The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Design of Improved Neuro-Fuzzy Controller for the Development of Fast Response and Stability of DC Servo Motor

직류 서보 전동기의 속응성 및 안정성 향상을 위한 개선된 뉴로-퍼지 제어기의 설계

  • Published : 2002.06.01
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Abstract

We designed a neuro-fuzzy controller to improve some problems that are happened when the DC servo motor is controlled by a PID controller or a fuzzy logic controller. Our model proposed in this paper has the stable and accurate responses, and shortened settling time. To prove the capability of the neuro-fuzzy controller designed in this paper, the proposed controller is applied to the speed control of DC servo motor. The results showed that the proposed controller did not produce the overshoot, which happens when PID controller is used, and also it did not produce the steady state error when FLC is used. And also, it reduced the settling time about 10%. In addition, we could by aware that our model was only about 60% of the value of current peak of PID controller.

Keywords

References

  1. Katsuhiko Ogata, 'Modern Control Engineering', second edition, pp. 2-7, pp. 627-633, Prentice_Hall, 1990
  2. 유지윤, '산업용 유도전동기의 새로운 속도 제어 기술', 전기학회지, 42권 12호, pp. 65-73, 1993년 12월
  3. 이광형, 오길록, '퍼지 이론 및 응용', II권 응용, pp. 5-3-5, 홍릉과학출판사, 1992년
  4. Kevin Passino, Stephen Yurkovich,'Fuzzy Control', pp. 21-48, Addison Wesley Longman, 1998
  5. Bart Kosko, 'Neural Networks and Fuzzy Systems', pp. 39-52, Prentice-Hall, 1992
  6. 임영도, 이상부, '퍼지.신경망.유전진화', pp. 107-123, 영과일, 1997
  7. Martin Brown, Chris Harris, 'Neurofuzzy Adaptive Modelling and Control', pp. 75-110, Prentice Hall, 1994
  8. T. Iwata, K. Machida, Y. Toda, 'Fuzzy Control using Neural Networks Techniques', Proceeding of the International Joint Conference on Neural Networks, Vol.II, pp. 461-465, 1990 https://doi.org/10.1109/IJCNN.1990.137870
  9. S. Horikawa, T. Furuhashi, S. Okuma, Y. Ukikawa, 'A Fuzzy Controller using a Neural Network and its Capability to learn Expert's Control Rules', Proceeding of International Joint Conference on Fuzzy Logic & Neural Networks, pp. 103-106, 1990
  10. Hiroyuki Okada, Nobuo Watanabe, Akira Kawamura, Kazuo Asakawa, Tetsuro Taira, Katsuyo Ishida, Tohru Kaji and Masataka Narita, Knowledge Implementation Multilayer Networks wuth Fuzzy Logic, Proceedings of the 2nd International Con