Version 1
: Received: 21 October 2024 / Approved: 29 October 2024 / Online: 30 October 2024 (06:48:12 CET)
How to cite:
Bianchi, D.; Epicoco, N.; Di Ferdinando, M.; Di Gennaro, S.; Pepe, P. Physics-Informed Neural Networks for UAV System Estimation. Preprints2024, 2024102275. https://doi.org/10.20944/preprints202410.2275.v1
Bianchi, D.; Epicoco, N.; Di Ferdinando, M.; Di Gennaro, S.; Pepe, P. Physics-Informed Neural Networks for UAV System Estimation. Preprints 2024, 2024102275. https://doi.org/10.20944/preprints202410.2275.v1
Bianchi, D.; Epicoco, N.; Di Ferdinando, M.; Di Gennaro, S.; Pepe, P. Physics-Informed Neural Networks for UAV System Estimation. Preprints2024, 2024102275. https://doi.org/10.20944/preprints202410.2275.v1
APA Style
Bianchi, D., Epicoco, N., Di Ferdinando, M., Di Gennaro, S., & Pepe, P. (2024). Physics-Informed Neural Networks for UAV System Estimation. Preprints. https://doi.org/10.20944/preprints202410.2275.v1
Chicago/Turabian Style
Bianchi, D., Stefano Di Gennaro and Pierdomenico Pepe. 2024 "Physics-Informed Neural Networks for UAV System Estimation" Preprints. https://doi.org/10.20944/preprints202410.2275.v1
Abstract
The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factor. Consequently, operators grapple with escalating challenges in implementing real-time control actions.
This study delves into an approach for estimating the model of quadrotor Unmanned Aerial Vehicles using Physics-Informed Neural Networks (PINNs) when you have a limited amount of data available. PINNs offer the potential to tackle issues like heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability. The effectiveness of the estimator is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF).
Keywords
quadrotor control; system identification; physics-informed neural networks
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
