基于深度学习的高空风在线估计及预报方法

doi:10.7527/S1000-6893.2022.27860

Abstract

Abstract:

The in-flight wind of launch vehicles is difficult to measure during flight， and the prediction error is great occasionally. In this paper， a real-time in-flight wind estimation and prediction method is proposed based on deep learning. By computing the spatial and temporal distribution of actual measurement data of in-flight wind in past 15 years， we acquire the rule that the wind velocity is higher and the wind direction is concentrated at 270° in winter， which is taken as the basis of sample set generation and wind prediction. Response of flight state to in-flight wind is obtained. Based on the obtained result， a deep neural network is designed to estimate the in-flight wind via the launch vehicle’s flight state. Next， a wind prediction method is proposed based on spatial and temporal distribution of in-flight wind. The rationality of wind prediction via deep learning is also given. We carry out a flight experiment on launch vehicle to verify the accuracy and real-timeliness of the proposed method. The proposed method is feasible for engineering implementation.

Key words: launch vehicle, in-flight wind, deep learning, estimation and prediction, spatial and temporal distribution of in-flight wind

CLC Number:

V475.1

Rongsheng ZHANG, Yansheng WU, Xudong QIN, Puzhuo ZHANG. A real⁃time in⁃flight wind estimation and prediction method based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(13): 327860-327860.

Figures/Tables 22

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References 33

1	JOHNSON D L， VAUGHAN W W. The wind environment interactions relative to launch vehicle design［J］. Journal of Aerospace Technology and Management， 2020（12）： 1-13.
2	LESTER H C， TOLEFSON H B. A study of launch-vehicle responses to detailed characteristics of the wind profile［J］. Journal of Applied Meteorology， 1964， 3（5）： 491-498.
3	王建明，林娜，张博戎. 某大型低温火箭高空风载荷分析［J］. 导弹与航天运载技术， 2021（5）： 137-141.
	WANG J M， LIN N， ZHANG B R. Analysis on flight wind-load for large cryogenic rocket［J］. Missiles and Space Vehicles， 2021（5）： 137-141 （in Chinese）.
4	余梦伦. CZ-2E火箭高空风弹道修正［J］. 导弹与航天运载技术， 2001（1）： 9-15.
	YU M L. CZ-2E ballistic correction for high altitude wind［J］. Missiles and Space Vehicles， 2001（1）： 9-15 （in Chinese）.
5	程胡华，李娟，肖云清，等. 风偏差对火箭最大气动载荷精度的影响［J］. 北京航空航天大学学报， 2021， 47（10）： 2034-2042.
	CHENG H H， LI J， XIAO Y Q， et al. Influence of wind deviation on rocket maximum aerodynamic load accuracy［J］. Journal of Beijing University of Aeronautics and Astronautics， 2021， 47（10）： 2034-2042 （in Chinese）.
6	马林，昝兴海，张琦. 考虑随机误差传递的高空风计算方法［J］. 兵器装备工程学报， 2016， 37（8）： 173-176.
	MA L， ZAN X H， ZHANG Q. Calculation method of the upper-air wind considering random error transfer［J］. Journal of Ordnance Equipment Engineering， 2016， 37（8）： 173-176 （in Chinese）.
7	唐学海，刘伯阳. 一种基于椭球分层模型的电波折射修正算法［J］. 电子与信息学报， 2015， 37（6）： 1507-1512.
	TANG X H， LIU B Y. Radio wave refraction correction algorithm based on ellipsoid stratification atmospheric structure model［J］. Journal of Electronics & Information Technology， 2015， 37（6）： 1507-1512 （in Chinese）.
8	FU B S， QI H， XU J T， et al. Attitude control in ascent phase of missile considering actuator non-linearity and wind disturbance［J］. Applied Sciences， 2019， 9（23）： 5113.
9	GAO Z X， FU J. Robust LPV modeling and control of aircraft flying through wind disturbance［J］. Chinese Journal of Aeronautics， 2019， 32（7）： 1588-1602.
10	王景国，卞韩城，陈学林，等. CZ-2F火箭整流罩残骸落点预报方法研究［J］. 载人航天， 2014， 20（5）： 457-460.
	WANG J G， BIAN H C， CHEN X L， et al. Research on impact point prediction methods of CZ-2F rocket fairing debris［J］. Manned Spaceflight， 2014， 20（5）： 457-460 （in Chinese）.
11	LEGRAND K， PUECHMOREL S， DELAHAYE D， et al. Robust aircraft optimal trajectory in the presence of wind［J］. IEEE Aerospace and Electronic Systems Magazine， 2018， 33（11）： 30-38.
12	HUI Y L， NAN Y， CHEN S D， et al. Dynamic attack zone of air-to-air missile after being launched in random wind field［J］. Chinese Journal of Aeronautics， 2015， 28（5）： 1519-1528.
13	李争学，贺元军，张广春，等. 风干扰引起的飞行器附加攻角和附加侧滑角计算方法［J］. 导弹与航天运载技术， 2016（5）： 66-73.
	LI Z X， HE Y J， ZHANG G C， et al. Calculation of the additional attack angle and the additional sideslip angle of craft caused by wind disturbance［J］. Missiles and Space Vehicles， 2016（5）： 66-73 （in Chinese）.
14	杨小龙. 飞行器纵向风扰动的辨识方法研究［J］. 系统工程理论与实践， 1996， 16（9）： 81-84.
	YANG X L. Longitudinal wind disturbance identification for a flight vehicle［J］. Systems Engineering-Theory & Practice， 1996， 16（9）： 81-84 （in Chinese）.
15	孙友，杨广慧. 大气层内飞行器风速在线辨识方法［J］. 航天控制， 2012， 30（6）： 3-6.
	SUN Y， YANG G H. A method of on-line wind identification in the field of guided aerocraft［J］. Aerospace Control， 2012， 30（6）： 3-6 （in Chinese）.
16	李洪. 智慧火箭发展路线思考［J］. 宇航总体技术， 2017， 1（1）： 1-7.
	LI H. The developing roadmap of intelligent launch vehicle［J］. Astronautical Systems Engineering Technology， 2017， 1（1）： 1-7 （in Chinese）.
17	CHAI R Q， TSOURDOS A， SAVVARIS A， et al. Six-DOF spacecraft optimal trajectory planning and real-time attitude control： A deep neural network-based approach［J］. IEEE Transactions on Neural Networks and Learning Systems， 2020， 31（11）： 5005-5013.
18	张荣升，吴燕生，秦旭东，等. 运载火箭推力下降故障下的在线弹道重构方法［J］. 南京航空航天大学学报， 2021， 53（S1）： 25-31.
	ZHANG R S， WU Y S， QIN X D， et al. Online trajectory reconstruction of launch vehicle with thrust drop faults［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2021， 53（S1）： 25-31 （in Chinese）.
19	ZHANG R S， ZHANG P Z， QIN X D， et al. Real-time guidance for launch vehicles under thrust drop fault via deep learning［C］∥ 2021 China Automation Congress （CAC）. Piscataway： IEEE Press， 2022： 5520-5525.
20	高晓光，李新宇，岳勐琪，等. 基于深度学习的地空导弹发射区拟合算法［J］. 航空学报， 2019， 40（9）： 322858.
	GAO X G， LI X Y， YUE M Q， et al. Fitting algorithm of ground-to-air missile launching area based on deep learning［J］. Acta Aeronautica et Astronautica Sinica， 2019， 40（9）： 322858 （in Chinese）.
21	ZHANG C Y， JI R P， LIANG Y， et al. LSTM-based boost-phase ballistic missile tracking［C］∥ 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference （ITAIC）. Piscataway： IEEE Press， 2021： 931-936.
22	KIM J， LIM M C， PARK S S， et al. Ballistic object trajectory and launch point estimation from radar measurements using long-short term memory networks［C］∥ 2019 7th International Conference on Robot Intelligence Technology and Applications （RiTA）. Piscataway： IEEE Press， 2019： 26-31.
23	HUANG L M， CHEN W C. Deep learning midcourse guidance for interceptor missile［C］∥2019 IEEE 3rd Information Technology， Networking， Electronic and Automation Control Conference （ITNEC）. Piscataway： IEEE Press， 2019： 1129-1134.
24	汪韧，惠俊鹏，俞启东，等. 基于LSTM模型的飞行器智能制导技术研究［J］. 力学学报， 2021， 53（7）： 2047-2057.
	WANG R， HUI J P， YU Q D， et al. Research of LSTM model-based intelligent guidance of flight aircraft［J］. Chinese Journal of Theoretical and Applied Mechanics， 2021， 53（7）： 2047-2057 （in Chinese）.
25	王因翰，范世鹏，吴广，等. 基于GRU的敌方拦截弹制导律快速辨识方法［J］. 航空学报， 2022， 43（2）： 325024.
	WANG Y H， FAN S P， WU G， et al. Fast guidance law identification approach for incoming missile based on GRU network［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（2）： 325024 （in Chinese）.
26	JIAO R H， PENG K X， DONG J. Remaining useful life prediction for a roller in a hot strip mill based on deep recurrent neural networks［J］. IEEE/CAA Journal of Automatica Sinica， 2021， 8（7）： 1345-1354.
27	康守强，周月，王玉静，等. 基于改进SAE和双向LSTM的滚动轴承RUL预测方法［J］. 自动化学报， 2022， 48（9）： 2327-2336.
	KANG S Q， ZHOU Y， WANG Y J， et al. RUL prediction method of a rolling bearing based on improved SAE and Bi-LSTM［J］. Acta Automatica Sinica， 2022， 48（9）： 2327-2336 （in Chinese）.
28	任俊超，刘丁，万银. 基于混合集成建模的硅单晶直径自适应非线性预测控制［J］. 自动化学报， 2020， 46（5）： 1004-1016.
	REN J C， LIU D， WAN Y. Hybrid integrated modeling based adaptive nonlinear predictive control of silicon single crystal diameter［J］. Acta Automatica Sinica， 2020， 46（5）： 1004-1016 （in Chinese）.
29	HOU X R， WANG K， ZHONG C， et al. ST-trader： A spatial-temporal deep neural network for modeling stock market movement［J］. IEEE/CAA Journal of Automatica Sinica， 2021， 8（5）： 1015-1024.
30	SUN L H， YANG B Q， MA J. A trajectory prediction algorithm for HFVs based on LSTM［C］∥ 2021 40th Chinese Control Conference （CCC）. Piscataway： IEEE Press， 2021： 7927-7931.
31	SATIR A S， DEMIR U， SEVER G G， et al. Nonlinear model based guidance with deep learning based target trajectory prediction against aerial agile attack patterns［C］∥ 2021 American Control Conference （ACC）. Piscataway： IEEE Press， 2021： 2607-2612.
32	赵世军，高太长，刘涛，等. 基于北斗一号的高空风探测方法研究［J］. 气象科技， 2012， 40（2）： 170-174.
	ZHAO S J， GAO T C， LIU T， et al. Upper wind sounding method based on Beidou-Ⅰ navigation system［J］. Meteorological Science and Technology， 2012， 40（2）： 170-174 （in Chinese）.
33	徐延万. 液体弹道导弹与运载火箭系列控制系统（上）［M］. 北京：中国宇航出版社， 1989： 147-149.
	XU Y W. Control system design of liquid propellant ballistic missile and launch vehicle（I）［M］. Beijing： China Astronautic Publishing House， 1989： 147-149 （in Chinese）.

序号	层	神经元数
1	输入层	5
2	LSTM	150
3	Relu
4	FCNN	100
5	Relu
6	FCNN	50
7	Relu
8	FCNN	20
9	Relu
10	输出层	2

序号	层	神经元数
1	输入层	3
2	LSTM	100
3	FCNN	30
4	Relu
5	FCNN	30
6	Relu
7	FCNN	10
8	Relu
9	输出层	2

风速偏差	估计风	预报风
x风速偏差绝对值平均值/（m·s^-1）	0.85	1.32
x风速偏差90%分位数平均值/（m·s^-1）	2.13/-1.62	3.41/-2.22
x风速偏差99%分位数平均值/（m·s^-1）	3.93/-3.34	6.02/-4.11
y风速偏差绝对值平均值/（m·s^-1）	0.92	1.31
y风速偏差90%分位数平均值/（m·s^-1）	1.82/-1.98	2.50/-2.87
y风速偏差99%分位数平均值/（m·s^-1）	2.98/-3.33	6.30/-5.31

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A real⁃time in⁃flight wind estimation and prediction method based on deep learning

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