Attitude Control of Hypersonic Vehicle based on Reinforcement Learning

被引：0
|
作者：
Liu, Jingwen [1 ]
Fan, Hongdong [1 ]
Fan, Yonghua [2 ]
Cai, Guangbin [1 ]
机构：
[1] Rocket Force Univ Engn, Xian 710025, Peoples R China
[2] Northwestern Polytech Univ, Xian 710072, Peoples R China
来源：
2024 3RD CONFERENCE ON FULLY ACTUATED SYSTEM THEORY AND APPLICATIONS, FASTA 2024 | 2024年
基金：
中国国家自然科学基金;
关键词：
hypersonic vehicle; intelligent control; attitude control; DDQN;
D O I：
10.1109/FASTA61401.2024.10595336
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, the attitude control problem of hypersonic vehicle is studied. The DDQN (double deep Q network) algorithm is used to realize the online adaptive adjustment of the control parameters of the PID (proportional-integral-derivative) controller. Firstly, the dynamic model of hypersonic vehicle is established. Then, the implementation process of parameter adjustment using DDQN algorithm is described. By building two neural networks, the main network is used to determine the action to be performed, and the target network is used to determine the action value, so as to realize the adaptive adjustment of the control parameters. Finally, the effectiveness of the proposed method is verified by simulation experiments.
引用
收藏
页码：1503 / 1507
页数：5
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