Yunus Govdeli
Learning Control of Tandem-Wing Tilt-Rotor UAV with Unsteady Aerodynamic Model
Govdeli, Yunus; Bin Muzaffar, Sheikh Moheed; Raj, Raunak; Elhadidi, Basman; Kayacan, Erdal
Authors
Sheikh Moheed Bin Muzaffar
Raunak Raj
Basman Elhadidi
Erdal Kayacan
Abstract
This paper presents a novel transition flight mathematical model of tilt-rotor unmanned aerial vehicles and demonstrates an application of a novel learning controller on the developed model. Instead of conventional steady aerodynamic models, an unsteady aerodynamic model capable of representing rapid changes in the air flow is developed for the tilt-rotor transition flight. The vehicle is controlled by a neuro-fuzzy learning controller, consisting of a type-2 fuzzy neural network and a proportional-derivative controller. Its results are compared with the results of proportional-integral-derivative controllers. It is evident from the results that the learning controller is capable of capturing the rapid changes in the aerodynamics and outperforms its nonlearning counterpart under perturbed conditions.
Citation
Govdeli, Y., Bin Muzaffar, S. M., Raj, R., Elhadidi, B., & Kayacan, E. (2019). Learning Control of Tandem-Wing Tilt-Rotor UAV with Unsteady Aerodynamic Model. . https://doi.org/10.1109/fuzz-ieee.2019.8859023
Conference Name | 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
---|---|
Conference Location | New Orleans, LA, USA |
Start Date | Jun 23, 2019 |
End Date | Jun 26, 2019 |
Publication Date | 2019-06 |
Deposit Date | Nov 20, 2023 |
Publisher | Institute of Electrical and Electronics Engineers |
DOI | https://doi.org/10.1109/fuzz-ieee.2019.8859023 |
You might also like
Computational stress analysis of aluminium and polyethylene composites in marine vessel hull structures
(2023)
Conference Proceeding
Numerical simulation of multi-physical flows in biomimetic smart pumps
(2023)
Conference Proceeding
CFD simulation of the Boom Supersonic XB-1 Commercial Jet Delta Wing
(2023)
Conference Proceeding
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search