Fault Tolerant Control of Nonlinear Processes with Adaptive Diagonal Recurrent Neural Network Model

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.

Authors and Affiliations

1. Control Systems Research Group, School of Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK

   Ding-Li Yu, Thoonkhin Chang & Jin Wang

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

   Xiao-Feng Liao

3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

Reprints and permissions

© 2005 Springer-Verlag Berlin Heidelberg

Policies and ethics