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Performance Analysis of Field Oriented Induction Motor using Fuzzy PI and Fuzzy Logic based Model Reference Adaptive Control

by Bharat Bhushan, Madhusudan Singh, Prem Prakash
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 4
Year of Publication: 2011
Authors: Bharat Bhushan, Madhusudan Singh, Prem Prakash
10.5120/2211-2811

Bharat Bhushan, Madhusudan Singh, Prem Prakash . Performance Analysis of Field Oriented Induction Motor using Fuzzy PI and Fuzzy Logic based Model Reference Adaptive Control. International Journal of Computer Applications. 17, 4 ( March 2011), 5-12. DOI=10.5120/2211-2811

@article{ 10.5120/2211-2811,
author = { Bharat Bhushan, Madhusudan Singh, Prem Prakash },
title = { Performance Analysis of Field Oriented Induction Motor using Fuzzy PI and Fuzzy Logic based Model Reference Adaptive Control },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 4 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number4/2211-2811/ },
doi = { 10.5120/2211-2811 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:43.339528+05:30
%A Bharat Bhushan
%A Madhusudan Singh
%A Prem Prakash
%T Performance Analysis of Field Oriented Induction Motor using Fuzzy PI and Fuzzy Logic based Model Reference Adaptive Control
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 4
%P 5-12
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the dynamic performances of an indirect vector controlled induction motor (IVCIM) using a fuzzy logic (FL) based model reference adaptive control (MRAC) slip gain tuner for speed regulation in the drive. In high performance AC drives the motor speed should closely match with the specified reference speed irrespective of the variations in the load, motor parameters and model uncertainties. Two fuzzy controllers combined with MRAC reactive power and stator direct axis (d-axis) voltage estimator have been used to tune the slip gain of the IVCIM drive against parameter variations and model uncertainties. An integrated mathematical model of the control scheme has been developed and simulated in MATLAB for Indirect vector control of an Induction motor. The simulated performances of the FL-MRAC slip gain tuner based IVCIM drive is compared to fuzzy PI controller. The simulated results in different dynamic operating conditions such as sudden change in command speed, step change in load, etc are demonstrated through necessary waveforms. The comparison of simulated results show that the fuzzy logic MRAC slip gain tuner based IVCIM drive is more robust and effective in minimizing the detuning effect in the drive due to parameter variations and model uncertainties.

References
  1. P. Vas, Vector control of AC machines, New York: Clarendon, 1990.
  2. B.K. Bose, power electronics and AC Drive. Englewood Cliffs, NJ. Prentice hall, 1986
  3. N. Mariun, S. B Mohd Noor, J. Jasni, and O. S. Bennanes, “A Fuzzy Logic Based Controller For An Indirect Vector Controlled Three-Phase Induction Motor” IEEE IECON Conf. Rec., Vol. 4, pp. 1-4, Nov. 2004.
  4. F. Biaschke, “ The principle of field oriention as applied to new transvector closed loop control system for rotating field machine,” Siemens Rev, vol. 34,,pp 217-220’may 1972
  5. L. Zhen and L. Xu, “Fuzzy Learning Enhanced Speed Control of an Indirect Field Oriented Induction Machine Drive,” proc. of IEEE Int symposium on Intelligent Control, Dearborn, MI, pp.109-114, Sept, 1996.
  6. M. T. Cao, J.L. S. Neto and H. L. Huy, “Fuzzy Logic Based Controller for Induction Motor Drives,” proc. of IEEE Int. conf. , pp.631-634, 1996.
  7. M.T.Cao and H.L. Huy, “Rotor Resistance Estimation using Fuzzy Logic for High Performance Induction Motor Drives,” Proc. of IEEE Int. conf , pp.303- 308, 1998.
  8. M. N. Uddin, T. S. Radwan and M. A. Rahman, “Performances of Novel Fuzzy Logic Based Indirect Vector Control for Induction Motor Drive,” Proc. of IEEE Int. conf, pp. 1225-1231, 2000.
  9. L. Zhen and L. Xu, “Fuzzy Learning Enhanced Speed Control of an Indirect Field Oriented Induction Machine Drive,” IEEE Trans on Control Systems Technology, vol. 8, No. 2, pp. 270-278, March 2000.
  10. B. Karanayil, M. F. Rahman and C. Grantham, “PI and Fuzzy Estimators for On-line tracking of Rotor Resistance of Indirect Vector Controlled Induction Motor drive,” Proc. of IEEE Int. conf, pp. 820-825, 2001.
  11. N. Mariun, S.B.M. Noor, J. Jasni and O. S. Bennanes, “A Fuzzy Logic Based Controller for an Indirect Vector Controlled Three Phase Induction Motor,” Proc. of IEEE Int. conf, pp.1-4, 2004.
  12. H.U. Rehman and W. Mahmood, “A Fuzzy Model Reference Learning Controller Based Direct Field Oriented Control of Induction Machine,” IEEE Int. conf, 2006.
  13. K. Kouzi, L. Mokrani and M. S. N. Said, “A New Design of Fuzzy Logic Controller with Fuzzy Adapted Gains Based on Indirect Vector Control for Induction Motor,” IEEE Int. conf, pp. 362-366, 2003.
  14. M. Masiala, B. Vafakhah, A. Knight and J. Salmon, “Performances of PI and Fuzzy-Logic Speed Control of Field Oriented Induction Machine Drives,” IEEE Int. Conf, pp. 397-400, 2007.
  15. R. A. Gupta, R. Kumar and R. S. Surjuse, “ANFIS Based Intelligent Control of Vector Controlled Induction Motor Drive,” Int. Conf on Emerging Trends in Engineering and Tech., pp. 674-680, 2009.
  16. M. Imecs, I. I. Incze and C. Szabo, “Dual Field Orientation for Vector Controlled Cage Induction Motors,” IEEE Int. conf, pp. 143-148, 2009.
  17. A. Mechernene, M.Zerikat and S. Chekroun, “Indirect Field Oriented Adaptive Control of Induction Motor Based On Neuro-Fuzzy Controller,” Mediterranean conf. on Control & Automation, Morocco, pp. 1109-1114, 2010.
  18. A. M. Eltamaly, A. I. Alolah and B.M. Badr, “ Fuzzy Controller for Three Phases Induction Motor Drives,” IEEE Int. Conf, 2010.
  19. Vinod Kumar and R. R. Joshi, “Hybrid Controller based Intelligent Speed Control of Induction Motor”, Journal of Theoretical and Applied Information Technology, pp. 71-75, 2005.
  20. CHE-MUN ONG,”Dynamic simulation of Electrical Machaniry using MATLAB / Simulink”, Printic hall PTR in 1998.
  21. M M. N. Uddin, T. S. Radwan, and M. A. Rahman, “Performances of Fuzzy-Logic-Based Indirect Vector Control for Induction Motor Drive”, IEEE Transactions On Industry Applications, Vol. 38, No. 5, pp. 1219-1225, Sept./Oct. 2002.
  22. L.A. Zadeh,”fuzzy theory,” university of California, Berkely,1965.
  23. Li Zhen and Longya Xu, “On-Line Fuzzy Tuning of Indirect Field-Oriented an Induction Machine Drives”, IEEE Transactions on Power Electronics, Vol. 13, No. 1, pp. 134-138, January 1998.
  24. J.-S.R.Jang, “Fuzzy Controller Design Without Domain Experts”, IEEE International Conference on Fuzzy Systems, 8-12 March 1992 pp. 289 – 296.
  25. P. Vas, and J. Li, 1993, ‘Simulation Package for Vector Controlled Induction Motor Drives’, Oxford University press.
  26. Bimal K. Bose,2002,’Modern Power Electronics and AC Drives’, Pearson Education Asia.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy logic PI controller field oriented induction motor model reference adaptive control