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| Home > Publications database > Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study |
| Journal Article | FZJ-2023-00799 |
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2022
IEEE
New York, NY
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Please use a persistent id in citations: doi:10.1109/LCSYS.2022.3181443
Abstract: 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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