Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

作者：Yi, Boyu; Kang, Longyun^*; Tao, Sinian; Zhao, Xianxian; Jing, Zhaoxia

来源：Mathematical Problems in Engineering, 2013, 2013: 974974.

DOI：10.1155/2013/974974

• Computational burden
• Extended Kalman filtering
• Model informations
• Model uncertainties
• Permanent Magnet Synchronous Motor
• Sensor less control
• Sensorless field oriented control
• Sensorless operation

Extended Kalman filters (EKF) have been widely used for sensorless field oriented control (FOC) in permanent magnet synchronous motor (PMSM). The first key problem associated with EKF is that the estimator requires all the plant dynamics and noise processes are exactly known. To compensate inaccurate model information and improve tracking ability, adaptive fading extended Kalman filtering algorithms have been proposed for the nonlinear system. The second key problem is that the EKF suffers from computational burden and numerical problems when state dimension is large. The two-stage extended Kalman filter (TSEKF) with respect to this problem has been extensively studied in the past. Combining the advantages of both AFEKF and TSEKF, this paper presents an adaptive two-stage extended Kalman filter (ATEKF) for closed-loop position and speed estimation of a PMSM to achieve sensorless operation. Experimental results demonstrate that the proposed ATEKF algorithm for PMSMs has strong robustness against model uncertainties and very good real-time state tracking ability.

• 出版日期2013
• 单位华南理工大学; 湖南人文科技学院