  We propose a two-component data-driven controller to safely perform docking
maneuvers for satellites. Reinforcement Learning is used to deduce an optimal
control policy based on measurement data. To safeguard the learning phase, an
additional feedback law is implemented in the control unit, which guarantees
the evolution of the system within predefined performance bounds. We define
safe and safety-critical areas to train the feedback controller based on actual
measurements. To avoid chattering, a dwell-time activation scheme is
implemented. We provide numerical evidence for the performance of the proposed
controller for a satellite docking maneuver with collision avoidance.
