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Conference Paper: Robust H∞ filtering for 2-D stochastic systems

TitleRobust H∞ filtering for 2-D stochastic systems
Authors
Keywords2-D systems
H∞ filtering
Linear matrix inequality
Stochastic perturbation
Issue Date2004
PublisherIEEE.
Citation
Proceedings of the American Control Conference, 2004, v. 4, p. 3158-3163 How to Cite?
AbstractThis paper investigates the problem of H∞, filter design for 2-D stochastic systems. The stochastic perturbation is first introduced into the well-known Fornasini-Marchesini local state-space (FMLSS) model. Our attention is focused on the design of full-order and reduced-order filters, which guarantee the filtering error system to be mean-square asymptotically stable and has a prescribed H∞ disturbance attenuation performance. Sufficient conditions for the existence of such filters are established in terms of linear matrix inequalities (LMIs), and the corresponding filter design is cast into a convex optimization problem which can be efficiently handled by using available numerical software. In addition, the obtained results are further extended to more general cases where the system matrices also contain uncertain parameters. The most frequently used ways of dealing with parameter uncertainties, including polytopic and norm-bounded characterizations, are taken into consideration.
Persistent Identifierhttp://hdl.handle.net/10722/46678
ISSN
2023 SCImago Journal Rankings: 0.575
References

 

DC FieldValueLanguage
dc.contributor.authorGao, Hen_HK
dc.contributor.authorLam, Jen_HK
dc.contributor.authorWang, Cen_HK
dc.contributor.authorXu, Sen_HK
dc.date.accessioned2007-10-30T06:55:42Z-
dc.date.available2007-10-30T06:55:42Z-
dc.date.issued2004en_HK
dc.identifier.citationProceedings of the American Control Conference, 2004, v. 4, p. 3158-3163en_HK
dc.identifier.issn0743-1619en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46678-
dc.description.abstractThis paper investigates the problem of H∞, filter design for 2-D stochastic systems. The stochastic perturbation is first introduced into the well-known Fornasini-Marchesini local state-space (FMLSS) model. Our attention is focused on the design of full-order and reduced-order filters, which guarantee the filtering error system to be mean-square asymptotically stable and has a prescribed H∞ disturbance attenuation performance. Sufficient conditions for the existence of such filters are established in terms of linear matrix inequalities (LMIs), and the corresponding filter design is cast into a convex optimization problem which can be efficiently handled by using available numerical software. In addition, the obtained results are further extended to more general cases where the system matrices also contain uncertain parameters. The most frequently used ways of dealing with parameter uncertainties, including polytopic and norm-bounded characterizations, are taken into consideration.en_HK
dc.format.extent413266 bytes-
dc.format.extent10566 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings of the American Control Conferenceen_HK
dc.rights©2004 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.subject2-D systemsen_HK
dc.subjectH∞ filteringen_HK
dc.subjectLinear matrix inequalityen_HK
dc.subjectStochastic perturbationen_HK
dc.titleRobust H∞ filtering for 2-D stochastic systemsen_HK
dc.typeConference_Paperen_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.doi10.23919/ACC.2004.1384395en_HK
dc.identifier.scopuseid_2-s2.0-8744235969en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-8744235969&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.spage3158en_HK
dc.identifier.epage3163en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridGao, H=7402971422en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK
dc.identifier.scopusauthoridWang, C=35363066800en_HK
dc.identifier.scopusauthoridXu, S=7404438591en_HK
dc.identifier.issnl0743-1619-

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