Adaptive Neural Network Event-Triggered Output-Feedback Containment Control for Nonlinear MASs With Input Quantization.

This article investigates the event-triggered containment control problem for stochastic nonlinear multiagent systems with unknown Bouc–Wen hysteresis input. Based on the backstepping technique, an adaptive fuzzy event-triggered containment control scheme is proposed, which is conditionally updated only at the sampled instants. Fuzzy logic systems are used to approximate the unknown nonlinear functions. In addition, a Nussbaum function is utilized to eliminate the effect of unknown hysteresis. Moreover, an event-triggered mechanism is introduced to reduce the communication burden. By using stochastic Lyapunov stability theory and graph theory, it is proved that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Meanwhile, all the followers’ outputs converge to the dynamic convex hull spanned by the dynamic leaders. Finally, the effectiveness of the proposed control scheme is illustrated by some simulation results.

In this article, we investigate the event-triggered containment control problem for a class of multiagent systems, where agents are described by higher-order linear dynamics subject to stochastic disturbances. In event-triggered control scenarios, the control action is updated when a specified error reaches a given threshold. Due to the existence of stochastic factors, the given threshold may be reached in an any short time interval, which makes it hard to avoid Zeno behavior. In view of this, we propose two novel event-triggered containment control schemes for the underlying multiagent systems with stochastic disturbances. Using the stochastic control theory, graph theory, and Lyapunov functional method, it is proven that the followers' states can almost surely converge to the convex hull spanned by the leaders’ states. It is noted that the continuous communications among agents are not required and each agent's inter-event intervals are lower-bounded by a positive constant. As such, the proposed control schemes are both practical and implementable. Finally, a numerical example is presented to illustrate the developed schemes’ effectiveness.

This paper focuses on the problem of the event-triggered adaptive containment control for a class of nonlinear multiagent systems (MASs) with prescribed performance and immeasurable states. First, the radial basis function neural networks (RBFNNs) are adopted to approximate the uncertain smooth nonlinear function, and the neural network-based state observer is designed to estimate the unmeasurable state. Besides, to reduce the control resource consumption and get a better balance between the system performance and network constraints, the switching threshold-based event-triggered control strategy is introduced. Based on this, the novel distributed containment controller is designed by utilizing the adaptive backstepping technique and the dynamic surface control (DSC) technique to guarantee that the output of each follower converges to the convex hull formed by multileader. Moreover, the containment errors can be converged to the prescribed boundary and all signals in closed-loop system are semi-global uniformly ultimately bounded (SGUUB) as well. Finally, the simulation examples are carried out to illustrate the efficiency of the proposed controller.

This paper investigates containment control problem for a class of nonlinear multi-agent systems with multiple dynamic leaders, where the condition of zero control input for leader is not assumed. The dynamics of the each subsystem are assumed to be unknown and are estimated using neural networks. A local observer is used to estimate the states and an observer-based neural network controller is designed to guarantee that followers converge to the convex hull spanned by the leaders. Numerical simulations are finally given to show the effectiveness of the obtained results.

In this article, the containment control problem of multiagent systems (MASs) with active leaders under a directed communication graph is addressed. A novel pull-based event-triggered protocol combined with aperiodic sampled-data mechanism is first presented, where each agent merely updates controller at its own triggering instants. Under the proposed control protocols, the information is only exchanged and calculated at the aperiodic sampling instants, therefore, it can reduce communication congestion and save computing resources. Furthermore, the Zeno behavior is naturally excluded because the trigger instants are in the set of aperiodic sampled instants. By virtue of the algebraic graph theory and Lyapunov stability analysis, it is shown that the active leaders can reach their desired states predefined arbitrarily and all followers are driven to the convex hull formed by the leaders. Finally, an illustrative example is given to validate the theoretical results and emphasize the advantages of the proposed protocols.

This article studies the bipartite containment control problem for nonlinear multiagent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol such that the followers converge to a convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition approach of input quantization, an event-triggered control scheme is developed via backstepping technique. A fuzzy observer is constructed to estimate unmeasurable states. Moreover, the bipartite containment control scheme for nonlinear MASs is designed. It is demonstrated that all signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior is excluded. Finally, a simulation example is given to verify the validity of the designed method.

This paper considers the event‐triggered adaptive fuzzy bipartite containment control problem for switched nonlinear multi‐agent systems (MASs) with actuator attacks. To handle the completely unknown nonlinear functions in the considered system, fuzzy logic systems (FLSs) are applied to approximate the unknown nonlinear functions. In addition, an adaptive bipartite containment control approach is designed to deal with the actuator attacks, which can reduce the burden of communication. According to the designed adaptive bipartite containment control scheme, it is shown that all the signals are semi‐globally uniformly ultimately bounded (SGUUB), and all the outputs of the followers achieve the bipartite consensus and finally enter the convex set spanned by leaders' in the presence of actuator attacks. Eventually, a simulation example is presented to illustrate the availability of the developed strategy.

This paper studies the robust containment control problem for a class of nonlinear multi-agent systems in lower triangular form with parameter uncertainty. The agents are heterogenous with the particularity that the dynamics of the leaders are totally different from those of the followers. The interconnection topology is described by a general directed graph. Based on the distributed internal model, the robust containment control problem is first converted into the stabilisation problem for an augmented network. A distributed state feedback control is then proposed to solve the stabilisation problem of the subsequent augmented network by the block backstepping technique. Under the proposed control, all the followers' outputs will converge to the convex hull spanned by the leaders.

Event-triggered adaptive neural inverse optimal output-feedback control of steer-by-wire vehicle systems with prescribed performance

This paper investigates the consensus tracking problem with predefined performances requirements for a class of unknown nonlinear multi-agent systems with hysteresis quantizer and external disturbances under a directed graph topology. Neural network observers are designed to estimate unmeasurable states and the the consensus tracking problem with performance requirements is transformed to a stabilization problem by prescribed performance error transformation schemes. The novel consensus protocol can be applied to a more general class of nonlinear multi-agent systems since the Lipschitz condition is avoided and state information is not required. It is strictly proved that all signals in the closed-loop systems are cooperatively uniformly ultimately bounded and both the transient and steady performances of the consensus tracking satisfy prescribed performance requirements. Finally, two numerical examples are presented to validate the effectiveness of the proposed strategy.

This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.

Fixed-time event-triggered and periodic event-triggered containment control for heterogeneous multi-agent systems under DoS attacks

Improved sampled-data-based adaptive dynamic event-triggered containment control of multi-agent systems

Event-triggered containment control for multi-agent systems with switching topology in finite-time

In this paper, we are concerned with the problem of containment control for a class of nonlinear multi-agent system. The local dynamics under consideration is governed by a nonlinear system with time delays and white Gaussian noise. The multi-agent system consists of both leaders and followers, and the containment control protocol proposed is an interaction between the leaders and followers characterised by time-varying delays and switching topology. Under a certain assumption on the connectivity of the whole network, by constructing a Lyapunov–Krasovskii functional, and using the average dwell time approach and some new analytical techniques, the sufficient conditions are established to ensure that each follower converges in mean square to the convex hull spanned by the leaders. A numerical example is presented to illustrate the effectiveness of the theoretical results.