Reinforcement Learning-Based Fixed-Time Control for Multiagent Systems with Actuator Faults

This paper investigates a reinforcement learning-based fixed-time tracking control scheme for multiagent systems against actuator faults. Firstly, an optimal control scheme is presented based on the simplified optimized control. In the optimization process, synchronization errors are embedded into the cost function, which promotes the achievement of the optimization task. Then, a novel actor-critic algorithm is designed to improve the convergence rate of system signals. Moreover, actuator faults are addressed to maintain the system performance. Finally, the presented optimized method ensures that all signals of the closed-loop system are bounded, and the simulation example verifies the feasibility of the presented control scheme.