In this work, we present an approach to supervisory reinforcement learning
control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where
control decisions in response to disturbances in the environment have to be
made in the order of milliseconds. We formulate a supervisory control
architecture that interleaves with extant embedded control and demonstrates
robustness to environmental disturbances in the form of adverse wind
conditions. We run case studies with a Tarot T-18 Octorotor to demonstrate the
effectiveness of our approach and compare it against a classic cascade control
architecture used in most vehicles. While the results show the performance
difference is marginal for nominal operations, substantial performance
improvement is obtained with the supervisory RL approach under unseen wind
conditions.

本文提出了一种针对无人机的监督强化学习控制方法，通过与现有嵌入式控制交错的控制架构，以及针对恶劣风条件的稳健性表现，实现了对环境扰动的控制，使用 Tarot T-18 八旋翼机进行了案例研究，结果表明相对于大多数车辆中使用的经典级联控制结构，监督强化学习模式在面对未知的风力条件时取得了实质性的性能提高。