Sensorless Control of Synchronous Reluctance Motor Based on Active Flux Vector and Extended Kalman Filter

Abstract

The need for high performance and high control accuracy in electric motors has made modern motor control techniques popular. Modern control techniques often require position sensors integrated into the electric motor. As an alternative to position measurement, the position estimation methods offer a cost advantage within acceptable error results. In this study, a new sensorless control method that includes both Active Flux Observer (AFO) and Extended Kalman Filter (EKF) is proposed. The active flux vector is obtained using stator currents and voltage data. The active flux vector components are separated and rotor position estimation is made. An angular velocity estimation is made to this vector by different methods. In this article, EKF, which can make less angular velocity estimation errors than angular velocity estimation methods obtained by AFO and other methods, is proposed. To validate the proposed method, the synchronous reluctance motor was modeled and controlled with the predicted position information. By applying EKF to the model, the load torque is estimated as well as the rotor angular velocity. Regarding position estimation, the results show that the EKF method is up to 50% more successful in the ramp and step load torques than other methods such as the Phase Locked Loop (PLL) and Flux Derivation Method in terms of rotor angular velocity estimation error.