TY - GEN
T1 - Enhanced Control System for Morphing Hypersonic Aircraft Based on an Improved Proximal Policy Optimization Algorithm
AU - Zhang, Yudong
AU - Tang, Guanghe
AU - Guo, Jianguo
AU - Xu, Xinpeng
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper focuses on the application of deep reinforcement learning, specifically an improved PPO algorithm, to the design of an attitude control system for a hypersonic aircraft with a retractable wingspan. The derivation and enhancement of the PPO algorithm are thoroughly explained prior to the control system design. Following this, an Actor network with superior control performance is trained offline and utilized as the controller. Three-channel attitude tracking tests are conducted under three conditions: no noise, small noise, and large noise. Results demonstrate that the attitude controller based on the improved PPO algorithm achieves high accuracy and robust anti-jamming performance. This highlights the unique advantages of deep reinforcement learning in addressing highly nonlinear, uncertain, and time-varying control problems.
AB - This paper focuses on the application of deep reinforcement learning, specifically an improved PPO algorithm, to the design of an attitude control system for a hypersonic aircraft with a retractable wingspan. The derivation and enhancement of the PPO algorithm are thoroughly explained prior to the control system design. Following this, an Actor network with superior control performance is trained offline and utilized as the controller. Three-channel attitude tracking tests are conducted under three conditions: no noise, small noise, and large noise. Results demonstrate that the attitude controller based on the improved PPO algorithm achieves high accuracy and robust anti-jamming performance. This highlights the unique advantages of deep reinforcement learning in addressing highly nonlinear, uncertain, and time-varying control problems.
KW - Morphing Hypersonic Aircraft
KW - PPO
KW - Time-varying Control
UR - https://www.scopus.com/pages/publications/105011822045
U2 - 10.1109/DDCLS66240.2025.11065632
DO - 10.1109/DDCLS66240.2025.11065632
M3 - 会议稿件
AN - SCOPUS:105011822045
T3 - Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025
SP - 1072
EP - 1077
BT - Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025
A2 - Sun, Mingxuan
A2 - Chi, Ronghu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025
Y2 - 9 May 2025 through 11 May 2025
ER -