Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression
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.
Cite
@article{arxiv.2209.06550,
title = {Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression},
author = {Max van Meer and Gert Witvoet and Tom Oomen},
journal= {arXiv preprint arXiv:2209.06550},
year = {2022}
}
Comments
6 pages, 11 figures