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

Abstract

This 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.

Input‐constrained optimal output synchronization of heterogeneous multiagent systems via observer‐based model‐free reinforcement learning

“…Reinforcement learning (RL) technique [26], inspired by natural learning mechanisms, acquires learning information by receiving rewards (or feedback) for behaviors from the environment and is capable of developing the optimal control law without requiring knowledge of system dynamics [27]. Some RL-based results have been put forward for output synchronization of heterogeneous MASs [28][29][30][31][32][33][34]. Based on the Q-learning technique, model-free optimal control protocols with distributed observer were developed to achieve output synchronization for heterogeneous continuous-time (CT) [31] and discrete-time (DT) [29] MASs.…”