Design of a Robust and Adaptive Sensorless Speed Controller for Induction Motor Drives Using General regression Neural Network Based Fuzzy Approach

The main purpose of this paper is to apply the Fuzzy based General Regression Neural Network (FGRNN) to the speed control of induction motor. A General Regression Neural Network (GRNN) is adopted to estimate the motor speed and thus provide a sensorless speed estimator system. The performance of the proposed FGRNN speed controller is evaluated for a wide range of operating conditions for induction motor. These include startup and parameters variations. Obtained results show that the GRNN provides a very satisfactory speed estimation under the above mentioned operation conditions and also the sensorless FGRNN speed controller can achieve very robust and satisfactory performance and could be used to get the desired performance levels. The response time is also very fast despite the fact that the control strategy is based on bounded rationality. To evaluate the usefulness of the proposed method, we compare the response of this method with PID controller. The simulation results show that our method has the better control performance than PID controller. Key-Words: Induction motor derives, general regression neural network, Fuzzy logic, Speed Control