On Improving TLS Identication Results Using Nuisance Variables with Application on PMSM

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dc.contributor.authorFriml, Dominikcs
dc.contributor.authorKozubík, Michalcs
dc.contributor.authorVáclavek, Pavelcs
dc.date.accessioned2021-11-16T09:52:51Z
dc.date.available2021-11-16T09:52:51Z
dc.date.issued2021-11-13cs
dc.description.abstractThis article presents a novel total least-squares based method for errors-in-variables model identication with a known structure. This method considers the errors of both input and output variables and thus achieves more accurate estimates compared to conventional ordinary least-squares based methods. The introduced method consists of two recursive total least-squares algorithms connected in a hierarchical structure, which allows for exploitation of nuisance variables and a priori known structure of the identied model. The total least-squares (TLS) method is introduced, and a new “nuisance improved hierarchical total least-squares” (nHTLS) method is derived. Its properties are discussed and proved by simulations. Furthermore, the method is applied in a practical experiment consisting of the state-space identication of the permanent magnet synchronous motor (PMSM). The introduced method is compared with TLS and proven to provide measurably superior dynamical behavior and smaller estimation error of results.en
dc.formattextcs
dc.format.extent1-6cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. 2021, p. 1-6.en
dc.identifier.doi10.1109/IECON48115.2021.9589402cs
dc.identifier.isbn978-1-6654-3554-3cs
dc.identifier.other173146cs
dc.identifier.urihttp://hdl.handle.net/11012/202278
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Societycs
dc.relation.urihttps://ieeexplore.ieee.org/document/9589402cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectTotal Least-Squaresen
dc.subjectErrors-in-Variablesen
dc.subjectHierarchical Total Least-Squaresen
dc.subjectNuisance Variablesen
dc.subjectPMSM Identicationen
dc.titleOn Improving TLS Identication Results Using Nuisance Variables with Application on PMSMen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-173146en
sync.item.dbtypeVAVen
sync.item.insts2023.02.17 16:55:07en
sync.item.modts2023.02.17 16:14:43en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
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