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GENETİK TABANLI GELENEKSEL DENETLEYİCİLERLE ANAHTARLAMALI RELÜKTANS MOTORUN POZİSYON TAKİP KONTROLÜ

Year 2016, Volume: 8 Issue: 1, 66 - 75, 01.03.2016

Abstract

Bu çalışmada orantı (P) ve orantı+integral (PI) geleneksel denetleyiciler kullanılarak anahtarlamalı relüktans motorun (ARM) pozisyon takip kontrolü gerçekleştirilmiştir. Pozisyon döngüsünde P ve hız döngüsünde PI denetleyici kullanılmıştır. Geleneksel denetleyicilerin katsayılarının belirlenmesinde deneme-yanılma ya da analitik metotlar kullanılabilir. Bu yöntemler doğrusal sistemlerde uygun olmasına karşın ARM sürme sistemi gibi doğrusal olmayan sistemlerde yetersiz kalmaktadırlar. Bu nedenle bu çalışmada katsayılar genetik arama algoritması kullanılarak online olarak belirlenmiştir. Farklı pozisyon koşullarında ortaya koyulan denetim sisteminin benzetim çalışmaları gerçekleştirilmiştir. Elde edilen benzetim sonuçları, motorun gerçek pozisyonunun referans pozisyonu oldukça iyi biçimde takip ettiğini göstermiştir.

References

  • Man, K. F., Tang, K. S., Kwong, S. (1996). Genetic algorithms: concepts and applications, IEEE Transactions on Industrial Electronics, 43(5), 519–534.
  • Ustun, O. (2009). A nonlinear full model of switched reluctance motor with artiŞcial neural network, Energy Conversion and Management, 50 (9), 2413–2421.
  • Mademlis, C., Kioskeridis, I. (2010). Gain-scheduling regulator for high-performance position control of switched reluctance motor drives, IEEE Transactions on Industrial Electronics, 57(9), 2922-2931.
  • Rafael, S., Branco, P. J., Pires, A. J. (2012). A study and design of a position tracking control for an 8/6 switched reluctance machine, The 38th Annual Conference on IEEE
  • Industrial Electronics Society, 1643-1647.
  • Niwa,Y., Abe, T., Higuchi, T. (2013). A study of rotor position control for switched reluctance motor, IEEE 10th International Conference on Power Electronics and Drive Systems, 1039-1044.
  • Reay, D.S., Moud, M. M., Williams, B.W. (1995). On the appropriate uses of fuzzy systems: fuzzy sliding mode position control of a switched reluctance motor, IEEE
  • International Symposium on Intelligent Control, 371-376. Ustun, O. (2009). Determining of activation functions in a feedforward neural network by using genetic algorithm, Journal of Engineering Sciences, Pamukkale University Engineering Faculty, 15(3), 225-134.

A TRACKING POSITION CONTROL OF THE SWITCHED RELUCTANCE MOTOR WITH GENETIC BASED CONVENTIONAL CONTROLLERS

Year 2016, Volume: 8 Issue: 1, 66 - 75, 01.03.2016

Abstract

In this study, position tracking control of the switched reluctance motor (ARM) is realized with the proportional (P) and the proportional + integral (PI) conventional controllers. P and PI controllers are used for position loop and speed loop, respectively. However, trial-and-error or analytical methods can be utilized for determination of the coefficients of the conventional controllers. These methods are capable of linear systems, but they are incapable on such that ARM drive systems. For this reason, these parameters are defined as online using genetic search algorithms in this study. Simulation studies are achieved for the performance of the proposed control systems under different position conditions. The obtained simulation results show that the actual position of the motor closely follows the reference position of the motor.

References

  • Man, K. F., Tang, K. S., Kwong, S. (1996). Genetic algorithms: concepts and applications, IEEE Transactions on Industrial Electronics, 43(5), 519–534.
  • Ustun, O. (2009). A nonlinear full model of switched reluctance motor with artiŞcial neural network, Energy Conversion and Management, 50 (9), 2413–2421.
  • Mademlis, C., Kioskeridis, I. (2010). Gain-scheduling regulator for high-performance position control of switched reluctance motor drives, IEEE Transactions on Industrial Electronics, 57(9), 2922-2931.
  • Rafael, S., Branco, P. J., Pires, A. J. (2012). A study and design of a position tracking control for an 8/6 switched reluctance machine, The 38th Annual Conference on IEEE
  • Industrial Electronics Society, 1643-1647.
  • Niwa,Y., Abe, T., Higuchi, T. (2013). A study of rotor position control for switched reluctance motor, IEEE 10th International Conference on Power Electronics and Drive Systems, 1039-1044.
  • Reay, D.S., Moud, M. M., Williams, B.W. (1995). On the appropriate uses of fuzzy systems: fuzzy sliding mode position control of a switched reluctance motor, IEEE
  • International Symposium on Intelligent Control, 371-376. Ustun, O. (2009). Determining of activation functions in a feedforward neural network by using genetic algorithm, Journal of Engineering Sciences, Pamukkale University Engineering Faculty, 15(3), 225-134.
There are 8 citations in total.

Details

Other ID JA86AG84FF
Journal Section Articles
Authors

Oğuz Üstün This is me

Publication Date March 1, 2016
Published in Issue Year 2016 Volume: 8 Issue: 1

Cite

IEEE O. Üstün, “GENETİK TABANLI GELENEKSEL DENETLEYİCİLERLE ANAHTARLAMALI RELÜKTANS MOTORUN POZİSYON TAKİP KONTROLÜ”, IJTS, vol. 8, no. 1, pp. 66–75, 2016.

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