  Switched Reluctance Motors (SRMs) enable power-efficient actuation with
mechanically simple designs. This paper aims to identify the nonlinear
relationship between torque, rotor angle, and currents, to design commutation
functions that minimize torque ripple in SRMs. This is achieved by conducting
specific closed-loop experiments using purposely imperfect commutation
functions and identifying the nonlinear dynamics via Bayesian estimation. A
simulation example shows that the presented method is robust to
position-dependent disturbances, and experiments suggest that the
identification method enables the design of commutation functions that
significantly increase performance. The developed approach enables accurate
identification of the torque-current-angle relationship in SRMs, without the
need for torque sensors, an accurate linear model, or an accurate model of
position-dependent disturbances, making it easy to implement in production.
