Citation

Alghafir MN, Dunne JF. Veh. Syst. Dyn. 2012; 50(2): 167-197.

Copyright

(Copyright © 2012, Informa - Taylor and Francis Group)

DOI

10.1080/00423114.2011.575946

PMID

unavailable

Abstract

A computationally efficient NARX-type neural network model is developed to characterise highly nonlinear frequency-dependent thermally sensitive hydraulic dampers for use in the virtual tuning of passive suspension systems with high-frequency loading. Three input variables are chosen to account for high-frequency kinematics and temperature variations arising from continuous vehicle operation over non-smooth surfaces such as stone-covered streets, rough or off-road conditions. Two additional input variables are chosen to represent tuneable valve parameters. To assist in the development of the NARX model, a highly accurate but computationally excessive physical damper model [originally proposed by S. Duym and K. Reybrouck, Physical characterization of non-linear shock absorber dynamics, Eur. J. Mech. Eng. M 43(4) (1998), pp. 181-188] is extended to allow for high-frequency input kinematics. Experimental verification of this extended version uses measured damper data obtained from an industrial damper test machine under near-isothermal conditions for fixed valve settings, with input kinematics corresponding to harmonic and random road profiles. The extended model is then used only for simulating data for training and testing the NARX model with specified temperature profiles and different valve parameters, both in isolation and within quarter-car vehicle simulations. A heat generation and dissipation model is also developed and experimentally verified for use within the simulations. Virtual tuning using the quarter-car simulation model then exploits the NARX damper to achieve a compromise between ride and handling under transient thermal conditions with harmonic and random road profiles. For quarter-car simulations, the paper shows that a single tuneable NARX damper makes virtual tuning computationally very attractive.