Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (6): 1294-1308.

• CONTROL THEORY AND APPLICATION •

Chengming ZHANG(), Yanwei ZHU(), Leping YANG(), Xin ZENG()

1. 1 College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410000, China
• Received:2021-04-25 Online:2022-12-18 Published:2022-12-24
• Contact: Yanwei ZHU E-mail:zhchm_vincent@163.com;zywnudt@163.com;ylpnudt@163.com;xzavier0214@outlook.com
• About author:
ZHANG Chengming was born in 1998. He received his B.S. and M.S. degrees from National University of Defense Technology (NUDT), Changsha, China, in 2019 and 2021, respectively. He is a graduate student with the College of Aerospace Science and Engineering, NUDT. His research interests include aerospace dynamics, guidance and control, and application of artificial intelligence to the control of astronautic systems. E-mail: zhchm_vincent@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 Aerospace Science and Engineering, NUDT. His research interests include aerospace dynamics, guidance and control, and astronautic mission planning and design. E-mail: zywnudt@163.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 Aerospace Science and Engineering, NUDT. His research interests include aerospace dynamics, guidance and control, and astronautic mission planning and design. E-mail: ylpnudt@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 student with the College of Aerospace Science and Engineering, NUDT. His research interests include aerospace dynamics, guidance and control, and 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:

With the development of space rendezvous and proximity operations (RPO) in recent years, the scenarios with non-cooperative spacecraft are attracting the attention of more and more researchers. A method based on the costate normalization technique and deep neural networks is presented to generate the optimal guidance law for free-time orbital pursuit-evasion game. Firstly, the 24-dimensional problem given by differential game theory is transformed into a three-parameter optimization problem through the dimension-reduction method which guarantees the uniqueness of solution for the specific scenario. Secondly, a close-loop interactive mechanism involving feedback is introduced to deep neural networks for generating precise initial solution. Thus the optimal guidance law is obtained efficiently and stably with the application of optimization algorithm initialed by the deep neural networks. Finally, the results of the comparison with another two methods and Monte Carlo simulation demonstrate the efficiency and robustness of the proposed optimal guidance method.