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@ARTICLE{Schulze:917604,
      author       = {Schulze, Jan C. and Mitsos, Alexander},
      title        = {{D}ata-{D}riven {N}onlinear {M}odel {R}eduction {U}sing
                      {K}oopman {T}heory: {I}ntegrated {C}ontrol {F}orm and {NMPC}
                      {C}ase {S}tudy},
      journal      = {IEEE control systems letters},
      volume       = {6},
      issn         = {2475-1456},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-00799},
      pages        = {2978 - 2983},
      year         = {2022},
      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.},
      cin          = {IEK-10},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000814618000004},
      doi          = {10.1109/LCSYS.2022.3181443},
      url          = {https://juser.fz-juelich.de/record/917604},
}