Optimal Consensus Control for Switching Uncertain Multiagent Systems Using Model Reference Control and Reinforcement Learning

• Published in: Journal of Advanced Computational Intelligence and Intelligent Informatics Vol. 29; no. 2; pp. 256 - 267
• Main Authors: He, Wenpeng, Chen, Xin, Sun, Yipu
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Ltd 20.03.2025; 富士技術出版株式会社; Fuji Technology Press Co. Ltd
• Subjects: Automation; Communication; Control systems; equivalent input disturbance; Game theory; Methods; Multiagent systems; optimal consensus; Optimal control; Stabilization; switching communication graph; Tracking errors; uncertain multiagent system
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2025.p0256

This paper addresses the optimal consensus problem in uncertain switching multiagent systems. The inherent uncertainty and time-varying structure of local tracking error system render conventional methods ineffective for deriving optimal control protocols. To overcome these challenges, we introduce a reference model for each agent and construct a modified augmented local tracking error (ALTE) system. This approach transforms the optimal consensus problem into two sub-problems: 1) model reference control (MRC) between agents and their reference models; 2) distributed optimal stabilization of the modified ALTE system. We propose a new control scheme that combines filtered tracking error with equivalent input disturbance method to achieve MRC. To realize distributed optimal stabilization of the modified ALTE, we introduce a deep deterministic policy gradient method based on value iteration. Through theoretical analysis, we demonstrate that the multiagent system achieves a near Nash equilibrium, which is further validated by numerical simulation.