Neuro-Fuzzy Control for Pneumatic Servo System

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概要

• 論文の詳細を見る

• A learning method for acquiring the appropriate fuzzy rules using error back propagation to improve the control performance of the pneumatic servo system is presented in this paper. In the proposed method, two criteria are defined and are adjusted so as to minimize them using error back propagation. These criteria are defined on the fuzzy rules, that is, shapes of membership functions of antecedent clause and real values of consequent clause in the fuzzy controller. Two differentiating coefficients of the plant, used in error back propagation with respect to those criteria, are estimated by the newly established neural network. Moreover, sigmoid function is introduced for the connection of the neural network to compensate for the effect of non-linearity of the system. The method was applied to an existent vertical type pneumatic servo system and proved its effectiveness for practical use.

• 一般社団法人日本機械学会の論文
• 2002-06-15

著者

• 神代 充

  岡山県立大学情報工学部

• SHIMIZU Akira

  Department of Electrical and Electronic Engineering, Tokyo Institute of Technology

• 神代 充

  岡山県立大学情報工学部情報システム工学科

• YAMAMOTO Tomonori

  Department of Cardiovascular Surgery, Nihon University School of medicine

• YAMAMOTO Tomonori

  Faculty of Engineering, Ehime University

• SHIBATA Satoru

  Department of Mechanical Engineering, Ehime University

• JINDAI Mitsuru

  Department of System Engineering, Faculty of Computer Science and System Engineering, Okayama Prefec

• Yamamoto T

  Department Of Mechanical Engineering Ehime University

• Yamamoto Tomonori

  Department Of Biological Science And Technology Science University Of Tokyo

• Shibata S

  Department Of Mechanical Engineering Ehime University

• Shibata Satoru

  Department Of Engineering For Production And Environments Ehime University

• Shimizu A

  Department Of Mechanical Engineering Ehime University

• Shimizu Akira

  Department Of Dermatology Gunma University Graduate School Of Medicine

• Shimizu Akira

  Department Of Biological Sciences Graduate School Of Science And Engineering Tokyo Metropolitan Univ

• Shimizu Akira

  Department Of Basic Science The University Of Tokyo

• Shimizu Akira

  Deparment Of Physics Rikkyo University

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