东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2007, Vol. 28 ›› Issue (7): 925-928.DOI: -

• 论著 • 上一篇    下一篇

基于两级神经网络的发酵过程多变量前馈解耦控制

常玉清;李玉朝;吕哲;王福利;   

  1. 东北大学流程工业综合自动化教育部重点实验室;沈阳理工大学机械工程学院;东北大学流程工业综合自动化教育部重点实验室;东北大学流程工业综合自动化教育部重点实验室 辽宁沈阳110004;东北大学信息科学与工程学院;辽宁沈阳110004;辽宁沈阳110168;辽宁沈阳110004;辽宁沈阳110004;东北大学信息科学与工程学院;辽宁沈阳110004
  • 收稿日期:2013-06-24 修回日期:2013-06-24 出版日期:2007-07-15 发布日期:2013-06-24
  • 通讯作者: Chang, Y.-Q.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60374003);;

Multivariable feedforward decoupling control based on double-level neural network in a fermentation process

Chang, Yu-Qing (1); Li, Yu-Chao (3); Lu, Zhe (1); Wang, Fu-Li (1)   

  1. (1) Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China; (2) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China; (3) School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110168, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2007-07-15 Published:2013-06-24
  • Contact: Chang, Y.-Q.
  • About author:-
  • Supported by:
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摘要： 针对具有时变、非线性、不确定性的多变量耦合生物发酵过程,提出了一种基于两级神经网络的多变量前馈解耦方法.一级神经网络利用可获得的过程信息拟和耦合通道的过程特性,实现耦合作用对被控量影响的估计;二级神经网络用来拟和控制通道的逆特性.通过两级网络的串联,消除系统间的耦合.实验结果表明,提出的解耦控制方法能适应生物发酵过程模型的不确定性和参数时变性,克服了前馈解耦方法依赖于过程模型和对模型参数的变化表现敏感的缺点.

关键词: 建模, 两级神经网络, 多变量, 解耦控制, 发酵过程

Abstract: A double-level neural network for feedforward decoupling control is proposed for the fermentation process characterized with time-variable, nonlinear, uncertain and multivariable coupling. The first-level network aims to build a characteristic model of coupling channel on account of the information on achievable process, and realize the prediction of how the coupling affects the controlled variables; On the other hand, the second-level network aims to build an inverse characteristic model of controlling channel, thus making the compensation output and realizing the decoupling control. Testing results showed that the control performance is better in multivariable fermentation process when based on the double-level neural network feedforward decoupling, thus getting rid of the disadvantage of feedforward decoupling method that not only relies on the exact process model but highly sensitive to the slight variation of parameters.

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