PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913416075018240 |
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| author | Ramezani, Mahya Alandihallaj, M. Amin Hein, Andreas M. |
| author_facet | Ramezani, Mahya Alandihallaj, M. Amin Hein, Andreas M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment Ramezani, Mahya Alandihallaj, M. Amin Hein, Andreas M. Robotics Artificial Intelligence Systems and Control 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. |
| title | PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2407.03224 |