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

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

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

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 outputequations for different inputs have been obtained. Control mechanisms have been explained bycomparing graphs that are obtained from the two algorithms.

___

  • [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)