@article{PENG2025, 
author = {Yuxiao PENG and Zhen HE and Jingwen QIU},
title = {Active deformation decision-making for four-wing variable sweep aircraft based on LSTM-DDPG algorithm},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {10},
pages = {3504-3514},
keywords = {morphing aircraft, flight control, deep reinforcement learning, deep deterministic policy gradient, long short-term memory recurrent neural network},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0513},
doi = {10.13700/j.bh.1001-5965.2023.0513},
abstract = {This paper presented an intelligent deformation control method based on the long short-term memory (LSTM) deep deterministic policy gradient (DDPG) algorithm, addressing the active deformation control challenges in variable configuration aircraft. A four-wing variable sweep aircraft with a tandem-wing configuration was studied, and its geometric model and aerodynamic parameters were calculated through OPENVSP, which was then used to establish the aircraft’s dynamics model. The LSTM-DDPG algorithm learning framework was designed for the accelerated climb process of the four-wing variable sweep aircraft. Under symmetrical deformation conditions, active deformation decision training was performed for longitudinal trajectory tracking. Simulation results show that the LSTM-DDPG algorithm applied to the active deformation control process converges quickly and achieves higher average rewards. Moreover, the trained active deformation controller exhibits good control performance in the trajectory tracking tasks of the four-wing variable sweep aircraft.}
}