Self-learning for translational control of elliptical orbit spacecraft formations

• Published in: Aircraft engineering and aerospace technology Vol. 96; no. 6; pp. 818 - 825
• Main Authors: Lu, Weijia, Zhang, Chengxi, Liu, Fei, Wu, Jin, Wang, Jihe, Tan, Lining
• Format: Journal Article
• Language: English
• Published: Emerald Publishing Limited 07.08.2024
• Subjects: Self-learning control; Spacecraft formation flying; Variable learning intensity
• ISSN: 1748-8842; 1748-8842
• DOI: 10.1108/AEAT-01-2024-0020

Purpose This paper aims to investigate the relative translational control for multiple spacecraft formation flying. This paper proposes an engineering-friendly, structurally simple, fast and model-free control algorithm. Design/methodology/approach This paper proposes a tanh-type self-learning control (SLC) approach with variable learning intensity (VLI) to guarantee global convergence of the tracking error. This control algorithm utilizes the controller's previous control information in addition to the current system state information and avoids complicating the control structure. Findings The proposed approach is model-free and can obtain the control law without accurate modeling of the spacecraft formation dynamics. The tanh function can tune the magnitude of the learning intensity to reduce the control saturation behavior when the tracking error is large. Practical implications This algorithm is model-free, robust to perturbations such as disturbances and system uncertainties, and has a simple structure that is very conducive to engineering applications. Originality/value This paper verified the control performance of the proposed algorithm for spacecraft formation in the presence of disturbances by simulation and achieved high steady-state accuracy and response speed over comparisons.