Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using traditional control design methods. We present Neural-Fly, a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly builds on two key observations that aerodynamics in different wind conditions share a common representation and that the wind-specific part lies in a low-dimensional space. To that end, Neural-Fly uses a proposed learning algorithm, domain adversarially invariant meta-learning (DAIML), to learn the shared representation, only using 12 minutes of flight data. With the learned representation as a basis, Neural-Fly then uses a composite adaptation law to update a set of linear coefficients for mixing the basis elements. When evaluated under challenging wind conditions generated with the Caltech Real Weather Wind Tunnel, with wind speeds up to 43.6 kilometers/hour (12.1 meters/second), Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers. In addition to strong empirical performance, the exponential stability of Neural-Fly results in robustness guarantees. Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.

翻译：在动态高速风场中执行安全精确的飞行机动，对于无人飞行器（UAV）的持续商业化至关重要。然而，由于各类风况条件及其对飞行器机动性影响的关联机制尚未被充分理解，采用传统控制设计方法构建有效的机器人控制器面临严峻挑战。我们提出Neural-Fly这一基于学习的方法，通过深度学习的预训练表征实现快速在线自适应。该方法基于两个关键发现：不同风况下的空气动力学特性共享共同表征，而风况特定部分存在于低维空间中。为此，Neural-Fly采用所提出的域对抗不变元学习（DAIML）算法，仅使用12分钟飞行数据即可学习共享表征。以习得表征为基础，Neural-Fly通过复合自适应律更新一组线性系数来混合基元。在加州理工学院真实风洞生成的强风条件下（风速高达43.6公里/小时，即12.1米/秒）进行评估时，Neural-Fly实现了精确的飞行控制，其跟踪误差显著小于当前最优的非线性与自适应控制器。除强劲的实证表现外，Neural-Fly的指数稳定性还提供了鲁棒性保障。最后，本控制设计能泛化至未见过的风况条件，在仅依赖机载传感器的户外飞行中展现有效性，并能在无人机间迁移且性能退化极小。