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Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning neural network control approach.
- Source :
-
Aerospace Science & Technology . Nov2023:Part A, Vol. 142, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- This paper addresses the robust attitude synchronization issue in a multi-spacecraft formation system subjected to limited communication, space disturbances, modeling uncertainties, and actuator faults. To accommodate limited inter-spacecraft communication, a dynamic event-triggered mechanism is designed to reduce the communication trigger frequency by dynamically adjusting the trigger threshold. Moreover, an event-based distributed self-learning neural-network control (SLN2C) law is developed to guarantee robust attitude synchronization during multi-spacecraft formation. In the SLN2C scheme, a learning radial basis function neural network (RBFNN) model is proposed to online approximate and compensate for lumped disturbances, in which an iterative learning algorithm with a variable learning intensity is adopted to update the weight matrix of the RBFNN model. Compared with the traditional fixed learning intensity, a variable one can reduce initial oscillation and weaken the saturation response. Numerical simulations and comparisons are performed to illustrate the effectiveness and superiority of the proposed event-based spacecraft attitude synchronization control method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 12709638
- Volume :
- 142
- Database :
- Academic Search Index
- Journal :
- Aerospace Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 173889286
- Full Text :
- https://doi.org/10.1016/j.ast.2023.108653