单个天基对地飞行器停泊轨道的优化设计

doi:10.3969/j.issn.1001-506X.2020.01.21

摘要/Abstract

摘要：

为了解决单个天基对地飞行器的停泊轨道优化设计中优化变量搜索范围大、初值敏感的问题，提出了基于模糊自适应粒子群（fuzzy adaptive particle swarm optimization，FAPSO）算法的停泊轨道优化设计方法。首先建立了给定变轨时刻下天基对地飞行器的落点范围求解模型，为求解停泊轨道优化设计的评价指标打下基础；其次提出了以覆盖预定落点数和覆盖不同预定落点数的平均时间为评价指标，建立了基于FAPSO算法的停泊轨道优化设计模型；最后仿真结果表明FAPSO算法能够获得收敛精度更高的结果，该方法能够更有效地解决单个天基对地飞行器的停泊轨道设计问题。

关键词: 天基对地飞行器, 模糊控制系统, 粒子群算法, 停泊轨道, Pontryagin极小值原理

Abstract:

An optimization design method for the parking orbit of a single space-to-ground vehicle based on the fuzzy adaptive particle swarm optimization (FAPSO) algorithm is proposed to solve the problem that the optimization variables are in large search ranges and are sensitive to the initial values. Firstly, given a maneuver moment, the striking range of the space-to-ground vehicle is established, which lays a foundation of the indexes of optimization design for the parking orbit; secondly, the number and the mean time of targets in the hitting range are taken as indexes, and the parking orbit optimization design model is established by FAPSO; finally, the simulation results show that the FAPSO algorithm has higher convergence accuracy and can effectively accomplish the design for the parking orbit of a single space-to-ground vehicle.

Key words: space-to-ground vehicle, fuzzy control system, particle swarm algorithm, parking orbit, Pontryagin minimum principle

中图分类号:

V474.2

刘庆国, 刘新学, 武健, 李亚雄. 单个天基对地飞行器停泊轨道的优化设计[J]. 系统工程与电子技术, 2020, 42(1): 157-165.

Qingguo LIU, Xinxue LIU, Jian WU, Yaxiong LI. Optimization design for the parking orbit of a single space-to-ground vehicle[J]. Systems Engineering and Electronics, 2020, 42(1): 157-165.

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参考文献 19

1	ZIARNICK B . The coming revolution in military space professionalism[J]. Air & Space Power Journal, 2018, 32 (2): 9- 20.
2	袁宴波, 李东伟. 天基对地打击武器最优过渡轨道研究[J]. 科学技术与工程, 2009, 9 (6): 1446- 1449. doi: 10.3969/j.issn.1671-1815.2009.06.014
	YUAN Y B , LI D W . Transition orbit optimization research of space-to-ground kinetic weapon[J]. Science Technology and Engineering, 2009, 9 (6): 1446- 1449. doi: 10.3969/j.issn.1671-1815.2009.06.014
3	胡正东, 沈荟萍, 张士峰, 等. SGKW轨道在线生成技术研究[J]. 固体火箭技术, 2009, 32 (6): 591- 595. doi: 10.3969/j.issn.1006-2793.2009.06.001
	HU Z D , SHEN H P , ZHANG S F , et al. Research on technique of onboard trajectory generation for SGKW[J]. Journal of Solid Rocket Technology, 2009, 32 (6): 591- 595. doi: 10.3969/j.issn.1006-2793.2009.06.001
4	边九州.组合动力天基对地打击飞行器轨迹设计与覆盖范围分析[D].哈尔滨:哈尔滨工业大学, 2014.
	BIAN J Z. Trajectory design and coverage area analysis of the space-based strike aircraft vehicle powered by combined cycle engine[D]. Harbin: Harbin Institute of Technology, 2014.
5	武健, 刘新学, 舒建生, 等. 在轨拦截器停泊轨道优化研究[J]. 飞行力学, 2014, 32 (3): 253- 257.
	WU J , LIU X X , SHU J S , et al. Research on parking orbit optimization for parking interceptor[J]. Flight Dynamics, 2014, 32 (3): 253- 257.
6	胡正东, 唐雪梅. 天基对地打击武器轨道规划与制导技术[M]. 北京: 国防工业出版社, 2013: 42- 43.
	HU Z D , TANG X M . Trajectory planning and guide technology of SGKW[M]. Beijing: National Industry Press, 2013: 42- 43.
7	XIE S B , HU X S , XIN Z K , et al. Pontryagin minimum principle based model predictive control of energy management for a plug-in hybrid electric bus[J]. Applied Energy, 2019, 236 (4): 893- 905.
8	MARCUS M L , SEDWICK R J . Low earth orbit debris removal technology assessment using genetic algorithms[J]. Journal of Spacecraft and Rockets, 2017, 54 (5): 1110- 1126. doi: 10.2514/1.A33671
9	OGHIM S , LEE C Y , LEEGHIM H . Minimum-energy spacecraft intercept on non-coplanar elliptical orbits using genetic algorithms[J]. International Journal of Aeronautical and Space Sciences, 2017, 18 (4): 729- 739. doi: 10.5139/IJASS.2017.18.4.729
10	MENG Z , GAO S , WANG Z S , et al. Trajectory design for extended mission of circumlunar return and reentry test[J]. Advances in the Astronautical Sciences, 2018, 165 (5): 1197- 1213.
11	LYU B Q , ZHANG J J , LI Z P , et al. Fuzzy partical swarm optimization based on filled function and transformation function[J]. Acta Automatica Sinica, 2018, 44 (1): 74- 86.
12	XU G P , CUI Q L , SHI X H , et al. Particle swarm optimization based on dimensional learning strategy[J]. Swarm and Evolutionary Computation, 2019, 45 (1): 33- 51.
13	CHEN Y G , LI L X , XIAO J H , et al. Particle swarm optimizer with crossover operation[J]. Engineering Applications of Artificial Intelligence, 2018, 70 (2): 159- 169.
14	TONG S C . Adaptive fuzzy control for uncertain nonlinear systems[J]. Journal of Control and Decision, 2019, 6 (1): 30- 40. doi: 10.1080/23307706.2018.1540311
15	DASS A , SRIVASTAVA S , CHAUDHARY G . Control and analysis of carbon monoxide concentration using fuzzy and recurrent fuzzy systems[J]. Journal of Information & Optimization Sciences, 2019, 40 (2): 493- 504.
16	CHENG M Y , PRAYOGO D . Fuzzy adaptive teaching-learning-based optimization for global numerical optimization[J]. Neural Computing and Applications, 2018, 29 (2): 309- 327. doi: 10.1007/s00521-016-2449-7
17	LI Y J , CHEN A H . Artificial bee colony algorithm based on taboo search[J]. Computer Engineering and Applications, 2017, 53 (4): 145- 151.
18	HERNANDEZ M , LUIS A , LEON B , et al. Exploration of DNA microarrays using data mining and a taboo search[J]. Computacion y Sistemas, 2018, 22 (2): 585- 597.
19	TASIR E . Corrigendum:simulated annealing with deterministic decisions[J]. Journal of Computer Science, 2018, 14 (3): 435- 441.

组号	M1	M2	M3	M4	M5	M6
1	(32.8, -172.8)	(26.3, -162.4)	(33.2, -158.3)	(25.5, -170.0)	(22.4, -170.2)	(29.9, -149.5)
2	(14.1, 150.8)	(16.6, 167.8)	(15.1, 157.8)	(25.9, 163.1)	(23.1, 179.6)	(19.6, 177.2)
3	(-29.9, -164.6)	(-18.6, -173.4)	(-23.8, -147.6)	(-29.9, -162.7)	(-21.2, -140.8)	(-32.2, -134.2)
4	(-29.8, 66.5)	(-26.6, 78.9)	(-35.4, 80.7)	(-46.7, 96.0)	(-44.9, 83.9)	(-53.2, 110.1)
5	(29.1, -50.4)	(21.5, -40.5)	(29.1, -30.3)	(20.4, -28.4)	(13.8, -32.5)	(8.3, -29.6)
6	(-1.5, -142.3)	(-9.6, -135.1)	(3.2, -135.4)	(-4.4, -135.3)	(-10.1, -124.3)	(-8.9, -133.9)
7	(0.2, 169.2)	(6.9, 179.2)	(-1.0, -177.9)	(4.1, 173.7)	(-0.7, 178.6)	(-16.7, -158.5)
8	(45.2, 169.2)	(37.7, 168.9)	(30.9, 177.1)	(20.9, -177.0)	(34.9, -175.7)	(27.7, -172.7)

组号	FAPSO	PSO	PSO-TS	GA	GA-SA
1	5.084	5.066	5.072	4.486	5.069
2	5.077	5.074	5.077	5.070	5.073
3	5.075	4.088	5.070	4.089	5.072
4	4.177	4.156	4.167	4.166	4.177
5	5.165	5.088	5.165	5.079	5.159
6	5.133	4.566	5.129	5.088	5.130
7	6.199	6.089	6.102	6.087	6.178
8	6.077	6.065	6.069	5.685	6.076

组号	a/km	e	i/(°)	Ω/(°)	ω/(°)	f/(°)
1	6 851	0.013 200	50.226	42.421	44.607	4.392
2	6 959	0.000 225	52.084	75.081	73.256	115.332
3	6 887	0.255 917	145.022	67.457	343.221	102.776
4	6 928	0.101 219	38.618	53.001	89.562	212.681
5	7 107	0.034 088	49.031	57.665	46.532	318.882
6	7 254	0.005 882	52.045	45.298	75.833	223.968
7	6 928	0.203 645	62.982	29.887	162.545	54.341
8	7 006	0.234 550	48.321	104.353	124.008	0.002

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