Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach
Aircraft Engineering and Aerospace Technology
ISSN: 0002-2667
Article publication date: 11 December 2024
Issue publication date: 30 January 2025
Abstract
Purpose
This paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication resources.
Design/methodology/approach
The authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication constraints.
Findings
The proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization accuracy.
Practical implications
This method requires the low communication resources, has a high control accuracy and is thus suitable for engineering applications.
Originality/value
A novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.
Keywords
Acknowledgements
This study was funded by State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster (Grant No. MS01240109).
Citation
Li, G., Jia, Q., Wu, Y., Liao, H. and Zheng, Z. (2025), "Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach", Aircraft Engineering and Aerospace Technology, Vol. 97 No. 2, pp. 162-169. https://doi.org/10.1108/AEAT-06-2024-0188
Publisher
:Emerald Publishing Limited
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