Learning prediction-correction guidance for impact time control

Liu Zichao; Wang Jiang; He ShaomingShin Hyo-SangTsourdos Antonios

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Learning prediction-correction guidance for impact time control

机译：Learning prediction-correction guidance for impact time control

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This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is developed based on a general prediction-correction concept: the exact time-to-go under proportional navigation guidance with realistic aerodynamic characteristics is estimated by a deep neural network and a biased command to nullify the impact time error is developed by utilizing the emerging reinforcement learning techniques. To deal with the problem of insufficient training data, a transfer-ensemble learning approach is proposed to train the deep neural network. The deep neural network is augmented into the reinforcement learning block to resolve the issue of sparse reward that has been observed in typical reinforcement learning formulation. Extensive numerical simulations are conducted to support the proposed algorithm. (C) 2021 Elsevier Masson SAS. All rights reserved.

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来源
《Aerospace science and technology》 |2021年第2期|107187.1-107187.12|共12页
作者
Liu Zichao; Wang Jiang; He ShaomingShin Hyo-SangTsourdos Antonios;
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作者单位

Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China;

Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类航空;
关键词
Missile guidance; Impact-time-control guidance; Prediction-correction; Transfer learning; Reinforcement learning;

Learning prediction-correction guidance for impact time control

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