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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共 35 条
  • [1] Zhang L, Chen Z Y, Yu X H, Yang J, Li S H., Sliding-mode-based robust output regulation and its application in PMSM servo systems, IEEE Transactions on Industrial Electronics, 70, 2, pp. 1852-1860, (2022)
  • [2] Enderes T, Gabriel J, Deutscher J., Cooperative output regulation for networks of hyperbolic systems using adaptive cooperative observers, Automatica, 162, (2024)
  • [3] Li Y S, Zhang Y, Li X D, Sun C Y., Regional multi-agent cooperative reinforcement learning for city-level traffic grid signal control, IEEE/CAA Journal of Automatica Sinica, 11, 9, pp. 1987-1998, (2024)
  • [4] Zhang X F, Wang G, Sun J., Data-driven control of consensus tracking for discrete-time multi-agent systems, Journal of the Franklin Institute, 360, 7, pp. 4661-4674, (2023)
  • [5] Zheng S Q, Shi P, Zhang H Y., Semiglobal periodic event-triggered output regulation for nonlinear multiagent systems, IEEE Transactions on Automatic Control, 68, 1, (2023)
  • [6] Liu T, Huang J., Adaptive cooperative output regulation of discrete-time linear multi-agent systems by a distributed feedback control law, IEEE Transactions on Automatic Control, 63, 12, pp. 4383-4390, (2018)
  • [7] Qasem O, Davari M, Gao W, Kirk D R, Chai T Y., Hybrid iteration ADP algorithm to solve cooperative, optimal output regulation problem for continuous-time, linear, multiagent systems: Theory and application in islanded modern microgrids with IBRs, IEEE Transactions on Industrial Electronics, 71, 1, (2023)
  • [8] Zhang H G, Liang H J, Wang Z S, Feng T., Optimal output regulation for heterogeneous multiagent systems via adaptive dynamic programming, IEEE Transactions on Neural Networks and Learning Systems, 28, 1, (2015)
  • [9] Jing G X, Huang B N, Sun J Y, Xie X P, Sun Q Y., Distributed cooperative control for power sharing of DC distribution network with event-triggered communication mechanism, International Journal of Robust and Nonlinear Control, 34, 5, pp. 3351-3373, (2024)
  • [10] Hong Y X, Su Y F, Cai H., Internal model based cooperative robust resilient control under DoS attacks with application to vehicles formation, IEEE Transactions on Industrial Informatics, 20, 11, pp. 13124-13134, (2024)
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