Optimal Fixed-Time Control for Human-in-the-Loop Multiagent Systems With Actuator Faults

This article delves into an optimal fixed-time tracking control scheme for human-in-the-loop (HiTL) multiagent systems (MASs) against actuator faults. For adapting various complex environments, an HiTL optimal control protocol is developed based on the simplified optimal control. Furthermore, the synchronization error containing the leader input is embedded into the cost function, which enables the achievement of the optimal control objective and ensures the execution of the tracking control under the HiTL control. By adding exponential terms, a novel reinforcement learning (RL) algorithm satisfying the fixed-time form is proposed to attain the optimal fixed-time controller, which prompts the convergence rate of system signals while ensuring minimum energy consumption effectively. Meanwhile, actuator faults are considered and compensated in the controller design process to attain exceptional system performance. Consequently, the presented optimal control scheme ensures that all signals of the closed-loop system maintain bounded in a fixed time. The simulation results verify the feasibility of the presented control method.