Spacecraft Proximity Operations in Elliptic Orbits

Vehicle Optimization, Dynamics, Control and Autonomy Lab

University of Michigan, MI, USA

PI: Professor Anouck Girard and Professor Ilya Kolmanovsky

Achievement:

(1) Taehyeun Kim, Robin Inho Kee * (: Equal contribution), Ilya Kolmanovsky, Anouck Girard, “Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Accelerated Time Shift Governor”, AIAA SCITECH 2025 Forum

Project: Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Accelerated Time Shift Governor

What I did:

(1) Developed an constraint-guided Learning-basted Time Shift Governor (L-TSG) for spacecraft rendezvous and docking in the Low Earth orbit and the elliptical orbit.

(2) Implemented a phase-adaptive sliding window and a constraint-specific loss function to optimize handling of dynamic input sizes, ensuring precise time shift predictions and adherence to stringent space mission constraints.

Abstract:

Our work 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.