A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft

doi:10.23919/JSEE.2022.000044

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (2): 438-446.doi: 10.23919/JSEE.2022.000044

• CONTROL THEORY AND APPLICATION • Previous Articles Next Articles

A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft

Fuyunxiang YANG(), Leping YANG(), Yanwei ZHU*(), Xin ZENG()

¹ College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

Received:2020-11-30 Online:2022-05-06 Published:2022-05-06
Contact: Yanwei ZHU E-mail:yfyxgood@hotmail.com;ylp_1964@163.com;zywnudt@163.com;xzavier0214@outlook.com
About author:|YANG Fuyunxiang was born in 1996. He received his B.S. degree from National University of Defense Technology (NUDT), Changsha, China, in 2018. He is a Ph.D. candidate with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, application of artificial intelligence to the control of astronautic systems.E-mail: yfyxgood@hotmail.com||YANG Leping was born in 1964. He received his B.S. and M.S. degrees from National University of Defense Technology (NUDT), Changsha, China, in 1984 and 1987, respectively. He is a professor with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, astronautic mission planning and design.E-mail: ylp_1964@163.com||ZHU Yanwei was born in 1981. He received his B.S., M.S., and Ph.D. degrees from National University of Defense Technology (NUDT), Changsha, China, in 2002, 2004 and 2009, respectively. He is an associate professor with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, astronautic mission planning and design.E-mail: zywnudt@163.com||ZENG Xin was born in 1992. He received his B.S. and M.S. degrees from National University of Defense Technology (NUDT), Changsha, China, in 2014 and 2016, respectively. He is a Ph.D. candidate with the College of Aeronautics and Astronautics, NUDT. His research interests include aerospace dynamics, guidance and control, application of artificial intelligence to the control of astronautic systems.E-mail: xzavier0214@outlook.com
Supported by:
This work was supported by the National Defense Science and Technology Innovation (18-163-15-Lz-001-004-13)

Abstract

Abstract:

Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization problems. This paper presents a data-free deep neural network (DNN) based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard constrained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to integrate interception dynamic model into the network and involve it in the process of gradient descent, which makes the network endowed with the knowledge of physical constraints and reduces the learning burden of the network. Thus, a DNN based method is proposed, which completely eliminates the demand of training datasets and improves the generalization capacity. Finally, numerical results demonstrate the feasibility and efficiency of our proposed method.

Key words: non-cooperative maneuvering spacecraft, neural network, differential game, trajectory optimization

Fuyunxiang YANG, Leping YANG, Yanwei ZHU, Xin ZENG. A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft[J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 438-446.

Figures/Tables 11

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ID	x/km	y/km	z/km	$\dot x $ /(m/s)	$\dot y $ /(m/s)	$\dot z $ /(m/s)
Target	?33.8	?50.4	6.7	2.0	4.9	0.0
Pursuer	0.0	0.0	0.0	0.0	0.0	0.0

ID	Relative distance/km	Relative velocity/(m/s)
Target	$ \sqrt {{x^2} + {y^2} + {z^2}} \leqslant 100 $	$ \sqrt {v_x^2 + v_y^2 + v_z^2} \leqslant 10 $
Pursuer	$ \sqrt {{x^2} + {y^2} + {z^2}} \leqslant 100 $	$ \sqrt {v_x^2 + v_y^2 + v_z^2} \leqslant 10 $

Method name	Dataset	${n_s}$ in Case 1?Case 7	${n_s}$ in Case 8?Case 10	${r_s}/{\text{%} }$
Method 1	500	3	0	30
Method 2	–	7	3	100

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A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft

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