JIC_V2_N1_RP2 Prashant Menghal A. Jaya Laxmi Journal on Instrumentation & Control Engineering 2321 – 1148 2 1 9 22 Proprtional Integrator(PI) Controller, Fuzzy Logic Controller (FLC), Neuro Network(NN), Intelligent Controller, Adaptive Neuro Fuzzy Inference System(ANFIS), Induction Motor (IM), Insulated Gate Bipolar Transistor (IGBT), Pulse Width Modulation (PWM) Induction Motors have many applications in the industries, because of the low maintenance and robustness. The speed control of induction motor is more important to achieve maximum torque and efficiency. The rapid development of power electronic devices and converter technologies in the past few decades, has made possible efficient speed control by varying the supply frequency and voltage, giving rise to various forms of adjustable-speed Induction Motor drives. In about the same period, there were also advances in control methods and Artificial Intelligence (AI) techniques, including expert system, fuzzy logic, neural networks and genetic algorithm. Researchers soon realized that the performance of induction motor drives can be enhanced by adopting Artificial Intelligent based methods. 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 and artificial neural network controllers are also introduced to the system for keeping the motor speed to be constant when the load varies. The performance of fuzzy logic and artificial neural network based controllers is compared with that of the conventional proportional integral controller. The performance of the Induction motor drive has been analyzed for constant and variable loads. November 2013 - January 2014 Copyright © 2014 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=2788