Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study

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.

References in corpus (6)

• A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
• Deep learning for universal linear embeddings of nonlinear dynamics
• Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
• Chaos as an Intermittently Forced Linear System
• Delay-coordinate maps and the spectra of Koopman operators
• Identification of MIMO Wiener-type Koopman Models for Data-Driven Model Reduction using Deep Learning