Abstract
Switched Reluctance Motors (SRMs) enable power-efficient actuation with mechanically simple designs. This paper aims to identify the nonlinear relationship between torque, rotor angle, and currents, to design commutation functions that minimize torque ripple in SRMs. This is achieved by conducting specific closed-loop experiments using purposely imperfect commutation functions and identifying the nonlinear dynamics via Bayesian estimation. A simulation example shows that the presented method is robust to position-dependent disturbances, and experiments suggest that the identification method enables the design of commutation functions that significantly increase performance. The developed approach enables accurate identification of the torque-current-angle relationship in SRMs, without the need for torque sensors, an accurate linear model, or an accurate model of position-dependent disturbances, making it easy to implement in production.
Original language | English |
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Title of host publication | Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023) |
Publisher | IEEE |
Pages | 5494-5499 |
Number of pages | 6 |
ISBN (Electronic) | 979-8-3503-0124-3 |
DOIs | |
Publication status | Published - 2023 |
Event | 62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 62nd IEEE Conference on Decision and Control, CDC 2023 |
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Country/Territory | Singapore |
City | Singapore |
Period | 13/12/23 → 15/12/23 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.