MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
This addresses fuel-efficient and disturbance-resilient control for satellite docking, advancing on-orbit refueling missions, but is incremental as it combines existing methods.
The paper tackles autonomous satellite docking with fuel sloshing by integrating reinforcement learning with model predictive control, achieving superior docking accuracy and higher success rates in simulations.
This paper presents an integrated Reinforcement Learning (RL) and Model Predictive Control (MPC) framework for autonomous satellite docking with a partially filled fuel tank. Traditional docking control faces challenges due to fuel sloshing in microgravity, which induces unpredictable forces affecting stability. To address this, we integrate Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) RL algorithms with MPC, leveraging MPC's predictive capabilities to accelerate RL training and improve control robustness. The proposed approach is validated through Zero-G Lab of SnT experiments for planar stabilization and high-fidelity numerical simulations for 6-DOF docking with fuel sloshing dynamics. Simulation results demonstrate that SAC-MPC achieves superior docking accuracy, higher success rates, and lower control effort, outperforming standalone RL and PPO-MPC methods. This study advances fuel-efficient and disturbance-resilient satellite docking, enhancing the feasibility of on-orbit refueling and servicing missions.