A novel fault detection method is presented in this paper for distributed satellite formation flight in the attitude control system. In order to detect the faults occurred in the reaction wheel of satellite formation flight, the dynamics of the three-axis in each satellite are modeled and the output-input feedback Elman neural network using resilient adaptive artificial fish-swarm algorithm is applied. The faults occurred in formation flight can be detected by the proposed fault detection scheme with two adjacent satellites in a certain period of time. Finally, different fault types are injected into satellite formation flight for testing the implementation of the proposed scheme and the simulation result illustrates the effectiveness and feasibility of the improved fault detection scheme.


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    Title :

    Fault Detection for Satellite Formation Flight Using Improved Elman Neural Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Yu, Xiang (editor) / Wang, Jiayi (author) / Chen, Hua (author) / Cui, Jia (author) / Wan, Bei (author) / Yang, Shengqing (author)


    Publication date :

    2021-10-30


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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