Optimal Consensus Control for Switching Uncertain Multiagent Systems Using Model Reference Control and Reinforcement Learning
Year: 2025
Type: article
Abstract: 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.
Authors: Wenpeng He, Xin Chen, Yipu Sun
Institution: China University of Geosciences
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408614247