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.

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. 

Tom Oomen received his MSc and PhD degree from the Eindhoven University of Technology, Eindhoven, The Netherlands. He has held long-term visiting positions at KTH, Stockholm, Sweden, and at The University of Newcastle, Australia, in addition to numerous short visits to international research centers. He is a recipient of both the VENI (2013) and VIDI (2017) personal research grants. He is a senior member of the IEEE, and is presently Associate Editor of IFAC Mechatronics and the IEEE Control Systems Letters (L-CSS). He has been Associate Editor on the IEEE Conference Editorial Board, as well as special issue guest editor for IFAC Mechatronics. He regularly organizes special sessions at international conferences, as well as workshops for academia and industry. He is a member of the Next-Gen board of the High-Tech Systems Center (HTSC) and the Eindhoven Young Academy of Engineering (EYAE).