Data-Enabled Predictive Control for Flexible Spacecraft

TL;DR

This paper applies data-enabled predictive control (DeePC) to boundary control of flexible spacecraft, leveraging recorded data to bypass explicit modeling, and demonstrates its advantages over traditional Lyapunov-based methods through simulations.

Contribution

It is the first to implement DeePC for flexible spacecraft boundary control, integrating dimension reduction for improved efficiency and performance.

Findings

DeePC outperforms Lyapunov-based control in simulations.

The method effectively handles nonlinear dynamics of flexible spacecraft.

Dimension reduction enhances computational speed.

Abstract

Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints. These appendages pose significant challenges for modeling and control due to their inherent nonlinearity. Data-driven control methods have gained traction to address such challenges. This paper introduces, to the best of the authors' knowledge, the first application of the data-enabled predictive control (DeePC) framework to boundary control for flexible spacecraft. Leveraging the fundamental lemma, DeePC constructs a non-parametric model by utilizing recorded past trajectories, eliminating the need for explicit model development. The developed method also incorporates dimension reduction techniques to enhance computational efficiency. Through comprehensive numerical simulations, this study compares the proposed method with Lyapunov-based control, demonstrating superior performance and offering a thorough evaluation of data-driven control for flexible spacecraft.