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.
