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Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

Genghuan Li, Qingxian Jia, Yunhua Wu, He Liao, Zhong Zheng

Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Article publication date: 11 December 2024

Issue publication date: 30 January 2025

56

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

Copyright © 2024, Emerald Publishing Limited

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