Differential flatness theory and Extended Kalman Filtering for sensorless control of induction motors

Abstract

Diffenential flatness theory and Extended Kalman Filtering (EKF) are used for implementing a sensorless control scheme for induction motor drives. It is shown that the induction motor is a differentially flat system, since all state variables of the circuits describing the motor's dynamics can be expressed as functions of the flat output and its derivatives. The flat output consists of the angle of the rotor and of the the angle of the magnetic field. Flatness-based control for the complete (sixth-order) induction motor model is analyzed while the Extended Kalman Filter is proposed to estimate the state vector of the nonlinear electric motor using a limited number of sensors. The efficiency of the above mentioned Extended Kalman Filter-based control scheme, is tested through simulation experiments.