Tom Oomen

We focus on learning from data in dynamical systems. This allows us to analyze their dynamical behavior and improve it through active control. Our activities range from data-driven modeling of dynamical systems, control of complex and uncertain systems, towards learning using sequential design of inputs for both improving the identified model quality and control performance. We are actively engaged in both fundamental and applied research. Regarding the latter, we take particular interest in developing key technologies for real-life applications, typically in close collaboration with industry. Our main application area lies in advanced motion control of mechatronic systems. These applications are complemented by collaborations on thermo-mechanical systems, automotive applications, medical applications, and energy systems. 

Control is the hidden technology that ensures our engineered systems actually work. The key question is what the performance limits of such controlled engineered systems are. We aim to find out by developing a framework that learns from data to control systems to the limits of their reproducible behavior.