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Article: A generalized parameter-dependent approach to robust H∞ filtering of stochastic systems
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TitleA generalized parameter-dependent approach to robust H∞ filtering of stochastic systems
 
AuthorsMeng, X2 1
Lam, J1
Fei, Z2
 
KeywordsH∞ Filtering
Linear Matrix Inequality
Robust Filtering
Stochastic Systems
Uncertain Systems
 
Issue Date2009
 
PublisherBirkhaeuser Boston. The Journal's web site is located at http://link.springer.de/link/service/journals/00034/
 
CitationCircuits, Systems, And Signal Processing, 2009, v. 28 n. 2, p. 191-204 [How to Cite?]
DOI: http://dx.doi.org/10.1007/s00034-008-9084-1
 
AbstractThis paper is concerned with the problem of robust H ∞ filtering for discrete-time stochastic systems with state-dependent stochastic noises and deterministic polytopic parameter uncertainties. We utilize the polynomial parameter-dependent approach to solve the robust H ∞ filtering problem, and the proposed approach includes results in the quadratic framework that entail fixed matrices for the entire uncertain domain and results in the linearly parameter-dependent framework that use linear convex combinations of matrices as special cases. New linear matrix inequality (LMI) conditions obtained for the existence of admissible filters are developed based on homogeneous polynomial parameter-dependent matrices of arbitrary degree. As the degree grows, a test of increasing precision is obtained, providing less conservative filter designs. A numerical example is provided to illustrate the effectiveness and advantages of the filter design methods proposed in this paper. © Birkhäuser Boston 2008.
 
ISSN0278-081X
2013 Impact Factor: 1.264
2013 SCImago Journal Rankings: 0.447
 
DOIhttp://dx.doi.org/10.1007/s00034-008-9084-1
 
ISI Accession Number IDWOS:000264517600002
Funding AgencyGrant Number
HKUCRCG 200611159157
National Nature Science Foundation of China60504008
The Research Fund for the Doctoral Programme of Higher Education of China20070213084
Fok Ying Tung Education Foundation111064
Key Laboratory of Integrated Automation for the Process Industry (Northeastern University)
Ministry of Education of China
Funding Information:

This work was supported by HKU CRCG 200611159157, the National Nature Science Foundation of China (60504008), The Research Fund for the Doctoral Programme of Higher Education of China (20070213084), the Fok Ying Tung Education Foundation (111064), and the Key Laboratory of Integrated Automation for the Process Industry (Northeastern University), Ministry of Education of China.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorMeng, X
 
dc.contributor.authorLam, J
 
dc.contributor.authorFei, Z
 
dc.date.accessioned2012-08-08T08:44:53Z
 
dc.date.available2012-08-08T08:44:53Z
 
dc.date.issued2009
 
dc.description.abstractThis paper is concerned with the problem of robust H ∞ filtering for discrete-time stochastic systems with state-dependent stochastic noises and deterministic polytopic parameter uncertainties. We utilize the polynomial parameter-dependent approach to solve the robust H ∞ filtering problem, and the proposed approach includes results in the quadratic framework that entail fixed matrices for the entire uncertain domain and results in the linearly parameter-dependent framework that use linear convex combinations of matrices as special cases. New linear matrix inequality (LMI) conditions obtained for the existence of admissible filters are developed based on homogeneous polynomial parameter-dependent matrices of arbitrary degree. As the degree grows, a test of increasing precision is obtained, providing less conservative filter designs. A numerical example is provided to illustrate the effectiveness and advantages of the filter design methods proposed in this paper. © Birkhäuser Boston 2008.
 
dc.description.natureLink_to_subscribed_fulltext
 
dc.identifier.citationCircuits, Systems, And Signal Processing, 2009, v. 28 n. 2, p. 191-204 [How to Cite?]
DOI: http://dx.doi.org/10.1007/s00034-008-9084-1
 
dc.identifier.citeulike3609185
 
dc.identifier.doihttp://dx.doi.org/10.1007/s00034-008-9084-1
 
dc.identifier.epage204
 
dc.identifier.isiWOS:000264517600002
Funding AgencyGrant Number
HKUCRCG 200611159157
National Nature Science Foundation of China60504008
The Research Fund for the Doctoral Programme of Higher Education of China20070213084
Fok Ying Tung Education Foundation111064
Key Laboratory of Integrated Automation for the Process Industry (Northeastern University)
Ministry of Education of China
Funding Information:

This work was supported by HKU CRCG 200611159157, the National Nature Science Foundation of China (60504008), The Research Fund for the Doctoral Programme of Higher Education of China (20070213084), the Fok Ying Tung Education Foundation (111064), and the Key Laboratory of Integrated Automation for the Process Industry (Northeastern University), Ministry of Education of China.

 
dc.identifier.issn0278-081X
2013 Impact Factor: 1.264
2013 SCImago Journal Rankings: 0.447
 
dc.identifier.issue2
 
dc.identifier.scopuseid_2-s2.0-63649135097
 
dc.identifier.spage191
 
dc.identifier.urihttp://hdl.handle.net/10722/157000
 
dc.identifier.volume28
 
dc.languageeng
 
dc.publisherBirkhaeuser Boston. The Journal's web site is located at http://link.springer.de/link/service/journals/00034/
 
dc.publisher.placeUnited States
 
dc.relation.ispartofCircuits, Systems, and Signal Processing
 
dc.relation.referencesReferences in Scopus
 
dc.subjectH∞ Filtering
 
dc.subjectLinear Matrix Inequality
 
dc.subjectRobust Filtering
 
dc.subjectStochastic Systems
 
dc.subjectUncertain Systems
 
dc.titleA generalized parameter-dependent approach to robust H∞ filtering of stochastic systems
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Harbin Institute of Technology