基于强化Meta学习的高超音速滑翔机自适应进场段制导
Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning
摘要：
本文采用强化Meta学习方法对滑翔式高超声速飞行器进场段的自适应制导系统进行了优化设计。适应性是通过优化一系列的非标称飞行条件，包括气动系数参数的扰动，致动器故障的情况下，传感器噪声。该系统将观测值直接映射到指令的倾斜角和攻角速率。这些观测包括使用平行导航制定的速度场跟踪误差，但适用于必须考虑地球曲率的长轨迹。使跟踪误差最小化保持到目标位置的弯曲空间视线与车辆的速度矢量对准。优化的制导系统将引导飞行器以指定的终端速度以高精度到达目标位置的轨迹，同时满足加热速率、载荷和动压约束。我们证明了制导系统的适应性，通过测试在优化过程中没有经历过的飞行条件。制导系统的性能，然后比较的线性二次型调节器跟踪最优轨迹。
英文摘要：
We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle. Adaptability is achieved by optimizing over a range of off-nominal flight conditions including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, and sensor noise. The system maps observations directly to commanded bank angle and angle of attack rates. These observations include a velocity field tracking error formulated using parallel navigation, but adapted to work over long trajectories where the Earth's curvature must be taken into account. Minimizing the tracking error keeps the curved space line of sight to the target location aligned with the vehicle's velocity vector. The optimized guidance system will then induce trajectories that bring the vehicle to the target location with a high degree of accuracy at the designated terminal speed, while satisfying heating rate, load, and dynamic pressure constraints. We demonstrate the adaptability of the guidance system by testing over flight conditions that were not experienced during optimization. The guidance system's performance is then compared to that of a linear quadratic regulator tracking an optimal trajectory.
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