Neural-Network-Based Self-Learning Disturbance Rejection Design for Continuous-Time Nonlinear Constrained Systems - Details

Author：

Li, Menghua (Li, Menghua.) | Wang, Ding (Wang, Ding.) | Qiao, Junfei (Qiao, Junfei.) | Xu, Xin (Xu, Xin.)

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EI Scopus

Abstract：

In　this　paper,　we　adopt　a　neural-network-based　adaptive　dynamic　programming　(ADP)　method　to　solve　the　disturbance　rejection　problem　for　continuous-time　nonlinear　systems　with　control　constraints.　First,　we　define　a　suitable　function　and　then　derive　the　optimal　control　law　with　control　constraints　and　the　worst　disturbance　law.　Besides,　the　constrained　Hamilton-Jacobi-Isaacs　equation　for　continuous-time　nonlinear　constrained　systems　is　derived.　Then,　only　one　critic　neural　network　is　used　to　approximate　the　optimal　cost　function.　Consequently,　the　approximate　optimal　control　law　and　the　approximate　worst　disturbance　law　are　obtained.　Additionally,　a　new　updating　rule　is　developed　in　the　process　of　neural　critic　learning.　Finally,　the　simulation　results　show　that　the　self-learning　optimal　control　is　realized　and　the　effectiveness　of　the　disturbance　rejection　is　verified　by　using　the　neural-network-based　ADP　method.　©　2021　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.

Keyword：

Cost functions Nonlinear equations Dynamic programming Disturbance rejection Adaptive control systems Continuous time systems Learning systems

Author Community：

[ 1 ] [Li, Menghua]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
[ 2 ] [Li, Menghua]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Li, Menghua]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Wang, Ding]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
[ 5 ] [Wang, Ding]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Wang, Ding]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
[ 7 ] [Qiao, Junfei]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
[ 8 ] [Qiao, Junfei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Qiao, Junfei]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Xu, Xin]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
[ 11 ] [Xu, Xin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 12 ] [Xu, Xin]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China

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Source ：

ISSN： 1934-1768

Year： 2021

Volume： 2021-July

Page： 2179-2184

Language： English

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 2

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 6

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