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000917604 1001_ $$0P:(DE-HGF)0$$aSchulze, Jan C.$$b0
000917604 245__ $$aData-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study
000917604 260__ $$aNew York, NY$$bIEEE$$c2022
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000917604 520__ $$aWe 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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000917604 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b1$$eCorresponding author$$ufzj
000917604 773__ $$0PERI:(DE-600)2894957-2$$a10.1109/LCSYS.2022.3181443$$gVol. 6, p. 2978 - 2983$$p2978 - 2983$$tIEEE control systems letters$$v6$$x2475-1456$$y2022
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