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dc.contributor.authorRotondo, Damiano
dc.contributor.authorHassani, Vahid
dc.contributor.authorCristofaro, Andrea
dc.date.accessioned2018-04-26T21:35:14Z
dc.date.available2018-04-26T21:35:14Z
dc.date.issued2017
dc.identifier.citationRotondo, D., Hassani, V., Cristofaro, A. A multiple model adaptive architecture for the state estimation in discrete-time uncertain LPV systems. A: American Control Conference. "2017 American Control Conference (ACC): 24-26 May 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 2393-2398.
dc.identifier.isbn978-1-5090-5992-8
dc.identifier.urihttp://hdl.handle.net/2117/116749
dc.description@2017 Personal use of these materials is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating news collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.description.abstractThis paper addresses the problem of multiple model adaptive estimation (MMAE) for discrete-time linear parameter varying (LPV) systems that are affected by parametric uncertainty. The MMAE system relies on a finite number of local observers, each designed using a selected model (SM) from the set of possible plant models. Each local observer is an LPV Kalman filter, obtained as a linear combination of linear time invariant (LTI) Kalman filters. It is shown that if some suitable distinguishability conditions are fulfilled, the MMAE will identify the SM corresponding to the local observer with smallest output prediction error energy. The convergence of the unknown parameter estimation, and its relation with the varying parameters, are discussed. Simulation results illustrate the application of the proposed method.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshDiscrete-time systems--Automation
dc.subject.otherAdaptation models
dc.subject.otherObservers
dc.subject.otherKalman filters
dc.subject.otherConvergence
dc.subject.otherLinear systems
dc.subject.otherParameter estimation
dc.titleA multiple model adaptive architecture for the state estimation in discrete-time uncertain LPV systems
dc.typeConference report
dc.subject.lemacSistemes de temps discret -- Automatització
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.identifier.doi10.23919/ACC.2017.7963311
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/7963311/
dc.rights.accessOpen Access
drac.iddocument21901554
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorRotondo, D., Hassani, V., Cristofaro, A.
upcommons.citation.contributorAmerican Control Conference
upcommons.citation.publishedtrue
upcommons.citation.publicationName2017 American Control Conference (ACC): 24-26 May 2017
upcommons.citation.startingPage2393
upcommons.citation.endingPage2398


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