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基于交互式多模型方法的线控车辆故障检测与隔离

Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles.

机构信息

LIVIC Laboratory, IFSTTAR, 25 Allée des Marronniers, 78000 Versailles, France.

Centre for Accident Research and Road Safety (CARRS-Q), Queensland University of Technology (QUT), Brisbane City, QLD 4000, Australia.

出版信息

Sensors (Basel). 2018 Jul 18;18(7):2332. doi: 10.3390/s18072332.

Abstract

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

摘要

目前,驾驶辅助系统在道路车辆中的地位正在大幅提升。线控技术为完全自动驾驶车辆铺平了道路,它可以提高车辆控制的潜力。这些新嵌入式系统的实施仍然受到限制,主要是出于可靠性原因,因此需要开发诊断机制。本文研究了线控道路车辆中传感器和执行器故障的检测和识别。提出了一种基于非线性车辆动力学观测器的交互多模型方法。讨论了不同概率观测器的适当性。基于实验车辆信号的结果表明,传感器故障的识别快速且稳健,而执行器故障则更具挑战性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/786f/6068968/af5f681d88bd/sensors-18-02332-g001.jpg

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