Neural network application for flux and speed estimation in the sensorless induction motor drive

Sensorless field-oriented control (SFOC) of induction motor drives requires the knowledge of instantaneous magnitude and position of the rotor flux as well as the rotor speed. This paper deals with the application of artificial neural networks (ANN) for estimation of the rotor flux vector and motor speed on the basis of phase current measurement only. Various structures of the neural estimators were simulated and their performances were compared. The influence of changing rotor parameters during the drive were tested. The neural network is able to estimate accurately the rotor flux and speed during line-start operation and load torque changes of the motor. The results of simulation experiments indicate that the neural network estimator may be a feasible alternative to other flux and speed estimation methods