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A robust observer based on H∞ filtering with parameter uncertainties combined with Neural Networks for estimation of vehicle roll angle

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URI: https://hdl.handle.net/10016/25343
ISSN: 0888-3270
DOI: https://doi.org/10.1016/j.ymssp.2017.06.044
UXXI: AR/0000020348
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robust_MSSP_2018_ps.pdf (739.11 KB)
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2018-01-18
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Elsevier Ltd.
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Abstract
Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.
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Vehicle dynamics, Roll angle estimation, NN, Robust observer, H∞ observer
Bibliographic citation
Mechanical Systems and Signal Processing, vol. 99 (2018), pp. 611-623
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