基于多种群混沌遗传算法的GEO目标服务任务规划

doi:10.12305/j.issn.1001-506X.2024.03.17

Abstract

Abstract:

Aiming to address diverse on-orbit service requirements, such as debris removal and fuel refueling in geosynchronous Earth orbit (GEO), the problem of spacecraft mission scheduling combining "fixed fuel station+round-trip spacecraft" is investigated. Firstly, a fuel-optimal bi-level mission scheduling model with a multi-mission hybrid is established, in which the outer layer is designed for target service sequence scheduling and the inner layer is designed orbit maneuver planning. Then, for this continuous-discrete mixed variable combinatorial optimization problem, a multi-group chaotic genetic algorithm (MGCGA) is proposed, in which the hybrid coding is employed to represent the decision variables and a cubic chaotic mapping operator is introduced to improve the quality of the initial population. Moreover, a multi-group and elite retention strategy is employed to significantly approach the optimal global solution during the solution process. Finally, a typical scenario is constructed using actual GEO target information. The scheduling results show that the proposed algorithm has the advantages of good global convergence and fast convergence.

Key words: on-orbit servicing, geosynchronous Earth orbit (GEO) target, multi-mission, fuel station, mission scheduling, multi-group chaotic genetic algorithm (MGCGA)

CLC Number:

V448.2

Shuai YIN, Jianhui YU, Bin SONG, Yanning GUO, Chuanjiang LI, Yueyong LYU. GEO target servicing mission scheduling based on multi-group chaotic genetic algorithm[J]. Systems Engineering and Electronics, 2024, 46(3): 914-921.

Figures/Tables 12

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Table 1

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名称	半长轴/km	偏心率	轨道倾角/(°)	升交点赤经/(°)	平近点角/(°)
储油站	42 164.169	0	0.017 6	117.227 8	4
服务航天器1	42 164.169	0	0.017 6	117.227 8	4
服务航天器2	42 164.169	0	0.017 6	117.227 8	4
服务航天器3	42 164.169	0	0.017 6	117.227 8	4
服务航天器4	42 164.169	0	0.017 6	117.227 8	4
目标1	42 164.169	0	1.923 2	48.498 5	201.506 1
目标2	42 164.169	0	1.007 3	55.634 6	48.047 4
目标3	42 164.169	0	1.366 0	64.759 4	174.224 0
目标4	42 164.169	0	1.763 1	84.555 5	182.437 9
目标5	42 164.169	0	1.007 3	55.634 6	181.137 5
目标6	42 164.169	0	1.799 4	90.460 5	171.747 0
目标7	42 164.169	0	2.396 3	284.152 6	120.629 3
目标8	42 164.169	0	0.081 4	87.368 7	83.535 1
目标9	42 164.169	0	0.062 2	14.807 0	113.247 2

名称	服务能力	干重/kg	比冲/(km/s)
服务航天器1	在轨加注/碎片清除	500	3
服务航天器2	在轨加注/碎片清除	500	3
服务航天器3	在轨加注	500	3
服务航天器4	碎片清除	500	3

名称	任务需求	需求燃料量(碎片质量)/kg
目标1	在轨加注	500
目标2	碎片清除	200
目标3	在轨加注	500
目标4	碎片清除	200
目标5	碎片清除	200
目标6	在轨加注	500
目标7	碎片清除	200
目标8	在轨加注	500
目标9	在轨加注	500

名称	任务规划方案	燃料消耗量/kg	时间/天
服务航天器1	0-8-0-4-3-0	1 082.87	14.628 8
服务航天器2	0-9-0-6-5-0	1 129.64	15.844 8
服务航天器3	0-1-0	573.35	4.916 2
服务航天器4	0-2-0-7-0	105.49	10.496 2

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GEO target servicing mission scheduling based on multi-group chaotic genetic algorithm

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