Fachgebiet Optimization-based Control

In the international scientific context, the term control encompasses both open-loop control and closed-loop control. Our research focuses on the interaction of open-loop and closed-loop control in modern methods ofnon-linear systems theory, in particular model predictive control (MPC). In MPC, the dynamic system behaviour is described - for example using ordinary and partial differential equations - and predicted as a function of input variables (optimization variables). The aim is to ensure desired system properties such as asymptotic stability in order to guarantee the reliability and efficiency of technical systems, for example. This is done using structural system properties (e.g. in the DFG project Exploiting nonlinear port-Hamiltonian structures for optimization-based and data-driven control) and data-based and machine learning methods, e.g. as part of the ALeSCo(Active Learning in Systems and Control) research group.

Prof. Worthmann's research combines methods from various areas of applied mathematics, including modelling, optimization and numerical methods, with techniques from the field of machine learning, among others. The broad spectrum of applications ranges from robotics (see DFG project Data-driven modeling and predictive control of non-holonomic systems in the Koopman framework) and medicine (see project KI-MSO-O) to energy and environmental technology (see project VerneDCt) and contributes to current technological and social challenges such as the energy transition.