This paper proposes a sensorless method for doubly salient electromagnetic motor (DSEM). The commutation signals are obtained through the comparison between the voltage of silent phase and a pre-set threshold value based on the characteristic that the excitation back electromotive force (EBEMF) at commutation points is unaffected by the saturation of excitation field. Furthermore, in order to improve the torque output performance of DSEM, a novel sensorless advanced angle control (AAC) is investigated. Unlike traditional AAC, through piecewise linear fitting EBEMF in advance commutation interval, this method does not require the detection of standard commutation points. The experiments implemented on a 12/8-pole DSEM validate the proposed methods over a wide range of speed and load conditions in both steady and dynamic procedures.
This article concentrates on the parameter estimation of brushless DC motor, where the stator current and winding back electromotive force are taken as the motor states, while the stator resistance and inductance are taken into consideration and augmented into the state vector. Based on this augmented model, a modified adaptive extended Kalman filter is proposed which updates the process noise covariance matrix in real time with the current input‐output data, and takes the state estimates by the traditional extended Kalman filter as one‐step estimation for the calculation of the covariance matrix. The convergence analysis is given to verify the theoretical results. Finally, the simulation results show that the proposed algorithm can effectively improve the estimation accuracy.
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