Modelling, simulation & analysis of induction motor using artificial intelligent controller

Abstract

This paper presents an integrated environment for speed control of induction motor (IM) using artificial intelligent controller. The main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, adaptive neuro-fuzzy inference system (ANFIS) controller is proposed in this paper. The rapid development of power electronic devices and converter technologies in the past few decades, however, has made possible efficient speed control by varying the supply frequency and voltage, giving rise to various forms of adjustable-speed induction motor drives. The integrated environment allows users to compare simulation results between classical and artificial intelligent controllers. The fuzzy logic controller (FLC), artificial neural network (ANN), and ANFIS controllers are also introduced to the system for keeping the motor speed to be constant when the load varies. Comparison between PI, fuzzy, ANN, and Adaptive neuro-fuzzy controller-based dynamic performance of induction motor drive has been presented. ANFIS-based control of induction motor will prove to be more reliable than other control methods. The performance of the induction motor drive has been analyzed for no load, constant load, and change in speed conditions. This paper also takes detailed review of the previous literature along with the contribution of us.