Neural network model adaptation and its application to process control

被引：22
|
作者：
Chang, TK [1 ]
Yu, DL [1 ]
Yu, DW [1 ]
机构：
[1] Liverpool John Moores Univ, Dept Engn, Control Syst Res Grp, Liverpool L3 3AF, Merseyside, England
来源：
ADVANCED ENGINEERING INFORMATICS | 2004年 / 18卷 / 01期
关键词：
adaptive neural networks; extended Kalman filter; model inversion; continuous-stirred tank reactor processes;
D O I：
10.1016/j.aei.2004.01.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A multi-layer perceptron network is made adaptive by weight updating using the extended Kalman filter (EKF). When the network is used as a model for a non-linear plant, the model can be on-line adapted with input/output, data to capture system time-varying dynamics and consequently used in adaptive control. The paper describes how the EKF algorithm is used to update the network model and gives the implementation procedure. The developed adaptive model is evaluated for on-line modelling and model inversion control of a simulated continuous-stirred tank reactor. The modelling and control results show the effectiveness of model adaptation to system disturbance and a global tracking control. (C) 2004 Elsevier Ltd. All rights reserved.
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页码：1 / 8
页数：8
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