Deep neural network based data-driven virtual sensor in vehicle semi-active suspension real-time control

    Paulius Kojis Affiliation
    ; Eldar Šabanovič Affiliation
    ; Viktor Skrickij Affiliation


This research presents a data-driven Neural Network (NN)-based Virtual Sensor (VS) that estimates vehicles’ Unsprung Mass (UM) vertical velocity in real-time. UM vertical velocity is an input parameter used to control a vehicle’s semi-active suspension. The extensive simulation-based dataset covering 95 scenarios was created and used to obtain training, validation and testing data for Deep Neural Network (DNN). The simulations have been performed with an experimentally validated full vehicle model using software for advanced vehicle dynamics simulation. VS was developed and tested, taking into account the Root Mean Square (RMS) of Sprung Mass (SM) acceleration as a comfort metric. The RMS was calculated for two cases: using actual UM velocity and estimations from the VS as input to the suspension controller. The comparison shows that RMS change is less than the difference threshold that vehicle occupants could perceive. The achieved result indicates the great potential of using the proposed VS in place of the physical sensor in vehicles.

Keyword : virtual sensor, real-time, semi-active suspension, vehicle dynamics, deep neural network, deep learning

How to Cite
Kojis, P., Šabanovič, E., & Skrickij, V. (2022). Deep neural network based data-driven virtual sensor in vehicle semi-active suspension real-time control. Transport, 37(1), 37–50.
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May 12, 2022
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