基于在线数据的窄轨车辆载荷分类

D. Isgro, G. Mantegazza, S. Formentin, Giulio Panzani, S. Savaresi
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引用次数: 4

摘要

在汽车应用中,了解车辆负载是一个至关重要的因素,可以显著提高安全性和性能,例如在ABS或半主动悬架控制中。在窄轨车辆中,这一点尤为重要,因为它的质量变化比标准车辆要大得多。本文的目的是提出一种仅基于惯性传感器的在线数据质量分类器。用一辆真实车辆的实验数据对该方法的有效性进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On-Line Data-Based Load Classification in Narrow-Track Vehicles
In automotive applications, the knowledge of the vehicle load is a crucial factor that can bring significant improvement on safety and performance, e.g. in ABS or semiactive suspensions control. In narrow-track vehicles, this aspect is even more important, considering that the mass variation - w.r.t to the vehicle one - is higher than in standard vehicles. The objective of this work is to present an on-line data-based mass classifier based on inertial sensors only. The effectiveness of the approach is assessed on experimental data taken from a real vehicle.
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