Stefan Streif

The research in the lab has a strong focus on controller development for the full cycle starting from system modelling to the end application. The particular topics also include advanced techniques of data analysis and classification along with model uncertainty assessment. Special attention is placed on controlled environment agriculture (CEA), energy and fuel cell systems, and automatic train operation as application areas. Our control methods include model predictive control, reinforcement learning, adaptive dynamic programming and other optimal control methods, with a special focus on learning aspects. Mathematical proofs and analysis of e.g. robustness properties of control methods in the presence of uncertainties is a part of virtually every new control method developed in the lab.