PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment

TL;DR

This paper presents a novel control method combining reinforcement learning and predictive control to manage uncertain floating platforms in zero-gravity, validated through simulations and experiments in a space environment.

Contribution

It introduces a PPO-MPC hybrid control framework specifically designed for zero-gravity floating platforms, enhancing adaptability and robustness over traditional methods.

Findings

PPO-MPC outperforms traditional control in uncertain conditions

The approach adapts to unmodeled dynamics and disturbances

Validated through simulations and Zero-G Lab experiments

Abstract

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.