Prof. Dr. Karl Worthmannis Heisenberg Professor for Optimization-based Control. The English term "control" in the denomination of his field includes both open-loop control and closed-loop control. It is precisely the interplay between open-loop and closed-loop control that is at the heart of modern methods of nonlinear system theory such as model predictive control (MPC). Here, the dynamic behavior of the (technical) systems under consideration is modeled, e.g. via ordinary or partial differential equations, and then predicted as a function of input variables (optimization variables) to be selected. Used correctly, this approach can achieve desired system properties such as asymptotic stability. Karl Worthmann's research uses methods from various mathematical fields, including modeling, optimization, and numerics. In addition, there is a wide range of applications in industry and business, e.g. in robotics, medical technology or in the course of the energy transition.

Research interests:

Dr. F. Philipp
  • Frame Theory
  • Harmonic Analysis
  • Indefinite Inner Product Spaces
  • Machine Learning
  • Mathematical Control Theory
P. Sauerteig, M. Sc.
  • Distributed Optimization
  • Machine Learning
  • Model Predictive Control
  • Numerical Optimization
  • Optimal Control
Dr. M. Schaller
  • Model Predictive Control
  • Numerics for PDEs
  • Optimal Control
  • PDE-constrained Optimization
  • Turnpike Theory