Cooperative Optimal Output Regulation for Multi-agent Systems Based on Distributed Adaptive Internal Model

被引：0
|
作者：
Dong, Yu-Chen [1 ]
Gao, Wei-Nan [1 ]
Jiang, Zhong-Ping [2 ]
机构：
[1] State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang
[2] Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, New York, 11201, NY
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2025年 / 51卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Adaptive dynamic programming; cooperative output regulation; distributed adaptive internal model; multi-agent systems; reinforcement learning;
D O I：
10.16383/j.aas.c240371
中图分类号：
学科分类号：
摘要：
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. © 2025 Science Press. All rights reserved.
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页码：678 / 691
页数：13
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