Fuzzy Optimal Prescribed Performance Control for Multi-Agent Systems via Reinforcement Learning

This paper proposes a fuzzy optimal adaptive prescribed performance control strategy for nonlinear multi-agent systems. Firstly, to solve the prescribed performance problem, an error transformation function is used to transform the constraint problem into a bounded problem. Subsequently, a distributed fuzzy control protocol is proposed, utilizing a reinforcement learning algorithm. The control scheme ensures that all closed-loop signals are bounded, and the consensus tracking error remains within a predefined range. Finally, the effectiveness of the proposed control scheme is verified through simulation.