In this article, we investigate the problem of adaptive safe critic control design for stochastic multiagent systems (MASs) subject to asymmetric state and input constraints. To systematically address asymmetric state constraints, a unified transformation function (UTF) is proposed to convert the constrained consensus control problem into the stability analysis of an unconstrained error system. In addition, a nonquadratic cost function is incorporated to address input limitations effectively. Building upon these developments, a time-varying Hamilton-Jacobi-Bellman equation (HJBE) is formulated by integrating the Bellman optimality principle with Itô's lemma, thereby accommodating stochastic disturbances and enhancing controller robustness. To improve data utilization and eliminate reliance on explicit drift dynamics, an integral reinforcement learning (IRL) algorithm is developed within this framework. Furthermore, a time-varying single-critic network is designed to approximate the solution to the HJBE and generate optimal control policies, thereby considerably reducing computational complexity. To further enhance learning efficiency and relax the persistent excitation (PE) condition, the experience replay (ER) technique is incorporated into the update process of the critic weight. Finally, two simulation examples are provided to verify the feasibility and effectiveness of the proposed approach.

This study addresses the adaptive optimal consensus control problem for nonlinear multiagent systems (MASs). To enhance the convergence speed of the consensus error, a predefined time performance technique is integrated into the optimal control framework. Unlike the conventional Hamilton–Jacobi–Bellman equation (HJBE), a time-varying HJBE is formulated to solve the optimal control problem for MASs. To further improve efficiency, an integral reinforcement learning (IRL) algorithm is developed, which eliminates the need for precise system dynamics during controller design. In addition, a single critic network is employed to simultaneously evaluate system performance and execute control actions, effectively reducing computational complexity. The experience replay technique is incorporated into the update law for the critic network weights, thus alleviating the requirement for persistent excitation. Finally, a simulation example is presented to validate the feasibility and effectiveness of the proposed method.

In this paper, a reinforcement learning (RL) control method is developed for the formation control of multiple wheeled mobile robots (WMRs) with visual servoing. First, a multi-robot system model is constructed based on the kinematic models of mobile robots, the camera model, and the multiple-view geometry principles. The leader-follower structure is then applied to derive the distributed error system. Next, the error term is separated from the optimal performance index function, and the Bellman residual error is obtained based on the Hamilton-Jacobi-Bellman equation (HJBE). Subsequently, the gradient descent method is employed to design the weight update rate, which is implemented in an actor-critic neural network (NN) architecture. The proposed RL control method achieves formation tracking and performance optimization simultaneously, which previous approaches have not accomplished. Furthermore, under the Lyapunov stability theory, it is proven that the follower robots can track the leader in a predefined formation, and the tracking error converges ultimately. The simulation outcomes verify the effectiveness of the developed approach.

This paper investigates a neuro-adaptive fixed-time tracking control issue for switched nonlinear systems subject to asymmetric time-varying constraints and unknown control gains. Unlike the current study on constraint problems, the system’s initial condition is unavailable in this article, which causes specific difficulties in constructing the Barrier Lyapunov Function. A novel shifting function is presented to unify the initial values of all system states. In addition, the system convergence time becomes known and adjustable by utilizing the Nussbaum gain technique and fixed-time stability criterion. An adaptive neural tracking control scheme is proposed based on the learning ability of neural networks and fixed-time theory. To alleviate the computational burden, we present the single learning parameter method such that the number of adaptive laws is reduced significantly. Furthermore, a novel switching threshold mechanism that considers the system errors is developed to balance the communication burden and control performance. Finally, the simulation example illustrates the feasibility of the proposed control strategy.

This article investigates a leader-following bipartite consensus issue for uncertain nonlinear heterogeneous multiagent systems (MASs). Initially, within the framework of optimal control theory, we employ the reinforcement learning (RL) algorithm to derive an approximate solution to the Hamilton-Jacobi-Bellman equation (HJBE). Specifically, the neural networks (NNs) are utilized to construct the Actor-Critic structure with the aim of implementing control behavior and evaluating system performance, respectively. An additional network is employed to address nonlinear uncertainties existing in the system. Furthermore, we design a static threshold event-triggered mechanism (ETM) to achieve the event-triggered state feedback-based control strategy. By utilizing this event-triggered state information, we reconstruct the approximate optimal controller and update laws of neural network weights, effectively reducing the communication burden while ensuring that all signals of the MASs remain bounded. Finally, two simulation examples are carried out to demonstrate the feasibility of the proposed method.

In this article, an event-triggered adaptive control strategy is proposed to achieve the leaderless-following consensus for a class of cyber–physical systems under the direct topology. Because the deception attacks, state time delay, and unknown external disturbance appear simultaneously, the existing method is hard to apply directly. The difficulty of this issue lies in that the actual system states are unavailable and control efficiencies are unknown, which caused by the deception attack. So as to tackle these knotty problems, the Nussbaum gain functions are employed to replace the control gains, and the available compromised system variables are applied in the controllers. In addition, the proposed disturbance observer based on the compromised state further improves the robustness of the system. To eliminate the influence of state time delays, the appropriate Lyapunov–Krasovskii functionals are used in the backstepping design process. Moreover, the computation and communication burden is dramatically decreased than by adopting the event-triggered mechanism and less adaptive laws. The boundedness of all signals in the closed-loop system is guaranteed via the Lyapunov stability theorem. Finally, the simulation results are provided to demonstrate the availability of the proposed control strategy.

This paper investigates the leader-following consensus control problem for a class of nonlinear multiagent systems subject to sensor and actuator faults under a fixed directed graph. First, a fault compensation mechanism is proposed because of multiple faults wherein the adaptive parameters substitute the fault coefficients. Then, the command filtering method is employed to avoid the burst of complexity rendered by the duplicative differentiation of the virtual control signal. Furthermore, the neural networks-based state observers are designed to reconstruct the unmeasurable states of the nonlinear multiagent systems. According to the given design approach, a switching threshold-based event-triggered adaptive fault-tolerant control strategy is developed and ensures all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, the simulation result is provided to demonstrate the validity of the presented method.