Self-learning for translational control of elliptical orbit spacecraft formations

Abstract

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.

Citation

Lu, W., Zhang, C., Liu, F., Wu, J., Wang, J. and Tan, L. (2024), "Self-learning for translational control of elliptical orbit spacecraft formations", Aircraft Engineering and Aerospace Technology, Vol. 96 No. 6, pp. 818-825. https://doi.org/10.1108/AEAT-01-2024-0020