274 citations · 1k across the 43 of their papers we have counts for
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Explicit multiobjective model predictive control for nonlinear systems with symmetries
Sina Ober-Blöbaum, Sebastian Peitz
Model predictive control is a prominent approach to construct a feedback control loop for dynamical systems. Due to real-time constraints, the major challenge in MPC is to solve mo…
Feedback control of nonlinear PDEs using data-efficient reduced order models based on the Koopman operator
Sebastian Peitz, Stefan Klus
In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linea…
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
Stefan Klus, Sebastian Peitz, Ingmar Schuster
Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are…
Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives
Sören Hanke, Sebastian Peitz, Oliver Wallscheid +3
Predictive control of power electronic systems always requires a suitable model of the plant. Using typical physics-based white box models, a trade-off between model complexity (i.…
On the hierarchical structure of Pareto critical sets
Bennet Gebken, Sebastian Peitz, Michael Dellnitz
In this article we show that the boundary of the Pareto critical set of an unconstrained multiobjective optimization problem (MOP) consists of Pareto critical points of subproblems…
Controlling nonlinear PDEs using low-dimensional bilinear approximations obtained from data
Sebastian Peitz
In a recent article, we presented a framework to control nonlinear partial differential equations (PDEs) by means of Koopman operator based reduced models and concepts from switche…