  A convex optimization-based model predictive control (MPC) algorithm for the
guidance of active debris removal (ADR) missions is proposed in this work. A
high-accuracy reference for the convex optimization is obtained through a
split-Edelbaum approach that takes the effects of J2, drag, and eclipses into
account. When the spacecraft deviates significantly from the reference
trajectory, a new reference is calculated through the same method to reach the
target debris. When required, phasing is integrated into the transfer. During
the mission, the phase of the spacecraft is adjusted to match that of the
target debris at the end of the transfer by introducing intermediate waiting
times. The robustness of the guidance scheme is tested in a high-fidelity
dynamical model that includes thrust errors and misthrust events. The guidance
algorithm performs well without requiring successive convex iterations.
Monte-Carlo simulations are conducted to analyze the impact of these thrust
uncertainties on the guidance. Simulation results show that the proposed
convex-MPC approach can ensure that the spacecraft can reach its target despite
significant uncertainties and long-duration misthrust events.

Optimization and Control