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Published in: Soft Computing 24/2018

03-08-2017 | Methodologies and Application

Automotive magnetorheological dampers: modelling and parameter identification using contrast-based fruit fly optimisation

Authors: Stratis Kanarachos, Dzmitry Savitski, Nikos Lagaros, Michael E. Fitzpatrick

Published in: Soft Computing | Issue 24/2018

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Abstract

The present study discusses the mechanical behaviour and modelling of a prototype automotive magnetorheological (MR) damper, which presents different viscous damping coefficients in jounce and rebound. The force generated by the MR damper is measured at different velocities and electrical currents, and a modified damper model is proposed to improve fitting of the experimental data. The model is calibrated by means of parameter identification, and for this purpose a new swarm intelligence algorithm is proposed, that we call the contrast-based Fruit Fly Optimisation Algorithm (c-FOA). The performance of c-FOA is compared with that of Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Artificial Bee Colony. The comparison is made on the basis of no a-priori knowledge of the damper model parameters range. The results confirm the good performance of c-FOA under parametric range uncertainty. A sensitivity analysis discusses c-FOA’s performance with respect to its tuning parameters. Finally, a ride comfort simulation study quantifies the discrepancies in the results, for different identified damper model sets. The discrepancies underline the importance of accurately describing MR damper nonlinear behaviour, considering that virtual sign-off processes are increasingly gaining momentum in the automotive industry.

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Appendix
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Metadata
Title
Automotive magnetorheological dampers: modelling and parameter identification using contrast-based fruit fly optimisation
Authors
Stratis Kanarachos
Dzmitry Savitski
Nikos Lagaros
Michael E. Fitzpatrick
Publication date
03-08-2017
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 24/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2757-6

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