A novel non-linear model predictive controller based on minimal resource allocation network and its application in CSTR PH process

Author

Haichuan, Lou ; Wenzhan, Dai

Author_Institution

Dept. of Autom. Control, Zhejiang Sci-Tech Univ., Hangzhou

fYear

2008

fDate

25-27 June 2008

Firstpage

5672

Lastpage

5676

Abstract

In this paper, a novel predictive controller based on minimal resource allocation network for non-linear system is presented. The controller combines the advantages of MRAN and neural predictor. The implemented neural predictive controller not only effectively eliminates the most significant obstacles but also be very robustness. At last, the algorithm is applied in a high nonlinear continuous stirred tank reactor (CSTR) pH process model and presents a better real-time control effect.

Keywords

chemical reactors; neurocontrollers; nonlinear control systems; pH control; predictive control; resource allocation; minimal resource allocation network; neural predictor; nonlinear continuous stirred tank reactor; nonlinear model predictive controller; nonlinear system; pH process model; Automatic control; Continuous-stirred tank reactor; Neural networks; Neurons; Nonlinear control systems; Prediction algorithms; Predictive control; Predictive models; Resource management; Robust control; CSTR pH process model; Minimal Resource Allocation network; Model predictive control; Non-linear system;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4593855

Filename

4593855