Adaptive control of a class of nonlinear discrete-time systems using support vector machine

Author

Xu, Jianqiang ; Chen, Shuzhong

Author_Institution

Center of Math. & Phys. Teaching, Shanghai Inst. of Technol., China

Volume

1

fYear

2004

fDate

15-19 June 2004

Firstpage

440

Abstract

In this paper, we introduce the use of least square support vector machine (LS-SVM) for the adaptive control of a class of nonlinear discrete-time systems. The solution is characterized by a set of linear equations. The results are discussed with radial basis function kernel. Advantages of LS-SVM control are that no number of hidden units has to be determined for the controller and that no centers have to be specified for the Gaussian kernels. The curse of dimensionality is avoided using the finite time window. Simulation results also verify the effectiveness of the approach.

Keywords

Gaussian processes; adaptive control; control system synthesis; discrete time systems; least squares approximations; nonlinear control systems; radial basis function networks; support vector machines; Gaussian kernels; SVM; adaptive control design; finite time window; least square support vector machine; linear equations; nonlinear discrete time systems; radial basis function kernel; Adaptive control; Equations; Kernel; Least squares approximation; Least squares methods; Mathematics; Multi-layer neural network; Neural networks; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1340610

Filename

1340610