Scalar control of an induction motor using artificial intelligent controller

In the industrial sector especially in the field of electric drives & control, induction motors play a vital role. Without proper controlling of the speed, it is virtually impossible to achieve the desired task for a specific application. Based on the inability of conventional control methods like PI, PID controllers to work under wide range of operation, artificial intelligent based controllers are widely used in the industry like ANN, Fuzzy controller, ANFIS, expert system, genetic algorithm. 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 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. This paper presents an integrated environment for speed control of induction motor (IM) using artificial intelligent controller. The integrated environment allows users to compare simulation results between classical and artificial intelligent controllers. The fuzzy logic controller, artificial neural network controller and ANFIS controllers are also introduced to the system for keeping the motor speed to be constant when the load varies. The comparison between Conventional PI, Fuzzy Controller, ANN and Adaptive neuro fuzzy controller based dynamic performance of induction motor drive has been presented. Adaptive Neuro Fuzzy 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 and variable loads.