Robust Model Predictive Control for Autonomous Spacecraft Close-Proximity Operations Around an Asteroid

To address the robustness of autonomous proximity trajectories in asteroid exploration missions under model uncertainties and external disturbances, this paper proposes a tube-based model predictive control (TBMPC) framework with disturbance identification for a six-degree-of-freedom nonlinear model. Specifically, the inner layer employs a sequential convex optimization-based nonlinear MPC framework to solve the nominal trajectory optimization problem, while the outer layer dynamically estimates the disturbance set using real-time measurement information through an online exogenous input identification mechanism and adaptively adjusts the size of the disturbance-invariant tube, thereby effectively reducing the conservatism caused by the fixed disturbance bounds in conventional TBMPC. In addition, a sensitivity analysis of the forgetting factor parameter is conducted to investigate the influence of different forgetting factor values on system performance. Finally, 100 Monte Carlo simulations are performed to further verify the robustness and stability of the proposed method under randomly bounded disturbances. The results show that all actual trajectories remain within the disturbance-invariant tube, demonstrating the good engineering applicability of the proposed method.