Icing on the wings of an aircraft increases drag and reduces lift, leading to a decrease in the aerodynamic performance and stability margin of UAV, which affects flight security. In order to solve the problem of icing state identification of UAV, this paper establishes a simulation model of icing on the wing of a certain type of UAV, analyses the impact of wing icing on the dynamic response of UAV, conducts simulation experiments with multiple state points and different degrees of icing, establishes a deep-learning neural network for icing state identification, and carries out the training and testing of three different strategies based on the simulation data. The results show that the icing identification method proposed in this paper has high accuracy in the prediction of icing degree and can provide a reference for the design of natural icing degree identification and prediction system for UAV.


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    Title :

    Deep-learning-based icing identification method for unmanned aerial vehicle


    Contributors:
    Zhang, Kun (editor) / Lorenz, Pascal (editor) / Li, Deshang (author)

    Conference:

    International Conference on Mechatronics and Intelligent Control (ICMIC 2024) ; 2024 ; Wuhan, China


    Published in:

    Proc. SPIE ; 13447


    Publication date :

    2025-01-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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