基于全扰动观测和支持向量机的无人滚轮GPS信号故障诊断

Chongsong Hu, K. Song, H. Xie
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引用次数: 0

摘要

滚轮是典型的多自由度铰接式多体车辆。准确可靠的位置和航向角测量是实现无人压路机精确路径跟踪的重要基础。由于压路机运行环境较差,定位信号经常出现漂移或跳变,影响系统的可靠运行。为了实现定位系统的可靠故障诊断,本文提出了一种将全扰动观测与支持向量机(SVM)分类相结合的定位系统故障诊断方法。建立了以方向盘角度和车速为输入,以经度、纬度和航向角为输出的多体运动学模型。模型估计值与实测值的差异被视为总扰动,由扩展状态观测器进行估计。然后将估计的总扰动与测量的位置和航向角一起输入支持向量机进行故障分类。实验结果表明,与仅基于支持向量机的传统方法相比,该方法的故障诊断准确率为95%,准确率和计算时间分别提高了9%和12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GPS Signal Fault Diagnosis for Unmanned Rollers Based on Total Disturbance Observation and Support Vector Machine
The roller is a typical articulated multi-body vehicle with multi-degree of freedom in motion. Accurate and reliable position and heading angle measurements are important foundations for the accurate path-following of unmanned rollers. Due to the poor operation environment of the roller, the positioning signal often drifts or jumps, which affects the reliable operation of the system. To achieve reliable fault diagnostic in the positioning system, in this paper, a novel solution that combines total disturbance observation and support vector machine (SVM) classification, is proposed. A multi-body kinematic model is established with steering wheel angle and vehicle speed as inputs, and with the longitude, latitude and heading angle as outputs. The discrepancy of model estimates from the measured value is treated as total disturbance, to be estimated by the extended state observer. Then the estimated total disturbance, together with the measured position and heading angle are input into the support vector machine for faults classification. Experimental results show that the fault diagnosis accuracy is 95%, the improvement in accuracy and computational time is 9% and 12% respectively, compared with the conventional solution that only based on SVM.
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