Dynamic neural network based nonlinear adaptive control for a distillation column

Author

Shurong, LI ; Feng, LI

Author_Institution

Univ. of Pet., Dongying, China

Volume

5

fYear

2000

fDate

2000

Firstpage

3087

Abstract

In this paper, a dynamic neural network is used to learn the input-output behaviors of a binary distillation column by combining the mechanistic property. The model can be online identified. The weight-training algorithm is proposed. The convergence of the algorithm is discussed by using the Lyapunov method. Based on the identified model, a nonlinear adaptive controller is designed, which can preserve the stability and robustness of the closed loop system. Some simulation results are illustrated to show the effectiveness of the controller

Keywords

Lyapunov methods; adaptive control; closed loop systems; convergence; distillation; neurocontrollers; nonlinear control systems; process control; stability; Lyapunov method; adaptive control; closed loop system; convergence; distillation column; dynamic neural network; nonlinear control systems; robustness; stability; weight-learning algorithm; Adaptive control; Convergence; Distillation equipment; Lyapunov method; Mechanical factors; Neural networks; Nonlinear control systems; Programmable control; Robust control; Robust stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.863026

Filename

863026