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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.
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]
Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 191635811
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PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
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  Data: Optimal multi-perturbation and multi-constraint mission planning for on-orbit refueling in GEO satellites.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Xinhan%22">Li, Xinhan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Shuai%22">Yan, Shuai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Lijiao%22">Wang, Lijiao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhi%22">Li, Zhi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Qi%22">Zhou, Qi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qizhou@hust.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Space+Research%22">Advances in Space Research</searchLink>. Mar2026, Vol. 77 Issue 5, p6011-6028. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Trajectory+optimization%22">Trajectory optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Geosynchronous+orbits%22">Geosynchronous orbits</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Orbital+mechanics%22">Orbital mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Fueling%22">Fueling</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Space+vehicles%22">Space vehicles</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.asr.2025.12.090
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 6011
    Subjects:
      – SubjectFull: Trajectory optimization
        Type: general
      – SubjectFull: Geosynchronous orbits
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Orbital mechanics
        Type: general
      – SubjectFull: Fueling
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Space vehicles
        Type: general
    Titles:
      – TitleFull: Optimal multi-perturbation and multi-constraint mission planning for on-orbit refueling in GEO satellites.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Li, Xinhan
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            NameFull: Yan, Shuai
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            NameFull: Wang, Lijiao
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            NameFull: Li, Zhi
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            NameFull: Zhou, Qi
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 77
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          Titles:
            – TitleFull: Advances in Space Research
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