In this paper, the attitude control problem of hypersonic vehicle is studied. The DDQN (double deep Q network) algorithm is used to realize the online adaptive adjustment of the control parameters of the PID (proportional-integral-derivative) controller. Firstly, the dynamic model of hypersonic vehicle is established. Then, the implementation process of parameter adjustment using DDQN algorithm is described. By building two neural networks, the main network is used to determine the action to be performed, and the target network is used to determine the action value, so as to realize the adaptive adjustment of the control parameters. Finally, the effectiveness of the proposed method is verified by simulation experiments.


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    Title :

    Attitude Control of Hypersonic Vehicle based on Reinforcement Learning


    Contributors:
    Liu, Jingwen (author) / Fan, Hongdong (author) / Fan, Yonghua (author) / Cai, Guangbin (author)


    Publication date :

    2024-05-10


    Size :

    966775 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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