  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.
