形状可変ニューラルネット・コントローラーを用いた非線形制御

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概要

• 論文の詳細を見る

• In this paper, a nonlinear control strategy based on using a shape-tunable neural network is developed for adaptive control of nonlinear processes. Based on the steepest descent method, a learning algorithm that enables the neural controller to possess the ability of automatic controller output range adjustment is derived. The novel feature of automatic output range adjustment provides the neural controller more flexibility and capability, and therefore the scaling procedure, which is usually unavoidable for the conventional fixed-shape neural controllers, becomes unnecessary. The advantages and effectiveness of the proposed nonlinear control strategy are demonstrated through the challenge problem of controlling an open-loop unstable nonlinear continuous stirred tank reactor (CSTR).

• 社団法人 化学工学会の論文

著者

• Chen C‐t

  Department Of Chemical Engineering Feng University

• Chen Chyi-tsong

  Department Of Chemical Engineering Feng Chia University

• Chang Wei-der

  Department Of Automatic Control Feng Chia University

• PENG Shih-tien

  Department of Chemical Engineering, Feng University

• Peng Shih-tien

  Department Of Chemical Engineering Feng University

• Peng Shih-tien

  Department Of Chemical Engineering Feng Chia University

• Chen Chyi-tsong

  Department Of Chemical Engineering

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• 形状可変ニューラルネット・コントローラーを用いた非線形制御
• A Sliding Mode Control Scheme for Uncertain Non-Minimum Phase CSTRs
• Multivariable Process Control Using Decentralized Single Neural Controllers
• Nonlinear Process Control Using a Direct Adaptive PI Controller
• Mathematical modeling of a liquid-liquid phase-transfer catalyzed reaction system.

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