Abstract
This paper focuses on an improved square root unscented Kalman filter (SRUKF) and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM). The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation under the conditions of step response or load disturbance.
Highlights
The extended Kalman filter (EKF) has been successfully implemented as a state observer for induction motor (IM) drives in various areas [1,2,3,4,5,6,7,8]
The performance of the improved square root unscented Kalman filter (SRUKF) observer is validated subject to a speed command
This paper has proposed and investigated an improved SRUKF filter for state estimation in sensorless permanent magnet synchronous motor (PMSM) drives
Summary
The extended Kalman filter (EKF) has been successfully implemented as a state observer for induction motor (IM) drives in various areas [1,2,3,4,5,6,7,8]. A symmetric sigma point solution, which used 2n + 1 points to match the mean and covariance of an n-dimensional random variable, was presented. With this set of points, the unscented transform guaranteed the same performance as the truncated second order filter, with the same order of calculations as an EKF but without the need to calculate any approximations or derivatives. A square root unscented Kalman filter (SRUKF) algorithm solves the problem of filtering divergence caused by non-positive of error covariance matrix in general EKF and UKF, and the stability of the algorithm is improved. In [12], SRUKF was used to estimate the speed and rotor
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