Publication detail
Hybrid Modeling Approach for Optimization Based Control of Multirotor Unmanned Aerial Vehicles
NOVÁK, J. HANÁK, J. CHUDÝ, P.
Original Title
Hybrid Modeling Approach for Optimization Based Control of Multirotor Unmanned Aerial Vehicles
Type
conference paper
Language
English
Original Abstract
The first principle based model synthesis is fundamental to Guidance, Navigation, and Control (GNC) solution development and integration. Optimization techniques such as Model Predictive Control (MPC) often rely on simplified governing equations of the system, omitting complex interactions, which are difficult to accurately model or pose numerical challenges for the optimization problem solver. This paper investigates a hybrid modeling approach based on Sparse Identification of Nonlinear Dynamics (SINDy) for local model adaptation within the MPC framework. The presented hybrid modeling approach benefits from the known structure of a physics-based model such that the learning process is computationally lightweight. Numerical experiments assume a multirotor Unmanned Aerial Vehicle (UAV) is subject to external phenomena typically encountered in urban environments, such as ground effects or wind gusts.
Keywords
Model Predictive Control, Sparse Identification of Nonlinear Dynamics, Unmanned Aerial Vehicle
Authors
NOVÁK, J.; HANÁK, J.; CHUDÝ, P.
Released
8. 10. 2024
Publisher
International Council of the Aeronautical Sciences
Location
Florence
ISBN
2958-4647
Periodical
ICAS Proceedings
Year of study
9
Number
10
State
Federal Republic of Germany
Pages from
1
Pages to
10
Pages count
10
URL
BibTex
@inproceedings{BUT189118,
author="Jiří {Novák} and Jiří {Hanák} and Peter {Chudý}",
title="Hybrid Modeling Approach for Optimization Based Control of Multirotor Unmanned Aerial Vehicles",
booktitle="ICAS Proceedings",
year="2024",
journal="ICAS Proceedings",
volume="9",
number="10",
pages="1--10",
publisher="International Council of the Aeronautical Sciences",
address="Florence",
issn="2958-4647",
url="https://www.icas.org/icas_archive/icas2024/data/preview/icas2024_0029.htm"
}