9 papers
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…
Exponential stability of data-driven nonlinear MPC based on input/output models
Lea Bold, Irene Schimperna, Karl Worthmann +1
We consider nonlinear model predictive control (MPC) schemes without stabilizing terminal conditions, where the model used in the optimization step is generated based on input-outp…
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…
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…
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…