Showing papers in "Iet Control Theory and Applications in 2017"
TL;DR: The designed ANITSMC of AUVs avoids the requirement of the prior knowledge of the lumped system uncertainty bounds, provides higher tracking accuracy than the existing globally finite-time stable tracking control (GFTSTC), and offers faster convergence rate and better robustness against dynamic uncertainties and time-varying external disturbances than the adaptive proportional-integral sliding mode control.
Abstract: This study proposes an adaptive non-singular integral terminal sliding mode control (ANITSMC) scheme for trajectory tracking of autonomous underwater vehicles (AUVs) with dynamic uncertainties and time-varying external disturbances. The ANITSMC is first proposed for a first-order uncertain non-linear dynamic system to eliminate the singularity problem in conventional terminal sliding mode control (TSMC) and avoid the requirement of the bound information of the lumped system uncertainty. The time taken to reach the equilibrium point from any initial error is guaranteed to be finite. The proposed ANITSMC is then applied to trajectory tracking control of AUVs. It guarantees that the velocity tracking errors locally converge to zero in finite time and after that the position tracking errors locally converge to zero exponentially. The designed ANITSMC of AUVs avoids the requirement of the prior knowledge of the lumped system uncertainty bounds as opposite to the existing globally finite-time stable tracking control (GFTSTC), provides higher tracking accuracy than the existing GFTSTC and adaptive non-singular TSMC (ANTSMC) and offers faster convergence rate and better robustness against dynamic uncertainties and time-varying external disturbances than the adaptive proportional-integral sliding mode control (APISMC). Comparative simulation results are presented to validate the superiority of the ANITSMC over the APISMC.
213 citations
TL;DR: The main concern of this study is to present a comprehensive introduction to logical networks and some other applications under the framework of STP of matrices.
Abstract: This study presents a detailed survey on recent development of logical networks and its applications, including the background of logical networks, the theory of a new matrix product called semi-tensor product (STP) of matrices, some fundamental works on logical networks, and some current research works. Particularly, some fundamental works on logical networks are presented for the past years, including controllability, stability and stabilisation, synchronisation, disturbance decoupling and so on. Due to the great potential of STP in dealing with logical networks, a surge of attraction from overseas is paid on the study of STP and its applications. Currently, some new research areas are widely studied including pinning control, function perturbations, system decomposition, trajectory control, output tracking issues, symbolic dynamics and so on. The main concern of this study is to present a comprehensive introduction to logical networks and some other applications under the framework of STP of matrices.
205 citations
TL;DR: Finite- and fixed-settling time of real-time differentiators utilising a higher-order sliding mode (HOSM) observer based on both non-recursive and recursive algorithm formulations are studied using constant observer gains.
Abstract: Finite- and fixed-settling time of real-time differentiators utilising a higher-order sliding mode (HOSM) observer based on both non-recursive and recursive algorithm formulations are studied using constant observer gains. Fixed convergence time estimation is achieved independent of initial conditions of the differentiation errors. The corresponding convergence/settling times are estimated. The HOSM differentiators are compared with the ideal differentiation formulation and MATLAB's differentiator. The analysis incorporates input noise to demonstrate via simulations the improved performance for real-time noisy input signals.
158 citations
TL;DR: In this article, a novel backstepping design is constructed via barrier Lyapunov function (BLF) combined with dynamic surface control (DSC), which guarantees the full state constraints are not violated and all the closed-loop signals remain bounded.
Abstract: This studies an adaptive control problem of pure-feedback non-linear systems with full state constraints. The mean value theorem is employed to deal with unknown non-linearities. A novel backstepping design is constructed via barrier Lyapunov function (BLF) combined with dynamic surface control (DSC). The BLF guarantees the full state constraints are not violated and all the closed-loop signals remain bounded. DSC solves the problems of restrictions on high order differentiability of stabilising functions and avoiding the complexity that arises due to the explosion of terms in backstepping design. It is shown that all the signals in the closed-loop system are ultimately bounded and the tracking error converges to an adjustable neighbourhood of the origin while the full state constraints remain unchanged. The performance of the BLF-based DSC is illustrated with two simulation examples.
129 citations
TL;DR: This study deals with the optimal control problem for a class of delta-domain networked control systems subjected to both matched and unmatched disturbances, and an upper bound for the ϵ -optimum is provided explicitly, and convex optimisation algorithms are given to compute such upper bound.
Abstract: This study deals with the optimal control problem for a class of delta-domain networked control systems (NCSs) subjected to both matched and unmatched disturbances. In the presence of the disturbances, the so-called ϵ -optimum is proposed to quantify the control performance. The purpose of the addressed problem is to design the optimal control strategy such that the cost function is minimised over the finite-/infinite-horizon under the network-induced constraints. In virtue of the dynamic programming method, sufficient conditions are established to guarantee the existence of the desired control strategies, and the controller parameters are designed. For the obtained optimal control strategy, an upper bound for the ϵ -optimum is provided explicitly, and convex optimisation algorithms are given to compute such upper bound. Both simulation and experimental results are provided to illustrate the usefulness and applicability of the proposed methods.
127 citations
TL;DR: In this article, a vector super-twisting-like algorithm is proposed to provide a direct extension of the conventional scalar supertwisting control, without any additional terms, and an upper estimate of its convergence (settling) time is calculated.
Abstract: The contribution of this study is threefold. First, a vector (multivariable) super-twisting algorithm is designed to provide a direct extension of the conventional scalar super-twisting control, without any additional terms. An upper estimate of its convergence (settling) time is calculated. Second, a fixed-time convergent continuous vector super-twisting-like algorithm is presented and its fixed convergence time is estimated. Third, an estimate of the finite convergence time of the scalar super-twisting algorithm is obtained as a particular case of the vector super-twisting one, which occurs to be less conservative than the one derived specially for the scalar case. The proposed theoretical concepts are illustrated in a number of scalar and multivariable examples.
115 citations
TL;DR: In this paper, a recursive least squares (RLS) estimation method based on the auxiliary model identification idea and the decomposition technique is presented for pseudo-linear system identification with missing data, and an interval-varying RLS algorithm is derived for estimating the system parameters.
Abstract: This study focuses on the parameter identification problems of pseudo-linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique. First, an auxiliary model-based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition-based RLS algorithm is presented. Then for the system identification with missing data, an interval-varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval-varying RLS algorithm and introduces the forgetting factors to track the time-varying parameters. The simulation results show that the proposed algorithms can work well.
112 citations
TL;DR: In this article, the authors considered the stability of hybrid stochastic differential equations by feedback control based on discrete-time state observations and established an upper bound on the duration τ between two consecutive state observations.
Abstract: The authors are concerned with the stability of hybrid stochastic differential equations by feedback controls based on discrete-time state observations. Under some reasonable conditions, they establish an upper bound on the duration τ between two consecutive state observations. Moreover, we can design the discrete-time state feedback control to stabilise the given hybrid stochastic differential equations in the sense of p
th moment exponential stability by developing a new theory. In comparison to the results given in the previous literature, this study has two new characteristics: (i) the stability criterion concerns p
th moment exponential stability, which is different from the existing works; (ii) discrete-time state observations depend on time delays.
106 citations
TL;DR: A backstepping-based adaptive fuzzy finite-time control scheme is proposed that guarantees that all the closed signals are semi-global practical finite- time stability while the tracking error converges to a small neighbourhood of the origin.
Abstract: The problem of adaptive practical finite-time control is considered for a class of single-input and single-output non-linear systems, in which the system non-linear functions are assumed to be unknown. By combining adaptive fuzzy control approach with the backstepping technology, a backstepping-based adaptive fuzzy finite-time control scheme is proposed. In the control design procedure, fuzzy logic systems are employed to identify the non-linear uncertainties. The stability analysis of the adaptive closed-loop systems is proposed based on the finite-time Lyapunov stability theory. The proposed adaptive fuzzy controller guarantees that all the closed signals are semi-global practical finite-time stability while the tracking error converges to a small neighbourhood of the origin. Finally, simulation results are presented to validate the effectiveness of our results.
96 citations
TL;DR: It is shown that the proposed variable gain super-twisting algorithm is an extension of the classical single-input case to the multi- input case and a bio-economic system is numerically simulated to verify the merits of the method proposed.
Abstract: This study investigates integral sliding mode control problems for Markovian jump T–S fuzzy descriptor systems via the super-twisting algorithm. A new integral sliding surface which is continuous is constructed and an integral sliding mode control scheme based on a variable gain super-twisting algorithm is presented to guarantee the well-posedness of the state trajectories between two consecutive switchings. The stability of the sliding motion is analysed by considering the descriptor redundancy and the properties of fuzzy membership functions. It is shown that the proposed variable gain super-twisting algorithm is an extension of the classical single-input case to the multi-input case. Finally, a bio-economic system is numerically simulated to verify the merits of the method proposed.
92 citations
TL;DR: In this article, the authors present a new approach for the design of distributed state estimation and fault detection and isolation (FDI) filters for a class of linear parameter-varying multi-agent systems, where the state space representations of the agents are not identical.
Abstract: In this study, the authors present a new approach for the design of distributed state estimation and fault detection and isolation (FDI) filters for a class of linear parameter-varying multi-agent systems, where the state-space representations of the agents are not identical. The developed formulation for the FDI offers a distributed filter design method, in which each agent uses sensor measurements both locally and from the neighbouring agents. Each FDI filter is in the ‘unknown input observer’ form which are designed so that their outputs, i.e. residual signals, are: (i) robust with respect to the external disturbance inputs and (ii) sensitive with respect to the fault signals. Moreover, it is shown that using the proposed methodology each agent is able to estimate not only its own states, but also states of its nearest neighbours in the presence of external disturbances and faults. Finally, a numerical example is given to illustrate the efficacy of the main results of the paper.
TL;DR: In this article, a fixed-time tracking control algorithm via the backstepping method in conjunction with a novel second-order sliding mode super-twisting-like structure adaptive fixed time disturbance observer is proposed.
Abstract: In this study, the state feedback fixed-time control problem is addressed for a class of high-order strict-feedback non-linear systems (SFNSs) with mismatching system uncertainties. Based on the fixed-time control technique and uniform exact difference method, a new fixed-time tracking control algorithm via the backstepping method in conjunction with a novel second-order sliding mode super-twisting-like structure adaptive fixed-time disturbance observer is proposed. Compared with many existing finite-time control results in which the convergence time grows unboundedly when the system initial conditions grow, the fixed-time control provides faster convergence rate by using high-degree terms and the settling time function derived only with controller parameters, is allowed to be adjusted independently of the system initial conditions. Moreover, to avoid the computation explosion, the singularity problem and to relax the assumption on the mismatching system uncertainties, the dynamics surface control method is employed based on a novel non-linear non-smooth first-order filter. Then the semi-globally uniformly fixed-time convergent performance of the high-order SFNSs is achieved. Finally, theoretical results are supported by simulation and experimental results.
TL;DR: This study addresses the problem of quasi-synchronisation of fractional-order memristor-based neural networks (FMNNs) with time delay in the presence of parameter mismatches with linear state feedback combined with delayed state feedback control law, which guarantees that for a predetermined synchronisation error bound, quasi- synchronisation of two FMNNs with mismatched parameters will be achieved.
Abstract: This study addresses the problem of quasi-synchronisation of fractional-order memristor-based neural networks (FMNNs) with time delay in the presence of parameter mismatches Under the framework of fractional-order differential inclusions and set-valued maps, quasi-synchronisation of delayed FMNNs is discussed and quasi-synchronisation criteria are established by means of constructing suitable Lyapunov function, together with introducing some fractional-order differential inequalities A new lemma on the estimate of Mittag–Leffler function is derived first, which extends the application of Mittag–Leffler function and plays a key role in the estimate of synchronisation error bound Then, linear state feedback combined with delayed state feedback control law is designed, which guarantees that for a predetermined synchronisation error bound, quasi-synchronisation of two FMNNs with mismatched parameters will be achieved provided that the feedback gains satisfy the newly-proposed criteria The obtained results extend and improve some previous published works on synchronisation of FMNNs Finally, two numerical examples are given to demonstrate the effectiveness of the obtained results
TL;DR: Three equivalent sufficient conditions are proposed to ensure the asymptotic stability of switched systems and one condition has a promising feature of affineness in system matrices, and it is extended to robust sampling case and ℒ 2 -gain analysis.
Abstract: The event-triggered control problem for switched linear system is addressed in this study. The periodical sampling scheme and event-triggering condition are incorporated in the closed-loop. The feedback control updates its value only at sampling instants as long as event-triggering condition is satisfied as well. In addition, the switchings are only allowed to occur at sampling instants and meanwhile the switching condition is satisfied. Three equivalent sufficient conditions are proposed to ensure the asymptotic stability of switched systems. In particular, one condition has a promising feature of affineness in system matrices, and as a consequence, it is extended to robust sampling case and ℒ
2
-gain analysis. Several examples are provided to illustrate the authors' results.
TL;DR: A non-singular fixed-time distributed control protocol for second-order multi-agent systems is designed, which only requires one-hop information of the neighbours without the global topology information and has the advantage of fast convergence performance both in the reaching phase and sliding phase.
Abstract: This paper is devoted to the fixed-time consensus tracking control for second-order multi-agent systems with bounded input uncertainties under a weighted directed topology. Firstly, a novel non-singular fixed-time fast terminal sliding mode (NFFTSM) surface with bounded convergence time in regardless of the initial states is designed, and the explicit expression of the settling time is provided. Fair and unprejudiced comparisons show that the proposed NFFTSM has faster convergence performance than most typical terminal sliding modes in the existed results. Subsequently, by employing the proposed NFFTSM, a non-singular fixed-time distributed control protocol for second-order multi-agent systems is designed, which only requires one-hop information of the neighbours without the global topology information and has the advantage of fast convergence performance both in the reaching phase and sliding phase. Rigorous proofs show that the fixed-time consensus tracking control for second-order multi-agent systems can be guaranteed by the proposed distributed control protocol. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed control scheme.
TL;DR: In this paper, the authors considered the problem of adaptive sliding mode control for discrete-time Takagi-Sugeno (T-S) fuzzy systems with actuator faults and external disturbances via the delta operator method.
Abstract: This study considers the problem of adaptive sliding mode control for discrete-time Takagi-Sugeno (T-S) fuzzy systems with actuator faults and external disturbances via the delta operator method. The delta operator approach is used to represent the discrete-time non-linear systems described by T-S fuzzy models. The actuator fault considered in this study is unknown and its fault-deviation is also unknown. A reduced-order system is utilised to design the sliding mode surface subject to linear matrix inequality constraint. By constructing the sliding mode surface, a novel adaptive sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Finally, two practical examples are presented to show the effectiveness and applicability of the developed fault-tolerant control scheme.
TL;DR: In this paper, the authors presented an inequality which can be used to analyse the stability of fractional order systems by constructing Lyapunov functions and showed that the fractional-order system is Mittag-Leffler stable if there is a convex and positive definite function such that its fractionalorder derivative is negative definite.
Abstract: This study presents an inequality which can be used to analyse the stability of fractional order systems by constructing Lyapunov functions. By using the presented inequality, it is shown that the fractional order system is Mittag-Leffler stable if there is a convex
and positive definite function such that its fractional order derivative is negative definite. This result generalises the existing works and gives a useful method to construct the Lyapunov function for the stability analysis of the fractional order systems. Finally, the authors illustrate the advantages of the proposed method by two examples and their numerical simulation.
TL;DR: In this article, a sliding mode control (SMC) was proposed for non-linear time-delay semi-Markovian jump systems with time-varying and dependent on the sojourn-time h. The authors derived sufficient conditions for the resulting sliding mode dynamics in the form of linear matrix inequalities.
Abstract: This study deals with the problem of robust stabilisation for non-linear time-delay semi-Markovian jump systems via sliding mode control (SMC). Such a switching is governed by a semi-Markovian process which is time-varying and dependent on the sojourn-time h. The time delay is considered as time-varying and meets the requirements of the upper and lower bounds. By introducing free-connection weighting matrix method and Lyapunov functional, sufficient conditions for the resulting sliding mode dynamics in the form of linear matrix inequalities are derived to guarantee the closed-loop system robustly stochastically stable for all admissible uncertainties and non-linear perturbations. Then, an SMC law is synthesised to drive the system trajectories onto the predefined switching surface in a finite time. Finally, an example illustrates the validity of the obtained results.
TL;DR: In this article, the stability analysis of non-linear time-varying systems by using Lyapunov function-based approach is concerned with stability analysis with the help of scalar stable functions.
Abstract: This study is concerned with stability analysis of non-linear time-varying systems by using Lyapunov function-based approach. The classical Lyapunov stability theorems are generalised in the sense that the time-derivative of the Lyapunov functions are allowed to be indefinite. The stability analysis is accomplished with the help of the scalar stable functions introduced in the author's previous study. Both asymptotic stability and input-to-state stability are considered. Particularly, for asymptotic stability, several concepts such as uniform and non-uniform asymptotic stability, and uniform and non-uniform exponential stability are studied. The effectiveness of the proposed theorems is illustrated by several numerical examples.
TL;DR: The parameter estimation problem for multi-input multi-output Hammerstein systems is considered and the modified Kalman filter algorithm is derived to estimate the unknown intermediate variables in the system and the MKF-based recursive least squares algorithm is presented to estimate all the unknown parameters.
Abstract: The parameter estimation problem for multi-input multi-output Hammerstein systems is considered. For the Hammerstein model to be identified, its dynamic time-invariant subsystem is described by a controlled autoregressive model with a communication delay. The modified Kalman filter (MKF) algorithm is derived to estimate the unknown intermediate variables in the system and the MKF-based recursive least squares (LS) algorithm is presented to estimate all the unknown parameters. Furthermore, the hierarchical identification is adopted to decompose the system into two fictitious subsystems: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear subsystem. Then an MKF-based hierarchical LS algorithm is derived. The convergence analysis shows the performance of the presented algorithms. The numerical simulation results indicate that the proposed algorithms are effective.
TL;DR: In this paper, a distributed extended Kalman filter (EKF) is developed for each node to guarantee an optimised upper bound on the state estimation error covariance despite consensus terms and linearisation errors.
Abstract: This study is concerned with the distributed state estimation problem for non-linear systems over sensor networks. By using the strategy of consensus on prior estimates, a distributed extended Kalman filter (EKF) is developed for each node to guarantee an optimised upper bound on the state estimation error covariance despite consensus terms and linearisation errors. The Kalman gain matrix is derived for each node by solving two Riccati-like difference equations. It is shown that the estimation error is bounded in mean square under certain conditions. The effectiveness of the proposed filter is evaluated on an indoor localisation of a mobile robot with visual tracking systems.
TL;DR: A new set of sufficient conditions in terms of linear matrix inequalities (LMIs) is established to not only guarantee the passivity and asymptotically stability of the resulting closed-loop system in the designed sliding surface, but also cover the issues of actuator saturation and performance constraints.
Abstract: This study examines the problem of fault-tolerant sliding mode control (SMC) design subject to actuator saturation for a class of Takagi-Sugeno fuzzy systems with time-varying delay and external disturbances. Our main attention is to propose the fault-tolerant SMC such that for given any initial condition, the system trajectories are forced to reach the sliding surface within a finite time. On the basis of the SM surface and Lyapunov stability theorem, a new set of sufficient conditions in terms of linear matrix inequalities (LMIs) is established to not only guarantee the passivity and asymptotically stability of the resulting closed-loop system in the designed sliding surface, but also cover the issues of actuator saturation and performance constraints. Then, the desired gain matrix of the fault-tolerant SMC is obtained in respect of the previously established LMIs such that the reachability of the predefined sliding surface is ensured. It is worth pointing out that the obtained sufficient conditions can preserve the trade-off between the maximisation of admissible upper bound of time-varying delay and enlarging the estimation about the domain of attraction for the closed-loop system. Eventually, the effectiveness and robustness of the proposed control approach are demonstrated via simulation results.
TL;DR: A singularity-free terminal sliding mode (TSM) control scheme with fast and fixed-time convergence for a class of second-order non-linear systems with matched uncertainties and external disturbances is proposed.
Abstract: A singularity-free terminal sliding mode (TSM) control scheme with fast and fixed-time convergence for a class of second-order non-linear systems with matched uncertainties and external disturbances is proposed. A novel singularity-free fast TSM (SFTSM) structure is constructed and the upper-bound of convergence time is independent of initial states and can be set arbitrarily in advance. Then, the proposed SFTSM controller is designed by combining the SFTSM structure and the composite fast reaching law and the globally fixed-time stability is guaranteed and derived with the phase plane analysis and Lyapunov stability theory. Finally, the simulation results for a single inverted pendulum tracking system are included to verify the effectiveness of the proposed control method.
TL;DR: In this article, a hierarchical extended stochastic gradient algorithm is presented to estimate the parameters of the nonlinear part and the linear part of a MIMO Hammerstein system.
Abstract: For the identification of a class of nonlinear multi-input multi-output (MIMO) Hammerstein systems with different types of coefficients: a matrix coefficient and scalar coefficients, it is difficult to parameterise such Hammerstein systems into an identification model to which the standard identification method can be easily applied to implement parameter estimation. By the matrix transformation and the over-parametrisation idea, this study transforms an MIMO Hammerstein system with different types of coefficients into an over-parametrisation regression identification model, and points out the aroused large computation problem. To overcome the large computational load of the over-parametrisation method, by the matrix transformation and the hierarchical identification principle, this study recasts the MIMO Hammerstein system into two models, each of which is expressed as a regression form in the parameters of the nonlinear part or in the parameters of the linear part. Then a hierarchical extended stochastic gradient algorithm is presented to alternatively estimate the parameters of the nonlinear part and the parameters of the linear part. The simulation results indicate that the proposed algorithm can effectively identify the nonlinear MIMO Hammerstein system.
TL;DR: A systematic control design approach to consider jointly the event-triggered communication mechanism and state-feedback control for switched linear systems and an estimate of the positive lower bound on the inter-execution times is derived to avoid Zeno behaviour.
Abstract: This study proposes a systematic control design approach to consider jointly the event-triggered communication mechanism and state-feedback control for switched linear systems. The systems determine the necessary samplings of the feedback signal by constructing predefined events that can reduce redundant signal transmission and updates. Specifically, the first main step in the design is to construct sufficient conditions for stability analysis in the form of linear matrix inequalities to utilise fully the idea of average dwell time. With the proposed event-triggering mechanism, the design renders the resulting switched closed-loop system finite-time bounded. Subsequently, the authors present the conditions for finding the parameter of the event-triggered sampling mechanism and the state-feedback sub-controller gains. Then, the results for the full state feedback control case are further extended to systems incorporating observer-based state-feedback control motivated by practical applications. For each case, an estimate of the positive lower bound on the inter-execution times is further derived to avoid Zeno behaviour. A numerical example is presented to illustrate the effectiveness of the proposed methods.
TL;DR: This study deals with the problem of asymptotic tracking and dynamic regulation of single-input single output (SISO) non-linear systems via output feedbacks by the discrete multi-dimensional Taylor network (MTN) controller, a novel controller with fixed structure and sampled-data control mechanism.
Abstract: For non-linear control, it is important to secure a generally structured controller that promises wide application and desirable performance. This study deals with the problem of asymptotic tracking and dynamic regulation of single-input single output (SISO) non-linear systems via output feedbacks by the discrete multi-dimensional Taylor network (MTN) controller, a novel controller with fixed structure and sampled-data control mechanism. For verification of its validity, differential geometry and polynomial approximation are adopted. Using the emulation technique and regional pole assignment, the asymptotic tracking and dynamic regulation without online optimisation of the system by discrete MTN controller is tested. With the dynamic change of error signals, the dynamic regulation by given index is realised. As a convex optimisation problem, the controller parameters can be acquired by parametric learning. Based on the delta operator model, the procedure of the controller design is given in detail. Simulation results confirm the feasibility and effectiveness of the proposed approach.
TL;DR: In this article, an adaptive neural network controller for a 3DOF robotic manipulator that is subject to backlash-like hysteresis and friction is proposed, which employs a hyperbolic tangent activation function.
Abstract: This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlash-like hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non-linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers.
TL;DR: Simulation results show the superiority of the proposed I-PD controller over conventional P/PI/PID ones for integrating processes and is validated through experiments on a temperature control process.
Abstract: An integral-proportional derivative (I-PD) control strategy for integrating plus time-delay processes is proposed. The proposed scheme consists of an inner PD loop and an outer I-loop for taking care of the servo as well as the regulatory action. Explicit formulas for tuning of the proposed controller are derived in terms of gain margin, phase margin and critical gain. Moreover, pole-placement and frequency loop-shaping design methods are also explored for designing the I-PD controller. Simulation results show the superiority of the proposed I-PD controller over conventional P/PI/PID ones for integrating processes. The design is also validated through experiments on a temperature control process.
TL;DR: In this article, the authors studied a class of event-triggered control for finite evolutionary networked games, where the control only works at some certain individual states and the game dynamics is converted into an algebraic form.
Abstract: Using the approach of semi-tensor product of matrices, this study studies a class of event-triggered control for finite evolutionary networked games, where the control only works at some certain individual states First, by identifying `control does not work' as a new specific control strategy, the controlled game dynamics is converted into an algebraic form Second, to make the game converge globally, two necessary and sufficient conditions for the existence of event-triggered control are obtained Meanwhile, a constructive procedure is proposed to design state feedback control strategy and an adjustment method is presented to minimise the control times Finally, the developed theory results are illustrated by a numerical method
TL;DR: This study investigates the cooperative containment control problem for heterogeneous discrete-time linear multi-agent systems with external disturbance under the directed communication topology with a novel distributed internal model compensator to tackle the uncertain parts of the dynamics.
Abstract: This study investigates the cooperative containment control problem for heterogeneous discrete-time linear multi-agent systems. The structures of the systems are uncertain, and there exists more than one leader in the authors' systems. For these leaders, it is unnecessary to establish links exchanging information between them. Instead, the authors assume that at least one leader has directed paths to all the follower nodes. A distributed discrete-time compensator is presented to estimate the convex hull information of the leaders. Then, based on the estimation of the convex hull information of the reference outputs, they design a novel distributed internal model compensator to tackle the uncertain parts of the dynamics. Finally, a distributed dynamic output feedback approach is utilised to study the distributed systems with external disturbance under the directed communication topology. A numerical example is provided to illustrate the validity of the theoretical results.