MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 35 references that are in the map. First posted on arXiv 25 Sep 2025.
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6 of 35 references.
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Same direction, newest first
Other references · 5
- 2017Proximal Policy Optimization Algorithmscited by 14,592
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,656
- 2023UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidancecited by 4
- 2024PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environmentcited by 4
- 2023Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumptioncited by 3
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Citing papers per year: 2026: 2.
2 papers.