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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16624 |
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| _version_ | 1866909968900292608 |
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| author | Benazet, Mark Ricca, Francesco Bralla, Dario Zeilinger, Melanie N. Carron, Andrea |
| author_facet | Benazet, Mark Ricca, Francesco Bralla, Dario Zeilinger, Melanie N. Carron, Andrea |
| contents | Learning-based model predictive control has emerged as a powerful approach for handling complex dynamics in mechatronic systems, enabling data-driven performance improvements while respecting safety constraints. However, when computational resources are severely limited, as in battery-powered tools with embedded processors, existing approaches struggle to meet real-time requirements. In this paper, we address the problem of real-time torque control for impact wrenches, where high-frequency control updates are necessary to accurately track the fast transients occurring during periodic impact events, while maintaining high-performance safety-critical control that mitigates harmful vibrations and component wear. The key novelty of the approach is that we combine data-driven model augmentation through Gaussian process regression with neural network approximation of the resulting control policy. This insight allows us to deploy predictive control on resource-constrained embedded platforms while maintaining both constraint satisfaction and microsecond-level inference times. The proposed framework is evaluated through numerical simulations and hardware experiments on a custom impact wrench testbed. The results show that our approach successfully achieves real-time control suitable for high-frequency operation while maintaining constraint satisfaction and improving tracking accuracy compared to baseline PID control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning-based Approximate Model Predictive Control for an Impact Wrench Tool Benazet, Mark Ricca, Francesco Bralla, Dario Zeilinger, Melanie N. Carron, Andrea Systems and Control Learning-based model predictive control has emerged as a powerful approach for handling complex dynamics in mechatronic systems, enabling data-driven performance improvements while respecting safety constraints. However, when computational resources are severely limited, as in battery-powered tools with embedded processors, existing approaches struggle to meet real-time requirements. In this paper, we address the problem of real-time torque control for impact wrenches, where high-frequency control updates are necessary to accurately track the fast transients occurring during periodic impact events, while maintaining high-performance safety-critical control that mitigates harmful vibrations and component wear. The key novelty of the approach is that we combine data-driven model augmentation through Gaussian process regression with neural network approximation of the resulting control policy. This insight allows us to deploy predictive control on resource-constrained embedded platforms while maintaining both constraint satisfaction and microsecond-level inference times. The proposed framework is evaluated through numerical simulations and hardware experiments on a custom impact wrench testbed. The results show that our approach successfully achieves real-time control suitable for high-frequency operation while maintaining constraint satisfaction and improving tracking accuracy compared to baseline PID control. |
| title | Learning-based Approximate Model Predictive Control for an Impact Wrench Tool |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.16624 |