Nonlinearity and non-stationarity analysis of dynamic response of vehicle–track coupling system enhanced by Huang transform

Shuangxi, Chen; Lin, Jianhui

doi:10.1016/j.measurement.2014.05.023

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The effectiveness of EMD based technique for signal decomposition has been demonstrated in [10], where EMD is applied to extract higher order statistical parameters for gear fault diagnosis. [11] has also used modification of EMD to quantify the nonlinearity and non-stationarity of dynamic response of a vehicle-track coupling system.…”

Section: D mentioning

confidence: 99%

Steering measurement decomposition for vehicle lane keeping – A study of driver behaviour

et al. 2018

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Steering control for vehicle lane keeping has attracted significant attention from both automotive industries and researchers. To describe intermittent pulse-like qualities imparted by drivers that are seen in real-world steering measurements, a pulse control model (PCM) is presented for vehicle lane keeping. Inspired by the PCM, a steering angle measurement is decomposed into a combination of trend, integrated sine components (ISCs) and sine components (SCs), where trend corresponds to the path curvature, ISCs to the heading angles, and SCs to the lateral positions. Trends are extracted through the use of empirical mode decomposition (EMD) and principal component analysis (PCA), with singular spectral analysis (SSA) and Fourier curve-fitting (FCF) being employed to determine the ISCs and SCs in the main pulses. Through statistical pattern analysis on experimental measurements of drivers' steering performance, it is revealed that (1) the pulse steering behaviour from real drivers shows the benefit of the proposed PCM for steering control during lane keeping, and (2) classification of pulse steering characteristics can be used for normal driver state identification and highlight abnormal driving behaviour, leading to the prospect of identifying driving characteristics typical of impaired concentration, substance misuse or tiredness, for instance. Highlights The PCM can describe intermittent pulse-like qualities in real-world steering measurements. Steering angle measurements can be decomposed into a combination of trend, ISCs and SCs. EMD and PCA present appropriate tools for measurement trend extraction. SSA and FCF are appropriate techniques for the extraction of ISC and SC pulses. Patterns of pulse characteristics are useful for PCM validation and driver identification.

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Section: D mentioning

confidence: 99%