Model Predictive Control for Magnetically-Actuated Cellbots
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913520616996864 |
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| author | Kermanshah, Mehdi Beaver, Logan E. Sokolich, Max Kirmizitas, Fatma Ceren Das, Sambeeta Tron, Roberto Weiss, Ron Belta, Calin |
| author_facet | Kermanshah, Mehdi Beaver, Logan E. Sokolich, Max Kirmizitas, Fatma Ceren Das, Sambeeta Tron, Roberto Weiss, Ron Belta, Calin |
| contents | This paper presents a control framework for magnetically actuated cellbots, which combines Model Predictive Control (MPC) with Gaussian Processes (GPs) as a disturbance estimator for precise trajectory tracking. To address the challenges posed by unmodeled dynamics, we integrate data-driven modeling with model-based control to accurately track desired trajectories using relatively small data. To the best of our knowledge, this is the first work to integrate data-driven modeling with model-based control for the magnetic actuation of cellbots. The GP effectively learns and predicts unmodeled disturbances, providing uncertainty bounds as well. We validate our method through experiments with cellbots, demonstrating improved trajectory tracking accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Predictive Control for Magnetically-Actuated Cellbots Kermanshah, Mehdi Beaver, Logan E. Sokolich, Max Kirmizitas, Fatma Ceren Das, Sambeeta Tron, Roberto Weiss, Ron Belta, Calin Robotics This paper presents a control framework for magnetically actuated cellbots, which combines Model Predictive Control (MPC) with Gaussian Processes (GPs) as a disturbance estimator for precise trajectory tracking. To address the challenges posed by unmodeled dynamics, we integrate data-driven modeling with model-based control to accurately track desired trajectories using relatively small data. To the best of our knowledge, this is the first work to integrate data-driven modeling with model-based control for the magnetic actuation of cellbots. The GP effectively learns and predicts unmodeled disturbances, providing uncertainty bounds as well. We validate our method through experiments with cellbots, demonstrating improved trajectory tracking accuracy. |
| title | Model Predictive Control for Magnetically-Actuated Cellbots |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.02722 |