Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks(Systems and Control)

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概要

• 論文の詳細を見る

• A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.

• 社団法人電子情報通信学会の論文
• 2006-05-01

著者

• Lian Kuang‐yow

  Chung‐yuan Christian Univ. Chung‐li Twn

• Lian Kuang-yow

  Department Of Electrical Engineering Chung-yuan Christian University

• SU Chien-Hsing

  Department of Electronic Engineering, National Taiwan University of Science and Technology

• HUANG Cheng-Sea

  Department of Electronic Engineering, National Taiwan University of Science and Technology

• Su Chien-hsing

  Department Of Electronic Engineering National Taiwan University Of Science And Technology

• Huang Cheng-sea

  Department Of Electronic Engineering National Taiwan University Of Science And Technology

関連論文

• Current Sensorless Regulation for Converters via Integral Fuzzy Control(Electronic Circuits)
• Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks(Systems and Control)

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