Convergence analysis of the modified adaptive extended Kalman filter for the parameter estimation of a brushless DC motor

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.