Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study

IEEE Control Systems Letters, vol. 6, pp. 2978–2983

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.

Authors 2

1. Jan C. Schulze Aachen Department of Mechanical Engineering Chair of Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Department of Mechanical Engineering, Chair of Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

2. Alexander Mitsos Aachen Department of Mechanical Engineering Chair of Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Department of Mechanical Engineering, Chair of Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany