A multi-model parameter and state estimation of mechanical systems

Author

Baruch, Ieroham ; Flores, J.M. ; Martinez, J.C. ; Garrido, Ruben

Author_Institution

Dept. de Contol Autom., Centro de Investigacion y de Estudios Avanzados, IPN, Mexico City, Mexico

Volume

2

fYear

2000

fDate

2000

Firstpage

700

Abstract

A parametric neural model and an identification learning algorithm for systems parameter and state estimation are described. For a complex nonlinear plants identification, a fuzzy-neural multi-model approach, is proposed. The proposed multi-model contains two parametric neural models, which are applied for real-time identification of a nonlinear mechanical system with friction. The simulation and experimental results confirms the multi-model applicability

Keywords

DC motor drives; compensation; electric machine analysis computing; friction; fuzzy neural nets; nonlinear systems; parameter estimation; state estimation; DC motor drive; complex nonlinear plants identification; friction; fuzzy-neural multi-model approach; identification learning algorithm; mechanical systems; multi-model parameter; nonlinear mechanical system; nonlinear systems; parametric neural model; parametric neural models; state estimation; systems parameter; Control systems; Frequency; Friction; Mechanical systems; Motion control; Motor drives; Neural networks; Pulse modulation; Servomechanisms; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 2000. ISIE 2000. Proceedings of the 2000 IEEE International Symposium on

Conference_Location

Cholula, Puebla

Print_ISBN

0-7803-6606-9

Type

conf

DOI

10.1109/ISIE.2000.930383

Filename

930383