Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

Max Van Meer, Gert Witvoet, Tom Oomen

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
39 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)302-307
Issue number37
Publication statusPublished - 2022
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022


  • Convex optimization
  • Feedback Control
  • Linearization
  • Nonparametric methods
  • Static optimization problems
  • Switched Reluctance Motor


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