Smoothed Kalman MRAS observer for speed sensorless indirect vector controlled induction motor drives

Abstract

This paper proposes a modified Kalman filter algorithm based on fixed lag smoothing which can be used to estimate the rotor speed and flux of three phase induction motor. The behaviour of a typical extended Kalman filter (EKF) algorithm is influenced by the process and measurement error covariance matrices. In EKF, these matrices are chosen by trial and error method and have to be varied according to the varying operating conditions of the motor. In this paper a smoothing-based extended Kalman filter (SKF) algorithm which uses additional data points for estimation is proposed. Since additional data is used, the algorithm is able to give a better estimate, with the same values of process and measurement error covariance matrices. The performance of the proposed algorithm is explored for sensorless indirect vector control application of three-phase induction motor. Finally, in order to validate the superiority of this algorithm, it is compared with the performance of EKF algorithm for various reference speeds in real-time.