Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 1992.07a
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- Pages.225-230
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- 1992
A Learning Fuzzy Logic Controller Using Neural Networks
신경회로망을 이용한 학습퍼지논리제어기
- Kim, B.S. (R & D Institute, Hyosung Industries Co., Ltd.) ;
- Ryu, K.B. (R & D Institute, Hyosung Industries Co., Ltd.) ;
- Min, S.S. (R & D Institute, Hyosung Industries Co., Ltd.) ;
- Lee, K.C. (R & D Institute, Hyosung Industries Co., Ltd.) ;
- Kim, C.E. (R & D Institute, Hyosung Industries Co., Ltd.) ;
- Cho, K.B. (R & D Institute, Hyosung Industries Co., Ltd.)
- 김병섭 (효성중공업(주) 기술연구소) ;
- 류근배 (효성중공업(주) 기술연구소) ;
- 민성식 (효성중공업(주) 기술연구소) ;
- 이규찬 (효성중공업(주) 기술연구소) ;
- 김창업 (효성중공업(주) 기술연구소) ;
- 조규복 (효성중공업(주) 기술연구소)
- Published : 1992.07.23
Abstract
In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.
Keywords