Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | en_US |
| dc.contributor.advisor | Yang, Lidong (ISE) | en_US |
| dc.contributor.advisor | Huang, Chao (ISE) | en_US |
| dc.creator | Han, Peng | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14520 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Design, deployment and on-orbit servicing optimization of LEO satellite constellations | en_US |
| dcterms.abstract | Low-Earth orbit (LEO) satellite constellations have become vital for global communications, navigation, and remote sensing due to their low latency and high communication bandwidth. However, current design and deployment methods mainly address global or non-continuous regional coverage, lacking solutions for complex continuous coverage needs in specific regions, such as those required for emergency response like local conflicts, emergency rescue. Additionally, lifecycle management and on-orbit servicing remain challenging, with issues like satellite failures and propellant depletion yet to be systematically resolved. | en_US |
| dcterms.abstract | Consequently, how to achieve efficient and economical design, deployment, and long-term operation of regional coverage LEO constellations have become emerging research problems in aerospace systems engineering. In response to these challenges, this dissertation addresses key system optimization problems throughout the LEO constellation lifecycle, including regional coverage design, launch and deployment optimization, and on-orbit servicing mission planning. The research proposes practical system optimization models and solution frameworks, whose effectiveness and superiority are validated through comprehensive simulations. The primary research contributions are outlined as follows: | en_US |
| dcterms.abstract | First, a constellation design method based on Repeat Ground Track (RGT) orbits is proposed to satisfy the continuous coverage requirements of ground regional targets. The circular convolution property between constellation pattern vectors and target visibility vectors is established, leading to a linear integer programming model for regional coverage constellations. This model is subsequently extended to a simultaneous orbit optimization and satellite deployment (SOOSD) model for multiple RGT orbits. To address the computational complexity arising from high-dimensional and sparse constraints in the SOOSD model, a two-stage optimization framework integrating differential evolution (DE) with integer programming solvers is developed, enabling collaborative optimization of orbital parameters and satellite deployment positions. Simulation results across multiple ground regions of varying geometries and locations demonstrate that the proposed method not only satisfies complex regional continuous coverage requirements but also significantly reduces the required satellite count, outperforming traditional symmetric constellation designs and direct solution approaches while exhibiting robust performance under varying communication elevation angles and other parameters. | en_US |
| dcterms.abstract | Second, for the launch and deployment (L&D) optimization of LEO constellations with orbital transfer vehicles (OTVs), a bi-objective combinatorial optimization model is formulated, incorporating constraints such as OTV fuel capacity, satellite payload per launch, and operational limitations. A genetic algorithm with cluster and nearest sorting initialization and local search operators (CNS-LSGA) is developed, employing the ε-constraint method to systematically explore the problem's Pareto frontier. The proposed solution framework efficiently generates high-quality initial solutions within the feasible domain and enhances solution quality through localized search strategies. Extensive simulations and comparative analyses validate the significant advantages of the proposed methodology in terms of solution quality, robustness, and diversity, providing an efficient computational tool and quantitative benchmark for constellation L&D mission planning. | en_US |
| dcterms.abstract | Finally, for on-orbit servicing (OOS) mission planning of LEO constellations under the service station-service satellite mode, a two-stage optimization framework is proposed. The framework decomposes the system mission planning problem into service station orbit design and service satellite mission scheduling. In the service station orbit design phase, a modified spectral clustering and nonlinear programming approach (MSC-NLP) is employed to achieve target satellite assignment and optimal orbit design for multiple service stations. An enhanced hybrid clustering metric based on constellation orbital characteristics is introduced, substantially reducing overall mission fuel consumption. In the service satellite scheduling phase, a genetic quantum algorithm (GQA) is utilized to solve the mixed-integer nonlinear programming model for service satellite scheduling, jointly optimizing the number of service satellite deployments and total fuel consumption. Case study simulations demonstrate that the proposed solution framework outperforms existing methodologies and is well-suited for large-scale OOS scenarios, exhibiting strong potential for engineering implementation. | en_US |
| dcterms.abstract | The dissertation intends to contribute to the LEO constellation systems engineering and provides technical guidance for the efficient construction and sustainable operation of LEO constellations. The proposed methodologies and findings offer valuable decision-making insights and technical resources for practical engineering challenges related to the design, launch and deployment, and on-orbit servicing of future regional coverage constellations. | en_US |
| dcterms.extent | xviii, 189 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2026 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Artificial satellites -- Orbits | en_US |
| dcterms.LCSH | Space vehicles | en_US |
| dcterms.LCSH | Artificial satellites in telecommunication | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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