Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies  

作　　者：Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI 

机构地区：[1]Chongqing Key Laboratory of Image Cognition,Chongqing University of Posts and Telecommunications,Chongqing 400065,China [2]Key Laboratory of Intelligent Perception and Computing of Anhui Province,Anqing Normal University,Anqing 246133,China [3]College of Electronic and Information Engineering,Southwest University,Chongqing 400715,China

出　　处：《Science China(Information Sciences)》2025年第1期303-318,共16页中国科学(信息科学)(英文版)

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.62276036,62221005,62006031);Major Project of Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-M202100602);Project of Natural Science Foundation of Chongqing(Grant Nos.cstc2021jcyj-msxmX1043,CSTB2024NSCQ-LZX0118);Anhui Provincial Research Programming Project(Grant Nos.2022AH051039,2022AH051054);Doctoral Talent Training Project of Chongqing University of Posts and Telecommunications(Grant No.BYJS202210).

摘　　要：This study uses event-triggered(ET)and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems.It proposes a novel distributed ET control strategy,which relies on a prioritized experience replay(PER)policy.This strategy not only conserves communication resources but also ensures acceptable system performance.To implement the proposed method,actor-critic(AC)dual-structured neural networks(NNs)are used to approximate the value function and control policy.In the AC NNs,the weight estimates for the NNs are updated at the moment of event triggering,resulting in a nonperiodic weight adjustment pattern.This approach decreases the computational cost in comparison with the traditional ET mechanism.The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource conservation.Moreover,it does not require the following two conditions in most existing studies:(1)requirement of the system dynamics model to be known,and(2)persistent excitation.In addition,Zeno behavior is excluded from this study.Finally,a simulation is conducted to confirm the validity of the suggested approach.

关 键 词：multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]