# Geometric Adaptive Control with Neural Networks for a Quadrotor UAV in Wind fields

arXiv: https://arxiv.org/abs/1903.02091 · HTML: https://mlsanity.com/paper/1903.02091/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions](https://mlsanity.com/paper/2103.01932/) (2021)
- [Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features](https://mlsanity.com/paper/2109.09974/) (2021)
- [Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust](https://mlsanity.com/paper/2101.12501/) (2021)
- [Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence](https://mlsanity.com/paper/2607.01528/) (2026)
- [Physics-Informed Neural Networks for Nonlinear Output Regulation](https://mlsanity.com/paper/2511.13595/) (2025)
- [Discrete Action Space as a Prerequisite for GRPO Convergence in Small-Model Continuous Control](https://mlsanity.com/paper/2607.21626/) (2026)

## Abstract (arXiv)

This paper proposes a geometric adaptive controller for a quadrotor unmanned aerial vehicle with artificial neural networks. It is assumed that the dynamics of a quadrotor is disturbed by arbitrary, unstructured forces and moments caused by wind. To address this, the proposed control system is augmented with multilayer neural networks, and the weights of neural networks are adjusted online according to an adaptive law. By utilizing the universal approximation theorem, it is shown that the effects of unknown disturbances can be mitigated. More specifically, under the proposed control system, the tracking errors in the position and the heading direction are uniformly ultimately bounded where the ultimate bound can be reduced arbitrarily. These are developed directly on the special Euclidean group to avoid complexities or singularities inherent to local parameterizations. The efficacy of the proposed control system is first illustrated by numerical examples. Then, several indoor flight experiments are presented to demonstrate that the proposed controller successfully rejects the effects of wind disturbances even for aggressive, agile maneuvers.