Implementation of neural network predictive control to a multivariable chemical reactor

被引：76

作者：

Yu, DL ^{[1
]}

Gomm, JB ^{[1
]}

机构：

[1] Liverpool John Moores Univ, Sch Engn, Dept Engn, Control Syst Res Grp, Liverpool L3 3AF, Merseyside, England

来源：

CONTROL ENGINEERING PRACTICE | 2003年 / 11卷 / 11期

关键词：

process control; multivariable systems; neural network modelling; model predictive control; on-line implementation;

D O I：

10.1016/S0967-0661(02)00258-7

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Implementation of a neural network model-based predictive control scheme to a laboratory-scaled multivariable chemical reactor is described in this paper. Three variables are controlled in the reactor-temperature, pH and dissolved oxygen. The reactor exhibits common features of industrial systems including non-linear dynamics, coupling effects among variables and is without a mathematical model. Multi-input, single-output sub-system models are developed using neural networks and combined to form a parallel process model for simulation and on-line prediction. The process modelling, model-based control simulation, implementation of the on-line control and performance evaluations are investigated and reported in detail in the paper. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：1315 / 1323

页数：9

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