核心概念
Deep reinforcement learning reduces sensor requirements for autonomous gust alleviation on UAVs.
摘要
This content discusses the application of deep reinforcement learning to create an autonomous gust alleviation controller for a camber-morphing wing on uncrewed aerial vehicles (UAVs). The study found that using only three pressure taps was statistically indistinguishable from using six taps, reducing sensor requirements while maintaining performance. This reduced-sensor fly-by-feel control method opens up opportunities for UAV missions in challenging environments.
The content is structured into sections covering Introduction, Wing Morphing Challenges, Autonomous Gust Rejection, Diminishing Effect of Sensors, Downward Gust Challenges, Adaptive Flight Applications, Materials and Methods, Testing Procedures, and Acknowledgements with References.
統計資料
"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."
"Controllers were trained to make decisions directly from pressure signals provided by up to six pressure taps."
"We found the number of pressure taps used for state observation significantly affected the trained GA controller performance."
"Controllers using all six pressure taps consistently achieved large mean gust rejections for each flight condition (high-lift: 84%; medium-lift: 84%; low-lift: 86%) relative to the respective gust-generated change in lift."
引述
"This result runs counter to the “Big Data” mentality that is pervasive in recent machine learning and distributed sensing applications."
"Instead of responding to a perturbation after it occurs, our fly-by-feel active GA senses environmental changes on the wing in real time."
"The success of fly-by-feel aircraft need not depend on our ability to implement highly complex large scale distributed networks if we can effectively identify a reduced set of sensors that provides comparable performance."