Adaptive neural network control for continuous stirred tank reactor

李东娟

doi:10.3969/j.issn.0438-1157.2013.12.060

您当前的位置：

首页 >

文章列表页 >

Adaptive neural network control for continuous stirred tank reactor

更新时间：2025-12-10

- Adaptive neural network control for continuous stirred tank reactor
- CIESC Journal Issue 12, Pages: 4674-4680(2013)
- 作者机构：
  
  辽宁工业大学化学与环境工程学院,辽宁,锦州,121001
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.060
  CLC： P273
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
移动端阅览
李东娟. Adaptive neural network control for continuous stirred tank reactor[J]. CIESC Journal, 2013, (12): 4674-4680.
DOI：

李东娟. Adaptive neural network control for continuous stirred tank reactor[J]. CIESC Journal, 2013, (12): 4674-4680. DOI： 10.3969/j.issn.0438-1157.2013.12.060.

摘要

基于神经网络的逼近特性，针对一类包含未知函数的串级连续搅拌釜式反应系统，提出了一种自适应控制算法。由于所考虑的反应系统具有非线性特性以及未知函数存在于各子系统的方程中，因此，该系统是复杂和难于控制的。为了克服困难，神经网络逼近系统中的未知函数，新奇的递归设计方法用于消除系统中的互联项，同时，需要定义特殊的被逼近非线性函数。利用李雅普诺夫稳定性分析方法，提出的控制算法保证了闭环系统的所有信号是有界的和系统的输出收敛到零的邻域内。仿真例子表明提出的控制算法是有效的。

Abstract

An adaptive control algorithm is proposed for continuous stirred tank reactor (CSTR) with unknown functions based on the approximation property of the neural networks.Because the considered reactor contains the nonlinear property and the unknown functions are included in the subsystem

it is a completed system and is very difficult to be controlled.In order to avoid the difficulties

a novel recursive procedure is given to remove the interconnection term and special approximated functions are defined to be approximated by using the neural networks.Using the Lyapunov method

the algorithm ensures that all the signals in the closed-loop are bounded and the output can converge to a neighborhood of zero.A simulation example is given to show effectiveness of the algorithm.

关键词

Keywords

references

Views

277

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Intelligent PID coordinated tuning of multivariable processes under variable working conditions in refining and chemical industries

Research and prospect of early warning and diagnosis technology for ORC power generation system process

Robust minimum covariance constrained control for C3 hydrogenation process and application

Model predictive control of nonlinear system based on adaptive fuzzy neural network

Electric heated water bath cascade control system research based on improved differential evolution algorithm-radial basis function neural network

Related Author

ZHOU Jianqiao

WANG Zhu

LUO Xionglin

WANG Jinjiang

LU Zhenjie

AN Weizheng

YANG Fengyun

QIN Xiaogang

Related Institution

Department of Automation, College of Artificial Intelligence, China University of Petroleum

College of Artificial Intelligence, China University of Petroleum

College of Safety and Ocean Engineering, China University of Petroleum

Key Laboratory of Oil and Gas Production Equipment Quality Inspection and Health Diagnosis, State Administration for Market Regulation

CNOOC Research Institute Co., Ltd.

⁰