Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

Switched reluctance motors are appealing because they inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics such way torque ripple reduced. To end, convex optimization problem posed directly penalizes between samples, as well power consumption, Gaussian Process regression used obtain continuous function. resulting fundamentally different from conventional functions, closed-loop simulations show significant reduction error. results offer new perspective on suitable functions for accurate control motors.