JILSA  Vol.2 No.2 , May 2010
Implementation of Adaptive Neuro Fuzzy Inference System in Speed Control of Induction Motor Drives
ABSTRACT
A new speed control approach based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to a closed-loop, variable speed induction motor (IM) drive is proposed in this paper. ANFIS provides a nonlinear modeling of motor drive system and the motor speed can accurately track the reference signal. ANFIS has the advantages of employing expert knowledge from the fuzzy inference system and the learning capability of neural networks. The various functional blocks of the system which govern the system behavior for small variations about the operating point are derived, and the transient responses are presented. The proposed (ANFIS) controller is compared with PI controller by computer simulation through the MATLAB/SIMULINK software. The obtained results demonstrate the effectiveness of the proposed control scheme.

Cite this paper
nullK. Sujatha and K. Vaisakh, "Implementation of Adaptive Neuro Fuzzy Inference System in Speed Control of Induction Motor Drives," Journal of Intelligent Learning Systems and Applications, Vol. 2 No. 2, 2010, pp. 110-118. doi: 10.4236/jilsa.2010.22014.
References
[1]   R. P. Basu, “A Variable Speed Induction Motor Using Thyristors in the Secondary Circuit,” IEEE Transactions on Parer Apparatus and Systems, Vol. 90, 1971, pp. 509-514.

[2]   M. Ramamoorthy and M. Arunachalam, “A Solid-State Controller for Slip Ring Induction Motors,” The IEEE Industry Applications Society Annual Meeting, Los Angeles, California, October 2-6, 1977.

[3]   M. Ramamoorthy and M. Arunachalam, “Dynamic Per-formance of a Closed Loop Induction Motor Speed Control System with Phase-Controlled SCR's in the Rotor,” IEEE Transactions on Industry Applications, Vol. 15, No. 5, 1979, pp. 489-493.

[4]   Y. Hsu and W. Chan, “Optimal Variable-Structure Controller for DC Motor Speed Control,” IEEE Proceedings D on Control Theory and Applications, Vol. 131, No. 6, 1984, pp. 233-237.

[5]   B. S. Zhang and J. M. Edmunds, “On Fuzzy Logic Controllers,” IEEE International Conference on Control, Edinburg, UK, 1991, pp. 961-965.

[6]   H. Ying, W. Siler and J. J. Buckley, “Fuzzy Control Theory: A nonlinear Case,” Automatica, Vol.26, No.3, 1990, pp. 513-520.

[7]   D. Dirankov, H. Hellendorn and M. Reinfrank, “An Introduction to Fuzzy Control,” Springer-Verlag, New York, 1993.

[8]   M. Maeda and S. Murakami, “A Self-Tuning Fuzzy Con-troller,” Fuzzy sets and Systems, Vol.51, No. 1, 1992, pp. 29-40.

[9]   T. J. Procyk and E. H. Mamdani, “A Linguistic Self- Organizing Process Controller,” Automatica, Vol. 15, No. 1, 1979, pp. 53-65.

[10]   R. Storn and K. Price, “Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces,” ICSI Technical Report, March 1995.

[11]   D. Karaboga and S. Okdem, “A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm,” Turk Journal of Electrical Engineering, Vol. 12, No. 1, 2004, pp. 53-60.

[12]   D. Borojevic, L. Garces and F. Lee, “Performance Comparison of Variable Structure Controls with PI Control for DC Motor Speed Regulator,” IEEE Industry Applications Conference, 1984, pp. 395-405.

[13]   J. Zhao and B. K. Bose, “Evaluation of Membership Functions for Fuzzy Logic Controlled Induction Motor Drive,” IEEE 2002 28th annual Conference of the Industrial Electronics Society, Vol. 1, 2002, pp. 229-234.

[14]   A. S. A. Farag, “State-Space Approach to the Analysis of DC Machines Controlled by SCRs,” IEEE Proceeding Publication-on the Control of Power Systems Conference, Oklahoma, March 10-12, 1976, pp. 157-163.

[15]   N. Mohan, “Electric Drives: An Integrative Approach,” Minnesota Power Electronics Research & Education, Minnesota, 2003.

[16]   N. Mohan, “Advanced Electric Drives: Analysis, Control and Modeling using Simulink®,” Minnesota Power Electronics Research & Education, Minnesota, 2001.

[17]   B. K. Bose, “Fuzzy Logic and Neural Network Applications in Power Electronics,” Proceedings of the IEEE, Vol. 82, No. 8, 1994, pp. 1303-1323.

[18]   M. G. Simoes and B. K. Bose, “Neural Network Based Estimation of Feedback Signals for Vector Controlled Induction Motor Drive,” IEEE Transactions on Industry Applications, Vol. 31, No. 3, 1995, pp. 620-629.

 
 
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