# Deep reinforcement learning reveals fewer sensors are needed for autonomous gust alleviation

**Authors:** Kevin PT. Haughn, Christina Harvey, Daniel J. Inman  
**arXiv:** [2304.03133](https://arxiv.org/abs/2304.03133)  
**Date:** 2023-04-06  
**Summary page:** https://www.summarizepaper.com/fr/paper/2304.03133v1/

## 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.