Robust sensorless Induction Motor control based on Extended Kalman Filter observer

This paper suggests a robust sensorless control of the Induction Motor (IM) using the famous Extended Kalman Filter (EKF). The quality of the estimated speed strongly depends on rotor resistance and stator resistance. A change in rotor resistance or stator resistance causes a significant speed estimation error, especially at low speed. The variation in these resistances affects not only the estimated speed but also the indirect Field-Oriented Control (FOC), as it would be demonstrated later in this paper. Unfortunately, these resistances are strongly affected by temperature and frequency variation. Therefore, the estimation of rotor resistance and stator resistance and rotor speed is necessary to enhance the robustness of the sensorless control of IM. This study also covers the load torque estimation used to improve the estimated speed transient state. In short, the contribution of this paper is represented by the estimation of the following states: currents, fluxes, rotor speed, load torque, stator resistance, and rotor resistance simultaneously by a single EKF without using a model of stator resistance or switching between two separate EKF algorithms. Simulation results obtained thanks to Matlab/Simulink software demonstrates the robustness of the EKF observer at low speed against variation of parameters and the robustness of Field Oriented Control against variation of rotor resistance.