PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment
This work addresses control challenges for floating platforms in space exploration, representing an incremental improvement by integrating existing methods.
The paper tackled the problem of controlling floating platforms in zero-gravity environments by combining Proximal Policy Optimization (PPO) with Model Predictive Control (MPC), resulting in a resilient control framework validated through simulations and experiments in the Zero-G Lab.
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.