Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor
This work addresses constraint enforcement for autonomous spacecraft rendezvous, which is a domain-specific problem, but it appears incremental as it builds on existing Time Shift Governor methods by adding an LSTM for approximation.
The paper tackled the problem of enforcing constraints during autonomous spacecraft rendezvous and docking missions by developing a Time Shift Governor-based control scheme integrated with an LSTM neural network, resulting in reduced computation time for the time shift parameter and successful completion of missions in simulation scenarios like Low Earth Orbit and Molniya orbit.
This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft. This modification of the commanded reference trajectory ensures that constraints are enforced while the time shift is reduced to zero to effect the rendezvous. Our approach to TSG implementation integrates an LSTM neural network which approximates the time shift parameter as a function of a sequence of past Deputy and Chief spacecraft states. This LSTM neural network is trained offline from simulation data. We report simulation results for RD missions in the Low Earth Orbit (LEO) and on the Molniya orbit to demonstrate the effectiveness of the proposed control scheme. The proposed scheme reduces the time to compute the time shift parameter in most of the scenarios and successfully completes rendezvous missions.