Search

This paper focuses on the leader-following consensus control problem of stochastic multi-agent systems with hysteresis inputs and nonlinear dynamics. A leader-following consensus scheme is presented for stochastic multi-agent systems directions under directed graphs, which can achieve predefined synchronisation error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded tracking stability of multi-agent systems recently. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronise with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and all the follower agents can keep consensus with the leader. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper addresses an observer-based consensus problem for leader-following control of a class of linear multi-agent systems (MASs) under a directed communication topology via event-triggered approaches. State observers are employed to tackle the scenario wherein inner information of the follower agents are not available for measurement. And then, an observer-based distributed leader-following control scheme is developed on the basis of event-triggered mechanisms. Further, to avoid continuous measurement information monitor, we present a technical approach for generation of the combinational information from their own neighbouring agents only at triggered instants. In theory, the stability of the resulting closed-loop system is rigorously investigated, and it is proven that all signals in the closed-loop system are bounded and Zeno behaviour is also excluded. Finally, simulation examples are presented for illustration of the theoretical claims. [ABSTRACT FROM AUTHOR]

Considering interconnections among subsystems, we propose an adaptive neural tracking control scheme for a class of multiple-input-multiple-output (MIMO) non-affine pure-feedback time-delay nonlinear systems with input saturation. Neural networks (NNs) are employed to approximate unknown functions in the design procedure, and the separation technology is introduced here to tackle the problem induced from unknown time-delay items. The adaptive neural tracking control scheme is constructed by combining Lyapunov–Krasovskii functionals, NNs, the auxiliary system, the implicit function theory and the mean value theorem along with the dynamic surface control technique. Also, it is proven that the strategy guarantees tracking errors converge to a small neighbourhood around the origin by appropriate choice of design parameters and all signals in the closed-loop system uniformly ultimately bounded. Numerical simulation results are presented to demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this note, the distributed event-triggered cooperative attitude control of multiple rigid bodies with leader–follower architecture is investigated, where both the cases of static and dynamic leaders are all considered. Two distributed triggering procedures are first introduced for the followers and leaders, and then the distributed cooperative controllers are designed under the proposed triggering schemes. Under the designed controllers with the event-triggered strategies, it is shown that the orientations of followers converge to the convex hull formed by the desired leaders’ orientations with zero angular velocities. Moreover, the communication pressure in network is reduced and the energy of each agent is saved. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

A new delay fraction technique is proposed to investigate the H ∞ control of uncertain systems with time delay in state or input. First, the variation interval of the time-varying delay is divided into several subintervals with equal length. By checking the variation of derivative of the Lyapunov functional in every subinterval, some new criteria on H ∞ performance analysis of the systems are derived in terms of the convexity properties of a matrix inequality and some other new analysis techniques. Then, criteria for the H ∞ control design are presented, which are concluded from the H ∞ performance analysis results. As applications of the derived results, H ∞ performance analysis and H ∞ control design are carried out for some systems, including two numerical systems, linearised truck trailer system and traffic network system. Discussions show that, the proposed method is effective for these systems and much less conservativeness appears than those given in the existing references. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem ofH∞filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of anH∞performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive approximately optimal tracking control approach is proposed for a class of non-affine nonlinear single-input single-output (SISO) systems via output feedback. With the help of fuzzy logic systems (FLSs), a fuzzy state observer is designed for the estimation of internal states by approximating unknown nonlinear functions, where a low-pass filter (LPF) is added for the algebraic loop problem. Then, the output feedback control approach, in the backstepping framework and the event-triggered mechanism, is presented. It contains the adaptive backstepping control and the approximately optimal control via adaptive dynamic programming (ADP) technology, and the computation cost and transmission load are reduced while guaranteeing that the performance index of the system is approximately minimised. Finally, two simulation examples are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]