Different Control Schemes for Sensor Less Vector Control of Induction Motor

Abstract

This paper deals with the design of different control schemes for sensorless vector control of induction motor.Induction motor is most widely used A.C. Motor but the major draw back is flux and torque cannot be controlled individually.This can be obtained by implementing sensorless vector control methods.The control strategy of induction motor is by different controllers like conventional control methods and artificial intelligence control methods.The conventional control methods are sensitive to parameter changes and will not be accurate. This paper proposes to design a controller to over come the above draw backs by using intelligent control techniques like fuzzy logic, artificial neutral networks (ANN) and genetic algorithm (GA). The above conventional control methods are compared with intelligent control techniques. The simulation studies are carried out using Matlab/Simulink and the wave forms for speed, torque and voltage components for various controlles are plotted. Numerical analysis for speed and torque components considering parameters like peakover shoot and peak time are presented. Keyword: Conventional controllers Fuzzy logic Genetic algorithms Induction motor (IM) Neural networks Sensor less vector control Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Srinivas Gangishetti, JNTUHCE, Hyderabad, India.Email: gangishetti07@gmail.com INTRODUCTIONIn the field of industrial sector induction motor drives are major contributors, the simulation of these drives can be done in several methods, one of such kind is Simulink model [1]- [2].Mathematical modeling of induction motor drives are involved in conventional methods where the system diverts from required performance due to system variable variation [3].The conventional methods like kalman filter,PI Controller are used by many researchers to develop dq0 & abc models [4]. The Kalman filter controller uses numerical analysis based on predictor-corrector approximation [5] and PI controller based on linearity concept for unique operating points which resulted in attaining high performance was discussed [6] by several authors. These methods are sensitive to rotor time and correct flux estimationis required.Sensor less Vector control gains its significance where induction motor can modelled similar to D.C Motor using Field orientation control where torque and flux are independently controlled.With the rapid development of power electronics and digital computation the errors in the above methods are nullified with adaptation of artificial intelligence methods,like Fuzzy controller,Artifical neural networks and Genetic Algorithms .Many authors created a control signal employing fuzzy systems [7], these controllers relay on firing rules which are framed by past experiences. Thus, the outcome of the controller is also random and optimal results are based on selecting the right rule depending on the situation.Artifical Neural networks was used to developed an adaptive speed control of drives and to predict th...

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