Analysis on existence of compact set in neural network control for nonlinear systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zou, Wencheng | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Xiang, Zhengrong | - |
dc.date.accessioned | 2021-08-30T13:56:44Z | - |
dc.date.available | 2021-08-30T13:56:44Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 0005-1098 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/53091 | - |
dc.description.abstract | Neural network method is an effective tool for approximating the unknown function in controller design for nonlinear systems. To guarantee the validity of the approximation, state variables in approximated unknown functions need to stay in a compact set. However, in most existing results, the existence of the compact set has not been correctly proven; therefore, the proof is not actually complete in these existing works. In this paper, we analyze the existence of compact sets for two typical nonlinear systems with novel neural network-based controllers and show the strict proof for the semi-global uniform ultimate boundedness of the closed-loop system. (C) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | TRACKING CONTROL | - |
dc.title | Analysis on existence of compact set in neural network control for nonlinear systems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.identifier.doi | 10.1016/j.automatica.2020.109155 | - |
dc.identifier.scopusid | 2-s2.0-85088567708 | - |
dc.identifier.wosid | 000564896800002 | - |
dc.identifier.bibliographicCitation | AUTOMATICA, v.120 | - |
dc.relation.isPartOf | AUTOMATICA | - |
dc.citation.title | AUTOMATICA | - |
dc.citation.volume | 120 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | TRACKING CONTROL | - |
dc.subject.keywordAuthor | Artificial neural networks | - |
dc.subject.keywordAuthor | Compact set | - |
dc.subject.keywordAuthor | Nonlinear systems | - |
dc.subject.keywordAuthor | Back-stepping | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.