A Novel Speed Estimation Algorithm for a Permanent Magnet Linear Synchronous Motor Using an Extended Kalman Filter With Multiple Fading Factors
Xiao Liu, Jingli Zhang, Haoran Xie, Chunfu Hu
Abstract
A Suboptimal Multiple Fading Extended Kalman Filter (SMFEKF) for a Permanent Magnet Linear Synchronous Motor (PMLSM) is proposed to achieve accurate speed estimation and high reliability of a PMLSM sensorless drive system when under parameter mismatch and sudden load disturbance. First, a dynamic mathematical model of the PMLSM is established. Second, an Extended Kalman Filter (EKF) is presented which can be used to estimate the speed and position of the PMLSM. An SMFEKF is also introduced to improve the estimation performance of the proposed algorithm under parameter mismatch and sudden changes in load conditions. Finally, EKF and SMFEKF-based PMLSM systems are simulated and experimentally compared. Results show that the proposed algorithm can realize accurate estimations of speed and position of the PMLSM under different conditions. This study verifies that the introduction of multiple fading factors can significantly improve the estimation effect of the algorithm.