RL-based Control of UAS Subject to Significant Disturbance
This work addresses the challenge of robust UAS control in unpredictable environments, offering an incremental improvement by integrating predictive cues into RL for better disturbance handling.
This paper tackles the problem of controlling an Unmanned Aerial System (UAS) under significant disturbances by proposing a Reinforcement Learning (RL)-based framework that uses a trigger signal to predict and counteract disturbances, resulting in a predictive policy that outperforms baseline and reactive policies by minimizing position deviations.
This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.