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eess.SY2025
Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Jan C. Schulze, Alexander Mitsos
Computationally cheap yet accurate dynamical models are a key requirement for real-time capable nonlinear optimization and model-based control. When given a computationally expensi…
eess.SY2024★ 17 cited
Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
Jan C. Schulze, Alexander Mitsos
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 measu…
eess.SY2023
Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Jan C. Schulze, Danimir T. Doncevic, Nils Erwes +1
Achieving real-time capability is an essential prerequisite for the industrial implementation of nonlinear model predictive control (NMPC). Data-driven model reduction offers a way…