Search for: All records

This paper illustrates an approach to integrate learning into spacecraft automated rendezvous, proximity maneuvering, and docking (ARPOD) operations. Spacecraft rendezvous plays a significant role in many spacecraft missions including orbital transfers, ISS re-supply, on-orbit refueling and servicing, and debris removal. On one hand, precise modeling and prediction of spacecraft dynamics can be challenging due to the uncertainties and perturbation forces in the spacecraft operating environment and due to multi-layered structure of its nominal control system. On the other hand, spacecraft maneuvers need to satisfy required constraints (thrust limits, line of sight cone constraints, relative velocity of approach, etc.) to ensure safety and achieve ARPOD objectives. This paper considers an application of a learning-based reference governor (LRG) to enforce constraints without relying on a dynamic model of the spacecraft during the mission. Similar to the conventional Reference Governor (RG), the LRG is an add-on supervisor to a closed-loop control system, serving as a pre-filter on the command generated by the ARPOD planner. As the RG, LRG modifies, if it becomes necessary, the command to a constraint-admissible reference to enforce specified constraints. The LRG is distinguished, however, by the ability to rely on learning instead of an explicit model of the system, and guarantees constraints satisfaction during and after the learning. Simulations of spacecraft constrained relative motion maneuvers on a low Earth orbit are reported that demonstrate the effectiveness of the proposed approach.