"Have you heard of industry 4.0, smart grids, smart buildings, smart cities, intelligent traffic systems and intelligent self-driving cars? Did you ever wonder what makes them smart and intelligent? The answer is control and automation and that’s what we do at the Automatic Control Laboratory (IfA). We use control theory, optimization, machine learning, and game theory to develop controllers and algorithms that are the backbone of nearly all modern technology. At our lab we span the whole area from pure theory to real-world applications and we are looking for you to help us push forward the state of the art. If you want to learn techniques and gain knowledge that enable you to work in any field from medical applications to spacecraft and from electrical grids to finance then the Automatic Control Laboratory is the place for you!"
I am always looking for talented students with interests in the area of optimal control, reinforcement learning, networked dynamical systems and related topics.
If you would like to apply, please send me an email with your CV and a transcript of records.
Data-driven optimal control via linear programming
Learning how to optimally regulate a dynamical system from data is a fundamental problem in control theory. This project focuses on investigating new theory and methods about the so-called linear programming approach to approximate dynamic programming.