11 papers · 1 filter
A constraint dissolving inexact penalty method for optimization problems with geometric constraints
Xiaoxi Jia, Leander Lerch, Stefan Streif +1
Optimization problems with geometric constraints have a broad range of applications, including machine learning, finance, and control. A powerful algorithmic tool to resolve these…
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…
Spatial exponential decay of perturbations in optimal control of general evolution equations
Simone Göttlich, Benedikt Oppeneiger, Manuel Schaller +1
We analyze the robustness of optimally controlled evolution equations with respect to spatially localized perturbations. We prove that if the involved operators are domain-uniforml…
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…
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…
Spatial decay of perturbations in hyperbolic equations with optimal boundary control
Benedikt Oppeneiger, Manuel Schaller, Karl Worthmann
Recently, domain-uniform stabilizability and detectability has been the central assumption %in order robustness results on the to ensure robustness in the sense of exponential deca…