# A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances

arXiv: https://arxiv.org/abs/2305.12543 · HTML: https://mlsanity.com/paper/2305.12543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 21 May 2023.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Neural Lander: Stable Drone Landing Control using Learned Dynamics](https://mlsanity.com/paper/1811.08027/) (2018)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems](https://mlsanity.com/paper/2103.04490/) (2021)
- [Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions](https://mlsanity.com/paper/1807.05290/) (2018)
- [Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control](https://mlsanity.com/paper/2008.04403/) (2020)
- [A Review of Control Algorithms for Autonomous Quadrotors](https://mlsanity.com/paper/1602.02622/) (2016)

## Abstract (arXiv)

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.