Neural network approach for linearizing control of nonlinear process plants

Author

Rahman, M. H R Fazlur ; Devanathan, Rajagopalan ; KuanYi, Zhu

Author_Institution

Dept. of Electr. Eng., Singapore Polytech., Singapore

Volume

47

Issue

2

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

470

Lastpage

477

Abstract

The application of a feedback linearization technique using artificial neural networks (ANNs) for a nonlinear industrial process plant is considered in this paper. The process plant is modeled first using an ANN, and then the dynamic neural network model acting as a process plant emulator is feedback linearized. A novel configuration for linearization of an ANN emulator using only backpropagation is used. Effective control of the linearized emulator is then exhibited using a linear controller. Experimentation and simulation results on the linearized emulator are presented to demonstrate the effectiveness of the feedback linearization technique

Keywords

backpropagation; control system analysis; control system synthesis; feedback; linearisation techniques; neurocontrollers; nonlinear control systems; process control; artificial neural networks; backpropagation; control design; control simulation; dynamic neural network model; feedback linearization technique; neural network linearisation approach; nonlinear industrial process plants control; process plant emulator; Artificial neural networks; Control systems; Linear feedback control systems; Linearization techniques; Modeling; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear systems; Process control;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/41.836363

Filename

836363