Comparative Evaluation of Speed and Position Estimators for Synchronous Reluctance Motor Drive with Parameter Mismatches and Current Sensor Faults

Sensorless control of synchronous reluctance motors (SynRMs) requires accurate rotor speed and position estimation that is robust to parameter uncertainties and sensor faults. This paper systematically investigates the influence of estimator architecture on its overall performance. Five estimators are compared, combining different motor-model representations (fundamental, active-flux, or extended electromotive force (EEMF)) with different observer structure (sliding-mode, Luenberger, Kalman filter, or extended Kalman filter). The estimators are evaluated in a unified simulation environment using four performance metrics: (1) sensitivity to stator resistance and inductance uncertainties, (2) sensitivity to gain and offset errors in phase-current sensors, (3) the overall estimation accuracy, and (4) computational complexity. The results reveal distinct performance characteristics among the considered approaches, with the extended Kalman filter and the EEMF-based Luenberger observer demonstrating the most favorable features.