%0 Journal Article
%A Schulze, Jan C.
%A Mitsos, Alexander
%T Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study
%J IEEE control systems letters
%V 6
%@ 2475-1456
%C New York, NY
%I IEEE
%M FZJ-2023-00799
%P 2978 - 2983
%D 2022
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000814618000004
%R 10.1109/LCSYS.2022.3181443
%U https://juser.fz-juelich.de/record/917604