DYNAMIC SPEED CONTROL OF THREE PHASE INDUCTION MOTOR USING ARTIFICIAL INTELLIGENCE BASED ON ANFIS
Author’s Name : Nandini P | Vaikundaselvan B | Venupriya S R
Volume 03 Issue 04 Year 2016 ISSN No: 2349-2503 Page no: 31-36
Abstract:
Induction motors are commonly used in industries due to low maintenance and robustness. By controlling the speed of Induction motor maximum efficiency and torque can be obtained. Using artificial intelligence particularly Fuzzy and Neural Networks, Induction motor performance can be improved. This paper presents dynamic speed control of induction motor drive using ANFIS. The integrated solution allows the user to compare the Neural Network and ANFIS technique. By using ANFIS the applied voltage frequency is controlled and thus the speed of the Induction motor is controlled to the required value. Rise time of the motor is decreased and pick –up speed is increased. By this the performance of the Induction motor is increased. The dynamic modelling and simulation of induction motor has been done using MATLAB/SIMULINK and the Induction motor drive performance has been analyzed for Artificial Intelligence controller.
Keywords:
Neuro Network(NNW), ANFIS Controller, Induction Motor, Fuzzy Logic
References:
- Ashok Kusagur et al (2009), ‘Modelling of Induction Motor & Control of Speed Using Hybrid Controller Technology’, Proc. Int. Journal of Theoretical Information & Technology (JATIT), Vol. 10, issue 2, pp. 117-126.
- Arulmozhiyal. R and Baskaran. K (2009), ‘Space Vector Pulse Width Modulation Based Speed Control of Induction Motor using Fuzzy PI Controller’, Proc. of the International Journal of Computer and Electrical Engg., Vol. 1, No. 1, pp. 98-103.
- Ernesto Araujo,(2008), ‘Improved Takagi-Sugeno Fuzzy Approach’, IEEE International Conference on Fuzzy Systems (FUZZ 2008), pp. 1154-1158.
- Haider A. F. Mohamed et al, (2008), ‘Fuzzy-SMC-PI Flux and Speed Control for Induction Motors’, Proc. of RAM-2008, pp. 325-328 .
- Kusagur. S. F. Kodad and Sankar Ram. B. V, (2009), ‘AI based design of a fuzzy logic scheme for speed control of induction motors using SVPWM technique’, Int. Jr. Comp. Sci. & Network Security, Vol. 9, No. 1, pp. 74 – 80.
- Menghal. P. M and Jaya Lakshmi. A (2013), ‘Artificial intelligence based induction motor drive’ Micheal Faraday IET India Summit, Kolkata , India , pp.208-212.
- Menghal.P.M ,and Jaya Laxmi. A “Artificial Intelligence Based Dynamic Simulation of Induction Motor Drives” IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) ISSN: 2278-1676 Volume 3, Issue 5 (Nov. – Dec. 2012), pp 37-45.
- Mokhtar Zerikat and Sofiane CHekroun, (2008), ‘High performance speed tracking of IM using an adaptive fuzzy NN control’, Int. Jr. Sciences & Techniques of Auto. Contr. & Computer Engg., IJ-STA Special Issue, CEM, pp. 516-531.
- Rehman. H and Dhaouadi. R,(2008), ‘A fuzzy learning-sliding mode controller for direct field-oriented induction machinese’, Neuro-computing, Vol. 71, pp. 2693–2701.
- Chen. J. Y and Wong. C. C, (2000), ‘Implementation of the Takagi-Sugeno model-based fuzzy control using an adaptive gain controller’, IEE Proc. – Control Theory Appl., Vol. 147, No. 5, pp. 509 – 514.
- Farzan Rashidi, (2004), ‘Sensorless Speed Control of Induction Motor Derives Using a Robust and Adaptive Neuro-Fuzzy Based Intelligent Controller’, IEEE International Conference an Industrial Technology (ICIT), pp. 617-627.
- Ernesto Araujo, (2008), ‘Improved Takagi-Sugeno Fuzzy Approach’, IEEE International Conference on Fuzzy Systems (FUZZ 2008), pp. 1154-1158.
- Khiar. D, (2007), ‘Robust takagi-sugeno fuzzy control of a spark ignition engine’, Control Engg. Practice.