Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study
Jan C. Schulze, Alexander Mitsos
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.
Topics & Concepts
Reduction (mathematics)Nonlinear systemNonlinear modelControl theory (sociology)Computer scienceControl (management)MathematicsPhysicsArtificial intelligenceQuantum mechanicsGeometryModel Reduction and Neural NetworksControl Systems and IdentificationProbabilistic and Robust Engineering Design