Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties

What the math gives to ML

The paper combines physics-informed observable lifting, finite-dimensional Koopman linearization, online adaptation, and receding-horizon quadratic programming for nonlinear attitude dynamics with abruptly changing inertia and disturbances. Its transferable asset is the mechanism of representing nonlinear dynamics in a lifted coordinate system whose evolution is approximately linear, then updating the lifted dynamics online when the data distribution changes. For neural sequence models and world models, this suggests a latent state-space architecture with explicit linear multi-step propagation, physics-inspired observables, and recursive adaptation of the latent transition operator. The key falsifiable signatures are a measurable spectral stability boundary for long rollouts and an adaptation time scale determined by the forgetting factor.