Adaptive approximately optimal control of unknown nonlinear systems based on locally weighted learning

Author

Dong, Wenjie ; Farrell, Jay A.

Author_Institution

Dept. of Electr. Eng., Univ. of Texas-Pan American, Edinburg, TX, USA

fYear

2009

fDate

15-18 Dec. 2009

Firstpage

345

Lastpage

350

Abstract

This paper considers the optimal control of unknown nonlinear systems. Adaptive approximately optimal controllers are proposed with the aid of learning techniques. The proposed controllers can update themselves according to the estimates of the value functions and converge to the optimal controller. To show effectiveness of the proposed controllers, numerical simulations are presented.

Keywords

adaptive control; learning (artificial intelligence); nonlinear control systems; numerical analysis; optimal control; adaptive approximately optimal control; locally weighted learning; numerical simulations; unknown nonlinear systems; value functions estimation; Adaptive control; Adaptive systems; Algorithm design and analysis; Control systems; Dynamic programming; Nonlinear equations; Nonlinear systems; Optimal control; Partial differential equations; Programmable control; Optimal control; approximately optimal control; learning; nonlinear system; uncertain systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on

Conference_Location

Shanghai

ISSN

0191-2216

Print_ISBN

978-1-4244-3871-6

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2009.5400918

Filename

5400918