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

TL;DR

This paper presents a deep reinforcement learning-based controller for UAV wings that significantly reduces gust impact using fewer sensors, enabling safer urban operations.

Contribution

The study introduces a novel deep learning approach for gust alleviation on UAVs that requires fewer sensors than traditional methods, improving response time and operational feasibility.

Findings

Gust impact was reduced by 84% using the controller.

Using only three pressure sensors was as effective as six.

The method enables UAV operation in complex urban environments.

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.