A Lag Compensation-Enhanced Adaptive Quasi-Fading Kalman Filter for Sensorless Control of Synchronous Reluctance Motor
Fengtao Gao, Zhonggang Yin, Cong Bai, Dongsheng Yuan, Jing Liu
Abstract
A novel position estimation strategy based on lag compensation-assisted adaptive quasi-fading Kalman filter (LC-AQFKF) is proposed for synchronous reluctance motor (SynRM) sensorless drive in this article. In LC-QFKF, the quasi-fading factor is derived to avoid the harsh assumptions of conventional adaptive fading Kalman filter, and accessibility of the method is improved while ensuring the estimation accuracy. The computational efficiency of LC-AQFKF is promoted by introducing the quasi-fading factor into the prediction error covariance matrix. Moreover, the frequency characteristic of AQFKF-based active back electromotive force observer is analyzed, and the phase lag problem in high-speed situations caused by the low-pass filtering property of AQFKF is overcome. In this way, the estimation accuracy of the rotor position is significantly enhanced. Besides, the double dynamic position compensation method is put forward to strengthen the position estimation performance of sensorless SynRM drive under dynamic conditions. The effectiveness of the proposed scheme is validated at a 1.5 kW SynRM drive.