Koopman Operator-Based Online Parameter Uncertainty Propagation in Nonlinear Dynamics

A method for uncertainty propagation based on the Koopman operator is proposed for nonlinear dynamics with parameter uncertainties. In the context of attitude dynamics with inertia moment uncertainties, the Koopman operator is approximated via the Galerkin method, and high-dimensional linear state transitions are computed for rapid trajectory extrapolation. The distribution and statistical characteristics of the state variables are obtained through Monte Carlo simulations. Finally, numerical simulations of a space target tumbling with random inertia moment identification results demonstrate that the proposed method can effectively and accurately propagate parameter uncertainties, indicating potential for online applications.