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
收藏
页码：137 / 144
页数：8
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