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Open AccessArticle
An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning
by
Shuai Xue
Shuai Xue 1,
Zhaolei Wang
Zhaolei Wang
Zhaolei Wang was born in 1986. He received his B.S. degree in automation from Beijing Institute of a [...]
Zhaolei Wang was born in 1986. He received his B.S. degree in automation from Beijing Institute of Technology in 2009. He was a Ph.D. candidate of navigation, guidance and control, in the School of Automation Science and Electrical Engineering, Beihang University. He is now an engineer of Beijing Aerospace Automatic Control Institute, Beijing. His research interests include fault diagnosis, networked control system, switched system theory and their application in missiles.
2,
Hongyang Bai
Hongyang Bai
Hongyang Bai received the Ph.D. degree in navigation, guidance and control from the Nanjing of and [...]
Hongyang Bai received the Ph.D. degree in navigation, guidance and control from the Nanjing University of Science and Technology, Nanjing, China, in 2012. He is currently an Professor with the Nanjing University of Science and Technology. His research interests include integrated navigation, terminal guidance technology, and precisely target recognition. Prof. Bai is currently an active Reviewer for many international academic journals.
1,*
,
Chunmei Yu
Chunmei Yu
Chunmei Yu was born in 1975. She received a bachelor's degree in inertia navigation from the Harbin [...]
Chunmei Yu was born in 1975. She received a bachelor's degree in inertia navigation from the Harbin Engineering University, Harbin, China, in 1997 and a Degree in aerospace engineering from the Beijing Institute of Technology in 2008. She iss working towards her Ph.D. degree at the National University of Defense Technology, Changsha, China. She is a Senior Engineer with the Beijing Aerospace Automatic Control Institute, Beijing, China. Her main research interests include automatic control, navigation guidance and control.
2,
Tianyu Deng
Tianyu Deng
Tianyu Deng received the bachelor's degree from the Nanjing University of Science and Technology in [...]
Tianyu Deng received the bachelor's degree from the Nanjing University of Science and Technology in 2022. He is currently working toward his Ph.D. degree majoring in armament science and technology with the School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing, China. His current research topics mainly include image recognition and intelligent perception.
1 and
Ruisheng Sun
Ruisheng Sun
Ruisheng Sun was born in 1978. He received his Ph.D. degree in navigation, guidance, and control of [...]
Ruisheng Sun was born in 1978. He received his Ph.D. degree in navigation, guidance, and control from Nanjing University of Science and Technology (NJUST), China, in 2010. He is currently a Professor at NJUST. His research interests include nonlinear adaptive control and guidance, adaptive observer design, and multidiscipline optimization.
1
1
School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing Aerospace Automatic Control Institute, Beijing 100854, China
*
Author to whom correspondence should be addressed.
Aerospace 2024, 11(8), 653; https://doi.org/10.3390/aerospace11080653 (registering DOI)
Submission received: 13 June 2024
/
Revised: 5 August 2024
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Accepted: 7 August 2024
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Published: 11 August 2024
Abstract
During aerial combat, when an aircraft is facing an infrared air-to-air missile strike, infrared baiting technology is an important means of penetration, and the strategy of effective delivery of infrared bait is critical. To address this issue, this study proposes an improved deep deterministic policy gradient (DDPG) algorithm-based intelligent bait-dropping control method. Firstly, by modeling the relative motion between aircraft, bait, and incoming missiles, the Markov decision process of aircraft-bait-missile infrared effect was constructed with visual distance and line of sight angle as states. Then, the DDPG algorithm was improved by means of pre-training and classification sampling. Significantly, the infrared bait-dropping decision network was trained through interaction with the environment and iterative learning, which led to the development of the bait-dropping strategy. Finally, the corresponding environment was transferred to the Nvidia Jetson TX2 embedded platform for comparative testing. The simulation results showed that the convergence speed of this method was 46.3% faster than the traditional DDPG algorithm. More importantly, it was able to generate an effective bait-throwing strategy, enabling the aircraft to successfully evade the attack of the incoming missile. The strategy instruction generation time is only about 2.5 ms, giving it the ability to make online decisions.
Share and Cite
MDPI and ACS Style
Xue, S.; Wang, Z.; Bai, H.; Yu, C.; Deng, T.; Sun, R.
An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning. Aerospace 2024, 11, 653.
https://doi.org/10.3390/aerospace11080653
AMA Style
Xue S, Wang Z, Bai H, Yu C, Deng T, Sun R.
An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning. Aerospace. 2024; 11(8):653.
https://doi.org/10.3390/aerospace11080653
Chicago/Turabian Style
Xue, Shuai, Zhaolei Wang, Hongyang Bai, Chunmei Yu, Tianyu Deng, and Ruisheng Sun.
2024. "An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning" Aerospace 11, no. 8: 653.
https://doi.org/10.3390/aerospace11080653
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