This study introduces a hybrid algorithm designed for trajectory tracking control during the transition flight of a tailsitter Unmanned Aerial Vehicle (UAV), employing an integration of neural networks and a genetic optimization algorithm. The tailsitter UAV ducted fan tailsitter, which has been augmented with two additional wings to enhance its flight capabilities. The foundation of the intelligent controller lies in the feedback linearization method, with performance enhancements achieved through online neural network training coupled with a genetic optimization algorithm. To validate the effectiveness of the proposed algorithm in controlling the UAV during transition flight, simulations were conducted within the Matlab- Simulink software environment.
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Ghanifar,M , Nikkhah,A and Khoshnood,A . (2024). Intelligent Control of a Tailsitter UAV during Transition Flight: A Feedback Linearization Approach. Journal of Aerospace Science and Technology, 17(2), 38-46. doi: 10.22034/jast.2024.297589.1088
MLA
Ghanifar,M , , Nikkhah,A , and Khoshnood,A . "Intelligent Control of a Tailsitter UAV during Transition Flight: A Feedback Linearization Approach", Journal of Aerospace Science and Technology, 17, 2, 2024, 38-46. doi: 10.22034/jast.2024.297589.1088
HARVARD
Ghanifar M, Nikkhah A, Khoshnood A. (2024). 'Intelligent Control of a Tailsitter UAV during Transition Flight: A Feedback Linearization Approach', Journal of Aerospace Science and Technology, 17(2), pp. 38-46. doi: 10.22034/jast.2024.297589.1088
CHICAGO
M Ghanifar, A Nikkhah and A Khoshnood, "Intelligent Control of a Tailsitter UAV during Transition Flight: A Feedback Linearization Approach," Journal of Aerospace Science and Technology, 17 2 (2024): 38-46, doi: 10.22034/jast.2024.297589.1088
VANCOUVER
Ghanifar M, Nikkhah A, Khoshnood A. Intelligent Control of a Tailsitter UAV during Transition Flight: A Feedback Linearization Approach. Journal of Aerospace Science and Technology. 2024;17(2):38-46. doi: 10.22034/jast.2024.297589.1088