The study focuses on the importance of understanding celestial and artificial satellite motion in aerospace engineering. It proposes a data-driven framework for system identification and global linearization of orbital problems using deep learning-based Koopman Theory. The method can accurately learn the dynamics of Two-Body and Circular Restricted Three-Body Problems, showcasing its ability to generalize to various systems without retraining. The approach aims to simplify control systems for satellites by achieving a globally linear representation of their dynamics.
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by George Nehma... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.08965.pdfDeeper Inquiries