Abstract
Performance of the field orientation in induction motors depends on the accurate estimation of the flux vector. The voltage model used for field orientation has in the flux calculation process an open integration problem, which is generally solved with a feedback loop. In this paper, a new method is developed for the feedback loop of the integrator. The method, as apart from studies in the literature, uses a fuzzy controller determined membership functions using a genetic algorithm (GA). For this purpose, a fuzzy controller is designed and tested on various motors of different power ratings. The proposed method is simulated by using MATLAB-SIMULINK and implemented on an experimental system using a TMS320C31 digital signal processor.
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References
T. Ohtani, N. Takada, K. Tanaka, “Vector Control of Induction Motor without Shaft Encoder”, IEEE Transactions on Industry Applications, vol. 28,no. 1, pp. 157–164, January/February, 1992.
Jos van der Burgt, The Voltage/Current Model in Field-Oriented AC Drives at Very Low Flux Frequencies, PhD Thesis, 1996.
B. K. Bose, N. R. Patel, “A Programmable Cascaded Low-Pass Filter-Based Flux Synthesis for a Stator Flux-Oriented Vector-Controlled Induction Motor Drive”, IEEE Transactions on Industrial Electronics, vol. 44,no. 1, pp. 140–143, February, 1997.
Akin E., A New Method for Rotor Flux Orientation of Induction Motor Via Stator Fluxes, Firat University, PhD Thesis, 1994.
E. Akin, H. Can, H. B. Ertan, Y. Üçtuğ “Comparison of Integration Algoritms for Vector Control”, ICEM98, Vol. 3, pp. 1626–1631, September, 2-4, 1998, Istanbul, Turkey.
C. L. Karr, “Design of an Adaptive Fuzzy Controller Using a Genetic Algorithm Proc. of the 4th Intl. Conf. on Genetic Algorithms”, 1991.
D. L. Meredith, C. L. Karr and K. Krishna Kamur, “The use of genetic algorithms in the design of fuzzy logic controllers”, 3rd workshop on Neural Network WNN’92, 1992.
M. A. Lee and H. Takagi, “Integrating Design Stages of Fuzzy Systems Using Genetic Algorithms”, Second IEEE Intl. Conference on Fuzzy Systems, 1993.
A. Arslan and M. Kaya, “Determination of fuzzy logic membership functions using genetic algorithms”, Fuzzy Sets and Systems, vol:118no:2, pp:297–306, 2001.
B. Hu, G. K. I. Mann, R. G. Gosine, “New Methodology for Analytical and Optimal Design of Fuzzy PID Controllers”, IEEE Trans. On Fuzzy Systems, vol. 7,no. 5, 521–539, October 1999.
K. M. Passino, S. Yurkovich, Fuzzy Control, Addison-Wesley, 1998.
J. H. Holland, “Adaptation in Natural and Artificial Systems” Ann Arbor, MI: Univ. Mich. Press, 1975.
F. Herrara, M. Lozano and J. L. Verdegay, “Tuning fuzzy logic controllers by genetic algorithms”, International Journal of Approximate Reasoning, 12(3/):299–315, 1995.
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Karakose, M., Kaya, M., Akin, E. (2001). Design of a Fuzzy Controller Using a Genetic Algorithm for Stator Flux Estimation. In: Alexandrov, V.N., Dongarra, J.J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds) Computational Science - ICCS 2001. ICCS 2001. Lecture Notes in Computer Science, vol 2074. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45718-6_31
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DOI: https://doi.org/10.1007/3-540-45718-6_31
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