Adaptive control for multi-agent systems with actuator fault via reinforcement learning and its application on multi-unmanned surface vehicle

Abstract

In this paper, the adaptive cooperative control problem is studied for a class of second order multi-agent systems (MASs) with actuator fault via reinforcement learning (RL). The strategic utility function is approximated by the critic neural network (NN), and the uncertain dynamics in MASs are estimated by the action NN. The NN weight vectors are updated by employing the gradient descent strategy. Then, the distributed RL control strategy is developed to solve the consensus control design problem. In comparison with the existent RL control results, the actuator fault is taken into consideration in the controller design. The stability analysis is given based on the Lyapunov theory, and all signals in the closed-loop system are guaranteed to semi-globally uniformly ultimately bounded (SGUUB). Two simulation examples, including a multi-unmanned surface vehicle system, are presented to demonstrate the validation of this strategy.