A Novel Neural Network Model Applied to Modeling of a Tandem-Wing Quadplane Drone
Authors:
- Michał Okulski,
- Maciej Ławryńczuk
Abstract
This research focuses on modeling one of the Quadplane flight phases: a hover state, similar to a regular Quadcopter hovering. The process is highly non-linear, and additionally, there are more phenomena to take into account - it is related to air turbulence around the wings and the fuselage. This work thoroughly studies the effectiveness of various types of neural networks to model the drone using the data recorded from real free-flight experiments. Finally, we introduce a novel type of neural-based model: the Feature-Sequence-To-Sequence (fseq2seq) Recurrent Neural Network Model. The new Model has interesting features: the input-data-driven initialization of RNN’s internal states and a simplification of the input layer (significant reduction of used neurons’ weights). We demonstrate that the new network outperforms all classic model types
- Record ID
- WUT98f245e708454303ba2e765cef0df730
- Author
- Journal series
- IEEE Access, ISSN 2169-3536
- Issue year
- 2021
- Vol
- 9
- Pages
- 14159-14178
- Publication size in sheets
- 0.95
- Keywords in English
- Drone, neural model, neural network, quadplane, quad-plane, recurrent neural network, system identi�cation, UAV.
- ASJC Classification
- ; ;
- DOI
- DOI:10.1109/ACCESS.2021.3051878 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/9323029 Opening in a new tab
- Language
- (en) English
- License
- File
-
- File: 1
- A Novel Neural Network Model Applied to Modeling of a Tandem-Wing Quadplane Drone, File Okulski_Lawrynczuk2021.pdf / 2 MB
- Okulski_Lawrynczuk2021.pdf
- publication date: 28-01-2021
- A Novel Neural Network Model Applied to Modeling of a Tandem-Wing Quadplane Drone, File Okulski_Lawrynczuk2021.pdf / 2 MB
-
- Score (nominal)
- 100
- Score source
- journalList
- Score
- = 100.0, 18-05-2022, ArticleFromJournal
- Publication indicators
- = 1; = 2; = 3; : 2018 = 1.718; : 2020 (2 years) = 3.367 - 2020 (5 years) =3.671
- Citation count
- 3
Share
- Uniform Resource Identifier
- https://repo.pw.edu.pl/info/article/WUT98f245e708454303ba2e765cef0df730/
- URN
urn:pw-repo:WUT98f245e708454303ba2e765cef0df730
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.