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
收藏
页码：86 / 91
页数：6
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