Optimal multi-perturbation and multi-constraint mission planning for on-orbit refueling in GEO satellites.
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| Title: | Optimal multi-perturbation and multi-constraint mission planning for on-orbit refueling in GEO satellites. |
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| Authors: | Li, Xinhan1 (AUTHOR), Yan, Shuai1 (AUTHOR), Wang, Lijiao2 (AUTHOR), Li, Zhi3 (AUTHOR), Zhou, Qi1 (AUTHOR) qizhou@hust.edu.cn |
| Source: | Advances in Space Research. Mar2026, Vol. 77 Issue 5, p6011-6028. 18p. |
| Subjects: | Trajectory optimization, Geosynchronous orbits, Genetic algorithms, Orbital mechanics, Fueling, Energy consumption, Space vehicles |
| Abstract: | Many-to-Many On-Orbit Refueling (M2M-OOR) mission planning for Geostationary Earth orbit (GEO) satellites is vital for improving service efficiency and reducing costs. This paper proposes a Multi-Perturbation Compensation Bi-Level Planning Model (MPC-BPM) to address trajectory deviations from orbital perturbations and the complexity of task allocation under constraints. The outer layer employs an Improved Genetic Algorithm (IGA) for the discrete optimization of task allocation and sequencing, while the inner layer applies a Velocity-Updated Neural Population Dynamics Optimization Algorithm (VNPDOA) for continuous trajectory optimization and orbital correction. A multi-perturbation compensation model is embedded in the inner layer, explicitly accounting for J 2 perturbation, solar radiation pressure, and third-body gravity to improve orbital accuracy and mission reliability. Simulation results using real satellite data demonstrate that MPC-BPM outperforms two benchmark methods in convergence speed, fuel efficiency, and trajectory accuracy. In particular, it achieves fuel savings of about 7.7% compared with multi-group chaotic genetic algorithm-Simulated Annealing (MGCGA-SA) and 4.0% compared with nested bilevel improved genetic algorithm (NBIGA), while reducing orbital transfer deviations to the meter level in numerical simulations under idealized assumptions. These findings confirm the effectiveness and robustness of MPC-BPM for complex OOR mission planning and highlight its potential for reliable operations in perturbation-rich space environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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