Adaptive Extended Kalman Filter for Speed-Sensorless Control of Induction Motors

Abstract

This paper presents an adaptive extended Kalman filter (AEKF) algorithm estimating the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque for speed-sensorless control applications of induction motors (IMs). The performance of a standard extended Kalman filter (SEKF) algorithm depends on the correct selection of system and measurement noise covariance matrices. In SEKF algorithms, these matrices are generally assumed as constant and determined by the trial-and-error method. However, they are affected by the operating conditions of IM and should be updated considering the operating conditions. For this purpose, instead of the time-consuming trial-and-error method for determining these matrices, an innovation-based adaptive estimation approach having the capability of online update is used in this paper. Finally, in order to verify the superiority of the AEKF algorithm, estimation performances of AEKF and SEKF algorithms are compared under challenging scenarios for a wide speed range considering computational burdens and parameter variations.