Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ni, Junkang | - |
dc.contributor.author | Ahn, Choon Ki | - |
dc.contributor.author | Liu, Ling | - |
dc.contributor.author | Liu, Chongxin | - |
dc.date.accessioned | 2021-09-01T01:53:58Z | - |
dc.date.available | 2021-09-01T01:53:58Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-10-21 | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/62180 | - |
dc.description.abstract | This paper investigates fixed-time prescribed performance control problem for uncertain strict-feedback nonlinear systems with unknown dead zone. First, a novel prescribed performance function (PPF) is proposed and a coordinate transformation is employed to transform the prescribed performance constrained system into an unconstrained one. Next, recurrent neural network is introduced to estimate the uncertain dynamics and fixed-time differentiator is utilized to obtain the derivative of virtual control. Then, a fixed-time dynamic surface control is developed to deal with dead zone and guarantee the convergence of the tracking error within a fixed time. Lyapunov stability analysis shows that the presented control scheme can achieve the fixed-time convergence of the error variables, while the other closed-loop system signals are bounded. Finally, numerical simulation validates the effectiveness of the presented control scheme. (C) 2019 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | DYNAMIC SURFACE CONTROL | - |
dc.subject | SLIDING-MODE CONTROL | - |
dc.subject | 2ND-ORDER MULTIAGENT SYSTEMS | - |
dc.subject | TRACKING CONTROL | - |
dc.subject | DEADZONE COMPENSATION | - |
dc.subject | ADAPTIVE-CONTROL | - |
dc.subject | CONSENSUS | - |
dc.subject | SYNCHRONIZATION | - |
dc.subject | STABILIZATION | - |
dc.subject | DESIGN | - |
dc.title | Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Choon Ki | - |
dc.identifier.doi | 10.1016/j.neucom.2019.07.053 | - |
dc.identifier.scopusid | 2-s2.0-85069704341 | - |
dc.identifier.wosid | 000484005300032 | - |
dc.identifier.bibliographicCitation | NEUROCOMPUTING, v.363, pp.351 - 365 | - |
dc.relation.isPartOf | NEUROCOMPUTING | - |
dc.citation.title | NEUROCOMPUTING | - |
dc.citation.volume | 363 | - |
dc.citation.startPage | 351 | - |
dc.citation.endPage | 365 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | DYNAMIC SURFACE CONTROL | - |
dc.subject.keywordPlus | SLIDING-MODE CONTROL | - |
dc.subject.keywordPlus | 2ND-ORDER MULTIAGENT SYSTEMS | - |
dc.subject.keywordPlus | TRACKING CONTROL | - |
dc.subject.keywordPlus | DEADZONE COMPENSATION | - |
dc.subject.keywordPlus | ADAPTIVE-CONTROL | - |
dc.subject.keywordPlus | CONSENSUS | - |
dc.subject.keywordPlus | SYNCHRONIZATION | - |
dc.subject.keywordPlus | STABILIZATION | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordAuthor | Prescribed performance control | - |
dc.subject.keywordAuthor | Fixed-time control | - |
dc.subject.keywordAuthor | Recurrent neural network control | - |
dc.subject.keywordAuthor | Dead zone | - |
dc.subject.keywordAuthor | Uncertain nonlinear system | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
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.