TY - GEN
T1 - Adaptive Radial Basis Function Neural Network-Based Active Fault-Tolerant Control for Spacecraft Formation Flying System
AU - Shu, Rui
AU - Jia, Qinxian
AU - Gui, Yule
AU - Li, Huayi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - In this work, actuator fault reconstruction and Fault-Tolerant Control (FTC) of Spacecraft Formation Flying (SFF) system subjects to space perturbation and actuator faults is investigated based on adaptive Radial Basis Function Neural Network (RBFNN) and adaptive sliding mode control. First, establish Lipschitz nonlinear motion model of the SFF system; then an adaptive RBFNN estimator is introduced to accurately evaluate the actuator faults. Based on the reconstructed fault signals, an adaptive neural sliding mode FTC algorithm is developed to realize the tracking of the desired formation trajectory. At last, a simulation instance is given to prove the performance and feasibility of the presented fault reconstruction and FTC strategy.
AB - In this work, actuator fault reconstruction and Fault-Tolerant Control (FTC) of Spacecraft Formation Flying (SFF) system subjects to space perturbation and actuator faults is investigated based on adaptive Radial Basis Function Neural Network (RBFNN) and adaptive sliding mode control. First, establish Lipschitz nonlinear motion model of the SFF system; then an adaptive RBFNN estimator is introduced to accurately evaluate the actuator faults. Based on the reconstructed fault signals, an adaptive neural sliding mode FTC algorithm is developed to realize the tracking of the desired formation trajectory. At last, a simulation instance is given to prove the performance and feasibility of the presented fault reconstruction and FTC strategy.
KW - Adaptive neural sliding mode control
KW - Adaptive radial basis function neural network
KW - Fault reconstruction
KW - Fault tolerant control
UR - https://www.scopus.com/pages/publications/85151141828
U2 - 10.1007/978-981-19-6613-2_15
DO - 10.1007/978-981-19-6613-2_15
M3 - 会议稿件
AN - SCOPUS:85151141828
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 134
EP - 143
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -