Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling

In this paper, we propose a physics-informed neural network controller for quadcopter dynamics modeling. Physics-aware machine learning methods, such as physics-informed neural networks, consider the UAV dynamics model, solving the system of ordinary differential equations entirely, unlike proportio...

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Главные авторы:	Abdulkadirov, R. I., Абдулкадиров, Р. И., Lyakhov, P. A., Ляхов, П. А., Nagornov, N. N., Нагорнов, Н. Н., Kalita, D. I., Калита, Д. И.
Формат:	Статья
Язык:	English
Опубликовано:	Multidisciplinary Digital Publishing Institute (MDPI) 2025
Темы:	Optimization UAV dynamics modeling Remote sensing Proportional–integral–derivative Physics-informed neural networks
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/30442
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id	ir-123456789-30442
record_format	dspace
spelling	ir-123456789-304422025-05-07T13:05:21Z Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Kalita, D. I. Калита, Д. И. Optimization UAV dynamics modeling Remote sensing Proportional–integral–derivative Physics-informed neural networks In this paper, we propose a physics-informed neural network controller for quadcopter dynamics modeling. Physics-aware machine learning methods, such as physics-informed neural networks, consider the UAV dynamics model, solving the system of ordinary differential equations entirely, unlike proportional–integral–derivative controllers. The more accurate control action on the quadcopter reduces flight time and power consumption. We applied our fractional optimization algorithms to decreasing the solution error of quadcopter dynamics. Including advanced optimizers in the reinforcement learning model, we achieved the trajectory of UAV flight more accurately than state-of-the-art proportional–integral–derivative controllers. The advanced optimizers allowed the proposed controller to increase the quality of the building trajectory of the UAV compared to the state-of-the-art approach by 10 percentage points. Our model had less error value in spatial coordinates and Euler angles by 25–35% and 30–44%, respectively. 2025-05-07T13:03:53Z 2025-05-07T13:03:53Z 2025 Статья Abdulkadirov R., Lyakhov P., Butusov D., Nagornov N., Kalita D. Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling // Drones. - 2025. - 9 (3). - art. no. 187. - DOI: 10.3390/drones9030187 https://dspace.ncfu.ru/handle/123456789/30442 en Drones application/pdf application/pdf Multidisciplinary Digital Publishing Institute (MDPI)
institution	СКФУ
collection	Репозиторий
language	English
topic	Optimization UAV dynamics modeling Remote sensing Proportional–integral–derivative Physics-informed neural networks
spellingShingle	Optimization UAV dynamics modeling Remote sensing Proportional–integral–derivative Physics-informed neural networks Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Kalita, D. I. Калита, Д. И. Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
description	In this paper, we propose a physics-informed neural network controller for quadcopter dynamics modeling. Physics-aware machine learning methods, such as physics-informed neural networks, consider the UAV dynamics model, solving the system of ordinary differential equations entirely, unlike proportional–integral–derivative controllers. The more accurate control action on the quadcopter reduces flight time and power consumption. We applied our fractional optimization algorithms to decreasing the solution error of quadcopter dynamics. Including advanced optimizers in the reinforcement learning model, we achieved the trajectory of UAV flight more accurately than state-of-the-art proportional–integral–derivative controllers. The advanced optimizers allowed the proposed controller to increase the quality of the building trajectory of the UAV compared to the state-of-the-art approach by 10 percentage points. Our model had less error value in spatial coordinates and Euler angles by 25–35% and 30–44%, respectively.
format	Статья
author	Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Kalita, D. I. Калита, Д. И.
author_facet	Abdulkadirov, R. I. Абдулкадиров, Р. И. Lyakhov, P. A. Ляхов, П. А. Nagornov, N. N. Нагорнов, Н. Н. Kalita, D. I. Калита, Д. И.
author_sort	Abdulkadirov, R. I.
title	Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
title_short	Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
title_full	Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
title_fullStr	Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
title_full_unstemmed	Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
title_sort	physics-aware machine learning approach for high-precision quadcopter dynamics modeling
publisher	Multidisciplinary Digital Publishing Institute (MDPI)
publishDate	2025
url	https://dspace.ncfu.ru/handle/123456789/30442
work_keys_str_mv	AT abdulkadirovri physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT abdulkadirovri physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT lyakhovpa physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT lâhovpa physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT nagornovnn physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT nagornovnn physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT kalitadi physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling AT kalitadi physicsawaremachinelearningapproachforhighprecisionquadcopterdynamicsmodeling
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Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling

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