We aim to develop methods to control future engineered systems to their limits of performance.

Many engineered systems have behavior that is very predictable given past measurement data. Our aim is to develop methods for learning, modeling, and control to achieve the limits of performance.

We develop methods to control real machines to the limits of performance by learning from data. Based in the high-tech hotspot Eindhoven, our key application domain is the development of advanced motion control solutions for precision mechatronics. Driven by the challenges occurring in future precision mechatronics, our research focusses on fundamental issues arising in learning to control complex dynamical systems from data with robustness guarantees. As such, it is primarily positioned in the field of system identification and control, intersecting with machine learning, artificial intelligence, and mechatronics. Applications are broadly addressed through collaborations with many companies and institutions in mechatronics, ranging from semiconductor equipment and printing to space and astronomy. In addition, we enjoy many fruitful collaborations with domain experts in the health, energy, and mobility areas.