Abstract
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.
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Acknowledgments
This paper is partially supported by National Natural Science Foundation of China (Grant No. 61703276), National Defense Science and Technology Funds for Excellent Young Scholar (Grant No. 2017-JCJQ-ZQ-034), High-level Innovation and Entrepreneurship Talents Introduction Program of Jiangsu Province of China (2019) and NUAA Scientific Research and Practice Innovation Program (Grant No. Xcxjh20211503).
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Shu, R., Jia, Q., Gui, Y., Li, H. (2023). Adaptive Radial Basis Function Neural Network-Based Active Fault-Tolerant Control for Spacecraft Formation Flying System. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_15
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DOI: https://doi.org/10.1007/978-981-19-6613-2_15
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