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System identification of MISO fractional systems: Parameter and differentiation order estimation

dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorVICTOR, Stéphane
ORCID: 0000-0002-0575-0383
IDREF: 148688942
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorMAYOUFI, Abir
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorMALTI, Rachid
IDREF: 137761856
dc.contributor.authorCHETOUI, Manel
dc.contributor.authorAOUN, Mohamed
dc.date.accessioned2022-07-13T13:03:22Z
dc.date.available2022-07-13T13:03:22Z
dc.date.issued2022-07
dc.identifier.issn0005-1098en_US
dc.identifier.urioai:crossref.org:10.1016/j.automatica.2022.110268
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/140484
dc.description.abstractEnThis paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis.
dc.language.isoENen_US
dc.sourcecrossref
dc.subject.enSystem identification
dc.subject.enContinuous-time
dc.subject.enInstrumental variable
dc.subject.enMultiple-input single-output (MISO) system
dc.subject.enOrder optimization
dc.subject.enFractional model
dc.title.enSystem identification of MISO fractional systems: Parameter and differentiation order estimation
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.automatica.2022.110268en_US
dc.subject.halSciences de l'ingénieur [physics]/Automatique / Robotiqueen_US
bordeaux.journalAutomaticaen_US
bordeaux.page110268en_US
bordeaux.volume141en_US
bordeaux.hal.laboratoriesLaboratoire d’Intégration du Matériau au Système (IMS) - UMR 5218en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.identifierhal-03722681
hal.version1
hal.date.transferred2022-07-13T13:03:24Z
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Automatica&rft.date=2022-07&rft.volume=141&rft.spage=110268&rft.epage=110268&rft.eissn=0005-1098&rft.issn=0005-1098&rft.au=VICTOR,%20St%C3%A9phane&MAYOUFI,%20Abir&MALTI,%20Rachid&CHETOUI,%20Manel&AOUN,%20Mohamed&rft.genre=article


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