Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling DOI Creative Commons
Ruslan Abdulkadirov, Pavel Lyakhov, Денис Бутусов

и другие.

Drones, Год журнала: 2025, Номер 9(3), С. 187 - 187

Опубликована: Март 3, 2025

In this paper, we propose a physics-informed neural network controller for quadcopter dynamics modeling. Physics-aware machine learning methods, such as networks, consider the UAV model, solving system of ordinary differential equations entirely, unlike proportional–integral–derivative controllers. The more accurate control action on reduces flight time and power consumption. We applied our fractional optimization algorithms to decreasing solution error dynamics. Including advanced optimizers in reinforcement achieved trajectory accurately than state-of-the-art allowed proposed increase quality building compared approach by 10 percentage points. Our model had less value spatial coordinates Euler angles 25–35% 30–44%, respectively.

Язык: Английский

Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling DOI Creative Commons
Ruslan Abdulkadirov, Pavel Lyakhov, Денис Бутусов

и другие.

Drones, Год журнала: 2025, Номер 9(3), С. 187 - 187

Опубликована: Март 3, 2025

In this paper, we propose a physics-informed neural network controller for quadcopter dynamics modeling. Physics-aware machine learning methods, such as networks, consider the UAV model, solving system of ordinary differential equations entirely, unlike proportional–integral–derivative controllers. The more accurate control action on reduces flight time and power consumption. We applied our fractional optimization algorithms to decreasing solution error dynamics. Including advanced optimizers in reinforcement achieved trajectory accurately than state-of-the-art allowed proposed increase quality building compared approach by 10 percentage points. Our model had less value spatial coordinates Euler angles 25–35% 30–44%, respectively.

Язык: Английский

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