# MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation

arXiv: https://arxiv.org/abs/2509.21045 · HTML: https://mlsanity.com/paper/2509.21045/

## Key facts

- Cited by 2 papers in the map (data through 14 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance](https://mlsanity.com/paper/2302.10669/) (2023)
- [PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment](https://mlsanity.com/paper/2407.03224/) (2024)
- [Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption](https://mlsanity.com/paper/2309.12004/) (2023)
- [Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05996/) (2025)

## Built on by (most cited first, 2 of 2)

- [Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration](https://mlsanity.com/paper/2601.12952/) (2026)
- [Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training](https://mlsanity.com/paper/2604.26833/) (2026)

## 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.