Autonomous contingency station-keeping for lunar halo orbits via reinforcement meta-learning

This work considers the problem of autonomous contingency station-keeping in lunar halo orbits under engine failures and communication delays with the Earth. The objective is to develop a control design methodology that autonomously maps observations to control actions, enabling the spacecraft to return to its nominal orbit while adapting to errors in both the magnitude and direction of thrust. We investigate various measurement sets that include direction, range, and radial velocity with respect to the Moon. The proposed control function is parameterized by a neural network and optimized via reinforcement meta-learning methods, which are well suited to handling uncertainty and contingencies. This approach is applicable to missions in both unstable and stable halo orbits associated with the L1 and L2. The results include performance analysis of the designed control functions, characteristic velocities required for station-keeping, and station-keeping precision. Findings are presented for both unstable and nearly stable halo orbits that are used in real missions.