In this project we aim to reduce the influence of model uncertainties and external noise on complex dynamical systems.

In robust control, the discrepancy between a real process and the model chosen for its description, is taken into account for controller design. This is of essential significance in practice, since mathematical models can only describe a real process approximately. Therefore, it is necessary that desired performance requirements such as the suppression of external disturbances and a good reference tracking, as well as the stability of the closed-loop system are not only guaranteed for the nominal model but for a family of models. In this manner, modelling and approximation errors can be addressed. The goal of this project is the development of novel design techniques for (robust) H∞-controllers for the case of dynamical systems with a large state-space dimension and/or with delays. To address this issue we plan to build optimization-based procedure that constructs reduced controllers by using adaptive interpolatory model reduction techniques on the given plant model. 

Dauer des Projekts

01.01.2020 - 31.12.2022

Mitarbeiter

Prof. Dr. Matthias Voigt Assistenzprofessor in Mathematik, FernUni Schweiz
Paul Schwerdtner Research Assistant and Doctoral Student, Technische Universität Berlin, Germany
Amon Lahr Student Research Assistant and Master Student (until August 2021), Technische Universität Berlin, Germany

Finanzierung

  • Deutsche Forschungsgemeinschaft (reference number VO2243/2-1, in total € 300’966 (incl. overhead))