基于分布式自适应内模的多智能体系统协同最优输出调节

In this paper, a distributed data-driven adaptive control strategy is proposed for the problem of cooperative optimal output regulation of discrete-time multi-agent systems, in the absence of precise information of multiagent system matrices. Based on adaptive dynamic programming and distributed adaptive internal model, two reinforcement learning algorithms, value iteration and policy iteration, are introduced to learn the optimal controller by using online data, so as to achieve the cooperative output regulation of multi-agent systems. Considering that the followers can only access the estimated value of the leader for online learning, in order to prove that the learned control gain converges to the optimal control gain, this paper provides a rigorous analysis of the stability of the closed-loop system and the convergence of the learning algorithm. The simulation results verify the effectiveness of the proposed control method.