Published November 4, 2022 | Version v1
Conference paper Open

Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

  • 1. Eindhoven University of Technology


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.



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IMOCO4.E – Intelligent Motion Control under Industry 4.E 101007311
European Commission