1. Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning neural network control approach.
- Author
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Jia, Qingxian, Gao, Junnan, Zhang, Chengxi, Ahn, Choon Ki, and Yu, Dan
- Subjects
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MACHINE learning , *ARTIFICIAL satellite attitude control systems , *RADIAL basis functions , *SYNCHRONIZATION , *AUTODIDACTICISM - 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]
- Published
- 2023
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