TY - GEN
T1 - Distributed Vibration Control of Large Flexible Satellite Solar Panel Via Reinforcement Learning
AU - Huang, Jingwen
AU - Liu, Xiangdong
AU - Liu, Haikuo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper proposes a distributed vibration control strategy based on reinforcement learning to solve the vibration control problem of large flexible satellite solar panel. The main contents are as follows: Firstly, a multiple substructures model for satellite solar panel is established, and a interaction topology is constructed to describe the information flow among substructures. Secondly, a distributed vibration controller based on reinforcement learning is designed, and the optimal control is obtained by using the strategy iteration algorithm. It is proved theoretically that the iterative results converge to the solution of the HJB equation. The strategy algorithm is implemented by means of neural network, and the gradient of the output and value function of the system is sampled to update the network, which can achieve better convergence performance. In this process, the weight of the network reaches the ideal value and the approximate optimal distributed vibration control protocol is obtained. Finally, simulation results are given to prove that the distributed control algorithm in this paper can effectively suppress the vibration of flexible solar panel.
AB - This paper proposes a distributed vibration control strategy based on reinforcement learning to solve the vibration control problem of large flexible satellite solar panel. The main contents are as follows: Firstly, a multiple substructures model for satellite solar panel is established, and a interaction topology is constructed to describe the information flow among substructures. Secondly, a distributed vibration controller based on reinforcement learning is designed, and the optimal control is obtained by using the strategy iteration algorithm. It is proved theoretically that the iterative results converge to the solution of the HJB equation. The strategy algorithm is implemented by means of neural network, and the gradient of the output and value function of the system is sampled to update the network, which can achieve better convergence performance. In this process, the weight of the network reaches the ideal value and the approximate optimal distributed vibration control protocol is obtained. Finally, simulation results are given to prove that the distributed control algorithm in this paper can effectively suppress the vibration of flexible solar panel.
KW - Active Vibration Control
KW - Distributed Control
KW - Large Satellite Solar Panel
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85181843917&partnerID=8YFLogxK
U2 - 10.1109/CCDC58219.2023.10327318
DO - 10.1109/CCDC58219.2023.10327318
M3 - Conference contribution
AN - SCOPUS:85181843917
T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
SP - 4036
EP - 4042
BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th Chinese Control and Decision Conference, CCDC 2023
Y2 - 20 May 2023 through 22 May 2023
ER -