Optimal mission planning of the reconfiguration process of satellite constellations through orbital maneuvers: A novel technical framework

Mahshid, Soleymani; Fakoor, Mahdi; Bakhtiari, Majid

doi:10.1016/j.asr.2019.02.003

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…After separation of the satellite from the launcher, the propulsion subsystem has two main tasks. The first one is orbital transfer in which the satellite should be placed in an appropriate position in the constellation [34] and in a suitable orbit. The other one is orbital modification.…”

Section: Activities For Propulsion Subsystem Designmentioning

confidence: 99%

Conceptual Design Process for LEO Satellite Constellations Based on System Engineering Disciplines

Salehi¹,

Fakoor²,

Kosari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Satellite design process is an interdisciplinary subject in which the need for collaboration among various science and engineering disciplines is evident. Meanwhile, finding an optimal process for conceptual design of a satellite, which can optimize time and cost, is still an important issue. In this paper, based on system engineering approach, an optimal design process is proposed for LEO satellite constellations. In the proposed method, design process, design sequences, and data flow are established. In this regard, the conceptual design process is divided into two levels of mission (or constellation) and system (or satellite) as well as 15 main activities based on the mission profile, previous experiences of the authors, and existing literature. Then, the relationships between these activities have been determined by considering the importance of relationships according to their priority. Finally, these relations are optimized based on design structure matrix (DSM). By utilizing this approach, system design process of a telecommunication satellite constellation in LEO is formulated in conceptual design phase. Performance and capability of the proposed approach in optimal design process of the satellite constellation are investigated by comparing the outcome with existing results in the literature.

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Section: Activities For Propulsion Subsystem Designmentioning

confidence: 99%

Conceptual Design Process for LEO Satellite Constellations Based on System Engineering Disciplines

Salehi¹,

Fakoor²,

Kosari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, McGrath et al [17] presented a satellite constellation reconfiguration problem, in which a restricted low-thrust Lambert rendezvous scenario was included. Soleymani et al [18] investigated an optimal mission planning problem of the reconfiguration process of satellite constellations. They applied a combination of particle swarm optimization and genetic algorithm to find the optimal departure and arrival positions of each satellite.…”

Section: Introductionmentioning

confidence: 99%

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Luo

Peng

et al. 2022

Remote Sensing

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Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.

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“…Constellation reconfiguration, defined as "a deliberate change of the relative arrangements of satellites in a constellation by addition or subtraction of satellites and orbital maneuvering in order to achieve desired changes in coverage or capacity " [1], has a wide range of applications in system recovery after satellite failure [2,3], constellation staged deployment [1], rapid emergency response [4], and so on. To positively respond to future uncertainties and rapidly recover or enhance system overall performance, constellation reconfiguration has profound significance in both civilian and military aspects.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research in constellation reconfiguration mainly focuses on how to reposition particular satellites into another configuration in a shorter time and at a lower cost. Weck et al proposed a generic framework for reconfiguration problems using an auction algorithm to determine the optimal allocation efficiently and reliably and applied the framework to the optimal design of low-orbit communications constellations [1]. Soleymani et al proposed a particle swarm/genetic hybrid algorithm based on Lambert's theory to determine the optimal reconfiguration with two constraints: the total reconfiguration cost and the final pattern [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm

Lee

Huang

et al. 2021

J. of Syst. Eng. Electron.

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Constellation reconfiguration is a critical issue to recover from the satellite failure, maintain the regular operation, and enhance the overall performance. The constellation reconfiguration problem faces the difficulties of high dimensionality of design variables and extremely large decision space due to the great and continuously growing constellation size. To solve such real-world problems that can be hardly solved by traditional algorithms, the evolutionary operators should be promoted with available domain knowledge to guide the algorithm to explore the promising regions of the trade space. An adaptive innovationdriven multi-objective evolutionary algorithm (MOEA-AI) employing automated innovation (AI) and adaptive operator selection (AOS) is proposed to extract and apply domain knowledge. The available knowledge is extracted from the final or intermediate solution sets and integrated into an operator by the automated innovation mechanism. To prevent the overuse of knowledgedependent operators, AOS provides top-level management between the knowledge-dependent operators and conventional evolutionary operators. It evaluates and selects operators according to their actual performance, which helps to identify useful operators from the candidate set. The efficacy of the MOEA-AI framework is demonstrated by the simulation of emergency missions. It was verified that the proposed algorithm can discover a non-dominant solution set with better quality, more homogeneous distribution, and better adaptation to practical situations.

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Optimal mission planning of the reconfiguration process of satellite constellations through orbital maneuvers: A novel technical framework

Cited by 13 publications

References 18 publications

Conceptual Design Process for LEO Satellite Constellations Based on System Engineering Disciplines

Conceptual Design Process for LEO Satellite Constellations Based on System Engineering Disciplines

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm

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