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Assessment of the Shock-Absorption Performance of a High Capacity Suspension System by Neural Networks

Published online by Cambridge University Press:  14 November 2013

Y.-W. Lee
Y.-W. Lee*
Affiliation:
Department of Mechatronic, Energy and Aerospace Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan, Taiwan 33448, R.O.C.
*
yaweilee@ ndu.edu.tw
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Abstract

The objective of this study is to develop a framework using nonlinear autoregressive model process with exogenous inputs (NARX) neural networks (NNs) to identify the dynamic hysteresis of high load capacity suspension systems. Here, a vertical excitation test is used to simulate various terrains at an oscillation frequency range from 0.1Hz to 10Hz by using NARX NNs. The model results are in good agreement with suspension component oscillation responses that manifest as variations in model order selection as the excitation frequency approaches 7Hz. Furthermore, mapping the models into generalized frequency response functions (GFRFs), elucidates any unanticipated couplings between surroundings and mechanical hysteresis within the suspension. The proposed approach's systematic design procedure is advantageous because it provides a cost-efficient method that achieves precise identification of online data.

Keywords

NARX networksDouble wishbone suspensionGeneralized frequency response functions (GFRFs)
Type
Research Article
Information
Journal of Mechanics , Volume 30 , Issue 1 , February 2014 , pp. 39 - 48
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2014 

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