vehicle with single-wheel actuators a model-based feed-forward control is derived. Using the accelerator and brake pedal position as well as the steering wheel angle, the feed-forward control generates a desired vehicle motion together with appropriate longitudinal and lateral forces on the vehicle mass and a corresponding yaw moment. Since no detailed vehicle model, e.g., the single-track model, is required it is possible to tune the horizontal vehicle dynamics arbitrarily without changing the vehicle's geometry. Subsequently, the obtained forces and the yaw moment are allocated to the eight horizontal tire forces. Since the vehicle considered is over-actuated a secondary objective, i.e., the safety maximization, can be pursued in addition to realizing the desired vehicle motion. Solving this optimization problem numerically in a real vehicle is time-critical, so an analytical solution is introduced.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Engine modelling and control, Automotive system identification and modelling
Abstract: Improvement of energy efficiency and ecological cleanliness of internal combustion engines with spark ignition is a task of vital importance and alternative fuels are one of the ways to solve it. Many researches and tests conducted by various institutes all over the world revealed advantages of different alternative fuels, unfortunately, alongside with disadvantages. Mixing fuels was proposed to eradicate known downsides, starting new wave of IC engine process researches running on alternative fuel mixtures. Authors consider propane widely spread in Russian Federation for low cost and reducing emissions of harmful substances in exhaust gases. To deal with its main limitations: reduction in engine power and torque when used in gas phase, it is proposed to add portions of gasoline to propane. As parameters of this mixture differs from gasoline or propane alone, it is necessary to develop new methods for IC engine control taking into account peculiarities of this mixture to improve energetic, ecological and economical figures. Paper presents control method that includes IC engine process model, algorithms to set control parameters (ignition and fuel systems) and approach to decrease knock occurrence probability. Mathematical model is based on Vibe function and extended for 2-component fuel mixture processing. It allows calculation of work, efficiency, mechanical and thermal load, state of working substance and engine performance figures. Authors propose algorithm for ignition timing calculation in real time using in-cylinder pressure peak position as a criterion of optimality and algorithm of propane/gasoline ratio variation to allow using optimal spark advance angles and prevent knock. Knock prevention approach includes forecasting part and 2-stage reacting part for keeping optimal ignition as long as possible. The control method proposed is confirmed by experiments. Car model “Lada 21108M” with 1.8 liter 16 valve engine was equipped by propane/petroleum mixture supply system. Test runs on MAHA LPS 3000 dyno stand under control or developed method showed increase of engine power and torque. Knock, emissions and fuel cost were decreased.

Keywords: Kalman filtering techniques in automotive control, Automotive sensors and actuators, Vehicle dynamic systems
Abstract: Electrically Power Assisted Cycles (EPACs) have been gaining increasing attention worldwide during the past few years. The delivery of a good assistance during inclines makes or destroys an EPAC. This paper addresses the low-cost estimation of road slope on bicycle without pedaling torque measurement. Two estimation algorithms are discussed. A full 6 Degrees of Freedom kinematic Extended Kalman Filter and a simpler, more cost-effective 2 DoF Kalman filter based on the longitudinal kinematic model. The proposed reduced-sensor algorithm is shown to be very accurate during straight running; however it is affected by errors during cornering. This issue is addressed by augmenting the filter with a curve correction algorithm. The curve correction algorithm, based on a time-varying low pass filter is detailed and validated on experimental data, comparing the estimated road slope against cartographic data.

Keywords: Intelligent driver aids, Autonomous Vehicles, Trajectory and Path Planning
Abstract: A hierarchical, two-level architecture for manoeuvre generation and vehicle control for automated highway driving is presented. The high-level planner computes a manoeuvre in terms of a (X,Y)-trajectory as well as a longitudinal velocity profile, utilizing a simplified point-mass model and linear collision avoidance constraints. The low-level controller utilizes a non-linear vehicle model in order to compute the vehicle control inputs required to execute the planned manoeuvre. Both the high-level planner and low-level controller are formulated based on the model predictive control methodology. Simulation results demonstrates the ability of the high-level planner to compute appropriate, traffic-dependent manoeuvres, that can be tracked by the low-level controller in real-time.

Keywords: Control architectures in automotive control
Abstract: A comparative analysis between two different heuristic suspension control strategies is presented, the approaches have been designed to control the vertical dynamics of a full size pick-up truck model, equipped with Magneto-Rheological (MR) dampers. The control schemes to compare are: (1) one global suspension controller and, (2) four independent controllers, one for each Quarter of Vehicle (QoV) model. The main idea is to compare the control performances and the implications that these heuristic controllers have; which only depend on measurements. Experimental data are used to model an MR damper in each corner. Two tests have been used to compare the control performances when the vehicle is driven in straight ways: (1) a running test on the road based on the standard ISO 8606 and, (2) a road that excites the vertical dynamics at different frequencies (Bounce Sine Sweep test). Results in CarSim show that the comfort performance is better when the controller is well coordinated among the wheel-stations (global suspension controller); the improvement is 33% in the heave motion and 35% in the pitch angle. The QoV-based controller presents un coordination that complicates the movement attenuation into the vehicle body; however, the road holding is improved.

Keywords: Vehicle dynamic systems, In-vehicle communication networks, Modeling and simulation of transportation systems
Abstract: This paper concerns the problem of networked control for vehicle lateral dynamic stability. Based on the Takagi-Sugeno (T-S) fuzzy representation of the vehicle, the state feedback and observer-based control designs are addressed. These controllers should guarantee the global stability of the resulting closed-loop fuzzy system with a prescribed H_{infty} disturbance attenuation level. Digital communication network conditions, such as network-induced delays, data packet dropouts and limited communication capacity due to signal quantization are taken into consideration. Using fuzzy Lyapunov-Krasovski functional, we derive a less conservative delay-dependent criterion for stability analysis and control synthesis of networked control systems with a quantizer. Simulation results illustrate the effectiveness of the proposed approach.

Keywords: Vehicle dynamic systems, Intelligent driver aids, Nonlinear and optimal automotive control
Abstract: The emerging new idea of lane-keeping electronic stability control is investigated. In a critical situation, such as entering a road curve at excessive speed, the optimal behavior may differ from the behavior of traditional ESC, for example, by prioritizing braking over steering response. The important question that naturally arises is if this has a significant effect on safety. The main contribution here is to give a method for some first quantitative measures of this. It is based on optimal control, applied to a double-track chassis model with wheel dynamics and high-fidelity tire-force modeling. The severity of accidents grows with the square of the kinetic energy for high velocities, so using kinetic energy as a measure will at least not overestimate the usefulness of the new safety system principle. The main result is that the safety gain is significant compared to traditional approaches based on yaw rotation, for several situations and different road-condition parameters.

Keywords: Adaptive and robust control of automotive systems, Autonomous Vehicles, Learning and adaptation in autonomous vehicles
Abstract: This paper presents an Triple-Level Adaptive Multiple Models Fuzzy Logic Controller (TAM-FLC) with model identification used for speed control in an unmanned vertical parking process. Adaptive Multiple Models fuzzy control ensures good performance with fast convergence and minimal chattering despite of variation in external disturbances. In contrast to conventional fuzzy logic controller where intensive controller tuning is required to achieve optimal control performance, the addition of Triple-Level adaptation in the proposed TAM-FLC increases the controller robustness to uncertainties in system parameters, and also the use of multiple models increase the accuracy of controller significantly. As a result, a properly designed TAM-FLC can be applied to different systems operating under various environments without compromising the performance. The proposed TAM-FLC is proven to stabilize and experimental results is given to prove the improvement compared with previous designs.

Keywords: Human and vehicle interaction, Navigation, Guidance and Control, Nonlinear and optimal automotive control
Abstract: This paper proposes a framework for a navigation system of electric vehicles (EVs) that minimizes the expectation of the energy consumption of the entire users while enhancing the Quality of Life (QoL), i.e., reducing the travel time and cost in our context, of individual users. To this end, we provide users optional flexibility of selecting a preferable route based on the individual travel time and cost among the multiple candidates indicated by the system, while use an incentive approach to make users select a route that requires as low energy consumption as possible. We show by numerical simulations on Chukyo Area in Japan that the proposed method is effective.

Keywords: Human and vehicle interaction, Intelligent driver aids, Man-machine interfaces
Abstract: Recently, automatic steering systems for emergency obstacle avoidance have been studied extensively. The control input of such active steering control systems can be classified into the steering angle and the steering torque input. The steering torque based control provides some degree of freedom for drivers to control the vehicle motion, thus it has potential for development as steering assistance system. This paper describes the evaluation of shared control characteristics between human drivers and the active steering system for obstacle avoidance assistance system based on steering torque input. The shared control characteristics between the driver and the active steering system are investigated by using the driving simulator reconstructing a dangerous scenario.

Keywords: Human and vehicle interaction, Intelligent transportation systems, Man-machine interface in transportation
Abstract: The work described in this paper is part of a research program named ABV (Low Speed Automation) where the goal is the automation of road vehicle at low speed while ensuring the sharing of driving between the driver and the assistance. This paper focuses on the problem of human-machine cooperation in the specic context of vehicle driving, with a view of shared control between driver and automation, considering the acceptability of the system and the driver distractions and drowsiness.

Keywords: Human and vehicle interaction, Navigation, Guidance and Control, Trajectory and Path Planning
Abstract: GPS data are available from a large amount of sources. Individuals and vehicles carry their receivers and are often willing to share their locations. These GPS traces are inexpensive compared to dedicated collection techniques. Therefore, using GPS traces in map extraction has attracted a number of researchers in the past decade. In the case of mobile machines, dedicated collection of GPS data is usually even impossible, so one has to confine to the data collected by the machine itself. This paper introduces an algorithm for extracting a map-like graph from mobile machine GPS traces with large uncertainties. The result is a topological map with intersections and route segments. It can be used for operator support instead of displaying the raw GPS traces, or information interchange between operators. In the future, even automatic route optimization is possible with the help of a graph like this.

Keywords: Teaching curricula developments for control and other engineers
Abstract: There are multiple challenges and opportunities that are presented to young investigators preparing for careers in science and engineering. The session will address important control engineering education issues of innovative teaching, balancing math, science and technology in engineering education but also how to prepare students for the complexity of the problems in the modern world. Invited international control engineering scholars and educators from academia and industry will share their wisdom in addressing the following and other related questions: How do we integrate research and education? What we, scientists, engineers and educators, should do about cultivating student interest in science, math and engineering? Is it important for control engineering students to know math and science? Should control engineering education focus mostly on engineering? What kind of control engineering textbooks are popular among students? Do they need textbooks? Should engineering education focus mostly on technology? What are the most successful methods in innovative teaching of current students? The expected output of this session would be to make recommendations to the IFAC community for cultivating students interest in science, math and engineering by encouraging the control community to do better at communicating the excitement, power and beauty of control systems integrated with science and math to the broader group of engineering students and to the public. Collectively we must develop understandings, look at relationships among things that influence our every day lives, the environment in which we live now, look at how to develop new products, new technology, new models, new applications in different disciplines, look at issues of health or economic well- being in new ways. Those are only some issues that seem to be very important for our discussion in which we all are engaged.

Keywords: Modeling and simulation of power systems, Real time simulation and dispatching, Control system design
Abstract: The forthcoming smart grids are comprised of integrated microgrids operating in grid-connected and isolated mode with local generation, storage and demand response (DR) programs. The proposed model is based on three successive complementary steps for power transaction in the market environment. The first step is characterized as a microgrid’s internal market; the second concerns negotiations between distinct interconnected microgrids; and finally, the third refers to the actual electricity market. The proposed approach is modeled and tested using a MAS framework directed to the study of the smart grids environment, including the simulation of electricity markets. This is achieved through the integration of the proposed approach with the MASGriP (Multi-Agent Smart Grid Platform) system.

Keywords: Power systems stability, Control of renewable energy resources, Smart grids
Abstract: We consider the problems of frequency stability, voltage stability and power sharing in droop-controlled inverter-based microgrids with meshed topologies and dominantly inductive power lines. Assuming that the conductances in the microgrid can be neglected, a port-Hamiltonian description of a droop-controlled microgrid is derived. The model is used to establish sufficient conditions for local stability. Furthermore, we propose a condition for the controller parameters such that a desired steady-state active power distribution is achieved. The robustness of the stability condition with respect to the presence of conductances is analyzed via a simulation example based on the CIGRE benchmark medium voltage distribution network.

Keywords: Control of renewable energy resources, Control system design, Power systems stability
Abstract: This paper contains a control scheme for power sharing in islanded microgrids with inverter-sourced distributed energy resources that combines robust control and droop control. As the load within the microgrid changes, the inverter-sourced generators will share this change in load; this paper shows that the uncontrolled load sharing among the generators will be arbitrary and methods such as droop control achieve a regulated change. This paper includes a background on control schemes for power sharing and highlights the difficulty in exact reactive power sharing. The performance of the proposed controller is demonstrated using a test microgrid system.

Keywords: Smart grids, Optimal operation and control of power systems
Abstract: Energy scheduling plays a critical role in the safe and economical operation of the Microgrid. Several methods exist that can be leveraged to improve the cost efficiency of Microgrid operation in grid-connected mode. However, existing works overemphasize the economic benefit of Microgrid operation in grid-connected mode such that in case of sudden transition to islanded mode, the operation reliability, an important performance index in practical operation can’t be guaranteed. In this paper, a two-stage scheduling scheme is proposed that takes the reliability issue into consideration by leveraging the upstream grid failure probability. Through real-world data sets based simulation evaluation, we evaluate our proposed two-stage scheduling scheme and validate its effectiveness.

Keywords: Optimal operation and control of power systems, Smart grids, Intelligent control of power systems
Abstract: The increasing penetration of renewable energy generation (REG) in the microgrid paradigm has brought with it larger uncertainty in the scheduled generation. This along with the inevitable variation between actual load and forecasted load has further accentuated the issue of real-time power balancing. With the advent of smart loads and meters supported by advanced communication technologies, several new possibilities for demand side management have opened up. In this paper, a real time optimization strategy for load side energy management system (EMS) and for power balance is proposed. The proposed strategy achieves power balance by optimizing load reduction. The objective is to ascertain uninterrupted power to critical loads and reduce non-critical loads depending on the priorities for various loads. To further enhance the flexibility of the system, the addition of a battery to the management model is also discussed. The proposed algorithm also makes the system fault tolerant if battery is added to the system. The effectiveness of the proposed online power balancing strategy via optimization is demonstrated through various simulation case studies.

Keywords: Optimal operation and control of power systems, Distribution automation, Constraint and security monitoring and control
Abstract: Fuel cost is a major concern for naval and commercial ship operations. One approach to fuel efficiency improvement is to integrate propulsion and electric generating systems. This paper examines the use of hybrid electric drives as a means for reducing fuel costs in naval ship operations. Fuel efficiency can be improved by committing only the necessary power generating resources to supply the propulsion and electric power requirements for the mission. However, it is also necessary to insure a degree of reliability of power supply, which typically implies committing more resources than actually needed. This paper examines fuel optimization in the presence of service reliability constraints. It considers optimal strategies for contingency response as a means of reducing the need to commit excess on-line power generating resources.

Keywords: Stability of nonlinear systems, Stability of hybrid systems, Lyapunov methods
Abstract: This paper derives a stability statement for a novel, switching based limit cycle control. The stability proof is based on multiple Lyapunov functions and a new interpretation of contraction analysis. By showing that the dissipated energy on the cycle increases with increasing velocity, while the injected energy is constant, the emergence of an attractive limit cycle is shown. The approach applies for general, nonlinear, and compliantly actuated second-order systems, with positive definite plant parameters and non-aperiodic solutions. An analysis of the controller parameters reveals, that for the majority of parameters, global attractiveness of the limit cycle can be guaranteed.

Keywords: Stability of hybrid systems, Lyapunov methods
Abstract: In this paper, new Lyapunov-based reset rules are constructed to improve L2 gain performance of linear-time-invariant (LTI) systems. By using the hybrid system framework, sufficient conditions for exponential and finite gain L2 stability are presented. It is shown that the L2 gain of the closed loop system with resets can be improved compared with the base system. Numerical example supports our results.

Keywords: Stability of hybrid systems, Stability of nonlinear systems, Lyapunov methods
Abstract: In this paper, stability and bifurcation of piecewise affine systems is investigated. In the stability analysis, stability of the origin and existence of limit cycles are investigated by means of the radial growth rate, which describes the behavior of the radial direction of the trajectories. In the bifurcation analysis, Hopf bifurcation and saddle-node bifurcation is discussed. The radial growth rate is also used for characterizing the return map in the discussion.

Keywords: Robust control, Stability of nonlinear systems, Switching stability and control
Abstract: This paper discusses the stability of feedback systems in which both plant and controller are switched. Switched systems considered here have all their subsystems satisfying the `mixed'-negative imaginary property. A definition for dissipativity (for switched systems) is proposed, and dissipative switched systems are shown to be stable (under certain conditions). Switched systems with `mixed'-negative imaginary property are shown to be dissipative and conditions for stability are derived. As an illustration of the results, a switched controller is designed for a nanopositioning stage, which has a `mixed'-negative imaginary frequency response function. Simulations show that the closed loop is stable and the designed controller damps the resonances satisfactorily.

Keywords: Control of switched systems, Switching stability and control
Abstract: This paper investigates the state feedback controller design for a class of linear switched systems with uncontrollable subsystems. After stating the problem discussed in this paper and some preliminaries, we design a controller that makes the given linear switched closed-loop system states converging to zero and show its effectiveness by analyzing the system states norm. Furthermore, we interpret that the presented controller design can be turned into a LMIs(linear Matrices Inequations) feasible problem which can be solved via existing softwares. In addition, an illustrative numerical example is presented to demonstrate the utility of the proposed state feedback controller.

Keywords: Tracking, Output feedback control (linear case), Switching stability and control
Abstract: In this paper, the problem of the switched H_{infty} tracking output feedback control problem is studied. The control design problem is addressed in the context of discrete-time switched linear systems. Then, the design of continuous-time case becomes trivial. Linear Matrix Inequality (LMI) and Linear Matrix Equality (LME) representations are used to express all sufficient conditions to solve the control problem. Some transformations leading to sufficient conditions for the control problem are also used. All conditions are established for any switching using a switched Lyapunov function and a common Lyapunov function. The effectiveness of the proposed control approach is shown through a steering vehicle control implementation. Interesting simulation results are obtained using real data acquired by an instrumented car.

Keywords: Continuous time system estimation
Abstract: Transient temperature response measurements of semiconductor devices such as high- powered Light-Emitting-Diodes (LEDs) can be used to detect possible thermal defects. The thermal transient responses of these LEDs appear to be stiff which can be represented by a model with both fast and slow dynamics. It is shown how direct continuous-time model estimation methods, such as the Simplified Refined Instrumental Variable method for Continuous systems (SRIVC), can directly identify with high accuracy a model with both small and large time-constants that can reproduce the thermal effects of the LEDs while conventional discrete-time model identification fails in this stiff response situation.

Keywords: Identification for control, Iterative learning control, Adaptive control -applications
Abstract: Individual pitch control (IPC) is gaining increasing acceptance as a method for mitigation of periodic disturbances in wind turbines. This paper aims at formulating a repetitive control (RC) methodology capable of adapting online to changing turbine dynamics. This is achieved by performing system identification in a reduced-dimensional space using basis functions and using the identified parameters to synthesise an RC law to reject periodic disturbances: this methodology is termed Subspace Predictive Repetitive Control (SPRC). The method is tested on an industrial simulation test bench and is able to identify and perform load control on the turbine without affecting its power production.

Keywords: Identification for control
Abstract: This paper addresses the performance of electric servo-actuators as part of a helicopter control linkage system. In a helicopter control system design process, one of the most important tasks is the development of a non-linear model capable of accurately representing the dynamics of the target system. The first stage in a helicopter non-liner model is representing the servo-actuator dynamics, which transmit the control inputs to the main rotor system. This research will perform a series of system identification experiments for a cyclic servo-actuator under different operating conditions. Then step responses for the actuators are estimated using frequency sampling filters. A comparison of performance will be made for a zero load bench test and the hover flight condition to determine the most accurate representation of the servo-actuator dynamic for helicopter flight applications.

Keywords: Nonlinear system identification, Continuous time system estimation, Identification for control
Abstract: A new approach is proposed for developing a model for High Voltage (HV) Lithium-ion (Li-Ion) battery pack using Hybrid Electrical Vehicle (HEV) drive cycles data. A variety of drive cycle’s data have been collected from the test vehicle at various operating conditions. The equivalent electrical circuit for the HV battery is formulated using mathematical equations and discrete state space equations. The Open Circuit Voltage (OCV), Offset Voltage at Zero Load conditions (VZL), battery pack internal resistance (Rbatt) are considered as various components of the mathematical equations and battery pack RC filter states are represented using discrete state space equations at various battery temperature (Batt Temp) conditions (-7 degC to 45 degC). The parameter estimation problem is then considered as the problem of simultaneously estimating the parameters of all the coefficients formulated in the battery pack mathematical and state space equations. This is mathematically posed as a constrained optimization problem and a variant of genetic algorithm (GA) is used to solve it. The main advantages of the proposed approach are, (i) The HV Battery Pack model can be developed without additional instrumentation on the HV Battery (which is expensive),except hall effect current sensor, (ii) Proposed methodology uses the sensor readings at battery pack level rather than accessing the individual cell data (iii) Standard and specially designed drive cycles are used to generate the required measurement data rather than specially design lab tests on the battery pack., (iv) The use of constrained optimization formulation enables to use apriori knowledge and experience during the parameter estimation, and (v) The use of genetic algorithm almost ensures that the estimated parameters are global optima within the specified constraints. Modeling is done using MATLAB®/Simulink®.The developed model is validated on actual vehicle drive cycle data. The results obtained by the proposed approach are closely matching with the actual HV Battery responses.

Keywords: Mechanical and aerospace estimation, Grey box modelling, Identification for control
Abstract: In this paper, a method for estimating physical parameters using limited sensors is investigated. As a case study, measurements from an IMU are used for estimating the change in mass and the change in center of mass of a ship. The roll motion is studied and an instrumental variable method estimating the parameters of a transfer function from the tangential acceleration to the angular velocity is presented. It is shown that only a subset of the unknown parameters are identifiable simultaneously. A multi-stage identification approach is presented as a remedy for this. A limited simulation study is also presented to show the properties of the estimator. This shows that the method is indeed promising but that more work is needed to reduce the variance of the estimator.

Keywords: Nonlinear system identification, Continuous time system estimation
Abstract: This paper proposes a solution to the problem of velocity and position estimation for a class of oscillating systems whose position, velocity and acceleration are zero mean signals. The proposed scheme considers that the dynamic model of the system is unknown and only noisy acceleration measurements are available

Keywords: Control over networks, Control and estimation with data loss
Abstract: We raise the question when a continuous-time system can be stabilized over a packet based communication system with loss and delay. Thereby, packet loss is assumed to be an iid random process; packet delay follows a known delay distribution and packets that arrive after a maximal waiting time are ignored, i.e., dropped. Moreover, due to the packet based communication system, the continuous-time system must be sampled. By setting the maximal waiting time for a packet identical to the sampling period, we are able to derive conditions on the sampling period for the existence of a stabilizing controller.

Keywords: Control under communication constraints, Networked embedded control systems, Stability and stabilization of hybrid systems
Abstract: We prove two more results on controllability of systems with switching delays. Firstly, we improve our previous results on controllability verification (when arbitrarily large look-ahead of the switching signal is allowed) by providing an algorithm which is not restricted to regular matrices. In particular we provide a more detailed analysis for nihilpotent matrices (i.e. matrices with only zero eigenvalues): we restate this problem as a completely combinatorial one and provide a polynomial time algorithm, which has much better efficiency than a naive brute force algorithm. Secondly, we show that when the switching signal cannot be measured it can be necessary to use nonlinear controllers for stabilizing a linear plant.

Keywords: Sensor networks, Control over networks
Abstract: In this paper, we will present LMI-based synthesis conditions for designing dynamic output-based controllers for networked control systems (NCSs). In particular, the controller is designed to render a linear time-invariant plant stable given a network that causes the transmission intervals and delays to be time varying and that precludes that all sensors and actuators data can be sent simultaneously. The latter fact necessitates the use of a scheduling protocol and, in this paper, we will focus on a periodic protocol. We will formulate the networked plant model as a discrete-time switched linear parameter-varying system and we will use convex overapproximation techniques to arrive at a model that is amenable for controller synthesis. The controller synthesis result is inspired from controller synthesis results for switched systems and yields a switched controller that is robust for the aforementioned network phenomena. We will illustrate the synthesis results on a benchmark example of a batch reactor, which shows that our novel results can outperform existing results on controller synthesis.

Keywords: Networked systems
Abstract: This paper is concerned with the stability analysis of networked control systems with communication constraints, variable delays and variable sampling intervals. The scheduling of sensor communication is defined by a stochastic protocol. The activation probability of each sensor node is a given constant, whereas it is assumed that collisions occur with a certain probability. The resulting closed-loop system is a stochastic impulsive system with delays in the continuous dynamics and in the reset equations. The system matrices have stochastic parameters with Bernoulli distributions. Sufficient conditions for the exponential mean-square stability are derived via a Lyapunov-Krasovskii-based method. The efficiency of the method is illustrated on the examples of a cart-pendulum system and a batch reactor. It is demonstrated how our time-delay approach allows treating network-induced delays larger than the sampling intervals.

Keywords: Sensor networks, Control of distributed systems, Process performance monitoring/statistical process control
Abstract: This paper investigates the control problem of distributed parameter systems (DPS) with time-varying delay by employing mobile actuator-sensor networks. It is assumed that each agent in the networks has a sensor device which can measure spatial state, and an actuator device that can dispense control signals to spatially distributed process and can communicate with its neighbors. To better control the DPS with time-varying delay, the strategy how to navigate the agents is considered. By constructing Lyapunov functionals and using inequality analysis, criterias for stability of the DPS with time-varying delays are derived. Meanwhile, the guidance scheme of every agent with augmented vehicle dynamics is derived. Simulation results show that such mobile actuator-sensor networks can improve the control performance of the DPS with time delay.

Keywords: Output feedback control, Networked systems, Nonlinear observers and filter design
Abstract: Focusing on networked control systems with state delay and packet dropout, a state feedback control for a class of nonlinear networked control systems with system delays and packet dropout is proposed. Then an observer is designed to estimate the system state. Finally, we achieve stabilization of this class of systems through a dynamic output feedback without augmentation of the state space model. From an appropriate Lyapunov-Krasovskii function, sufficient conditions which guarantee the convergence of the state variables and state estimation errors to the origin are deduced and expressed in terms of simple LMIs. Usability and simplicity are the advantages of this approach. A numerical example is given to illustrate the effectiveness of the proposed approach.

Keywords: Linear systems, Data-based control
Abstract: This paper explores some important characteristics of a system of linear equations containing parameters. Such a system of equations arises in many branches of engineering including electrical circuits, hydraulic networks and truss structures. A parametrized solution of a set of linear equations can be obtained by applying Cramer's rule. In many practically important cases the parameters appear with rank one dependency, resulting in parametrized solutions to be of a rational multilinear form, which will be monotonic in each parameter. This monotonic characteristic has practical importance in the analysis and design of linear systems with parameters having interval uncertainties. In particular, extremal values of system variables occur at the vertices of the parameter boxes.

Keywords: Linear systems, Robust control (linear case), Robustness analysis
Abstract: Repetitive control (RC) is well known as a method for tracking a periodic command as well as eliminating the influence of a periodic disturbance. An issue that can be encountered in applications is that the period of the disturbance can vary. In some cases one can monitor a drift in the disturbance period, but in other cases the period can fluctuate sufficiently fast that one wants an RC law that is robust to period fluctuations. Several studies address this problem using higher order RC incorporating a negative weight on errors from some previous period. This paper presents a systematic design procedure that can be used to improve RC robustness to disturbance period fluctuations. Methods are given to present the information needed to make the necessary tradeoffs when tuning the set of design parameters.

Keywords: Linear systems, Time-invariant systems, Disturbance rejection (linear case)
Abstract: The concepts of ultimate bounds and invariant sets play a key role in several control theory problems, as they replace the notion of asymptotic stability in the presence of unknown disturbances. However, when the disturbances are unbounded, as in the case of Gaussian white noise, no ultimate bounds nor invariant sets can in general be found. To overcome this limitation we introduced, in previous work, the notions of probabilistic ultimate bound (PUB) and probabilistic invariant set (PIS) for discrete-time systems. This article extends the notions of PUB and PIS to continuous-time systems, studying their main properties and providing tools for their calculation. An application of these concepts to robust control design is also presented.

Keywords: Time-invariant systems, Linear systems, Optimal control theory
Abstract: This paper considers the problem of solving parameter-dependent Riccati equations. In this paper, a tractable iterative scheme involving mainly additions and multiplications is developed for finding solutions to arbitrary accuracy. It is first presented in the parameter-independent case and then extended to the parametric case. It hinges upon two results: (i) a palindromic quadratic polynomial matrix characterization of the matrix sign and square root functions. (ii) a particular representation of parameter-dependent matrices with negative and positive power series with respect to parameters. Several numerical examples are given throughout the paper to prove the validity of the proposed results.

Keywords: Observers for linear systems, Robust controller synthesis, Convex optimization
Abstract: Design of robust observers is considered in the context of linear discrete-time, time invariant systems. Robustness is achieved with respect to polytopic type uncertainties that affect the dynamics of the plant. At the difference with the uncertainty-free situation, state-feedback / observer separation principle does not hold. Therefore, the observer design has to take into account the state-feedback gain. Results are derived with linear matrix inequality formalism and involve up-to-date slack-variables approach. A numerical example illustrates the results. Limitations of the method are discussed and prospective work for improving these is exposed.

Keywords: Observers for linear systems, Linear systems
Abstract: This paper proposes a new ellipsoid-based guaranteed state estimation approach for linear discrete-time systems with bounded perturbations and bounded measurement noise. This approach is based on the minimization of the radius of the ellipsoidal state estimation set. Firstly, the ellipsoidal state estimation is computed by off-line solving a Linear Matrix Inequality optimization problem. Secondly, a new online method is developed in order to improve the accuracy of the estimation but it leads to an increase of the online computation load. A new scaling technique is proposed to reduce the computation time, while keeping a good accuracy of the state estimation. An illustrative example is analyzed in order to show the advantages of the proposed approach.

Keywords: Motion Control Systems, Micro and Nano Mechatronic Systems, Vibration control
Abstract: Integral Resonant Control (IRC) is a simple and robust control scheme for vibration damping. Combined with an integral tracking controller, the IRC has been shown to improve the performance of a wide range of nanopositioners. However, the overall improvement in positioning performance is limited by the pole induced by the IRC. Through selective zero placement, the induced pole can be placed near origin. A structured PI tracking controller, where the PI gains are selected to place a zero at a specific location, is used to cancel the pole. This effectively reduces the order of the system by one. For this system, controller gains are derived analytically in order to maximize tracking bandwidth. Simulation results for one axis of a nanopositioner are provided for both standard and modified IRC schemes. Compared to the standard IRC scheme, the modified IRC scheme is found to provide a 55% increase in the ±1 dB bandwidth and a reduction of both maximum and rms tracking errors.

Keywords: Micro and Nano Mechatronic Systems, Motion Control Systems, Reliable measurement and actuation
Abstract: This paper presents a displacement sensing technique that can be integrated into a microfabricated microelectromechanical system (MEMS) device. This sensor determines displacement by measuring the capacitance of a MEMS electrostatic drive, as the capacitance is a function of the displacement. The electrostatic drive is incorporated into an LC oscillator whose frequency varies with the capacitance. A lock-in amplifier is used to extract the frequency signal. The sensitivity of the sensor was -1.153 V/µm and exhibited no dynamics up to the 1.2 kHz bandwidth of the MEMS device it was implemented in. The electrostatic drive in this technique is used for both actuation and sensing. This effectively increases the transduction efficiency of both the actuator and sensor as more space on the die can be dedicated to the one drive. Additionally, the scheme allows for one terminal of the drive to be grounded. Thus, this scheme can be used on MEMS devices with more than one drive connected to a common mechanical structure which is electrically grounded.

Keywords: Motion Control Systems, Mechatronic systems, Micro and Nano Mechatronic Systems
Abstract: Actuators composing dual stage actuation usually have different purposes and mechanisms to complement each other. Hence, they potentially have different types of control challenges to achieve high-precision motion performance. This paper presents the use of single-input single-output controllers to efficiently overcome the potential problems by selecting proper control techniques to each actuator. The setup is a low-stiffness dual stage actuator capable of positioning with nanometer resolution without additional vibration isolation. The system consists of a CD/DVD laser pickup and a linear motor with roller bearings, which suffer from environmental vibrations and friction, respectively. By selecting and implementing a tamed PID controller for the pickup and a sliding mode controller for the linear motor, the dual stage actuator is able to move over 100mm with a maximum velocity of 0.34m/s and position with a static precision of +/-2.5nm (peak-to-peak).

Keywords: Motion Control Systems, Smart Sensors and Actuators, Micro and Nano Mechatronic Systems
Abstract: The present paper deals with the motion control of a piezoelectric cantilevered actuator. The motion behavior of the piezoelectric cantilever is affected by two main parasitic effects, the hysteresis and creep effects, which are considered as a generalized disturbance. Additionally, cantilevers's position is the only available measured state. The latter operational scenario reveals that the trajectory-tracking or regulation problem is a non-obvious task. Prior to the controller design, we have focused on the estimation of the velocity using a sliding-mode observer (SMO) in order to avoid the numerical derivative. On the other hand, the control design takes into account the limited response of the control input (bounded states) and it is obtained using the Backstepping framework assuring the stability of origin. Numerical simulations are first presented to verify the efficiency of the observer and controller algorithm. This shows satisfactory performance to fullling the control objective. Then experimental results were performed and validate the predicted performances.

Keywords: Micro and Nano Mechatronic Systems, Smart Sensors and Actuators, Smart Structures
Abstract: The design of a phase-locked loop (PLL)-based proportional integral (PI) controller for improving the spiral scanning of a piezoelectric tube scanner (PTS), used in an atomic force microscope (AFM), is demonstrated in this paper. Spiral motion of the PTS for scanning of material surfaces or biological samples using an AFM is achieved by applying two sinusoidal signals with a 90 degree phase-shift and of varying amplitudes to the X and Y-axes of the scanner. If there is an exact 90-degree phase shift between the sinusoidal displacements of the two scanner axes, a true circular image is generated. Otherwise, the image will distort into an elliptical shape, which can lead to missing data points or changes in the lattice structure of the sample surface to be scanned. The phase error between the X and Y-axes positions increases with increasing scanning speeds. In this proposed control scheme, the phase error between the displacements of the two lateral axes of the scanner is measured by a phase detector and is minimized using a PI controller. Experimental results for reference tracking and imaging performance confirm the efficiency of the proposed control scheme.

Keywords: Control of physiological and clinical variables, Model formulation, experiment design, Clinical trial
Abstract: Feasibility of closed-loop anesthesia has been shown in a number of clinical studies. Demonstration of patient safety will be essential to convince regulatory authorities of the benefits of such systems. This paper considers safety constraints for closed-loop propofol anesthesia based on its therapeutic range. Simulation scenarios are proposed for evaluation of control strategies in the presence of these constraints. The scenarios reproduce realistic situations encountered in clinical practice. Using the proposed scenarios, the performance of L2 anti-windup is compared to sliding mode reference conditioning and to back-calculation anti-windup. It is concluded that L2 anti-windup might not be appropriate for this problem. The sliding mode solution results in behaviour comparable to the Hanus conditioned controller and there seems to be no need for fast switching. The back-calculation anti-windup performs well in a variety of situations.

Keywords: Control of physiological and clinical variables, Biomedical system modeling, simulation and visualization, Cellular, metabolic, cardiovascular, pulmonary, neuro-systems
Abstract: A patient with hypoxia requires immediate oxygen therapy to enhance oxygenation. Individual model including disease and parameter variation is needed, which limits all model-based approaches for the task of reference tracking control. In this article, a non-identifier based control algorithm, called funnel control, is proposed to solve this clinical problem in real practice. With this control technique, it requires less a priori knowledge of the complicated nonlinear and time-delay plant. The design of a funnel controller relies only upon an output feedback and the difficult control problem of such system is simplified by the design of a funnel boundary, which is a time-varying gain to force error dynamics inside this boundary. Funnel controllers with relative degree one and two are presented and simulated with the proposed cardiopulmonary system for low tidal volume, simulating the existing and abrupt change of atelectasis.

Keywords: Control of physiological and clinical variables, Modeling and identification, Cellular, metabolic, cardiovascular, pulmonary, neuro-systems
Abstract: Heart assist devices provide mechanical circulatory support for patients with end-stage heart failure. Extracorporeal heart assist devices are applied to adult and pediatric patients in the case of uni- and biventricular assistance. Modern driving units provide more mobility what is an immense benefit to the quality of life of the patients. The treated heart assist device in this article is the EXCOR system (Berlin Heart GmbH, Germany). This device is pneumatically operated by a piston drive. Pump assistance for the native heart of the patient should be provided by an automatic control system. This could be achieved by the control of the piston movement and the enclosed air mass in the pneumatic system. The model based design of a control system for the extracorporeal assist device is described in this paper.

Keywords: Rehabilitation engineering and healthcare delivery, Identification and validation, Control of physiological and clinical variables
Abstract: Disorders of the central nervous system like stroke often lead to a paresis of the muscles responsible for dorsiflexion and eversion of the foot during swing phase. Functional electrical stimulation (FES) is commonly used to improve the foot movement. A precise placement of two single surface electrodes on the shank as well as a manual tuning of heel-switch triggered stimulation is required when using standard drop-foot stimulators.

In this work, the use of automatically tuned array electrodes for selective nerve/muscle stimulation and the application of iterative learning control for adjusting stimulation intensities are investigated with the aim to obtain an optimal foot movement. The stimulation effect is observed by means of two inertial measurement units mounted on the foot and shank, respectively. Two array electrodes are placed over the areas covering the peroneal nerve and the muscle tibialis anterior. A fast identification procedure finds three suitable sets of array elements, forming so-called virtual electrodes, in both arrays. Two stimulation channels are established over the three virtual electrodes. Both channels produce dorsiflexion, but one promotes eversion and the other inversion. A decoupling matrix is applied to the stimulation intensities of both channels in order to obtain independent control over dorsiflexion and eversion. Finally, two independent iterative learning controllers are employed to achieve desired angle profiles. The proposed stimulation and control scheme is initially tested on a healthy person sitting on a table with the shank and foot free to swing. A tracking RMS error of less than one degree is achieved within six walking steps.

Keywords: Intensive and chronic care or treatment, Model formulation, experiment design, Developments in measurement, signal processing
Abstract: In this paper, we present a novel method to supervise several discrete events and continuous processes causing failures in a blood pump. These are potential hazards which regularly cause problems in intensive care routine. We propose an indicator that considers the nonlinear shear thinning ow properties of blood. Based on a threefold of physiological motivated measures, we calculate an indicator which is not only able to detect ongoing events like gas in the blood phase but also to predict upcoming events like the suction of the withdrawing cannula to the surrounding vessel's wall. We present an algorithm that is embedded in a distributed 32 bit microcontroller network and holding hard real-time constraints. We were able to evaluate out algorithms in-vivo. For this algorithm we analyzed online data of more than 140 hours of animal experiments.

Keywords: Decision support and control, Kinetic modeling and control of biological systems, Rehabilitation engineering and healthcare delivery
Abstract: In this contribution we present the novel electro-pneumatic adaptable impedance actuator (EPAIA) consisting of a brushless DC-motor and a rotational pneumatic actuator, applied in series to the gear train. The pneumatic actuator is employed as a pneumatic torsional spring with a continuously adaptable stiffness, controlled by air inflow and pressure control. Thus, a rotary variable stiffness actuator is obtained, for which the stiffness can be expressed as a smooth nonlinear function of chamber pressures. The mechanical design furthermore utilises a planetary and a bevel gear reduction, to provide a torque amplification ratio necessary for human assistive knee joint actuation. Besides large and precise torque generation, the actuator provides a smoothly adjustable output impedance, backdrivability and a low cost, yet quality sensitive torque sensor. Following a detailed description of the mechanical design of the actuator, a dynamical model is derived using the Lagrange formalism. We propose a robust mathcal{H}_2-torque-controller design procedure for the linearised actuator model, for which a constraint for passivity of the load transfer function is employed. The resulting controller is tested in simulations using the nonlinear validated model.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Control architectures in automotive control, Hybrid and alternative drive vehicles
Abstract: This paper proposes a time-ecient method for sub-optimal design of a plug-in hybrid electric vehicle with a parallel powertrain topology. The method finds the optimal design of the vehicle by iteratively using dynamic programming (DP) and convex optimization to minimize sum of operational and component costs over a given driving cycle. In particular, DP is used to optimize energy management, gear shifting and engine on-o for given component sizes, and convex optimization is used to optimize energy management and component sizes using the gear shifting and engine on-o strategies obtained by DP. Next, DP is re-optimized with the component sizes obtained by convex optimization, and the procedure is repeated until the component sizes converge. The result of this iterative method is compared by using DP on a grid of possible component sizes. It is shown that the iterative method gives a result very close to the global optimum in a comparably short time.

Keywords: Hybrid and alternative drive vehicles, Nonlinear and optimal automotive control, Modeling, supervision, control and diagnosis of automotive systems
Abstract: Hybrid electric vehicles (HEV) enable higher fuel-efficiency compared to conventional vehicles. The main potential for fuel efficiency lies in the choice of torque split between the internal combustion engine and the electric motor and the gear shifting strategy. In the framework of a predictive control strategy, the vehicle velocity can be considered as another degree of freedom. To achieve minimum fuel consumption, the right choice for these three degrees of freedom over time has to be found. In this paper, a discrete dynamic programming approach is used to find a global optimal solution for fuel efficiency potential analysis, optimizing torque split, gear shifting and velocity trajectory. For illustration, the results for a parallel hybrid electric passenger car are shown.

Keywords: Hybrid and alternative drive vehicles, Automotive sensors and actuators
Abstract: Recently, the possibility of upgrading conventional vehicles to Hybrid Electric Vehicles (HEV’s) is gaining interest. Among the different options for hybridization, researchers are focusing on electrification of rear wheels in front-driven vehicles, transforming the vehicle in a Through-The-Road (TTR) parallel HEV. This paper deals with the research work on the development of a kit for converting a conventional vehicle into a TTR Hybrid Solar Vehicle (HSV). In order to develop an effective and safe control strategy for wheel-motors, a precise real-time knowledge of the Driver Intention is required. In particular, the detection of the active gear is needed. In this paper, a mathematical model for the real-time identification of the active gear, using only data measured by the On Board Diagnostics (OBD) port, is presented and validated over on road experimental data.

Keywords: Hybrid and alternative drive vehicles
Abstract: Continuously variable transmissions play an important role in mechanical kinetic energy recovery systems. In this work, a clutch based continuously variable transmission comprising two stages of three clutches is considered. A detailed mathematical model is derived and a control strategy for the load torque is given. The model covers losses in bearings, clutches and fixed transmissions as well as flywheel losses due to gas friction.

Keywords: Engine modelling and control, Hybrid and alternative drive vehicles, Control architectures in automotive control
Abstract: Three Stochastic Dynamic Programming (SDP) implementations are developed for control of a diesel-electric wheel loader transmission. The implementations each use a stochastic description of the load, with the probabilities either independent of the states, dependent on previous power or on distance driven. Both the cycles used for the controller development and for the evaluation are derived from a measured sequence of cycles.

The evaluation shows that SDP can be used for control of the engine speed and that the resulting trajectories from the three implementations are very similar. The most surprising part is that the method which has constant load probability is able to adjust to the actual load. The combination of the calculation eorts and the outcomes leads to the conclusion that the constant load probability implementation is superior to the other versions.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Engine modelling and control, Hybrid and alternative drive vehicles
Abstract: A simple multi-0D model of a 3-Way Catalytic Converter (3WCC) is built from physical equations, integrating the temperature dynamics and a pollutant emission conversion map. The validated model involves complexity and performances suitable to be integrated in a high delity powertrain model of a gasoline-Hybrid Electric Vehicle (HEV). Next, a pollutant constrained optimal energy management is derived from the Pontryagin Minimum Principle. The approach allows the joint minimization of pollution and fuel consumption with only one parameter to tune, by considering all the standardized pollutant emissions. The proposed strategy signicantly reduces pollutant emissions with only a slight fuel consumption increase. Using a complex HEV model shows the feasibility of the pollution constraint integration in on-line energy management.