Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909066571284480 |
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| author | Schulze, Jan C. Mitsos, Alexander |
| author_facet | Schulze, Jan C. Mitsos, Alexander |
| contents | We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. We present a deep-learning approach to train the proposed models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04508 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study Schulze, Jan C. Mitsos, Alexander Systems and Control Machine Learning Dynamical Systems Optimization and Control We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. We present a deep-learning approach to train the proposed models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column. |
| title | Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study |
| topic | Systems and Control Machine Learning Dynamical Systems Optimization and Control |
| url | https://arxiv.org/abs/2401.04508 |