Inverse optimal nonlinear recurrent high order neural observer

Author

Ricalde, Luis J. ; Sanchez, Edgar N.

Author_Institution

CINVESTAV, Jalisco, Mexico

Volume

1

fYear

2005

fDate

31 July-4 Aug. 2005

Firstpage

361

Abstract

This paper presents the design of an adaptive recurrent neural observer for nonlinear systems which model is assumed to be unknown. The neural observer is composed of a recurrent high order neural network which builds an online model of the unknown plant and a learning adaptation law for the neural network weights. This law is obtained by the Lyapunov methodology. The feedback law which guarantees stability of the estimation error is proved to be optimal with respect to a well defined cost functional.

Keywords

Lyapunov methods; feedback; inverse problems; learning (artificial intelligence); nonlinear systems; observers; recurrent neural nets; Lyapunov method; adaptive recurrent neural observer; estimation error stability; feedback law; inverse optimal nonlinear recurrent high order neural observer; learning adaptation law; nonlinear systems; recurrent high order neural network; Control systems; Cost function; Estimation error; Neural networks; Nonlinear control systems; Nonlinear systems; Observers; Recurrent neural networks; Stability; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on

Print_ISBN

0-7803-9048-2

Type

conf

DOI

10.1109/IJCNN.2005.1555857

Filename

1555857