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THE POSITION CONTROL OF THE DC MACHINE BY PID ALGORITM AND TRAINING WITH ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Year 2007, Issue: 014, 55 - 64, 17.12.2007

Abstract

In this study, the position of DC machines has been controlled by PID (Proportional-Integral-Derivative)
algorithm, has been trained by ANFIS (Adaptive Neuro Fuzzy Inference System) algorithm and output
equations for different inputs have been obtained. Control mechanisms have been explained by
comparing graphs that are obtained from the two algorithms.

References

  • [1] ST, AN280. Application note controlling voltage transiensts in full bridge driver applications.
  • [2] Klee Andrew, “Development of a speed control system using matlab and simulink, implemented with a digital signal processor”, Master of Science in the Department of Electrical and Computer Engineering - In the College of Engineering and Computer Science at the University of Central Florida, Orlando, Florida, Spring Term, 2005
  • [3] TMS320F2810, TMS320F2811, TMS320F2812, TMS320C2810, TMS320C2811, TMS320C2812, Digitalsignal processors, data manual. literature number: SPRS174L. April 2001 − Revised December 2004
  • [4] Maas, J., “Industrial Electronics”, Prentice-Hall, New Jersey, 844-860 (1995)
  • [5] MATLAB Fuzzy Logic Toolbox-2 User’s Guide, COPYRIGHT 1995–2007 The MathWorks, Inc.
  • [6] J.-S. R. Jang, C.-T. Sun ve E. Mizutani, Neuro-fuzzy and soft computing, Prentice Hall, New Jersey, 1997.
  • [7] Elmas,Ç.,”Bulanık Mantık denetleyiciler”, Seçkin yayınları,Ankara,188-197 (2003)

DOĞRU AKIM MAKİNALARININ PID ALGORİTMASI İLE KONUM DENETİMİ ve UYARLANIR SİNİR BULANIK ÇIKARIM SİSTEMİ (ANFIS) İLE EĞİTİMİ

Year 2007, Issue: 014, 55 - 64, 17.12.2007

Abstract

Bu çalışmada, doğru akım (DC) makinaların konumu PID (Proportional-Integral-Derivative) algoritması kullanılarak
denetlenmiş, ANFIS (Adaptive Neuro Fuzzy Inference System) kullanılarak eğitimi yapılmış ve farklı girdiler için
çıktı denklemleri elde edilmiştir. Her iki algoritmadan elde edilen sonuç grafikleri karşılaştırılarak denetim
yöntemleri hakkında açıklamalar yapılmıştır.

References

  • [1] ST, AN280. Application note controlling voltage transiensts in full bridge driver applications.
  • [2] Klee Andrew, “Development of a speed control system using matlab and simulink, implemented with a digital signal processor”, Master of Science in the Department of Electrical and Computer Engineering - In the College of Engineering and Computer Science at the University of Central Florida, Orlando, Florida, Spring Term, 2005
  • [3] TMS320F2810, TMS320F2811, TMS320F2812, TMS320C2810, TMS320C2811, TMS320C2812, Digitalsignal processors, data manual. literature number: SPRS174L. April 2001 − Revised December 2004
  • [4] Maas, J., “Industrial Electronics”, Prentice-Hall, New Jersey, 844-860 (1995)
  • [5] MATLAB Fuzzy Logic Toolbox-2 User’s Guide, COPYRIGHT 1995–2007 The MathWorks, Inc.
  • [6] J.-S. R. Jang, C.-T. Sun ve E. Mizutani, Neuro-fuzzy and soft computing, Prentice Hall, New Jersey, 1997.
  • [7] Elmas,Ç.,”Bulanık Mantık denetleyiciler”, Seçkin yayınları,Ankara,188-197 (2003)
There are 7 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ayhan Gün This is me

Publication Date December 17, 2007
Published in Issue Year 2007 Issue: 014

Cite

APA Gün, A. (2007). THE POSITION CONTROL OF THE DC MACHINE BY PID ALGORITM AND TRAINING WITH ADAPTIVE NEURO FUZZY INFERENCE SYSTEM. Journal of Science and Technology of Dumlupınar University(014), 55-64.

HAZİRAN 2020'den itibaren Journal of Scientific Reports-A adı altında ingilizce olarak yayın hayatına devam edecektir.