A deep learning control approach for autonomous landing of the quadrotor unmanned aerial vehicle (UAV) is investigated. First, the error position dynamics concerning the moving target and the error attitude dynamics are described. Then, the force and torque controllers are developed, where the deep learning neural network (DNN) compositing of output-layers, input-layers and modular neural functions is utilized to counteract the system uncertainty. Stability analysis demonstrates that the closed-loop systems are uniformly ultimately bounded. Finally, the proposed strategy is validated through simulation examples.


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    Title :

    A Deep Learning Neural Network Control Approach for Quadrotor UAV Landing on a Moving Platform


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Peng, Jiahan (author) / Xia, Kewei (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Publication date :

    2025-03-06


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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