Siroos Talaee - Malek Ashtar University of Technology (MUT), Tehran, Iran
Saeed Nasrollahi - Sharif University of Technology, Tehran, Iran
Mohammad Reza Arvan - Malek Ashtar University of Technology (MUT), Tehran, Iran

In this paper we analyze the permanent magnet synchronous motor. The state equations governing the permanent magnet synchronous motor are nonlinear, so the angular position and velocity of its rotor are estimated using nonlinear filters. Since the current passing through the armature is measurable, this measurement is used to estimate system states (i.e. angular position and angular velocity). Discrete extended Kalman filter and hybrid extended Kalman filter algorithms are presented. The comparison between simulation results of rotor’s position and velocity estimation using discrete extended Kalman filter and hybrid Kalman filter shows that hybrid Kalman filter estimator has better performance

discrete-time extended Kalman filter, hybrid extended Kalman filter, motor state estimation, nonlinear systems, permanent magnet synchronous motor