运载火箭推力下降时入轨能力评估与轨迹重构方法

doi:10.13229/j.cnki.jdxbgxb.20211120

Abstract

Abstract:

To avoid the failure of orbit insertion caused by thrust decline， a mission capability evaluation and trajectory reconstruction method is proposed based on the local collocation method and neural network. First， the local collocation method is used to solve the fuel-optimal launch ascent problem， leading to the establishment of the trajectory database for the target and secondary rescue orbit insertion scenarios with various thrust decline percentages. Then， the mapping between the thrust decline percentage and the mission capability is learned from the trajectory database by using the neural network. Thus， the mission capability evaluation and decision can be made in real time when the thrust declines. As last， the online trajectory reconstruction is carried out using the local collocation method to perform the orbit insertion. Numerical simulation results show that the proposed method can evaluate the mission capability and reconstruct the ascent trajectory when the thrust of the launch vehicle declines， thereby improving the success rate of the launch mission.

Key words: launch vehicle, trajectory reconstruction, thrust decline, neural networks, local collocation method

CLC Number:

V488.23

Shuang LI,Zi-rui LIN,Song YE,Xu LIU,Ji-song ZHAO. Orbital capability evaluation and trajectory reconstruction for launch vehicle with thrust decline[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2245-2253.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Simulation parameters for core engine thrust linear decline"

故障发生时刻 $t$ /s	故障比例系数 $k C l$	次级轨道高度 $r f$ /km
50	0.68	6578
150	0.77	6678
250	0.83	6778
350	0.84	6900

Table 1

Table 2

Simulation parameters for core engine thrust proportional decline"

故障发生时刻 $t$ /s	故障比例系数 $k C k$	次级轨道高度 $r f$ /km
50	0.78	6578
200	0.86	6678
250	0.90	6778
350	0.91	6900

Table 2

Fig.9

References 22

1	李学锋. 运载火箭智慧控制系统技术研究[J]. 宇航总体技术, 2018, 2(2): 43-48.
	Li Xue-feng. Research on GNC system of new generation intelligent launch vehicle[J]. Astronautical Systems Engineering Technology, 2018, 2(2): 43-48.
2	胡珊. 载人运载火箭主动段故障检测技术研究[D]. 西安: 西北工业大学航天学院, 2005.
	Hu Shan. Research on fault detection of manned launch vehicle boosting stage[D]. Xi'an: School of Astronautics, Northwestern Polytechnical University, 2005.
3	黄盘兴. 重型运载火箭可重构控制系统设计研究[D]. 哈尔滨: 哈尔滨工业大学航天学院, 2012.
	Huang Pan-xing. Research on reconfigurable control system design of heavy launch vehicle[D]. Harbin: School of Astronautics, Harbin Institute of Technology, 2012.
4	Arther E, Bryson J, Ho Y C. Applied Optimal Control[M]. London: Blaisdell Publishing Company, 1969.
5	Dukeman G. Atmospheric ascent guidance for rocket-powered launch vehicles[C]∥AIAA Guidance, Navigation, and Control Conference and Exhibit, Monterey, California, USA, 2002: 1-11.
6	Lu P, Sun H, Tsai B. Closed-loop endoatmospheric ascent guidance[J]. Journal of Guidance, Control, and Dynamics, 2003, 26(2): 283-294.
7	Lu P, Griffin B J, Dukeman G A, et al. Rapid optimal multi-burn ascent planning and guidance[J]. Journal of Guidance, Control, and Dynamics, 2008, 31(6): 1656-1664.
8	Murillo O, Lu P. Fast ascent trajectory optimization for hypersonic air-breathing vehicles[C]∥AIAA Guidance, Navigation, and Control Conference, Toronto, Ontario, Canada, 2010: 1-24.
9	韩雪颖, 马英, 程兴, 等. 运载火箭推力故障下的弹道重构策略研究[J]. 导弹与航天运载技术, 2019 (2): 7-11.
	Han Xue-ying, Ma Ying, Cheng Xing, et al. Trajectory reconfiguration strategy research on launch vehicle with thrust failure[J]. Missiles and Apace Vehicles, 2019(2): 7-11.
10	湛康意, 陈海朋, 贺从园, 等. 基于间接法在线能力评估和自主规划技术研究[J]. 中国空间科学技术, 2021, 41(3): 31-38.
	Zhan Kang-yi, Chen Hai-peng, He Cong-yuan, et al. Research on online capability assessment and autonomous planning technology based on indirect method[J]. Chinese Space Science and Technology, 2021, 41(3): 31-38.
11	Darby C L, Hager W W, Rao A V. Direct trajectory optimization using a variable low-order adaptive pseudospectral method[J]. Journal of Spacecraft and Rockets, 2011, 48(3): 433-445.
12	Betts J T, Huffiman W P. Mesh refinement in direct transcription methods for optimal control[J]. Optimal Control Applications and Methods, 1998, 19(1): 1-21.
13	Jain S, Tsiotras P. Multiresolution-based direct trajectory optimization[C]∥The 46th IEEE Conference on Decision and Control, Piscataway, New Jersey, USA, 2007: 5991-5996.
14	Zhao J S, Li S. Modified multiresolution technique for mesh refinement in numerical optimal control[J]. Journal of Guidance Control and Dynamics, 2017, 40(12): 3328-3338.
15	Liu F, Hager W W, Rao A V. Adaptive mesh refinement method for optimal control using nonsmoothness detection and mesh size reduction[J]. Journal of the Franklin Institute, 2015, 352(10): 4081-4106.
16	Zhao J S, Li S. Adaptive mesh refinement method for solving optimal control problems using interpolation error analysis and improved data compression[J]. Journal of the Franklin Institute, 2020, 357(3): 1603-1627.
17	宋征宇, 王聪, 巩庆海. 运载火箭上升段推力下降故障的自主轨迹规划方法[J]. 中国科学: 信息科学, 2019, 49(11): 1472-1487.
	Song Zheng-yu, Wang Cong, Gong Qing-hai. Autonomous trajectory planning for launch vehicle under thrust drop failure[J]. Scientia Sinica Informationis, 2019, 49(11): 1472-1487.
18	周鼎, 梁艳迁, 梁建国, 等. 运载火箭任务降级轨迹规划问题特性分析研究[J]. 上海航天, 2020, 37(): 53-58, 70.
	Zhou Ding, Liang Yan-qian, Liang Jian-guo, et al. Characteristic analysis of trajectory planning problem of orbit descent for launch vehicles[J]. Aerospace Shanghai, 2020, 37(Sup.2): 53-58, 70.
19	杨卓乔, 吕春红. 考虑时间约束的多飞行器轨迹优化方法研究[J]. 航天控制, 2021, 39(2): 11-16.
	Yang Zhuo-qiao, Lv Chun-hong. Exploiting sparsity in local collocation methods for solving trajectory optimization problems[J]. Aerospace Control, 2021, 39(2): 11-16.
20	刘云昭. 基于神经网络预测的主动段轨迹在线生成方法研究[D]. 西安: 西安电子科技大学空间科学与技术学院, 2020.
	Liu Yun-zhao. Research on the method of online trajectory generation for powered phase based on neural network prediction[D]. Xi'an: School of Aerospace Science and Technology, Xidian University, 2020.
21	赵吉松. 求解轨迹优化问题的局部配点法的稀疏性研究[J]. 宇航学报, 2017, 38(12): 49-58.
	Zhao Ji-song. Exploiting sparsity in local collocation methods for solving trajectory optimization problems[J]. Journal of Astronautics, 2017, 38(12): 49-58.
22	赵吉松, 谷良贤, 佘文学. 配点法和网格细化技术用于非光滑轨迹优化[J]. 宇航学报, 2013, 34(11): 1442-1450.
	Zhao Ji-song, Gu Liang-xian, She Wen-xue. Application of local collocation method and mesh refinement to nonsmooth trajectory optimization[J]. Journal of Astronautics, 2013, 34(11): 1442-1450.

Related Articles 15

[1]	Ming-hua GAO,Can YANG. Traffic target detection method based on improved convolution neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1353-1361.
[2]	Huai-jiang YANG,Er-shuai WANG,Yong-xin SUI,Feng YAN,Yue ZHOU. Simplified residual structure and fast deep residual networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1413-1421.
[3]	Xiang-jun LI,Jie-ying TU,Zhi-bin ZHAO. Validity classification of melting curve based on multi⁃scale fusion convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 633-639.
[4]	Tao CHEN,Jing QIN,Hua ZHAO,Qing-peng SU,Yong LYU,Kai ZHONG,Ying-bo WANG,Yi-qiang PEI. Prediction of gasoline engine steady state exhaust based on model group prediction method [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1565-1574.
[5]	Hui ZHONG,Heng KANG,Ying-da LYU,Zhen-jian LI,Hong LI,Ruo-chuan OUYANG. Image manipulation localization algorithm based on channel attention convolutional neural networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1838-1844.
[6]	Hou⁃jie LI,Fa⁃sheng WANG,Jian⁃jun HE,Yu ZHOU,Wei LI,Yu⁃xuan DOU. Pseudo sample regularization Faster R⁃CNN for traffic sign detection [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1251-1260.
[7]	Dan⁃tong OUYANG,Jun XIAO,Yu⁃xin YE. Distant supervision for relation extraction with weakconstraints of entity pairs [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 912-919.
[8]	GAO Ming-liang, YU Sheng-bao, ZHENG Jian-bo, XU Chang, ZHANG Kun, LUAN Hui. Application of PSBP method in high-density two-dimensional resistivity inversion [J]. 吉林大学学报(工学版), 2015, 45(6): 2026-2033.
[9]	LI Di-fei, TIAN Di, HU Xiong-wei. Standard literature language model based on deep learning [J]. 吉林大学学报(工学版), 2015, 45(2): 596-599.
[10]	YAN Chu-liang, HAO Yun-xiao, LIU Ke-ge. Fatigue life prediction of materials based on BP neural networks optimized by genetic algorithm [J]. 吉林大学学报(工学版), 2014, 44(6): 1710-1715.
[11]	CHEN Li, SUN Yong-hai, FU Tian-yu, DING Jian-feng. Method of rapid estimation of extracellular polysaccharide of Morchella Esculenta [J]. 吉林大学学报(工学版), 2014, 44(2): 567-572.
[12]	YANG Na, CHEN Hou-jin, LI Yan-feng, HAO Xiao-li, YAO Chang. Algorithm of image segmentation based on the improved pulse coupled neural network model [J]. 吉林大学学报(工学版), 2013, 43(03): 758-763.
[13]	GONG Ya-feng, CHENG Yong-chun, JIAO Yu-bo. Damage identification of urban overpasses based on static strain data and neural networks optimized by genetic algorithm [J]. 吉林大学学报(工学版), 2011, 41(增刊2): 164-169.
[14]	FANG Hui-juan, LUO Ji-liang, HUANG Cai-hong, LI Ping. Fast simulation of spiking neural networks [J]. 吉林大学学报(工学版), 2011, 41(增刊1): 227-230.
[15]	FU Zhao-yang,GUO Lei,CHANG Wei-wei. Fusion algorithm of hyperspectral images based on wavelet transform and multichannel PCNN [J]. 吉林大学学报(工学版), 2011, 41(03): 838-843.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Orbital capability evaluation and trajectory reconstruction for launch vehicle with thrust decline

RICH HTML

PDF (PC)