Optimal Control of a Class of Nonlinear Systems Using Radial Basis Function Neural Networks

Author

Medagam, Peda V. ; Pourboghrat, Farzad

Author_Institution

Southern Illinois Univ., Carbondale

Volume

4

fYear

2007

fDate

13-15 Dec. 2007

Firstpage

3

Lastpage

7

Abstract

This paper presents an online optimal control technique for a class of nonlinear systems. The technique is based on approximating the solution to the corresponding generalized Hamilton-Jacobi-Bellman (GHJB) equation for optimal control using radial basis function neural networks (RBFNN). The GHJB equation is solved by adjusting the parameters (weights and centers) of RBFNN online. The proposed optimal control algorithm provides good accuracy and numerical examples illustrate the merits of the proposed approach.

Keywords

neurocontrollers; nonlinear control systems; optimal control; radial basis function networks; generalized Hamilton-Jacobi-Bellman equation; nonlinear system; optimal control; radial basis function neural network; Control systems; Function approximation; Least squares approximation; Neural networks; Nonlinear control systems; Nonlinear equations; Nonlinear systems; Optimal control; Radial basis function networks; Riccati equations;

fLanguage

English

Publisher

ieee

Conference_Titel

Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on

Conference_Location

Sivakasi, Tamil Nadu

Print_ISBN

0-7695-3050-8

Type

conf

DOI

10.1109/ICCIMA.2007.321

Filename

4426440