Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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A study on Induction Motor Servo System using Self-learning Neural-Fuzzy Networks

자기학습형 뉴럴-퍼지 제어기에 의한 유도전동기 서어보시스템

  • Yang, Seung-Ho (Department of Electrical Engineering, Sungkyunkwan University) ;
  • Kim, Se-Chan (Department of Electrical Engineering, Sungkyunkwan University) ;
  • Won, Chung-Yuen (Department of Electrical Engineering, Sungkyunkwan University) ;
  • Kim, Duk-Heon (Department of Electrical Engineering, Sungkyunkwan University)
  • 양승호 (성균관대학교 전기공학과) ;
  • 김세찬 (성균관대학교 전기공학과) ;
  • 원충연 (성균관대학교 전기공학과) ;
  • 김덕헌 (성균관대학교 전기공학과)
  • Published : 1993.11.26
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Abstract

In this study, a Self-learning Neural-Fuzzy Networks is presented, Because of the fuzzy controller property, the designing problems of fuzzy if-then rules, membership functions and inference methods are very complex task. Thus in this paper we proposed the Neural-Fuzzy Networks composed by Sugeno and Takagi's fuzzy inference method and learned by using temporal back propagation algorithm. The proposed method can refine automatically the fuzzy if-then rules without human expert's knowledges. The induction motor servo system is used to demonstrate the effectiveness of the proposed control scheme and the feasibility of the acquired fuzzy controller. All results are supported by simulation.

Keywords