Torque Estimation using Kalman Filter and Extended Kalman Filter Algorithms for a sensorless Direct Torque Controlled BLDC Motor drive: A Comparative Study

Abstract

Torque estimation for Direct Torque Controlled BLDC motor drive using Kalman Filter and Extended Kalman Filter are described in this paper. In conventional direct torque control method, the torque estimation is done based on the position information, which in turn requires position sensors for accurate estimation. In this paper, torque is directly estimated using Kalman filter and Extended Kalman Filter algorithms without the aid of any feedback information, and a comparison is made between the two. Both the algorithms are perceptive to follow the actual torque; the error between the actual and reference is used for the selection of appropriate voltage vector for inverter switching. The reflection of load changes in the control algorithm is also a part of the interest in this work. The potential of Extended Kalman Filter to follow exactly the changes in the load torque is utilized here and Extended Kalman Filter based sensorless drive is proposed for the torque estimation in Direct Torque Controlled BLDC motor. The performance parameters, viz. computational effort, torque ripple reduction are found to be superior with the Extended Kalman Filter algorithm, which is validated.