用神经微分方程建模飞机飞行轨迹，降段预测更准。
A Neural ODE Approach to Aircraft Flight Dynamics Modelling
- 结合物理规律与数据驱动，用神经微分方程建模飞行动态。
- 在下降阶段的高精度轨迹还原，优于传统BADA4模型。
- 适合航空管理、飞行仿真与性能评估场景使用。
精准的飞机轨迹预测对空中交通管理、航空公司运营及环境评估至关重要。本文提出基于神经常微分方程的飞行动力学模型NODE-FDM，利用快速访问记录器（QAR）数据进行训练。通过融合解析运动学关系与数据驱动组件，该模型在飞行下降阶段对实际轨迹的还原精度显著优于现有先进模型（如结合轨迹控制算法的BADA4性能模型），在高度、速度和质量动态方面均有明显提升。尽管当前存在物理约束不足及QAR数据有限等局限，结果表明物理信息引导的神经微分方程在高保真数据驱动飞机性能建模中具有潜力。未来工作将扩展该框架以完整建模飞机侧向运动。
原文摘要 · Abstract (English)
Accurate aircraft trajectory prediction is critical for air traffic management, airline operations, and environmental assessment. This paper introduces NODE-FDM, a Neural Ordinary Differential Equations-based Flight Dynamics Model trained on Quick Access Recorder (QAR) data. By combining analytical kinematic relations with data-driven components, NODE-FDM achieves a more accurate reproduction of recorded trajectories than state-of-the-art models such as a BADA-based trajectory generation methodology (BADA4 performance model combined with trajectory control routines), particularly in the descent phase of the flight. The analysis demonstrates marked improvements across altitude, speed, and mass dynamics. Despite current limitations, including limited physical constraints and the limited availability of QAR data, the results demonstrate the potential of physics-informed neural ordinary differential equations as a high-fidelity, data-driven approach to aircraft performance modelling. Future work will extend the framework to incorporate a full modelling of the lateral dynamics of the aircraft.
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