In this paper, we propose an approximate optimal control strategy for drag-free satellites using reinforcement learning, where state constraints related to the position of test masses are taken into consideration. To handle six-degrees-of-freedom position constraints, a barrier function is embedded in the performance cost function. Then, one critic neural network is utilized to learn cost function online and get the optimal control law. Meanwhile, in order to relax excitation condition, historical data are introduced into the weight adaptive law. The Lyapunov method is utilized to ensure that the weight error and state error of the drag-free satellites are uniformly ultimately bounded. Numerical simulations confirm the efficacy of the proposed method.


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    Title :

    Learning-Based Drag-Free and Attitude Control of Spacecraft with State Constraints


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Li, Haoran (author) / Shao, Xiaodong (author) / Hu, Qinglei (author) / Zhang, Yonghe (author) / Wang, Pengcheng (author)

    Conference:

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



    Publication date :

    2025-03-04


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English