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This article studies the containment control problem of nonlinear multiagent systems (MASs) subjected to communication link faults and dead-zone inputs. In case of an unknown fault in the communication link, there is no constant Laplacian matrix anymore and each follower agent cannot be informed of the global information simultaneously. To deal with this problem, an adaptive compensating estimator is constructed to estimate the signal spanned by the leaders. Instead of using the linear filter, a nonlinear filter is employed, which both solves the classical complexity explosion in the traditional backstepping method and flushes out the usefulness of the boundary layer error. Considering the dead zone input, we propose two event-triggered schemes, that is, the update-triggered scheme and the transmit-triggered scheme. In the former, the threshold function involves the tracking errors and additional dynamic variable, which can provide the desirable tradeoff between the containment control performance of the considered MASs and saving communication resources. In the latter, the triggered condition is designed according to the characteristic of dead zone, which makes the communication burden be reduced further. Following the backstepping design framework, an adaptive containment control is constructed, it is shown that the containment error can converge to an adjustable residual set even if MASs are subjected to the unknown and bounded communication link faults and dead-zone inputs. Finally, an example is given to show the effectiveness of the proposed results.

This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers’ states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.

This article investigates an optimized containment control problem for multiagent systems (MASs), where all followers are subject to deferred full-state constraints. A universal nonlinear transformation is proposed for simultaneously handling the cases with and without constraints. Particularly, for the constrained case, initial values of states are flexibly managed to the midpoint between upper and lower boundaries by utilizing a state-shifting function, thus eliminating the initial restriction conditions. By deferred constraints, the state is forced to fall back into the restrictive boundaries within a preassigned time. A neural network (NN)-based reinforcement learning (RL) algorithm is executed under the identifier-critic–actor architecture, where the Hamilton-Jacobi–Bellman (HJB) equation is built in every subsystem to optimize control performance. For actor and critic NNs, updating laws are simplified, since the gradient descent method is performed based on a simple positive function rather than square of Bellman residual error. In view of the Lyapunov stability theorem and graph theory, it is proved that all signals are bounded and the outputs of followers can eventually enter into the convex hull constituted by leaders. Finally, simulations confirm the validity of the proposed approach.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

This article investigates the optimal control problem for the discrete-time (DT) nonlinear semi-Markovian jump systems (s-MJSs) that possess unknown dynamics. The study uses the semi-Markovian kernel approach to address the problem of mode-switching in these systems. This approach employs the transition probability and the sojourn-time distribution function to jointly determine the transitions between different modes. Then, with a neural network (NN) identifier, the demand for accurate information on the system dynamics is eliminated, and an optimal control method for the nonlinear s-MJSs is utilized to solve the Hamilton-Jacobi–Bellman equation (HJBE) built upon adaptive dynamic programming methodology. Additionally, a detailed analysis of the convergence of a value iteration-based algorithm, which solves the optimal control issue for the DT s-MJSs, is thoroughly discussed. Furthermore, an actor-critic NN is trained to attain an estimated solution to the relevant HJBE. Finally, to validate the designed approach, two simulations are performed to prove its effectiveness.

This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

This article researches the sliding mode control (SMC) for fuzzy fractional-order multiagent system (FOMAS) subject to time-varying delays over directed networks based on reinforcement learning (RL), $\alpha \in (0,1)$ . First, since there is information communication between an agent and another agent, a new distributed control policy $\xi _{i}(t)$ is introduced so that the sharing of signals is implemented through RL, whose propose is to minimize the error variables with learning. Then, different from the existed papers studying normal fuzzy MASs, a new stability basis of fuzzy FOMASs with time-varying delay terms is presented to guarantee that the states of each agent eventually converge to the smallest possible domain of 0 using Lyapunov–Krasovskii functionals, free weight matrix, and linear matrix inequality (LMI). Furthermore, in order to provide appropriate parameters for SMC, the RL algorithm is combined with SMC strategy, and the constraints on the initial conditions of the control input $u_{i}(t)$ are eliminated, so that the sliding motion satisfy the reachable condition within a finite time. Finally, to illustrate that the proposed protocol is valid, the results of the simulation and numerical examples are presented.

In this brief, for time-varying nonlinear systems, a novel approximate dynamic programming structure is first proposed to obtain the approximate optimal solution of the control system. In addition, considering that the system is time-varying and it is difficult to obtain the system dynamics, we propose the idea of time normalization to identify the time-varying system dynamics through a model network. Based on the traditional heuristic dynamic programming structure, an action network is added to approximate the next time step action. With the increase of feedback channels, the convergence speed of the action network is accelerated. And we also prove that the weight errors of the three improved neural network structure are uniformly ultimately bounded. The effectiveness of our proposed new neural network structure is demonstrated by numerical simulation results.