  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.
