基于模糊逻辑的运输机尾流轨迹自动识别

Aziz Al-Mahadin, F. Bouslama
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引用次数: 3

摘要

飞机尾涡有时会对尾随的飞机造成很大的干扰。有时过高估计了领先和跟随飞机之间的分离标准,从而降低了机场容量。对旋涡分离形成和修正的一个重要贡献在于飞行员报告对尾流涡穿越的识别,以及对飞行数据记录仪(FDRs)常规记录的飞行数据的人工分析。这个过程有许多缺点,因此,希望有一种自动识别技术,它可以节省时间,既准确又易于实现。本文采用模糊逻辑(FL)对涡旋相遇进行建模和识别。FL容忍数据不精确,并能很好地处理模拟涡旋遭遇的复杂性。模糊语言变量用于FDR数据建模。模糊规则来自54名飞行员的涡旋遭遇报告和210份fdr的飞行记录。分析了FL识别模型,优化了模糊规则库。平均鉴定成功率为83.7%。该自动识别系统可使航空当局定期检讨尾流涡旋分离准则的适当性,以加强安全及增加机场容量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic identification of wake vortex traverse by transport aircraft using fuzzy logic
Aircraft trailing vortices result sometimes in significant disturbance to following aircraft. Separation standards between leading and following aircraft are sometimes over estimated, hence reducing airport capacity. An important contribution to the formation and revision of vortex separations lies in the recognition of wake vortex traverse by pilot reports together with a manual analysis of the flight data routinely recorded by flight data recorders (FDRs). This process has many disadvantages and, therefore, it is desirable to have an automatic identification technique, which can save time, and is both accurate and simple to implement. In this paper, fuzzy logic (FL) is used to model and identify vortex encounters. FL tolerates data imprecision and cope well with complexities in modeling the vortex encounters. Fuzzy linguistic variables are used to model FDR data. Fuzzy rules are derived from a collection of 54 pilot reports of vortex encounters and 210 flight records from FDRs. FL identification models are analyzed and the fuzzy rule base is optimized. An average success rate of identification of 83.7% is obtained. This automatic identification system should enable the aviation authorities to review regularly the appropriateness of wake vortex separation criteria to enhance safety and increase airport capacities.
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