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This paper deals with the issue of state estimator design for nonlinear switched systems. A multiplemode adaptive estimator is proposed under mode-dependent average dwell time (MDADT) switching, and the switching signal with MDADT constraint is also obtained to guarantee the exponential stability of estimation error dynamics, where the Lipschitz constant may be unknown since it is adaptively adjusted by designing an adaptation law. Based on both Lyapunov stable theory and the feasible solution of an optimization problem with linear matrix inequality constraint, the gain matrices and switching signals are provided, respectively. The sufficient conditions of the existence of multiple-mode adaptive switched estimator are also derived. Meanwhile, the above methods are also extended to the case of the average dwell time (ADT) switching, and an algorithm is given to summarize the implementation of the proposed estimators. Finally, the effectiveness of the designed methods is illustrated by simulation examples.

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.

In this paper, the issue of adaptive neural tracking control for uncertain switched multi-input multi-output (MIMO) nonstrict-feedback nonlinear systems with average dwell time is studied. The system under consideration includes unknown dead-zone inputs and output constraints. The uncertain nonlinear functions are identified via neural networks. Also, neural networks-based switched observer is constructed to approximate all unmeasurable states. By means of the information for dead-zone slopes and barrier Lyapunov function (BLF), the problems of dead-zone inputs and output constraints are tackled. Furthermore, dynamic surface control (DSC) scheme is employed to ensure that the computation burden is greatly reduced. Then, an observer-based adaptive neural control strategy is developed on the basis of backstepping technique and multiple Lyapunov functions approach. Under the designed controller, all the signals existing in switched closed-loop system are bounded, and system outputs can track the target trajectories within small bounded errors. Finally, the feasibility of the presented control algorithm is proved via simulation results.

This paper is devoted to weighted H ∞ consensus design for continuous-time/discrete-time stochastic multi-agent systems with average dwell time (ADT) switching topologies and external disturbances via output feedback. By introducing a linear transformation, the closed-loop systems are changed into reduced-order systems and, at the same time, the issue of weighted H ∞ consensus design is transformed into a weighted H ∞ control problem. Then, Lyapunov conditions are established for the mean-square asymptotic stability and weighted H ∞ disturbance attenuation of the reduced-order systems. Based on them, two sufficient conditions are derived for the existence of desired output-feedback control protocols through the feasible solution of a series of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results.

This paper studies the asynchronous bumpless transfer control problem for a class of switched impulsive positive system. The main novelty is to propose a new asynchronous bumpless transfer definition that suppresses the control signal only at impulsive and controller switching instants instead of the entire time interval. Using multiple linear copositive Lyapunov function approach, the controller and switching signal are co-designed to ensure positivity and stability while achieving bumpless transfer performance under asynchronous switching. Moreover, the switching signal allows both average dwell time and the running time ratio of matched period and mismatched period between the subsystem and controller to be mode-dependent, which increases design flexibility. Finally, the effectiveness of the designed approach is verified through simulation.

PurposeThis paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.Design/methodology/approachA control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.FindingsBased on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.Originality/valueThe “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

This paper studies the problem of adaptive fuzzy control for switched multi-input and multi-output (MIMO) nonlinear systems with full state constraints and unknown dead zones. First, fuzzy logic systems (FLSs) serve as approximate instruments of unknown nonlinear functions, which are used to tackle the issue of parameter uncertainties. The unmeasured states are estimated by designing a switched MIMO state observer. Then, an adaptive fuzzy control strategy based on the command filter technique and average dwell time (ADT) method is proposed, which tackles the issue of “explosion of complexity” in the conventional backstepping recursive procedure, and also overcomes the drawback of dynamic surface control by designing compensating signals. Furthermore, the dead zone nonlinearities are handled using the information for dead zone slopes. In particular, barrier Lyapunov functions are employed to testify the stability of the system under our proposed control signals while preventing the constraint violations. Finally, two simulation examples demonstrate the availability and effectiveness of the presented adaptive controller.

In this paper, we study the finite-time stability and applications of positive switched linear delayed systems under synchronous impulse control, which includes two types of random switching and average dwell time switching. By constructing a type of linear time-varying co-positive Lyapunov functional, we first propose several new finite-time stability criteria. It should be emphasized that the linear term coefficient of the linear vector of the Lyapunov functional is adjusted to the difference between the weighting vector and the given vector. Then, we apply the obtained stability criteria to the linear time-varying delayed systems with impulsive effects. At last, three examples are given to demonstrate the validity of the obtained results, which includes the specific linear programming algorithm process.

This paper is concerned with the exponential H∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli stochastic variable. Based on the derived H∞ performance analysis results, the H∞ filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.

Switched systems are an important class of hybrid systems. In recent years, such systems have drawn considerable attentions in control field. A switched fuzzy system is a switching system, for which all subsystems are fuzzy systems. This paper investigates the robust state estimation problem for a class of uncertain switched fuzzy systems with time-varying delays. By using appropriate switched Lyapunov functional approach, average dwell time scheme and filtering theory, delay dependent sufficient conditions for the solvability of this problem are stablished in terms of linear matrix inequalities (LMIs). An illustrative example is provided to show the effectiveness of the proposed theoretical results.