Control Set Reduction for PMSM Predictive Controller via Assisted Learning Algorithm

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dc.contributor.authorKozubík, Michalcs
dc.contributor.authorVáclavek, Pavelcs
dc.date.issued2024-06-18cs
dc.description.abstractThis paper introduces innovative methods for reducing the control set in finite control set model predictive control of the Permanent Magnet Synchronous Motor powered by a 3-level voltage source inverter. The primary objective of this reduction is to address a crucial factor in the computational burden of the control algorithm-the exponential growth in the number of potential switching state combinations forming the controller’s control set with an increasing prediction horizon length. The proposed methods aim to decrease the number of switching states necessary for evaluation, mitigating the aforementioned exponential growth. These methods leverage information about the controller’s behavior. The first method relies solely on the count of transitions between individual switching states. Additionally, the second method incorporates information about the states of the controlled motor to construct a decision tree, forming the new control set. The behavior of the controllers with reduced and complete control sets is compared in the simulation experiment, emphasizing the proper tracking of the requested angular speed and their overall computational complexity.en
dc.formattextcs
dc.format.extent1-6cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citation2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE). 2024, p. 1-6.en
dc.identifier.doi10.1109/ISIE54533.2024.10595683cs
dc.identifier.isbn979-8-3503-9408-5cs
dc.identifier.orcid0000-0002-6887-202Xcs
dc.identifier.orcid0000-0001-8624-5874cs
dc.identifier.other189125cs
dc.identifier.researcheridA-3448-2009cs
dc.identifier.scopus8448897700cs
dc.identifier.urihttp://hdl.handle.net/11012/249361
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartof2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE)cs
dc.relation.urihttps://ieeexplore.ieee.org/abstract/document/10595683cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.subjectfinite control seten
dc.subjectmodel predictive controlen
dc.subjectnonlinear controlen
dc.subjectpermanent magnet synchronous motoren
dc.subjectsupervised learningen
dc.titleControl Set Reduction for PMSM Predictive Controller via Assisted Learning Algorithmen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-189125en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:35en
sync.item.modts2025.01.17 15:18:00en
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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