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

摘要: 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 plant dynamics and noise processes are exactly known. To compensate inaccurate model information improve tracking ability, adaptive fading extended filtering algorithms proposed nonlinear system. second suffers from computational burden numerical problems when state dimension large. two-stage filter (TSEKF) respect to this has extensively studied past. Combining advantages of both AFEKF TSEKF, paper presents an (ATEKF) closed-loop position speed estimation a PMSM achieve operation. Experimental results demonstrate ATEKF algorithm PMSMs strong robustness against uncertainties very good real-time ability.