Abstract
This paper proposes a novel sensorless technique for PMSM based on reduced-order linear model Kalman filter (LKF). Through the investigation into the comparison with extended Kalman filter (EKF) and flux linkage observer (FLO), the proposed LKF applied in position estimation can obtain higher precision than EKF and almost same precision as FLO, simpler tuning on parameters than that of covariance for EKF and excellent dynamic speed response. Experimental results prove the method based LKF overcomes inherent withdraws of FLO while keeping the benefit of EKF
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