A combination of multiple gravity-assist and impulsive Delta-V maneuvers is often required for interplanetary and interstellar space missions, such as NASA's Voyager 1 and 2, Galileo, and Cassini missions. The design of such complex interplanetary missions is difficult with traditional mission analysis techniques because the mission must be prepruned to determine potential trajectories. Prepruning has been necessary because these mission often require the optimization of dozens of variables in a highly nonlinear and discontinuous design space. This process risks pruning nonintuitive solutions, which may potentially contain the optimal trajectory. In this paper, a hybrid optimization algorithm that is capable of determining optimal interplanetary trajectories, including the number of gravity assists and the planetary flyby order, is developed. The hybrid optimization algorithm uses a stochastic genetic algorithm to globally search the design space, as well as traditional nonlinear programming gradient-based optimization tools to determine locally optimal trajectories. By combining the global convergence properties of genetic algorithms with the accuracy of gradient-based solvers, a robust optimization algorithm capable of determining near-optimal solutions for a complex interplanetary space mission is developed.


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    Titel :

    Hybrid Algorithm for Multiple Gravity-Assist and Impulsive Delta-V Maneuvers


    Beteiligte:


    Erscheinungsdatum :

    2015




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.54 Flugführung
    Lokalklassifikation TIB:    770/7040




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