In the field of space exploration, floating platforms play a crucial role in scientific investigations and technological advancements. However, controlling these platforms in zero-gravity environments presents unique challenges, including uncertainties and disturbances. This paper introduces an innovative approach that combines Proximal Policy Optimization (PPO) with Model Predictive Control (MPC) in the zero-gravity laboratory (Zero-G Lab) at the University of Luxembourg. This approach leverages PPO's reinforcement learning power and MPC's precision to navigate the complex control dynamics of floating platforms. Unlike traditional control methods, this PPO-MPC approach learns from MPC predictions, adapting to unmodeled dynamics and disturbances, resulting in a resilient control framework tailored to the zero-gravity environment. Simulations and experiments in the Zero-G Lab validate this approach, showcasing the adaptability of the PPO agent. This research opens new possibilities for controlling floating platforms in zero-gravity settings, promising advancements in space exploration.

翻译：在空间探索领域，浮动平台在科学研究和科技进步中发挥着关键作用。然而，在零重力环境下控制这些平台面临着独特的挑战，包括不确定性和扰动。本文提出了一种创新方法，将近似策略优化（PPO）与模型预测控制（MPC）相结合，应用于卢森堡大学的零重力实验室（Zero-G Lab）。该方法利用PPO的强化学习能力和MPC的精确性，以应对浮动平台复杂的控制动力学。与传统控制方法不同，这种PPO-MPC方法从MPC预测中学习，适应未建模动力学和扰动，从而构建了一个专为零重力环境设计的鲁棒控制框架。在Zero-G Lab进行的仿真和实验验证了该方法的有效性，展示了PPO智能体的适应能力。这项研究为零重力环境下浮动平台的控制开辟了新的可能性，有望推动空间探索的进步。