Rotor Speed Estimation Based on Extended Kalman Filter for Sensorless Vector Control of Induction Motor

In this paper, a sensorless vector control method is introduced where rotor speed is estimated based on extended Kalman filter (EKF). The EKF is a recursive optimum stochastic state estimator. The proposed EKF is designed to obtain a small estimation speed error in both transient and steady state over a wide speed range. A major challenge at very low and zero speed is the lost coupling effect from the rotor to the stator, which makes the information on rotor variables unobservable on the stator side. To solve this problem, in the proposed EKF, the load torque and the rotor speed with unmeasurable information are estimated simultaneously. The rotor speed is considered via the equation of motion and the estimation of load torque, on the other hand, is performed as a constant parameter. Hardware and software for experiment of the AC induction motor drive are introduced. The experimental results are presented to show the effectiveness of the proposed system.