Multi-agent differential game based cooperative synchronization control using a data-drivenmethod  

作　　者：Yu SHI Yongzhao HUA Jianglong YU Xiwang DONG Zhang REN 

机构地区：[1]School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China [2]Institute of Artificial Intelligence,Beihang University,Beijing 100191,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2022年第7期1043-1056,共14页信息与电子工程前沿（英文版）

基　　金：Project supported by the Science and Technology Innovation 2030,China(No.2020AAA0108200);the National Natural Science Foundation of China(Nos.61873011,61973013,61922008,and 61803014);the Defense Industrial Technology Development Program,China(No.JCKY2019601C106);the Innovation Zone Project,China(No.18-163-00-TS-001-001-34);the Foundation Strengthening Program Technology Field Fund,China(No.2019-JCJQ-JJ-243);the Fund from the Key Laboratory of Dependable Service Computing in Cyber Physical Society,China(No.CPSDSC202001)。

摘　　要：This paper studies the multi-agent differential game based problem and its application to cooperative synchronization control.A systematized formulation and analysis method for the multi-agent differential game is proposed and a data-driven methodology based on the reinforcement learning(RL)technique is given.First,it is pointed out that typical distributed controllers may not necessarily lead to global Nash equilibrium of the differential game in general cases because of the coupling of networked interactions.Second,to this end,an alternative local Nash solution is derived by defining the best response concept,while the problem is decomposed into local differential games.An off-policy RL algorithm using neighboring interactive data is constructed to update the controller without requiring a system model,while the stability and robustness properties are proved.Third,to further tackle the dilemma,another differential game configuration is investigated based on modified coupling index functions.The distributed solution can achieve global Nash equilibrium in contrast to the previous case while guaranteeing the stability.An equivalent parallel RL method is constructed corresponding to this Nash solution.Finally,the effectiveness of the learning process and the stability of synchronization control are illustrated in simulation results.

关 键 词：Multi-agent system Differential game Synchronization control DATA-DRIVEN Reinforcement learning 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]