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

Jan C. Schulze, Alexander Mitsos
1/9/2024
eess.SYcs.LGmath.DS

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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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Cite this paper

@article{schulze2024datadriven,
  title  = {Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study},
  author = {Jan C. Schulze and Alexander Mitsos},
  year   = {2024},
  eprint = {2401.04508},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.04508v1}
}

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