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Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm

The quadplane has become the favorite UAV platform for drone delivery. The dynamics and control of the quadplane UAV are extremely complex, unstable, and nonlinear due to its required capability to operate in multiple flight modes. The paper presents a method for autotuning attitude PID for a quadp...

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Main Authors: Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq
Format: Article
Language:English
Published: 2024
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/11090/1/J17582_c2993fc3ed72129571875c728246999c.pdf
http://eprints.uthm.edu.my/11090/
https://doi.org/10.6125/JoAAA.202403_56(1S).23
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spelling my.uthm.eprints.110902024-06-04T03:05:26Z http://eprints.uthm.edu.my/11090/ Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm Pairan, Mohammad Fahmi Shamsudin, Syariful Syafiq T Technology (General) The quadplane has become the favorite UAV platform for drone delivery. The dynamics and control of the quadplane UAV are extremely complex, unstable, and nonlinear due to its required capability to operate in multiple flight modes. The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. This study uses the DE algorithm and a radial basis function neural network (RBF NN) to create an autotuning PID and unified dynamics model for quadplanes, with the goal of improving their performance in delivery missions. The unified RBF model is then used for autotuning the PID controller using the DE algorithm. The proposed RBF-NN system identification can successfully predict the attitude dynamics of a quadplane in all flight modes with a coefficient of determination of R2 greater than 0.9 and a computing mean time of less than 5 ms. By iteratively modifying the control settings to achieve optimal performance, the DE algorithm replaces the requirement for manual PID tuning, which can be time-consuming and suboptimal. Comparing DE tuning to manual tuning, the results demonstrate a considerable improvement in quadplane roll and pitch performance of about 58.5%, 65.0%, and 74.0% in the overshoot, peak time, and rising time, respectively. 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11090/1/J17582_c2993fc3ed72129571875c728246999c.pdf Pairan, Mohammad Fahmi and Shamsudin, Syariful Syafiq (2024) Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm. Journal of Aeronautics, Astronautics and Aviation, 56 (15). pp. 341-356. https://doi.org/10.6125/JoAAA.202403_56(1S).23
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Pairan, Mohammad Fahmi
Shamsudin, Syariful Syafiq
Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
description The quadplane has become the favorite UAV platform for drone delivery. The dynamics and control of the quadplane UAV are extremely complex, unstable, and nonlinear due to its required capability to operate in multiple flight modes. The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. This study uses the DE algorithm and a radial basis function neural network (RBF NN) to create an autotuning PID and unified dynamics model for quadplanes, with the goal of improving their performance in delivery missions. The unified RBF model is then used for autotuning the PID controller using the DE algorithm. The proposed RBF-NN system identification can successfully predict the attitude dynamics of a quadplane in all flight modes with a coefficient of determination of R2 greater than 0.9 and a computing mean time of less than 5 ms. By iteratively modifying the control settings to achieve optimal performance, the DE algorithm replaces the requirement for manual PID tuning, which can be time-consuming and suboptimal. Comparing DE tuning to manual tuning, the results demonstrate a considerable improvement in quadplane roll and pitch performance of about 58.5%, 65.0%, and 74.0% in the overshoot, peak time, and rising time, respectively.
format Article
author Pairan, Mohammad Fahmi
Shamsudin, Syariful Syafiq
author_facet Pairan, Mohammad Fahmi
Shamsudin, Syariful Syafiq
author_sort Pairan, Mohammad Fahmi
title Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
title_short Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
title_full Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
title_fullStr Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
title_full_unstemmed Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
title_sort autotuning pid controllers for quadplane hybrid uav using differential evolution algorithm
publishDate 2024
url http://eprints.uthm.edu.my/11090/1/J17582_c2993fc3ed72129571875c728246999c.pdf
http://eprints.uthm.edu.my/11090/
https://doi.org/10.6125/JoAAA.202403_56(1S).23
_version_ 1803337336053825536
score 13.211869

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