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| 001 | 917604 | ||
| 005 | 20240712112858.0 | ||
| 024 | 7 | _ | |a 10.1109/LCSYS.2022.3181443 |2 doi |
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| 037 | _ | _ | |a FZJ-2023-00799 |
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| 100 | 1 | _ | |a Schulze, Jan C. |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study |
| 260 | _ | _ | |a New York, NY |c 2022 |b IEEE |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Mitsos, Alexander |0 P:(DE-Juel1)172025 |b 1 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1109/LCSYS.2022.3181443 |g Vol. 6, p. 2978 - 2983 |0 PERI:(DE-600)2894957-2 |p 2978 - 2983 |t IEEE control systems letters |v 6 |y 2022 |x 2475-1456 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/917604/files/Schulze_2022_postreferee_copyright.pdf |y Restricted |
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