TY - GEN
T1 - Intelligent Deformation Decision Algorithm for Morphing Aircraft with Distributed Seamless Wing
AU - Shao, Shuai
AU - Liu, Junjui
AU - Han, Wanru
AU - Shan, Jiayuan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Aiming at the deformation decision-making problem of morphing aircraft, an intelligent deformation decision-making method based on Deep Deterministic Policy Gradient (DDPG) is proposed. Firstly, the dynamic model of the aircraft is established, and the aerodynamic parameters of the aircraft are obtained by computational fluid dynamics (CFD). Secondly, considering the performance indexes including optimal lift-drag ratio and control error of altitude loop, an intelligent deformation decision-making algorithm based on DDPG is designed. Furthermore, the intelligent agent of morphing aircraft with PID controller is trained to obtain the optimal configuration decision under given angle of attack (AOA) trajectory in real time. Finally, the simulation results show that the proposed algorithm can realize the AOA command tracking of the optimal lift-drag ratio. Compared with the traditional aircraft, the lift-drag ratio in different AOA can be optimized by morphing, and the attitude dynamic tracking error can be further reduced.
AB - Aiming at the deformation decision-making problem of morphing aircraft, an intelligent deformation decision-making method based on Deep Deterministic Policy Gradient (DDPG) is proposed. Firstly, the dynamic model of the aircraft is established, and the aerodynamic parameters of the aircraft are obtained by computational fluid dynamics (CFD). Secondly, considering the performance indexes including optimal lift-drag ratio and control error of altitude loop, an intelligent deformation decision-making algorithm based on DDPG is designed. Furthermore, the intelligent agent of morphing aircraft with PID controller is trained to obtain the optimal configuration decision under given angle of attack (AOA) trajectory in real time. Finally, the simulation results show that the proposed algorithm can realize the AOA command tracking of the optimal lift-drag ratio. Compared with the traditional aircraft, the lift-drag ratio in different AOA can be optimized by morphing, and the attitude dynamic tracking error can be further reduced.
KW - Deep Deterministic Policy Gradient (DDPG)
KW - intelligent deformation decision
KW - morphing aircraft
UR - http://www.scopus.com/inward/record.url?scp=85199333194&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-3336-1_43
DO - 10.1007/978-981-97-3336-1_43
M3 - Conference contribution
AN - SCOPUS:85199333194
SN - 9789819733354
T3 - Lecture Notes in Electrical Engineering
SP - 503
EP - 514
BT - Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Decision and Planning Technologies
A2 - Li, Xiaoduo
A2 - Song, Xun
A2 - Zhou, Yingjiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023
Y2 - 24 November 2023 through 27 November 2023
ER -