The active shape adjustment of large-diameter mesh antennas has been widely explored for achieving high on-orbit performances. The surface reconfiguration of the cable net reflector is the essential precondition for active shape adjustment of mesh antennas. In this paper, we proposed a surface reconfiguration method based on the modal theory and transfer learning method. The proposed surface reconfiguration method was divided into three steps. The first step is that the axial error distributions of preselected points of the cable net reflector is derived analytically by the electromechanical coupling method together with far-field mapping. The second step uses a modal theory to predict axial mechanical deformations of discrete nodes of cable net reflectors by the derived axial errors of preselected points. In the last step, the transfer learning theory together with the deep learning algorithm is adopted to predict radial mechanical deformations of discrete nodes of cable net reflectors. Simulation and comparative results show the proposed surface reconfiguration method can accurately predict displacement deformations of mesh antennas.


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    Title :

    Surface Reconfiguration Method of Mesh Antennas by Electrical Performance


    Contributors:
    Xu, Xiang (author) / Li, Tuanjie (author) / Wang, Zuowei (author)

    Published in:

    AIAA Journal ; 60 , 4 ; 2644-2653


    Publication date :

    2022-01-08


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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