TY  - JOUR
AU  - Schulze, Jan C.
AU  - Mitsos, Alexander
TI  - Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study
JO  - IEEE control systems letters
VL  - 6
SN  - 2475-1456
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2023-00799
SP  - 2978 - 2983
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000814618000004
DO  - DOI:10.1109/LCSYS.2022.3181443
UR  - https://juser.fz-juelich.de/record/917604
ER  -