4 papers · 1 filter
Kernel-based Koopman approximants for control: Flexible sampling, error analysis, and stability
Lea Bold, Friedrich M. Philipp, Manuel Schaller +1
Data-driven techniques for analysis, modeling, and control of complex dynamical systems are on the uptake. Koopman theory provides the theoretical foundation for the popular kernel…
Two-component controller design to safeguard data-driven predictive control
Lea Bold, Lukas Lanza, Karl Worthmann
We design a two-component controller to achieve reference tracking with output constraints - exemplified on systems of relative degree two. One component is a data-driven or learni…
Offset-free Nonlinear MPC with Koopman-based Surrogate Models
Irene Schimperna, Lea Bold, Karl Worthmann
In this paper, we design offset-free nonlinear Model Predictive Control (MPC) for surrogate models based on Extended Dynamic Mode Decomposition (EDMD). The model used for predictio…
Kernel EDMD for data-driven nonlinear Koopman MPC with stability guarantees
Lea Bold, Manuel Schaller, Irene Schimperna +1
Extended dynamic mode decomposition (EDMD) is a popular data-driven method to predict the action of the Koopman operator, i.e., the evolution of an observable function along the fl…