Affiliation:
1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering Jiangnan University Wuxi China
2. Department of Chemical & Materials Engineering University of Alberta Edmonton Alberta Canada
Abstract
AbstractThis article develops distributed optimal control policies via Q‐learning for multi‐agent systems (MASs) by solving dual games. According to game theory, first, the distributed consensus problem is formulated as a multi‐player non‐zero‐sum game, where each agent is viewed as a player focusing only on its local performance and the whole MAS achieves Nash equilibrium. Second, for each agent, the anti‐disturbance problem is formulated as a two‐player zero‐sum game, in which the control input and external disturbance are a pair of opponents. Specifically, (1) an offline data‐driven off‐policy for distributed tracking algorithm based on momentum policy gradient (MPG) is developed, which can effectively achieve consensus of MASs with guaranteed ‐bounded synchronization error. (2) An actor‐critic‐disturbance neural network is employed to implement the MPG algorithm and obtain optimal policies. Finally, numerical and practical simulation results are conducted to verify the effectiveness of the developed tracking policies via MPG algorithm.
Funder
National Natural Science Foundation of China
China Scholarship Council
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Aerospace Engineering,Biomedical Engineering,General Chemical Engineering,Control and Systems Engineering