引用本文:孙玉坤,王博,嵇小辅,黄永红.基于模糊神经网络α阶逆系统的发酵过程多变量解耦控制(英文)[J].控制理论与应用,2010,27(2):188~192.[点击复制]
SUN Yu-kun,WANG Bo,JI Xiao-fu,HUANG Yong-hong.Multivariable decoupling control based on fuzzy-neural network αth-order inverse system in fermentation process[J].Control Theory and Technology,2010,27(2):188~192.[点击复制]
基于模糊神经网络α阶逆系统的发酵过程多变量解耦控制(英文)
Multivariable decoupling control based on fuzzy-neural network αth-order inverse system in fermentation process
摘要点击 2337  全文点击 1486  投稿时间：2009-06-22  修订日期：2009-11-02
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DOI编号  10.7641/j.issn.1000-8152.2010.2.ICTA090804
  2010,27(2):188-192
中文关键词  生化反应过程  模糊神经网络  逆系统方法  解耦控制  专家控制器
英文关键词  bioprocesses  fuzzy-neural network  inverse system method  decoupling control  expert controller
基金项目  supported by the National High-Tech Research and Development Program under grant 2007AA04Z179; the Research Found for the Doctoral Program of Higher Education of China under grant 20070299010; the Professional Research Foundation for Advanced Talents of Jiangsu University under grant 07JDG037; the Open Project of the National Key Laboratory of Industrial Control Technology in Zhejiang University under grant ICT0910.
作者单位E-mail
孙玉坤* 江苏大学 电气信息工程学院 xiaocao8@sina.com.cn 
王博 江苏大学 电气信息工程学院  
嵇小辅 江苏大学 电气信息工程学院  
黄永红 江苏大学 电气信息工程学院  
中文摘要
      将逆系统方法与模糊神经网络相结合, 提出一种基于模糊神经网络®阶逆系统的发酵过程解耦控制方法. 在分析了系统可逆性的基础上, 利用模糊神经网络建立发酵过程的非线性逆模型, 然后将得到的模糊神经α阶逆系统与发酵过程串联复合成伪线性系统, 最后设计专家控制器实现高性能闭环解耦控制. 仿真结果表明, 提出的解耦控制方法能够适应发酵过程模型的不确定性和参数的时变性, 具有较强的鲁棒性, 克服了解析逆系统解耦控制方法依赖于过程模型和对模型参数的变化很敏感的缺点, 且结构简单, 易于实现.
英文摘要
      This paper proposes a nonlinear multivariable decoupling control strategy based on fuzzy-neural network αth-order inverse method that combines inverse system theory with fuzzy-neural network for fermentation process. A nonlinear inverse model is developed based on the reversibility analysis of the process model. A fuzzy-neural network ®th-order inverse system is then constructed, which is cascaded with this process to transform the original nonlinear system to a pseudo-linear system. Finally, an expert controller is used to closed-loop synthesis. The effectiveness of the presented method is illustrated by a simulation experiment.