Modeling and compensation of frictional uncertainties in motion control: a neural network based approach

Author

Ciliz, M. Kemal ; Tomizuka, Masayoshi

Author_Institution

Dept. of Electr. Eng., Bogazici Univ., Istanbul, Turkey

Volume

5

fYear

1995

fDate

21-23 Jun 1995

Firstpage

3269

Abstract

Frictional uncertainties are known to be a major cause of performance degradation in motion control systems. This paper investigates the modeling and compensation of nonlinear friction dynamics in direct drive servo mechanisms. Different modeling techniques such as, model based adaptive identification, modeling based on experimental data and neural network based approximation are discussed and experimentally tested on the first link of a direct drive manipulator

Keywords

compensation; friction; manipulators; motion control; neural nets; servomechanisms; compensation; direct drive manipulator; direct drive servo mechanisms; frictional uncertainties; model based adaptive identification; motion control systems; neural network based approach; neural network based approximation; nonlinear friction dynamics; performance degradation; Artificial neural networks; Degradation; Friction; Intelligent networks; Mechanical engineering; Motion control; Neural networks; Parametric statistics; Servomechanisms; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, Proceedings of the 1995

Conference_Location

Seattle, WA

Print_ISBN

0-7803-2445-5

Type

conf

DOI

10.1109/ACC.1995.532207

Filename

532207