State estimation of permanent magnet synchronous motor using modified square-root UKF algorithm

Concerning the problem of permanent magnet synchronous motor state estimation, an estimation method based on modified square root UKF (SRUKF) is derived. To avoid the problem of significant calculation caused by increasing the amount of sigma points, based on the UT transformation, the spherical simplex sampling method was put forward. So the amount of calculation was lessened greatly with greater performance of UKF. With regard to the non-linearity of system, the SRUKF estimation method was adopted to solve the state estimation and avoid the linearization error of extended Kalman filtering (EKF). What' more, to avoid the divergence of filter and raise the velocity of convergence and stability of filter algorithm, the Cholesky, QR decomposition and the covariance square root matrix instead of covariance matrix were used in the process of estimation. Simulation results show that the method can reduce the amount of calculation and raise the estimation precision in contrast to extended Kalman filtering and SRUKF.