Article: Hankel norm model reduction of uncertain neutral stochastic time-delay systems

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TitleHankel norm model reduction of uncertain neutral stochastic time-delay systems
AuthorsLi, Y1
Lam, J2
Luo, X3
KeywordsCone complementarity linearization
Hankel norm
Linear matrix inequality
Model reduction
Neutral stochastic systems
Issue Date2009
PublisherI C I C International. The Journal's web site is located at http://www.ijicic.org/home.htm
CitationInternational Journal Of Innovative Computing, Information And Control, 2009, v. 5 n. 9, p. 2819-2828 [How to Cite?]
AbstractThis paper investigates the problems of robust Hankel norm model reduction for uncertain neutral stochastic time-delay systems with time-varying norm-bounded parameter uncertainties appearing in the state matrices. For a given mean square asymptotically stable system, our purpose is to construct reduced-order systems, which approximate the original system well in the Hankel norm sense. The Hankel norm gain criterion is first established for neutral stochastic time-delay systems, and the corresponding model reduction problem is solved by using the projection lemma, and sufficient conditions are obtained for the existence of admissible reduced-order models in terms of linear matrix inequalities (LMIs) plus matrix inverse constraints. Since these obtained conditions are not expressed as strict LMIs, the cone complementarity linearization (CCL) method is exploited to cast them into nonlinear minimization problems subject to LMI constraints, which can be readily solved by standard numerical software. The efficiency of the proposed methods is demonstrated via a numerical example. © 2009 ISSN.
ISSN1349-4198
2011 SCImago Journal Rankings: 0.060
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorLi, Y
dc.contributor.authorLam, J
dc.contributor.authorLuo, X
dc.date.accessioned2010-10-31T10:57:37Z
dc.date.available2010-10-31T10:57:37Z
dc.date.issued2009
dc.description.abstractThis paper investigates the problems of robust Hankel norm model reduction for uncertain neutral stochastic time-delay systems with time-varying norm-bounded parameter uncertainties appearing in the state matrices. For a given mean square asymptotically stable system, our purpose is to construct reduced-order systems, which approximate the original system well in the Hankel norm sense. The Hankel norm gain criterion is first established for neutral stochastic time-delay systems, and the corresponding model reduction problem is solved by using the projection lemma, and sufficient conditions are obtained for the existence of admissible reduced-order models in terms of linear matrix inequalities (LMIs) plus matrix inverse constraints. Since these obtained conditions are not expressed as strict LMIs, the cone complementarity linearization (CCL) method is exploited to cast them into nonlinear minimization problems subject to LMI constraints, which can be readily solved by standard numerical software. The efficiency of the proposed methods is demonstrated via a numerical example. © 2009 ISSN.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationInternational Journal Of Innovative Computing, Information And Control, 2009, v. 5 n. 9, p. 2819-2828 [How to Cite?]
dc.identifier.epage2828
dc.identifier.hkuros179599
dc.identifier.issn1349-4198
2011 SCImago Journal Rankings: 0.060
dc.identifier.issue9
dc.identifier.scopuseid_2-s2.0-69949113157
dc.identifier.spage2819
dc.identifier.urihttp://hdl.handle.net/10722/124849
dc.identifier.volume5
dc.languageeng
dc.publisherI C I C International. The Journal's web site is located at http://www.ijicic.org/home.htm
dc.publisher.placeJapan
dc.relation.ispartofInternational Journal of Innovative Computing, Information and Control
dc.relation.referencesReferences in Scopus
dc.subjectCone complementarity linearization
dc.subjectHankel norm
dc.subjectLinear matrix inequality
dc.subjectModel reduction
dc.subjectNeutral stochastic systems
dc.titleHankel norm model reduction of uncertain neutral stochastic time-delay systems
dc.typeArticle
Author Affiliations
  1. Daqing Petroleum Institute
  2. The University of Hong Kong
  3. China University of Petroleum - Beijing