Bakar, A, Ke, L, Liu, H et al. (2 more authors) (2021) Design of Low Altitude Long Endurance Solar-Powered UAV Using Genetic Algorithm. Aerospace, 8 (8). 228. ISSN 2226-4310
Abstract
This paper presents a novel framework for the design of a low altitude long endurance solar-powered UAV for multiple-day flight. The genetic algorithm is used to optimize wing airfoil using CST parameterization, along with wing, horizontal and vertical tail geometry. The mass estimation model presented in this paper is based on structural layout, design and available materials used in the fabrication of similar UAVs. This model also caters for additional weight due to the change in wing airfoil. The configuration is optimized for a user-defined static margin, thereby incorporating static stability in the optimization. Longitudinal and lateral control systems are developed for the optimized configuration using the inner–outer loop strategy with an LQR and PID controller, respectively. A six degree-of-freedom nonlinear simulation is performed for the validation of the proposed control scheme. The results of nonlinear simulations are in good agreement with static analysis, validating the complete design process.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. This is an author produced version of an article published in Aerospace. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | solar-powered UAV (unmanned aerial vehicle); genetic algorithm; optimization; LQR (linear quadratic regulator); PID (proportional integral derivative) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Feb 2022 12:04 |
Last Modified: | 22 Feb 2022 12:04 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/aerospace8080228 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183629 |