Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
Systems and Control
2024-01-10 v1 Machine Learning
Systems and Control
Dynamical Systems
Optimization and Control
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.
Keywords
Cite
@article{arxiv.2401.04508,
title = {Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study},
author = {Jan C. Schulze and Alexander Mitsos},
journal= {arXiv preprint arXiv:2401.04508},
year = {2024}
}