# RL-based Control of UAS Subject to Significant Disturbance

arXiv: https://arxiv.org/abs/2504.08114 · HTML: https://mlsanity.com/paper/2504.08114/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots](https://mlsanity.com/paper/2503.01471/) (2025)
- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13285/) (2023)
- [The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control](https://mlsanity.com/paper/2410.07686/) (2024)

## Abstract (arXiv)

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.