A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances
This work addresses robust control for UAVs in dynamic environments, but it appears incremental as it builds on existing supervisory architectures with reinforcement learning.
The paper tackled the problem of robust supervisory control for UAVs under disturbances like adverse wind conditions, resulting in substantial performance improvement compared to a classic cascade control architecture, with marginal differences in nominal operations.
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.