Rotor Speed Identification Using Two Nonlinear Observers for Induction Motor Drive

This paper proposes two approaches to achieve robust speed estimation in induction motor sensorless control, namely: Model Reference Adaptive System (MRAS), its structure consists of a reference model that doesn't involve the variable being estimated and an adjustable model that does. The error between these two models is proportional to the error in the estimation of the unknown variable. This value is used to improve the estimation of the adaptive model so that its output is equal to the output of the reference model. An alternative approach to the MRAS observer is to use an Extended Kalman Filter, EKF. This is a well established technique for controlling complicated processes involving noisy measured signals that are further disturbed by random noise. It does this by working on the assumption that noise in the measured signal is uncorrelated to the disturbance noise. The Kalman Filter's ability to provide an optimum observation in complex non linear environments means that it could potentially be used for sensorless induction motor control. The validity of the proposed methods is confirmed by the simulative results.