A hybrid intelligent control based torque tracking approach for doubly fed asynchronous motor (DFAM)‎ drive

Abstract EN

In this paper a hybrid intelligent control based torque tracking approach for Doubly Fed Asynchronous Motor (DFAM) drive is proposed.

First, a mathematical model of DFAM written in an appropriate d-q reference frame is established to investigate simulations.

In order to control the rotor currents of DFAM, a torque tracking control law is synthesized using PI controllers ; under conditions of the stator side power factor is controlled at unity level.

A four layer Neural Network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC).

The back propagation learning algorithm is used for training this network.

The performances of neuro-fuzzy controller (NFC) which is based on the torque tracking control algorithm are investigated and compared to those obtained from the PI controller.

Results obtained in Matlab / Simulink environment show that the NFC is more robust, superior dynamic performance and hence found to be a suitable replacement of the conventional PI controller for the high performance drive applications.