基于METRIC理论的超大规模卫星星座多级备份策略

doi:10.12305/j.issn.1001-506X.2022.07.14

Abstract

Abstract:

Aiming at the possible problems of high operation and maintenance cost and low mission availability of super large-scale satellite constellation, combined with the current situation of mass production of satellites and the use of one rocket to put multiple satellites into orbit (one rocket with multiple satellites), a multi-level backup strategy of super large-scale satellite constellation is proposed based on the optimization method of multi-echelon technology for recoverable item control (METRIC). Firstly, the principle of the multi-stage backup strategy model of a satellite constellation based on METRIC theory is explained. The backup strategy considers three backup modes: ground backup, parking orbit backup, and constellation orbit backup. The constellation availability model considers the satellites' average annual demand number, expected demand number, and expected shortage number. The constellation backup cost model includes manufacturing cost and launch cost, and the mathematical optimization model with constellation availability as the constraint and constellation annual backup cost as the optimization objective. Then, a case study of a super large-scale constellation is developed and solved by using both genetic algorithm and marginal analysis. Finally, the advantages and disadvantages of the genetic algorithm and marginal analysis in solving this optimization problem are discussed. Result shows that the model significantly reduces the backup cost of a large-scale satellite constellation while ensuring the constellation availability, and provides guidance for the accurate allocation of the number of backup satellites at each level.

Key words: satellite constellation, multi-echelon technique for recoverable item control (METRIC) theory, backup strategy, genetic algorithm (GA)

CLC Number:

Tiansu LUO, Lingfeng ZHAO, Yunwen FENG, Xiaofeng XUE, Cheng LU. Super large-scale satellite constellation multi-level backup strategy based on METRIC theory[J]. Systems Engineering and Electronics, 2022, 44(7): 2181-2190.

Figures/Tables 14

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优化参数	取值范围
N_tb/条	[1, 10]
h_tb/km	[800, 1 200]
Q_xz/颗	[1, 20]
s_xz/颗	[1, 20]
Q_tb/颗	[1, 100]
s_tb/颗	[1, 100]
s_dm/颗	[1, 100]

固定参数	取值
h_xz/km	1 200
i/(°)	50
N_xz/条	40
n_i/个	40
h_tb/km	800
λ/(次/年)	0.1
T_cl/d	90
p_zc/(百万/年)	0.05
U_hj/个	34
m_gz/kg	150
P_zz/百万	0.5
P_fs/百万	1.5
ν/(km/s)	2.16
ε/(百万/千克)	0.001

优化参数	计算结果
N_tb/条	3
h_tb/km	831.74
Q_xz/颗	4
s_xz/颗	1
Q_tb/颗	32
s_tb/颗	40
c/(百万/年)	464.32
A_c	0.995 0

符号	Q/颗	s_xz/颗	s_ground/颗	c/(百万/年)	A_c
计算结果	28	2	85	672.43	0.990 4

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Super large-scale satellite constellation multi-level backup strategy based on METRIC theory

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