Sensorless Speed Control of Synchronous Reluctance Motor Drives Based on Extended Kalman Filter and Neural Magnetic Model

Abstract

Due to their robustness and adaptability, position estimators based on the extended Kalman filter have been used in permanent magnet synchronous motors for decades. The time has come to extend their use to reluctance motors as well and this work focuses on the elements that hinder the transition. All passes through the availability of an accurate and analytical magnetic model, which is obtained by Artificial Intelligence tools. It is proved that the sensorless control of synchronous reluctance motors using the extended Kalman filter is possible over broad speed and torque ranges. The experimental session compares different implementation possibilities, concluding with the proposal of a new hybrid algorithm that greatly reduces the computational load.