Model Predictive Path Integral Control for Roll-to-Roll Manufacturing
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909993900441600 |
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| author | Martin, Christopher Patil, Apurva Li, Wei Tanaka, Takashi Chen, Dongmei |
| author_facet | Martin, Christopher Patil, Apurva Li, Wei Tanaka, Takashi Chen, Dongmei |
| contents | Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains challenging due to subsystem interactions, nonlinearities, and process disturbances. This paper proposes a Model Predictive Path Integral (MPPI) control formulation for R2R systems, leveraging a GPU-based Monte-Carlo sampling approach to efficiently approximate optimal controls online. Crucially, MPPI easily handles non-differentiable cost functions, enabling the incorporation of complex performance criteria relevant to advanced manufacturing processes. A case study is presented that demonstrates that MPPI significantly improves tension regulation performance compared to conventional model predictive control (MPC), highlighting its suitability for real-time control in advanced manufacturing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Predictive Path Integral Control for Roll-to-Roll Manufacturing Martin, Christopher Patil, Apurva Li, Wei Tanaka, Takashi Chen, Dongmei Systems and Control Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains challenging due to subsystem interactions, nonlinearities, and process disturbances. This paper proposes a Model Predictive Path Integral (MPPI) control formulation for R2R systems, leveraging a GPU-based Monte-Carlo sampling approach to efficiently approximate optimal controls online. Crucially, MPPI easily handles non-differentiable cost functions, enabling the incorporation of complex performance criteria relevant to advanced manufacturing processes. A case study is presented that demonstrates that MPPI significantly improves tension regulation performance compared to conventional model predictive control (MPC), highlighting its suitability for real-time control in advanced manufacturing. |
| title | Model Predictive Path Integral Control for Roll-to-Roll Manufacturing |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.06547 |