Core Concepts
Data-driven framework for global linearization of Two-Body and Circular Restricted Three-Body Problems using Koopman Theory.
Abstract
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.
Stats
Global linearization allows engineers to control satellite systems efficiently.
Deep learning-based Koopman Theory identifies underlying dynamics.
Data-driven methods like Extended Dynamic Mode Decomposition are used for approximation.
Neural Networks with custom architecture approximate Koopman operator.