CFP last date
20 May 2024
Reseach Article

Design of Speed Controller for Squirrel-cage Induction Motor using Fuzzy Logic based Techniques

by Amit Mishra, Zaheeruddin
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 22
Year of Publication: 2012
Authors: Amit Mishra, Zaheeruddin
10.5120/9429-3791

Amit Mishra, Zaheeruddin . Design of Speed Controller for Squirrel-cage Induction Motor using Fuzzy Logic based Techniques. International Journal of Computer Applications. 58, 22 ( November 2012), 10-18. DOI=10.5120/9429-3791

@article{ 10.5120/9429-3791,
author = { Amit Mishra, Zaheeruddin },
title = { Design of Speed Controller for Squirrel-cage Induction Motor using Fuzzy Logic based Techniques },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 22 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 10-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number22/9429-3791/ },
doi = { 10.5120/9429-3791 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:03:11.379024+05:30
%A Amit Mishra
%A Zaheeruddin
%T Design of Speed Controller for Squirrel-cage Induction Motor using Fuzzy Logic based Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 22
%P 10-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a performance based comparative study of various fuzzy logic controllers (FLCs) to control the speed of squirrel-cage induction motor (SCIM) by replacing the conventional proportional??integral (PI) controller. The fuzzy logic based controller does not require any identification of motor dynamic to control its speed and also assures the disturbance rejection with high robustness. Performances of the different fuzzy controllers (i. e. PD??, PI?? and PID??like ) are also compared with the conventional PI speed controller in terms of several performance measures such as peak overshoot (Mp%), settling time (ts), rise time (tr), steady state error (ess), integral absolute error (IAE), integral squared error (ISE), integral of time-multiplied absolute error (ITAE) and integral of time-multiplied squared error (ITSE), at different values of load (torque). The simulation results show the effectiveness of the controllers based on fuzzy logic techniques and, for each performance index, the PI??like fuzzy speed controller outperformed its conventional counterpart. Moreover, the performance of proportional??integral??derivative (PID??like) fuzzy speed controller is found best among all the fuzzy controllers discussed in this paper.

References
  1. Ming-Tzu Ho and Chia-Yi Lin, PID Controller Design for robust performance,IEEE Transactions on Automatic Control, vol. 48, no. 8, pp. 1404–1409, Aug. (2003).
  2. Krishnamoorthy Natarajan, Robust PID Controller Design for Hydro turbines,IEEE Transactions on Energy conversions, vol. 20, no. 3, pp. 661–667, Sep. (2005).
  3. B. K. Bose, Modern Power Electronics and AC Drives, Pearson Education, Inc. , (2002). 0.
  4. L. A. Zadeh, Fuzzy sets, Informat. Control, vol. 8, pp. 338- 353, (1965).
  5. E. H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Mun Much. Studies. , vol. 7, no. 1, pp. 113, (1975).
  6. M. Sugeno and K. Murakami, Fuzzy parking control of model car, in 23rd IEEE Conf. on Decision and Control, Las Vegas, (1984).
  7. O. Yagishita, O. Itoh, and M. Sugeno, Application of fuzzy reasoning to the water purification process, in Industrial Applications of Fuzzy Control. , M. Sugeno, Ed. Amsterdam: North Holland, pp. 19-40, (1985).
  8. S. Yasunobu and S. Miyamoto, Automatic train operation by predictive fuzzy control, in Industrial Application of Fuzzy Control, M. Sugeno, Ed. Amsterdam: North-Holland, pp. 118, (1985).
  9. S. Yasunobu and T. Hasegawa, Evaluation of an automatic container crane operation system based on predictive fuzzy control, Control Theory Adv. Technol, vol. 2, no. 3, pp. 419- 432, (1986).
  10. M. Kinoshita, T. Fukuzaki, T. Satoh, and M. Miyake, An automatic operation method for control rods in BWR plants, in Proc. Specialists Meeting on In-Core Instrumentation and Reactor Core Assessment, Cadarache, France, (1988).
  11. T. Yamakawa and K. Sasaki, Fuzzy memory device, in Proc. 2nd IFSA Congress, Tokyo, Japan, pp. 551-555, (1987).
  12. D. Dirankov, H. Hellendorn, and M. Reinfrank, An Introduction to Fuzzy Control. New York: Springer-Verlag, (1993).
  13. S. Galichet and L. Foulloy, Fuzzy controllers: Synthesis and equivalences, IEEE Trans. Fuzzy Syst. , vol. 3, pp. 140- 148 (1995).
  14. L. X. Wang, A Course in Fuzzy Systems and Control, Englewood Cliffs, NJ: Prentice-Hall, (1997).
  15. B. S. Moon, Equivalence between fuzzy logic controllers and PI controllers for single input systems,Fuzzy Sets Syst. , vol. 69, pp. 105-113 (1995).
  16. K. Ogata, Modern Control Engineering, Englewood Cliffs, NJ: Prentice-Hall, (1970).
  17. Rajani K. Mudi and Nikhil R. Pal, A Robust Self-Tuning Scheme for PI- and PD-Type Fuzzy Controllers, IEEE Transactions on Fuzzy Systems, vol. 7, no. 1, pp. 2-16, Feb. (1999).
  18. C. C. Lee, Fuzzy logic in control systems: Fuzzy logic controllerParts I, II. , IEEE Trans. Syst. , Man, Cybern. , vol. 20, pp. 404-435, Mar. /Apr (1990).
  19. J-S. R. Jang, ANFIS, Adaptive-Network-based Fuzzy Inference Systems, IEEE Transactions on System, Man, and Cybernetics, vol. 23, no. 5, pp. 665-685, (1993).
  20. Adel Gastli and Mohamed Magdy Ahmed, ANN-Based Soft Starting of Voltage-Controlled-Fed IM Drive System, IEEE Transactions on Energy Conversion, vol. 20, no. 3, pp. 497–503, Sep. (2005).
  21. Yu Zhang, Zhenhua Jiang, and Xunwei Yu, Indirect Field- Oriented Control of Induction Machines Based on Synergetic Control Theory, in IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–7, (2008).
  22. Leonid Reznik, Fuzzy Controllers, Newnes (a division of Reed Eductional and Professional Publishing Ltd. ), (1997).
  23. Tsung Tai, H. Y. Chung and J. Jye. Lin, A Fuzzy PID Controller Being like Parameter Varying PID, IEEE Fuzzy Systems Conference, pp. 269–276, Sep. (2001).
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy logic controller indirect field-oriented control proportional-integral-derivative (PID) controller squirrelcage induction motor. ifx