Modelling of a novel gas strut using neural networks

B. Gao, J. Darling, D. G. Tilley, R. A. Williams, A. Bean, J. Donahue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The strut is one of the most important components in a vehicle suspension system. Since it is highly non-linear it is difficult to predict its performance characteristics using a physical mathematical model. However, neural networks have been successfully used as universal 'black-box' models in the identification and control of non-linear systems. This approach has been used to model a novel gas strut and the neural network was trained with experimental data obtained in the laboratory from simulated road profiles. The results obtained from the neural network demonstrated good agreement with the experimental results over a wide range of operation conditions. In contrast a linearised mathematical model using least square estimates of system parameters was shown to perform badly due to the highly non-linear nature of the system. A quarter car mathematical model was developed to predict strut behavior. It was shown that the two models produced different predictions of ride performance and it was argued that the neural network was preferable as it included the effects of non-linearities. Although the neural network model does not provide a good understanding of the physical behavior of the strut it is a useful tool for assessing vehicle ride and NVH performance due to its good computational efficiency and accuracy.

Original languageEnglish
Title of host publicationASME International Mechanical Engineering Congress and Exposition
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages137-143
Number of pages7
DOIs
Publication statusPublished - 24 Mar 2008
Event2004 ASME International Mechanical Engineering Congress and Exposition, IMECE - Anaheim, CA, USA United States
Duration: 13 Nov 200419 Nov 2004

Conference

Conference2004 ASME International Mechanical Engineering Congress and Exposition, IMECE
CountryUSA United States
CityAnaheim, CA
Period13/11/0419/11/04

Keywords

  • Gas strut
  • Neural networks
  • Vehicle suspension and computer simulation

ASJC Scopus subject areas

  • Mechanical Engineering
  • Software

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