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

Autor:	Ramezani, Mahya, Alandihallaj, M. Amin, Hein, Andreas M.
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Pre-print version submitted to 2024 International Conference on Robotics and Automation (ICRA)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.03224 Zobrazit plný text záznamu View this record from Arxiv