A Nonlinear Model Predictive Control Based on NARX Model Identification using Least Squares Support Vector Machines

Author

Xiang, Lizhi ; Shi, Yuntao ; Gao, Dongjie

Author_Institution

Eng. Res. Center of Integrated Autom. Technol., Chinese Acad. of Sci., Beijing

Volume

1

fYear

0

fDate

0-0 0

Firstpage

901

Lastpage

905

Abstract

In the domain of industry process control, the model identification and predictive control of nonlinear systems are always difficult problems. To solve the problems, an identification method based on least squares support vector machines for function approximation is utilized to identify a nonlinear autoregressive external input (NARX) model. The NARX model is then used to construct a novel nonlinear model predictive controller. In deriving the control law, a quasi-Newton algorithm is selected to implement the nonlinear model predictive control (NMPC) algorithm. The simulation result illustrates the validity and feasibility of the nonlinear MPC algorithm

Keywords

autoregressive processes; function approximation; identification; least squares approximations; nonlinear control systems; predictive control; support vector machines; NARX model identification; function approximation; industrial process control; least square support vector machines; nonlinear autoregressive external input model; nonlinear model predictive control; nonlinear systems; quasiNewton algorithm; Electrical equipment industry; Function approximation; Industrial control; Least squares approximation; Least squares methods; Nonlinear systems; Predictive control; Predictive models; Process control; Support vector machines; Least squares support vector machines (LS-SVM); NARX model identification; Quasi-Newton algorithm; nonlinear model predictive control;

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.2006.1712474

Filename

1712474