1. Robust Active Fault-Tolerant Configuration Control for Spacecraft Formation via Learning RBFNN Approaches.
- Author
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Shu, Rui, Jia, Qingxian, Wu, Yunhua, Liao, He, and Zhang, Chengxi
- Subjects
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FAULT-tolerant control systems , *MACHINE learning , *FORMATION flying , *RADIAL basis functions , *ARTIFICIAL satellite attitude control systems - Abstract
This paper studies the issue of learning radial basis function neural network (RBFNN)-based robust reconfigurable fault-tolerant configuration control for spacecraft formation flying (SFF) systems subject to thruster faults and space perturbations. To robustly reconstruct thruster faults, a novel learning RBFNN estimator is innovatively explored, in which the P-type iterative learning algorithm is utilized to online update the weight matrix of the RBFNN model and the H∞ control technique is adopted to attenuate the effect of space perturbations. Further, a learning RBFNN output-feedback fault-tolerant control (FTC) method is developed for spacecraft formation configuration maintenance with high accuracy, and the learning RBFNN algorithm is used to update and compensate the synthesized perturbation. Finally, a numerical example is simulated to verify the presented learning RBFNN-based spacecraft formation FTC approach is feasible and superior. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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