Highlights The connection between a neural network (NN)-based adaptive controller and a classical PID controller is revealed for deep-space formation flight. Adaptive capability of the NN-based controller is weakly correlated to input layer and hidden layer. The PID controller has nearly the same adaptive capability as the NN-based counterpart.

    Abstract The high-precision control problem of deep-space formation flying is considered in this paper. To achieve accurate position control, a neural-network-based adaptive controller is adopted as a benchmark to counteract the unknown perturbations in deep space. This controller is enhanced by considering the dynamic environment of deep-space formation, yielding a proportional-integral-derivative (PID) adaptive controller. Compared with the benchmark, the PID adaptive controller has a simpler architecture with fewer parameters. Therefore, the PID adaptive controller avoids the challenge of parameter tuning, which is a longstanding disadvantage of neural-network-based adaptive controllers. Simulations show that the adopted neural-network-based adaptive controller and the PID adaptive controller have nearly equivalent adaptive capabilities.


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    Title :

    Analysis of a neural-network-based adaptive controller for deep-space formation flying


    Contributors:
    Yang, Chihang (author) / Zhang, Hao (author) / Gao, Yang (author)

    Published in:

    Publication date :

    2021-03-07


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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