Fault tolerant control of nonlinear processes with adaptive diagonal recurrent neural network model

被引：0

作者：

Yu, DL ^{[1
]}

Chang, TH ^{[1
]}

Wang, J ^{[1
]}

机构：

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

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS | 2005年 / 3498卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fault tolerant control (FTC) using an adaptive recurrent neural network model is developed in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. A model inversion control with the developed adaptive model is applied to nonlinear processes and fault tolerant control is achieved. The developed control scheme is evaluated by a simulated continuous stirred tank reactor (CSTR) and effectiveness is demonstrated.

引用

页码：86 / 91

页数：6

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