Title :
Predictive Control Based on Support Vector Machine Model
Author :
Wang, Jing ; Sun, Shuyi
Author_Institution :
Autom. Inst., Beijing Univ. of Chem. Technol.
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712639
