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

TL;DR

This paper introduces a learning-based control scheme using a Time Shift Governor with an LSTM neural network to enforce constraints during spacecraft rendezvous, improving computational efficiency and mission success.

Contribution

It presents a novel TSG control scheme with an LSTM neural network to adaptively generate reference trajectories for constrained spacecraft rendezvous.

Findings

Successfully enforces constraints during rendezvous

Reduces computation time for time shift parameter

Achieves successful rendezvous in LEO and Molniya orbits

Abstract

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.