Experimental ANN-based modeling of an adjustable damper

Author

Tudon-Martinez, Juan C. ; Morales-Menendez, Ruben ; Ramirez-Mendoza, Ricardo ; Garza-Castanon, Luis

fYear

2014

fDate

6-11 July 2014

Firstpage

2512

Lastpage

2518

Abstract

A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN model is focused to get the best architecture that manages the trade-off between computing cost and performance. Experimental data provided from two commercial MR dampers with different properties have been used to validate the performance of the proposed ANN model in comparison with the classical parametric model of Bingham. Based on the Root Mean Square Error index, an average error of 7.2 % is obtained by the ANN model, by taking into account 5 experiments with 10 replicas each one; while the Bingham model has 13.8 % of error.

Keywords

magnetorheology; mean square error methods; neural nets; shock absorbers; vehicle dynamics; vibration control; ANN model; Bingham model; adjustable damper; artificial neural networks; automotive suspension system; experimental ANN-based modeling; magneto-rheological damper; root mean square error index; Artificial neural networks; Computational modeling; Computer architecture; Damping; Force; Mathematical model; Shock absorbers;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889391

Filename

6889391