Robust Intelligent Guidance Method for Distant Retrograde Orbit Station-Keeping

Earth–moon distant retrograde orbits (DROs) periodically circle around the moon with high stability, suitable for the deployment of a cislunar station. However, due to the navigation and control errors and uncertainty in dynamic parameter, the actual trajectory drifts from the nominal DRO. To further improve the on-orbit station-keeping (SK) accuracy, robust guidance methods for DRO SK are provided. For reducing the influences of navigation and control errors, a robust SK point design method based on Lyapunov exponents is developed. Trajectory convergence under initial position and velocity error is studied using an equivalent method. The robust DRO-SK point is designed to perform trajectory guidance periodically. To deal with the uncertainty in dynamic parameter, a model-free uncertainty fitting method is develop based on reinforcement learning (RL). The uncertainty is identified, fitted, and predicted using the nominal and actual trajectories to improve the accuracy of trajectory propagation. The optimal state and reward for RL-based uncertainty fitting are investigated. Furthermore, a double-loop guidance framework for on-orbit guidance is established based on a predictor–corrector guidance method. In the inner loop, the RL-based uncertainty fitting method is employed; in the outer loop, the robust SK point is taken as a periodic SK point for the cislunar station. The performance of the robust guidance method is analyzed through numerical simulations.