Nonparametric identification of dynamic nonlinear systems

Author

Krzyzak, Adam ; Sasiadek, Jerzy Z.

Author_Institution

Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada

Volume

3

fYear

1997

fDate

10-12 Dec 1997

Firstpage

2996

Abstract

Nonlinear dynamic block oriented systems of Hammerstein and Wiener type are identified. Hammerstein system consists of a memoryless nonlinearity followed by a dynamic, linear system, while Wiener system is a cascade of a linear dynamic system connected to a memoryless nonlinearity. The class of nonlinearities considered is large and nonparametric. Identification algorithms based on input-output observations are proposed for both systems and their convergence and rates are studied. Simulation results are provided and possible applications of block oriented systems in robotics are discussed

Keywords

cascade systems; convergence; nonlinear dynamical systems; observers; Hammerstein system; I/O observations; Wiener system; block oriented systems; cascade system; dynamic linear system; input-output observations; memoryless nonlinearity; nonlinear dynamic block oriented systems; nonparametric identification; robotics; Adaptive control; Aerodynamics; Computer science; Convergence; Kernel; Linear systems; Nonlinear dynamical systems; Nonlinear systems; Robots; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.657907

Filename

657907