There is a growing need for uncrewed aerial vehicles (UAVs) to operate in cities. However, the uneven urban landscape and complex street systems cause large-scale wind gusts that challenge the safe and effective operation of UAVs. Current gust alleviation methods rely on traditional control surfaces and computationally expensive modeling to select a control action, leading to a slower response. Here, we used deep reinforcement learning to create an autonomous gust alleviation controller for a camber-morphing wing. This method reduced gust impact by 84%, directly from real-time, on-board pressure signals. Notably, we found that gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six signals. This reduced-sensor fly-by-feel control opens the door to UAV missions in previously inoperable locations.

翻译：城市环境下无人飞行器（UAV）的作业需求日益增长。然而，不均匀的城市地貌与复杂的街道系统会引发大规模阵风，对UAV的安全有效运行构成挑战。当前阵风缓解方法依赖传统控制面及计算成本高昂的建模来选择控制动作，导致响应速度较慢。本研究采用深度强化学习为弯度变形机翼构建自主阵风缓解控制器。该方法基于机载实时压力信号，可降低84%的阵风影响。值得注意的是，我们发现仅使用三个压力测点信号实现的阵风缓解效果与使用六个信号在统计学上无显著差异。这种减少传感器的"触觉飞行控制"技术为UAV在先前无法作业区域执行任务开辟了道路。