UAVs are increasingly prominent in the competition for space due to their multiple characteristics such as strong maneuverability, long flight distance, and high survivability. A new online safe flight control method based on constrained reinforcement learning has been proposed for intelligent safety control of UAVs. The method designed a constrained reinforcement learning algorithm based on extra safety budget, which introduces Lyapunov stability requirements to ensure flight safety and improves the robustness of the controller. By efficiently interacting with the constructed simulation environment, a control law model for UAVs is trained. Subsequently, a condition-triggered meta-learning online learning method is used to adjust the control rate online to complete the attitude angle tracking task. Simulation experimental results show that the control law can be learnt online to adjust UAVs control strategy, ensuring the safety and stability of UAVs during flight.


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    Title :

    Online Safe Flight Control Method Based on Constraint Reinforcement Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Zhao, Jiawei (author) / Xu, Haotian (author) / Wang, Zhaolei (author) / Zhang, Tao (author)

    Conference:

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



    Publication date :

    2025-03-02


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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