Comparison of state-of-the-art estimators for electrical parameter identification of PMSM

Abstract

In this paper, four state-of-the-art online estimation approaches, i.e. recursive least square (RLS) approach, model reference adaptive system (MRAS), extended Kalman filter (EKF) and unscented Kalman filter (UKF) for parameter identification of permanent magnet synchronous machines (PMSM) are implemented and compared. Moreover, a promising estimation method, the moving horizon estimator (MHE), is also investigated. The performance comparison is conducted with simulations and experiments under various scenarios on a permanent magnet synchronous motor among these five techniques.