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A time control entry guidance method for hypersonic glide vehicles based on deep reinforcement learning
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Zhenyu LIU, Gang LEI, Yong XIAN, Leliang REN, Shaopeng LI, Daqiao ZHANG. A time control entry guidance method for hypersonic glide vehicles based on deep reinforcement learning[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="A time control entry guidance method for hypersonic glide vehicles based on deep reinforcement learning",
author="Zhenyu LIU, Gang LEI, Yong XIAN, Leliang REN, Shaopeng LI, Daqiao ZHANG",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500144"
}
%0 Journal Article
%T A time control entry guidance method for hypersonic glide vehicles based on deep reinforcement learning
%A Zhenyu LIU
%A Gang LEI
%A Yong XIAN
%A Leliang REN
%A Shaopeng LI
%A Daqiao ZHANG
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500144
TY - JOUR
T1 - A time control entry guidance method for hypersonic glide vehicles based on deep reinforcement learning
A1 - Zhenyu LIU
A1 - Gang LEI
A1 - Yong XIAN
A1 - Leliang REN
A1 - Shaopeng LI
A1 - Daqiao ZHANG
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2500144
Abstract: To meet the requirement of simultaneous arrival for multiple hypersonic glide vehicles (HGVs), we propose a time control entry guidance (TCEG) method leveraging deep reinforcement learning. First, the entry guidance problem is solved with a reinforcement learning framework based on a designed reference flight profile. By appropriately designing the observation space and training environment, the well-trained agent demonstrates robust guidance performance under varying widths of the heading error corridor. Then, a novel method for predicting the remaining flight time is established, which consists of two main components. The first component estimates the remaining flight time using an analytical formula, while the second component employs a deep neural network (DNN) to predict the residual error between the estimated and the true value. Subsequently, based on the predicted arrival time error, the threshold of the heading error and the observation vector are corrected in real time, there-by guiding the agent to dynamically adjust its output actions. This enables precise control of the terminal arrival time. Since the generation of guidance commands only requires forward computation computations by the neural network, the proposed method exhibits excellent real-time performance. Finally, the effectiveness and robustness of the method are demonstrated through numerical simulations in various scenarios.
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