Title: Iterative learning algorithms-based multiplicative thrust fault reconstruction and tolerant control for spacecraft formation flying systems

Authors: Yule Gui; Qingxian Jia; Huayi Li; Zhong Zheng

Addresses: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ' Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150000, China ' College of Transportation Engineering, Nanjing Tech University, Nanjing 210016, China

Abstract: In this paper, the issues of multiplicative thruster fault reconstruction and reconfigurable fault-tolerant control for spacecraft formation flying system subject to loss of thruster effectiveness and a series of external space perturbations are investigated using iterative learning algorithms. Inspired by sliding mode methodology, a new robust iterative learning observer (RILO) is explored to reconstruct thrust effectiveness factor. Subsequently, a learning state-feedback fault-tolerant control approach is proposed based on the fault signals obtained from the RILO to guarantee the closed-loop spacecraft formation configuration is accurately maintained in the presence of multiplicative thrust faults and space perturbations. Finally, numerical simulations clearly validate the effectiveness and superiority of the proposed thrust fault-reconstructing and tolerant configuration maintenance control schemes for spacecraft formation flying systems.

Keywords: spacecraft formation; fault reconstruction; iterative learning observer; fault-tolerant control; learning state-feedback control.

DOI: 10.1504/IJAAC.2023.130563

International Journal of Automation and Control, 2023 Vol.17 No.3, pp.249 - 266

Received: 09 Nov 2021
Accepted: 10 Mar 2022
Published online: 28 Apr 2023 *

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