Event-based robust performance guarantee for nonaffine plants via system identification - Details

Author：

Wang, D. (Wang, D..) | Zhou, Z. (Zhou, Z..) | Li, M. (Li, M..) | Ren, J. (Ren, J..) | Qiao, J. (Qiao, J..)

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EI Scopus SCIE

Abstract：

In　this　paper,　by　making　use　of　recurrent　neural　networks　(RNNs),　a　novel　event-based　robust　optimal　controller　is　designed　for　a　category　of　continuous-time　(CT)　nonlinear　nonaffine　systems　with　disturbances.　For　tackling　the　optimal-robust　problem　transformation　and　the　performance　guarantee,　a　system　identifier　is　developed　to　reconstruct　the　system,　and　an　event-triggered　critic-only　strategy　is　investigated　to　replace　the　traditional　time-based　actor-critic　structure.　The　RNN　is　adopted　to　reconstruct　the　nonaffine　system　as　an　identifier　for　obtaining　the　affine　model.　It　simplifies　the　system　structure　and　reduces　computational　burdens.　In　addition,　the　stability　of　the　unknown　nominal　system　learned　by　the　RNN　is　proved.　Then,　using　the　obtained　system　identifier　and　the　adaptive　critic　learning　structure,　the　proposed　event-triggered　robust　control　with　performance　guarantee　is　realized.　At　last,　the　effectiveness　of　the　given　design　method　is　verified　by　two　simulation　examples.　©　2023　John　Wiley　&　Sons　Ltd.

Keyword：

system identification adaptive critic control event-triggered control robustness nonlinear plants optimal control

Author Community：

[ 1 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 2 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 3 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 4 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 5 ] [Zhou Z.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 6 ] [Zhou Z.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 7 ] [Zhou Z.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 8 ] [Zhou Z.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 9 ] [Li M.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 10 ] [Li M.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 11 ] [Li M.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 12 ] [Li M.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 13 ] [Ren J.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 14 ] [Ren J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 15 ] [Ren J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 16 ] [Ren J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, China
[ 17 ] [Qiao J.]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 18 ] [Qiao J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
[ 19 ] [Qiao J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
[ 20 ] [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： 10

Volume： 33

Page： 5365-5387

3 . 9 0 0

JCR@2022

ESI Discipline： ENGINEERING;

ESI HC Threshold：19

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 3

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 9

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