Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

TL;DR

This paper introduces a novel Gaussian Process regression-based method to optimize commutation functions in switched reluctance motors, significantly reducing torque ripple and improving control accuracy.

Contribution

It develops a new continuous commutation function using Gaussian Process regression that directly minimizes torque ripple and power consumption, advancing motor control techniques.

Findings

Significant reduction in torque ripple in simulations

The new commutation function outperforms conventional methods

Improved control accuracy demonstrated in closed-loop tests

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.