[2209.06550] Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression
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Electrical Engineering and Systems Science > Systems and Control

arXiv:2209.06550 (eess)
[Submitted on 14 Sep 2022]

Title:Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

Authors:Max van Meer, Gert Witvoet, Tom Oomen
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Abstract:Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.
Comments: 6 pages, 11 figures
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2209.06550 [eess.SY]
  (or arXiv:2209.06550v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2209.06550

Submission history

From: Max van Meer [view email]
[v1] Wed, 14 Sep 2022 10:57:37 UTC (918 KB)
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