Permanent Magnet Synchronous Motor Speed and Position Estimation Using Reduced -Order Extended Kalman Filter

Permanent Magnet Synchronous Motor (PMSM) plays a pivotal role in many applications. Precise control of the PMSM necessitates accurate estimation of its states, particularly the motor's speed and position. Conventionally, a full-order Extended Kalman filter (EKF) is employed for state estimation. However, since the winding currents are directly measurable, deploying a full-order observer to estimate all states becomes unnecessary. In this work, a novel Reduced-order Extended Kalman filter (ROEKF) is introduced to estimate the speed and position of a two-phase PMSM. Simulation results show that the proposed ROEKF brings equivalent estimation accuracy compared to the full-order EKF, while it reduces the computation cost significantly. In addition, the proposed reduced-order filtering approach can be easily adapted to other applications to reduce the computation cost as well.