Optimal synchronization control for heterogeneous multi‐agent systems: Online adaptive learning solutions

Abstract

AbstractThis paper presents an online adaptive learning solution to the optimal synchronization control problem of heterogeneous multi‐agent systems via a novel distributed policy iteration approach. For the leader‐follower multi‐agents, the dynamics of all the followers are heterogeneous with leader disturbance. To make the output of each follower synchronize with the leader's output, we propose a synchronization control protocol where the stability conditions for selecting the feedback gains are given. Next, with a minimization of the output tracking errors, we optimize the feedback gains for the synchronization control protocol, and the unique solutions for those feedback gains are learned via a novel distributed policy iteration approach. We prove that the proposed online adaptive learning solution will converge to the optimal control solution under some mild conditions. Finally, an illustrative numerical example is provided to show the effectiveness of the approach.