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arXiv · 2304.03133

Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing

Kevin PT. Haughn·Christina Harvey·Daniel J. Inman
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Abstract

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.

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Kevin PT. Haughn, Christina Harvey, Daniel J. Inman. 2023-04-06. Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing. https://doi.org/10.1038/s44172-024-00201-8

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