Distributed adaptive output feedback containment control for time-delay nonlinear multiagent systems

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

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 considers the consensus problem of two class of multi-agent systems with communication disturbance by using iterative learning control(ILC) method and both of them has a fixed information topology with a spanning tree. Firstly, a distributed iterative learning-based control protocol is designed for a time-varying linear multi-agent system, a sufficient condition is obtained such that the consensus problem is solved. By the same way, a sufficient condition is also given for a class of nonlinear time-varying multi-agent systems to solve consensus problem. The main contribution of the proposed results is that they can restrain any type of repetitive disturbance. The validity of them is verified by the simulation results.

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.

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.

Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies

SummaryIn this article, an adaptive containment control scheme is put forward for uncertain nonstrict‐feedback multi‐agent systems (MASs) under directed graphs. The dynamic surface control (DSC) approach is extended to the containment control of a class of nonstrict‐feedback MASs, and time‐varying gains are used to replace constant gains in the design process. Using the property of Gaussian function and Young's inequality, and introducing an auxiliary dynamic signal, nonstrict‐feedback structure problem and unmodeled dynamics are effectively resolved. The time‐varying gain controller can make all followers converge to the convex hull spanned by multiple leaders with smaller containment error, and all the signals are bounded. Finally, compared with the constant gain control method, the validity of the time‐varying gain control scheme is illustrated by numerical and actual simulation results.

This paper addresses the critical problem of secure containment control for fractional-order heterogeneous and nonlinear multi-agent systems (MASs) subjected to both mixed time-varying delays and malicious false data injection. Such issues are highly relevant in modern networked control systems where data integrity and robustness are increasingly threatened. Within a leader–follower framework, the attacker injects unknown, time-varying, and deceptive signals that can compromise consensus and system stability. To counteract this, we propose a novel distributed observer and adaptive fuzzy-based feedback controller design for each agent. The observer accurately estimates the agents’ states despite false data, while the controller ensures convergence and robustness under heterogeneous dynamics and delays. Stability of the closed-loop system is rigorously analyzed using a Lyapunov–Krasovskii functional tailored to fractional-order systems. Finally, simulation results demonstrate the effectiveness and resilience of the proposed control strategy against adversarial attacks.

This paper investigates the event-based prescribed performance fuzzy adaptive containment control problem for multi-agent systems with unknown control direction and sensor faults. In the proposed control strategy, the tracking error can converge to the prescribed boundary by designing a performance function. An adaptive faults compensation scheme is introduced to handle sensor faults. In addition, the technology of Nussbaum-type function is employed to cope with the difficulty of unknown control gains. Moreover, by utilizing the Lyapunov stability theory, it is proven that the developed control method can ensure all the signals of closed-loop system are semi-globally uniformly ultimately bounded. Finally, the effectiveness of the developed strategy is verified by some simulation results.KeywordsMulti-agent systemsEvent-triggered containment controlPrescribed performance controlUnknown control directionSensor faults

Within the multiagent systems framework, the relative states between neighbors can be acquired by some on-board sensors, and then the relative state saturations inevitably occur due to the limited sensing capabilities. This paper investigates the consensus problem of nonlinear multiagent systems subject to the relative state saturations. Utilizing the incidence matrix and the edge Laplacian, the consensus problem of nonlinear multiagent systems with the relative state saturations can be cast into the stabilization problem of edge dynamics operating on the constrained set. Three types of protocols, namely continuous, intermittent, and adaptive state feedback protocols are, respectively, proposed for achieving the constrained consensus, and meanwhile yielding consensus values. A consensus analysis is provided by virtue of state saturation theory, switched system theory, adaptive theory, and Lyapunov stability theory. Output feedback protocol is also designed. Finally, the obtained results are applied to connectivity preservation for first-order nonlinear multiagent systems, despite the presence of limited communication range and input constraints. The theoretical results are validated by two simulation examples.

This paper is devoted to a containment control problem with time-delay for second-order nonlinear multi-agent systems subject to external disturbances under a directed communication topology. First of all, a distributed delay-coupled protocol is proposed based on relative states among the neighboring agents. Then, sufficient conditions in terms of linear matrix inequality(LMI) are derived to make the followers asymptotically converge to the convex hull spanned by the leaders while satisfying desired H ∞ performance. It is proved that the time-delay protocol is sensitive to time delays. Finally, numerical simulations are provided to show the effectiveness of our theoretical results.

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

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.

Formation control for nonlinear multi-agent systems by robust output regulation

SummaryThis paper focuses on the problem of adaptive containment control about privacy preservation in nonlinear multi‐agent systems, where followers suffer from multiple cyber‐attacks and leaders have non‐zero control inputs. A multiple attacks mold consisting of denial‐of‐service (DoS) attacks and false data‐injection attacks is constructed for nonlinear systems. The containment control protocol is updated by applying an event‐triggered scheme. This scheme is proposed to dispatch the updating information in the presence of multiple attacks. Meanwhile, to deal with the information insecurity that may exist in the control process, the protocol for privacy preservation about initial information is developed by introducing time‐varying functions as masks. Tracking errors of agents are described by taking the inputs of leaders as the external disturbances. This method does not depend on the widths of boundary layers and reduce the computational complexity. The stability of multi‐agent systems is analyzed by applying the Cauchy inequality, the Young's inequality and the Lyapunov function theory. Finally, two simulation examples are shown to illustrate the effectiveness of the proposed control strategy.