8 citations · 19 across the 10 of their papers we have counts for
6 papers · 1 filter
Stability of data-driven Koopman MPC with terminal conditions
Irene Schimperna, Lea Bold, Johannes Köhler +2
This paper derives conditions under which Model Predictive Control (MPC) with terminal conditions, using a data-driven surrogate model as a prediction model, asymptotically stabili…
Excitation of control-affine systems and Koopman error bounds
Philipp Schmitz, Lea Bold, Friedrich M. Philipp +4
The Koopman operator and extended dynamic mode decomposition (EDMD) as a data-driven technique for its approximation have attracted considerable attention as a key tool for modelin…
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…
Data-driven Model Predictive Control: Asymptotic Stability despite Approximation Errors exemplified in the Koopman framework
Irene Schimperna, Karl Worthmann, Manuel Schaller +2
In this paper, we analyze stability of nonlinear model predictive control (MPC) using data-driven surrogate models in the optimization step. First, we establish asymptotic stabilit…
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…