Improved rotor speed estimation using two Kalman filter-based algorithms

This paper proposes the application of two new Kalman filter-based algorithms to realise a speed-sensorless vector control of induction motor drives. The first one is a linear Kalman filter (LKF)-based algorithm that estimates the equivalent disturbance torque, which is compensated by the injection of a feedforward signal. The latter is an extended Kalman filter (EKF)-based algorithm used to obtain a correct implementation of sensorless vector control, since it estimates both the rotor flux components and speed. The mathematical EKF-model is accurate because of the equivalent-disturbance compensation obtained from the LKF-based observer. The rotor speed estimate is very good in the whole velocity range including zero value. The results show the effectiveness of the proposed control scheme.