Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors

TL;DR

This paper introduces a Bayesian-based nonlinear identification method for Switched Reluctance Motors that improves commutation function design, reduces torque ripple, and enhances performance without requiring torque sensors or detailed models.

Contribution

It presents a novel Bayesian estimation approach for identifying nonlinear torque-current-angle relationships in SRMs, facilitating better commutation control.

Findings

Method is robust to disturbances

Enables torque ripple reduction

Improves motor performance

Abstract

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.