Design of a Self-tuning Neural-fuzzy Controller for the Speed Control of an Induction Motor

D.-S. Kim, W.-Y. Han, and C.-G. Lee (Korea)


Induction motor, Neural Network, Adaptive Fuzzy control


This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The Fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than an existent PID speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PID controller and is proved robust about parameter variation through simulation.

Important Links:

Go Back