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Article: Exponential ε-regulation for multi-input nonlinear systems using neural networks

TitleExponential ε-regulation for multi-input nonlinear systems using neural networks
Authors
KeywordsAdaptive control
Multi-input system
Neural network
Nonlinear system
Uncertain system
Uniform ultimate boundedness
Issue Date2005
PublisherIEEE.
Citation
Ieee Transactions On Neural Networks, 2005, v. 16 n. 6, p. 1710-1714 How to Cite?
AbstractThis paper considers the problem of robust exponential ε-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified ε-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/44938
ISSN
2011 Impact Factor: 2.952
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZhou, Sen_HK
dc.contributor.authorLam, Jen_HK
dc.contributor.authorFeng, Gen_HK
dc.contributor.authorHo, DWCen_HK
dc.date.accessioned2007-10-30T06:13:52Z-
dc.date.available2007-10-30T06:13:52Z-
dc.date.issued2005en_HK
dc.identifier.citationIeee Transactions On Neural Networks, 2005, v. 16 n. 6, p. 1710-1714en_HK
dc.identifier.issn1045-9227en_HK
dc.identifier.urihttp://hdl.handle.net/10722/44938-
dc.description.abstractThis paper considers the problem of robust exponential ε-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified ε-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case. © 2005 IEEE.en_HK
dc.format.extent215601 bytes-
dc.format.extent10566 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Neural Networksen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectAdaptive controlen_HK
dc.subjectMulti-input systemen_HK
dc.subjectNeural networken_HK
dc.subjectNonlinear systemen_HK
dc.subjectUncertain systemen_HK
dc.subjectUniform ultimate boundednessen_HK
dc.titleExponential ε-regulation for multi-input nonlinear systems using neural networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1045-9227&volume=16&issue=6&spage=1710&epage=1714&date=2005&atitle=Exponential+/spl+epsiv/-regulation+for+multi-input+nonlinear+systems+using+neural+networksen_HK
dc.identifier.emailLam, J:james.lam@hku.hken_HK
dc.identifier.authorityLam, J=rp00133en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TNN.2005.853335en_HK
dc.identifier.scopuseid_2-s2.0-28244499868en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-28244499868&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1710en_HK
dc.identifier.epage1714en_HK
dc.identifier.isiWOS:000233350300037-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhou, S=7404166480en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK
dc.identifier.scopusauthoridFeng, G=35232832800en_HK
dc.identifier.scopusauthoridHo, DWC=7402971938en_HK

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