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A New Non-Parametric Model Based on Neural Network for a MR Damper

[+] Author Affiliations
María Jesús L. Boada, José Antonio Calvo, Beatriz L. Boada, Vicente Díaz

Carlos III University, Madrid, Spain

Paper No. ESDA2008-59210, pp. 597-602; 6 pages
doi:10.1115/ESDA2008-59210
From:
  • ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis
  • Volume 2: Automotive Systems; Bioengineering and Biomedical Technology; Computational Mechanics; Controls; Dynamical Systems
  • Haifa, Israel, July 7–9, 2008
  • Conference Sponsors: International
  • ISBN: 978-0-7918-4836-4 | eISBN: 0-7918-3827-7
  • Copyright © 2008 by ASME

abstract

Currently dampers based on magnetorheological (MR) fluids are being used in many applications such as construction, biomechanical and semi-active suspension to improve their behaviour. The main advantage of MR dampers is its very low time response (≈ 10 ms). In many cases, it is necessary to establish a suitable model of MR damper which characterizes its behaviour so that this model can be used in the simulation stage. In this paper, a new non-parametric model is proposed based on neural networks using a recursive lazy learning to model the MR damper behaviour. The proposed method is validated by comparison with experimental obtained responses. Results show that the estimated model correlates very well with the data obtained experimentally and learns quickly.

Copyright © 2008 by ASME

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