Event-triggered robust adaptive critic control for nonlinear disturbed systems - Details

Author：

Wang, D. (Wang, D..) | Zhou, Z. (Zhou, Z..) | Liu, A. (Liu, A..) | Qiao, J. (Qiao, J..)

Indexed by：

EI Scopus SCIE

Abstract：

A　novel　dynamic　event-triggered　control　strategy　is　proposed　by　utilizing　the　adaptive　critic　learning　(ACL)　technique　for　nonlinear　continuous-time　systems　subject　to　disturbances　in　this　paper.　To　address　the　transformation　of　the　robust-optimal　control　problem,　a　modified　cost　function　containing　the　disturbance　term　is　introduced.　The　dynamic　event-triggered　controller　is　obtained　by　incorporating　an　internal　variable　into　the　static　triggering　strategy.　An　ACL　method　is　employed　to　design　static　and　dynamic　triggering　controllers.　Critic　neural　networks　are　used　to　approximate　the　cost　function　and　the　corresponding　Hamilton–Jacobi–Bellman　equation,　leading　to　the　establishment　of　adaptive　critic　event-triggered　controllers.　Stability　analysis　of　the　closed-loop　system　is　also　provided,　and　simulation　results　demonstrate　the　effectiveness　of　the　developed　dynamic　triggering　strategy　with　two　examples.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Nature　B.V.

Keyword：

Critic neural networks Nonlinear disturbed systems Dynamic event-triggered control strategies Adaptive critic learning Robust-optimal control design

Author Community：

[ 1 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Zhou Z.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Zhou Z.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Zhou Z.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Zhou Z.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Liu A.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Liu A.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 11 ] [Liu A.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 12 ] [Liu A.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
[ 13 ] [Qiao J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 14 ] [Qiao J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 15 ] [Qiao J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
[ 16 ] [Qiao J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China

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Source ：

Nonlinear Dynamics

ISSN： 0924-090X

Year： 2023

Issue： 21

Volume： 111

Page： 19963-19977

5 . 6 0 0

JCR@2022

ESI Discipline： ENGINEERING;

ESI HC Threshold：19

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 5

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 1

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