A Dead-Time Compensation Strategy Based on an Online Learned Artificial Neural Network

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dc.contributor.authorBuchta, Luděkcs
dc.contributor.authorKozovský, Matúšcs
dc.contributor.authorBlaha, Petrcs
dc.coverage.issue10cs
dc.coverage.volume72cs
dc.date.accessioned2025-09-15T08:56:10Z
dc.date.available2025-09-15T08:56:10Z
dc.date.issued2025-04-03cs
dc.description.abstractThis article presents an innovative approach to mitigate the harmonic distortion of the phase currents of a permanent magnet synchronous motor (PMSM) controlled by a field-oriented control (FOC) algorithm. The issue of phase current harmonic distortion is often a consequence of the output voltage deformation caused by the non-linearities of the voltage source inverter (VSI). The relationship between the disturbance voltages of the inverter and the phase currents of the motor is non-linear. Therefore, we used an artificial neural network (ANN) to identify the compensation voltages. The topology is designed to allow the neural network to solve complex problems with the limited computing resources available on the AURIX TC397 microcontroller. The input vector is assembled from quantities available in the PMSM FOC algorithm. The online learning process based on the back-propagation algorithm is adapted to operate directly on the microcontroller. The proposed strategy with ANN is verified on a real PMSM. The results show the excellent ability of the proposed ANN to suppress the harmonic distortion of the PMSM phase currents without knowledge of the VSI parameters.en
dc.formattextcs
dc.format.extent1-11cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Transactions on Industrial Electronics. 2025, vol. 72, issue 10, p. 1-11.en
dc.identifier.doi10.1109/TIE.2025.3544207cs
dc.identifier.issn0278-0046cs
dc.identifier.orcid0000-0002-8954-3495cs
dc.identifier.orcid0000-0002-1547-1003cs
dc.identifier.orcid0000-0001-5534-2065cs
dc.identifier.other197508cs
dc.identifier.researcheridG-8085-2014cs
dc.identifier.researcheridE-2371-2018cs
dc.identifier.researcheridD-6854-2012cs
dc.identifier.scopus56028720700cs
dc.identifier.scopus7006825993cs
dc.identifier.urihttps://hdl.handle.net/11012/255539
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Transactions on Industrial Electronicscs
dc.relation.urihttps://ieeexplore.ieee.org/document/10948334cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0278-0046/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectInverter non-linearities compensationen
dc.subjectdead-time effecten
dc.subjectartificial neural network (ANN)en
dc.subjectpermanent magnet synchronous motor (PMSM)en
dc.subjectvoltage source inverter (VSI)en
dc.titleA Dead-Time Compensation Strategy Based on an Online Learned Artificial Neural Networken
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197508en
sync.item.dbtypeVAVen
sync.item.insts2025.09.15 10:56:10en
sync.item.modts2025.09.15 10:33:03en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
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