Research of temperature predictive control based on LSSVM optimized by improved PSO for thick steel plate Roller hearth Normalizing Furnace

Author

Li, Jing ; Wang, Jing

Author_Institution

Eng. Res. Inst., Univ. of Sci. & Technol. Beijing, Beijing, China

fYear

2010

Firstpage

3717

Lastpage

3721

Abstract

According to process requirements of Roller-hearth Normalizing Furnace, and non-linear characteristics of the temperature, the paper proposes a new nonlinear system prediction control algorithm instead of the tradition, which the accuracy of model is not high. The new control algorithm uses least squares support vector machine (LSSVM) optimized by improved particle swarm optimization (APSO) to establish the predictive model. This model is simulated and studied by using lots of data acquired from the site. The result indicates that this prediction model based on APSO and LSSVM has higher control accuracy and good application in future.

Keywords

control engineering computing; furnaces; least squares approximations; metallurgical industries; nonlinear control systems; particle swarm optimisation; plates (structures); predictive control; rollers (machinery); steel; support vector machines; temperature control; LSSVM optimization; improved PSO; improved particle swarm optimization; least squares support vector machine; nonlinear characteristics; nonlinear system prediction control; temperature predictive control; thick steel plate roller hearth normalizing furnace; Furnaces; Particle swarm optimization; Prediction algorithms; Predictive models; Support vector machines; Temperature; Temperature control; PSO (particle swarm optimizer algorithm); SVM(support vector machine); chaos; predictive control; roller-hearth normalizing furnace;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Conference_Location

Jinan

Print_ISBN

978-1-4244-6712-9

Type

conf

DOI

10.1109/WCICA.2010.5555019

Filename

5555019