45 citations · 147 across the 71 of their papers we have counts for
16 papers · 1 filter
Error analysis of kernel EDMD for prediction and control in the Koopman framework
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is…
Safe data-driven reference tracking with prescribed performance
Philipp Schmitz, Lukas Lanza, Karl Worthmann
We study output reference tracking for unknown continuous-time systems with arbitrary relative degree. The control objective is to keep the tracking error within predefined time-va…
On model predictive control with sampled-data input for output tracking with prescribed performance
Dario Dennstädt, Lukas Lanza, Karl Worthmann
We propose a model predictive control (MPC) scheme with sampled-data input which ensures output-reference tracking within prescribed error bounds for relative-degree-one systems. H…
Koopman-based feedback design with stability guarantees
Robin Strässer, Manuel Schaller, Karl Worthmann +2
We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman-operator…
Alleviating the Curse of Dimensionality in Minkowski Sum Approximations of Storage Flexibility
Emrah Öztürk, Timm Faulwasser, Karl Worthmann +2
Many real-world applications require the joint optimization of a large number of flexible devices over time. The flexibility of, e.g., multiple batteries, thermostatically controll…
Data-driven MPC with stability guarantees using extended dynamic mode decomposition
Lea Bold, Lars Grüne, Manuel Schaller +1
For nonlinear (control) systems, extended dynamic mode decomposition (EDMD) is a popular method to obtain data-driven surrogate models. Its theoretical foundation is the Koopman fr…