Unscented Kalman Estimator for Estimating the State of Two-phase Permanent Magnet Synchronous Motor


This paper presents the unscented Kalman filters (UKF) for estimating the states (winding currents, rotor speed and rotor angular position) of two-phase Permanent Magnet Synchronous Motor (PMSM). The UKF is based on firstly specifying a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the Gaussian Random Variable (GRV), and when propagated through the true nonlinear system (motor model), capture the posterior mean and covariance accurately to the second order (Taylor series expansion). The results showed that the UK estimator could successively estimate the states of PMSM without need any Jacobian matrix.