Control of Time Varying Nonlinear System Based on RBFNN-DMC Algorithm

Author

Haichuan, Lou ; Wenzhan, Dai ; Jie, Su

Author_Institution

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

Volume

1

fYear

2008

fDate

13-17 Dec. 2008

Firstpage

134

Lastpage

138

Abstract

In this paper, a novel dynamic matrix control algorithm based on RBF neural network for time varying nonlinear system is presented. RBFNN is used for system model identification, as well as DMC is adopted as optimized controller. Besides, the predictive initiative value is solved by multi-steps prediction of RBF neural network, and nonlinear dynamic matrix coefficients are derived correspondingly. Compared to regular DMC algorithm, the RBFNN-DMC algorithm not only effectively overcomes the large disturbance but also be very robustness. At last, the algorithm is applied in a time varying, high nonlinear Continuous Stirred Tank Reactor (CSTR) pH process model and presents a better control performance.

Keywords

chemical reactors; control system synthesis; matrix algebra; neurocontrollers; nonlinear control systems; pH control; radial basis function networks; robust control; time-varying systems; RBF neural network; RBFNN-DMC Algorithm; dynamic matrix control algorithm; high nonlinear continuous stirred tank reactor pH process model; large disturbance; predictive initiative value; time varying nonlinear system; Automatic control; Continuous-stirred tank reactor; Control systems; Heuristic algorithms; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Predictive models; Time varying systems; CSTR pH process model; Dynamic matrix control (DMC). Time varying nonlinear system; Radial basis function neural network (RBFNN);

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2008. CIS '08. International Conference on

Conference_Location

Suzhou

Print_ISBN

978-0-7695-3508-1

Type

conf

DOI

10.1109/CIS.2008.110

Filename

4724629