A Cubature Kalman Filter based speed and position estimator for Permanent Magnet Synchronous Motor

This paper presents a Cubature Kalman Filter (CKF) based speed and position observer for a Permanent Magnet Synchronous Motor (PMSM). CKF is a new variety of Kalman filter which uses a third degree spherical-radial cubature rule to numerically compute multivariate moment integrals. Unlike in an Extended Kalman Filter(EKF), mean and covariance are propagated through the non-linear system, which minimizes the errors due to linearization. The proposed observer is incorporated in the sensorless control of an IPMSM of 1.5kW, 3000rpm rating. For the CKF algorithm, PMSM is modeled in stationary αβ reference frame. To get comparatively better transient performance and convergence of the CKF for a non-zero initial rotor position, system covariance matrix Q is chosen adaptively. Simulation results for a VSI fed IPMSM are presented and the convergence of CKF is shown for a variation of stator resistance. Performance of the proposed observer is compared with that of an EKF observer.