Hybrid neural network shock absorber model

Abstract

A hybrid neural network model is presented and described. The model is composed of a mechanical and thermodynamical part. The mechanical part is described by Akima spline in combination with a feed-forward neural network, while in the case of the thermodynamic part differential equation of dissipative heating is formulated. The interface between both part is provided by the neural network. To identify a proper parameter set of the hybrid model a shock absorber of a middle class passenger car is measured on a servo-hydraulic testing machine. As an excitation a stochastic signal with a predefined power spectral density (PSD) is used. Subsequently the hybrid shock absorber is implemented into a full vehicle model in ADAMS/Car to test its numerical performance and influence on the vertical vehicle dynamics. As reference a standard spline shock absorber model is taken.