System identification with partial-state measurement via dynamic multilayer neural networks

Author

Yu, Wen ; Poznyak, A.S.

Author_Institution

Seccion de Control Autom., CINVESTAV-IPN, Mexico City, Mexico

Volume

3

fYear

1999

fDate

1999

Firstpage

2081

Abstract

This paper proposes a new online identification method for a class of partial-state measurement nonlinear systems. Only input and output are available, the inner state and the structure are unknown. The design of this paper is based on the combination of the state observer with the neuro identifier. As no information of the nonlinear system can be used, first a model-free high-gain observer is designed to estimate the inner state. Then a dynamic multilayer neural network is used to identify the nonlinear system based on the full observed states. By means of a Lyapunov-like analysis we determine the stable learning algorithms for the observer-based neuro identifier

Keywords

Lyapunov methods; multilayer perceptrons; nonlinear systems; online operation; state estimation; Lyapunov-like analysis; dynamic multilayer neural networks; inner state estimation; model-free high-gain observer; nonlinear system; observer-based neuro identifier; online identification method; partial-state measurement nonlinear systems; stable learning algorithm determination; state observer; system identification; Function approximation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Observers; State estimation; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.832707

Filename

832707