# Online Adaptation for Flying Quadrotors in Tight Formations

arXiv: https://arxiv.org/abs/2506.17488 · HTML: https://mlsanity.com/paper/2506.17488/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [acados: a modular open-source framework for fast embedded optimal control](https://mlsanity.com/paper/1910.13753/) (2019)
- [Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions](https://mlsanity.com/paper/2012.05457/) (2020)
- [Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors](https://mlsanity.com/paper/2109.04210/) (2021)
- [Knowledge-Based Learning of Nonlinear Dynamics and Chaos](https://mlsanity.com/paper/2010.03415/) (2020)
- [Flying batteries: In-flight battery switching to increase multirotor flight time](https://mlsanity.com/paper/1909.10091/) (2019)
- [Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots](https://mlsanity.com/paper/2308.00570/) (2023)
- [Modeling Aggregate Downwash Forces for Dense Multirotor Flight](https://mlsanity.com/paper/2312.03488/) (2023)
- [Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control](https://mlsanity.com/paper/2410.09727/) (2024)
- [Learning Based MPC for Autonomous Driving Using a Low Dimensional Residual Model](https://mlsanity.com/paper/2412.03874/) (2024)

## Built on by (most cited first, 1 of 1)

- [Statistical Turbulence and High-Fidelity Disturbance Fields for Quadrotor Flight Control](https://mlsanity.com/paper/2610.06874/) (2026)

## Abstract (arXiv)

The task of flying in tight formations is challenging for teams of quadrotors because the complex aerodynamic wake interactions can destabilize individual team members as well as the team. Furthermore, these aerodynamic effects are highly nonlinear and fast-paced, making them difficult to model and predict. To overcome these challenges, we present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework that allows individual quadrotors to accurately track trajectories while adapting to time-varying aerodynamic interactions during formation flights. We evaluate L1 KNODE-DW MPC in two different three-quadrotor formations and show that it outperforms several MPC baselines. Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight. These findings show that the L1 adaptive module compensates for unmodeled disturbances most effectively when paired with an accurate dynamics model. A video showcasing our framework and the physical experiments is available here: https://youtu.be/9QX1Q5Ut9Rs