A novel guidance law based on proximal policy optimization

被引：0

作者：

Jiang, Yang ^{[1
]}

Yu, Jianglong ^{[1
]}

Li, Qingdong ^{[1
]}

Ren, Zhang ^{[1
]}

Done, Xiwang ^{[1
,2
]}

Hua, Yongzhao ^{[1
,2
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China

[2] Beihang Univ, Inst Artificial Intelligence, Beijing 100191, Peoples R China

来源：

2022 41ST CHINESE CONTROL CONFERENCE (CCC) | 2022年

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

reinforcement learning; proximal policy optimization; high-speed maneuvering target; SLIDING-MODE CONTROL;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new guidance law based on deep reinforcement learning is proposed for the high-speed maneuvering target attack problem. Firstly, the missile-target kinematic model is established, and the action space and state space of reinforcement learning are designed. Then, according to the missile strike process, the reward function suitable for this scenario is proposed. The proximal policy optimization (PPO) based guidance law construction is completed. Finally, the strike effect in multiple sets of experiments verifies the effectiveness of the method proposed in this paper.

引用

页码：3364 / 3369

页数：6

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