Adaptive critic control with multi-step policy evaluation for nonlinear zero-sum games - Details

Author：

Li, X. (Li, X..) | Wang, D. (Wang, D..) | Wang, J. (Wang, J..) | Qiao, J. (Qiao, J..)

Indexed by：

EI Scopus SCIE

Abstract：

To　attenuate　the　effect　of　disturbances　on　control　performance,　a　multi-step　adaptive　critic　control　(MsACC)　framework　is　developed　to　solve　zero-sum　games　for　discrete-time　nonlinear　systems.　The　MsACC　algorithm　utilizes　multi-step　policy　evaluation　to　obtain　the　solution　of　the　Hamilton–Jacobi–Isaac　equation,　which　is　faster　than　that　of　the　one-step　policy　evaluation.　The　convergence　rate　of　the　MsACC　algorithm　is　adjustable　by　varying　the　step　size　of　the　policy　evaluation.　In　addition,　the　stability　and　convergence　of　the　MsACC　algorithm　are　proved　under　certain　conditions.　In　order　to　realize　the　MsACC　algorithm,　three　neural　networks　are　established　to　approximate　the　control　input,　the　disturbance　input,　and　the　cost　function,　respectively.　Finally,　the　effectiveness　of　the　MsACC　algorithm　is　verified　by　two　simulation　examples,　including　a　linear　system　and　a　nonlinear　plant.　©　2023　John　Wiley　&　Sons　Ltd.

Keyword：

optimal control multi-step policy evaluation adaptive critic control zero-sum games nonlinear plants

Author Community：

[ 1 ] [Li X.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 2 ] [Li X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 3 ] [Li X.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 4 ] [Li X.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 5 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 6 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 7 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 8 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 9 ] [Wang J.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 10 ] [Wang J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 11 ] [Wang J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 12 ] [Wang J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 13 ] [Qiao J.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 14 ] [Qiao J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 15 ] [Qiao J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 16 ] [Qiao J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China

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Source ：

International Journal of Robust and Nonlinear Control

ISSN： 1049-8923

Year： 2023

Issue： 1

Volume： 34

Page： 551-566

3 . 9 0 0

JCR@2022

ESI Discipline： ENGINEERING;

ESI HC Threshold：19

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 1

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 3

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