Abstract Gravity assist is crucial for tour exploration of Jupiter’s Galilean moons. To achieve high accuracy and low cost gravity-assisted transfer trajectories, the circular, restricted, three-body problem (CR3BP) model is adopted. Since there is no analytical solution for CR3BP, the orbit propagation must be calculated by numerical integration, which requires considerable time for the design of complex Jupiter exploration missions. Artificial neural networks (ANNs) that can fully approximate complex nonlinear relationships are used to model accurate and computationally efficient maps between the orbit states before and after gravity assist. Thereafter, a new framework based on the ANN model is developed for the fast design of Jovian-moon gravity-assisted transfers, by which optimal trajectories are obtained using a genetic algorithm (GA). Finally, the accuracy and performance of the proposed method are verified by two representative test cases of the Jupiter-Europa and Jupiter-Ganymede systems. The simulation results show that using the trained ANN model can significantly improve the convergence speed of the genetic algorithm, and high-precision, optimal solutions can be obtained rapidly.
ANN-based method for fast optimization of Jovian-moon gravity-assisted trajectories in CR3BP
Advances in Space Research ; 69 , 7 ; 2865-2882
2022-01-14
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Jupiter gravity-assisted trajectories.
NTRS | 1969
|Jupiter gravity-assisted trajectories.
NTRS | 1968
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