Predictive Control Based on Support Vector Machine Model

Author

Wang, Jing ; Sun, Shuyi

Author_Institution

Autom. Inst., Beijing Univ. of Chem. Technol.

Volume

fYear

fDate

0-0 0

Firstpage

1683

Lastpage

1687

Abstract

To the nonlinear controlled objects that generally exist in industrial processes, a predictive control algorithm based on support vector machine (SVM) model was proposed. First, SVM model with RBF kernel function was constructed offline. Then, the future values of controlled variable were predicted and linearized online using the SVM model. Finally, generalized predictive control (GPC) was applied to realize control goal. The simulation proves that this method is effective

Keywords

nonlinear control systems; predictive control; radial basis function networks; support vector machines; RBF kernel function; generalized predictive control; nonlinear controlled objects; nonlinear predictive control; radial basis function; support vector machine model; Automatic control; Automation; Chemical industry; Chemical technology; Electronic mail; Industrial control; Predictive control; Predictive models; Sun; Support vector machines; GPC; linearize; nonlinear predictive control; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712639

Filename

1712639

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1752654