Air combat maneuver decision-making test based on deep reinforcement learning

被引：0
|
作者：
Zhang S. [1 ,2 ]
Zhou P. [1 ,2 ]
He Y. [1 ,2 ]
Huang J. [1 ,2 ]
Liu G. [2 ]
Tang J. [1 ,2 ]
Jia H. [3 ]
Du X. [1 ,2 ]
机构：
[1] Aerospace Technology Institute, China Aerodynamics Research and Development Center, Mianyang
[2] China Aerodynamics Research and Development Center, Mianyang
[3] School of Aeronautics, Northwestern Polytechnical University, Xi’an
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2023年 / 44卷 / 10期
基金：
中国国家自然科学基金;
关键词：
close-range air combat; deep reinforcement learning; flight test; human-machine combat; intelligent decision-making;
D O I：
10.7527/S1000-6893.2023.28094
中图分类号：
学科分类号：
摘要：
The air combat intelligent decision-making will greatly change the form of wars. Deep reinforcement learning decision-making machine，as an important technical paradigm to realize the intelligent decision-making in air combat，can explore the potential of unmanned aircraft. However，reports on its engineering implementation are rare. Aimed at the practical implementation of the maneuver intelligent decision-making based on deep reinforcement learning in the one-to-one fighters’close-range air combat，an online deep neural network maneuver decision-making model suitable for application is developed. The maneuver control scheme that the trajectory guidance decision-making commands are tracked with the flight control law is proposed. The corresponding software and hardware architectures are realized and the human-machine combat flight test is carried out，which achieves the transfer from virtual simulation to real flight in intelligent air combat. The research results show that，based on the close-range air combat maneuver decision-making and control method developed in this paper，the intelligent unmanned aircraft can make logical maneuver decisions quickly in favor of its own side and thus is soon in the advantageous situation by maneuver when combatting with human“pilots”. The flight test results demonstrate the potential application value of the deep neural network intelligent decision-making machine in air combat decision-making. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
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共 32 条
  • [1] FAN H T, YAN J., Evolution and development trend of air combat system［J］, Acta Aeronautica et Astronautica Sinica, 43, 10, (2022)
  • [2] SUN Z X, YANG S Q, Et al., A survey of air combat artificial intelligence［J］, Acta Aeronautica et Astronautica Sinica, 42, 8, (2021)
  • [3] SUN C., Development trend of future fighter：a review of evolution of winning mechanism in air combat［J］, Acta Aeronautica et Astronautica Sinica, 42, 8, (2021)
  • [4] NICHOLS S O., 21st century air-to-air short range weapon requirementsf, (1998)
  • [5] DONG Y Q, AI J L., Decision making in autonomous air combat：review and prospects［J］, Acta Aeronautica et Astronautica Sinica, 41, (2020)
  • [6] BURGIN G H., OWENS A J., An adaptive maneuvering logic computer program for the simulation of one-on-one air-to-air combat［R］, (1975)
  • [7] ISAACS R., Differential games：A mathematical theory with applications to warfare and pursuit，control and optimization, (1965)
  • [8] XUE Y, ZHANG Y Y，, Et al., Multiple UCAV cooperative jamming air combat decision making based on heuristic self-adaptive discrete differential evolution algorithm［J］, Acta Aeronautica et Astronautica Sinica, 34, 2, pp. 343-351, (2013)
  • [9] RODIN E Y，, LIROV Y, MITTNIK S, Et al., Artificial intelligence in air combat games［J］, Computers & Mathematics With Applications, 13, 1-3, pp. 261-274, (1987)
  • [10] ERNEST N, CARROLL D., Genetic fuzzy based artificial intelligence for unmanned combat aerial vehicle control in simulated air combat missions［J］, Journal of Defense Management, 6, 1, (2016)
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