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arXiv · 2401.04508

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study

Jan C. Schulze·Alexander Mitsos
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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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Jan C. Schulze, Alexander Mitsos. 2024-01-09. Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study. https://doi.org/10.1109/lcsys.2022.3181443

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