Online optimal consensus control of unknown linear multi-agent systems via time-based adaptive dynamic programming

被引：22

作者：

Liu, Yifan ^{[1
]}

Li, Tieshan ^{[1
,2
]}

Shan, Qihe ^{[1
]}

Yu, Renhai ^{[1
]}

Wu, Yue ^{[1
]}

Chen, C. L. Philip ^{[1
,3
]}

机构：

[1] Dalian Maritime Univ, Nav Coll, Dalian 116026, Liaoning, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Sichuan, Peoples R China

[3] Univ Macau, Dept Comp & Informat Sci, Fac Sci & Technol, Macau 999078, Peoples R China

来源：

NEUROCOMPUTING | 2020年 / 404卷

基金：

中国国家自然科学基金;

关键词：

Linear multi-agent systems (MASs); Adaptive dynamic programming (ADP); Consensus control; Discrete-time (DT) system; NEURAL-NETWORK; SYNCHRONIZATION; ALGORITHMS; DESIGN; GAMES;

D O I：

10.1016/j.neucom.2020.04.119

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper considers the online optimal consensus control problem for unknown linear discrete-time (DT) multi-agent systems (MASs). Based on time-based adaptive dynamic programming (ADP) method, the control policies are designed by utilizing the current and recorded data of unknown MASs. The critic-actor NN frameworks are employed to approximate the performance indexes and optimal control policies, respectively. The NN weights are updated once at the sampling instant to produce real-time online control. Furthermore, the control policies are proved to effectively drive the MASs to achieve consistency and satisfy the Nash equilibrium. Finally, a numerical example is implemented to shown the feasibility of the control scheme. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：137 / 144

页数：8

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