A Deep Stall Recovery Control Based on the Proximal Policy Optimization

Xinlong Xu, Ruichen Ming, Xiaoxiong Liu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The phenomenon of deep stall refers to the situation where an aircraft, due to the presence of a stable point at high angles of attack, tends to remain in this state once it enters deep stall. This condition leads to a reduction in lift and control effectiveness, making it challenging to recover using conventional controls. This paper proposes a control method based on a reinforcement learning algorithm to achieve deep stall recovery. The state and action spaces are determined based on the aircraft motion equations. The reward function is designed considering flight characteristics, and the Proximal Policy Optimization algorithm is employed to train the controller for end-to-end deep stall recovery. Simulation results demonstrate that the proposed deep stall recovery method effectively achieves recovery and maintains stable aircraft states after recovery. Additionally, this paper considers constraints on the smoothness of controller outputs and examines the robustness of the controller to state noise.

源语言英语
主期刊名Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 1
编辑Liang Yan, Haibin Duan, Yimin Deng
出版商Springer Science and Business Media Deutschland GmbH
538-549
页数12
ISBN(印刷版)9789819621996
DOI
出版状态已出版 - 2025
活动International Conference on Guidance, Navigation and Control, ICGNC 2024 - Changsha, 中国
期限: 9 8月 202411 8月 2024

出版系列

姓名Lecture Notes in Electrical Engineering
1337 LNEE
ISSN(印刷版)1876-1100
ISSN(电子版)1876-1119

会议

会议International Conference on Guidance, Navigation and Control, ICGNC 2024
国家/地区中国
Changsha
时期9/08/2411/08/24

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