Automotive Magneto-Rheological Dampers: Modelling and Parameter Identification using contrast-based Fruit Fly Optimisation

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

Research output: Contribution to journalArticle

4 Citations (Scopus)
66 Downloads (Pure)

Abstract

The present study discusses the mechanical behaviour and modelling of a prototype automotive magneto-rheological (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.

Original languageEnglish
Pages (from-to)8131–8149
Number of pages19
JournalSoft Computing
Volume22
Issue number24
Early online date3 Aug 2017
DOIs
Publication statusPublished - Dec 2018

Bibliographical note

Publisher Statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-017-2757-6

Keywords

  • Automotive magnetorheological dampers
  • Contrast-based fruit fly optimisation
  • Model identification
  • Ride comfort
  • Swarm intelligence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology

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