The Benelux Meeting 2020 will host the following renowned invited speakers:

Thomas Schön

Professor, Department of Information Technology, Uppsala University, Uppsala, Sweden 

 Title: Learning nonlinear dynamics using sequential Monte Carlo

Slides: part 1, part 2, parts 3

Abstract: We are mainly concerned with probabilistic modeling which provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. When it comes to learning probabilistic nonlinear state-space models there is no closed-form solution available, implying that we are forced to use approximations. The sequential Monte Carlo (SMC) method has proven to offer a practical approximation for estimating the states of a nonlinear state-space model (and many other models). After deriving the SMC method we will also show how it can be used in solving the nonlinear system identification problem. We will provide solutions to both the maximum likelihood problem and the Bayesian problem. The former will be solved using a new stochastic quasi-Newton algorithm whereas the latter is solved using a Metropolis Hastings algorithm. Both of these solutions are carefully tailored to the specific problem of learning a nonlinear state-space model.

Short biography: Thomas B. Schön is Professor of the Chair of Automatic Control in the Department of Information Technology at Uppsala University. He received the PhD degree in Automatic Control in Feb. 2006, the MSc degree in Applied Physics and Electrical Engineering in Sep. 2001,  the BSc degree in Business Administration and Economics in Jan. 2001, all from Linköping University. He has held visiting positions with the University of Cambridge (UK), the University of Newcastle (Australia) and Universidad Técnica Federico Santa María (Valparaíso, Chile). In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences (IVA) and The Royal Society of Sciences at Uppsala. He received the Tage Erlander prize for natural sciences and technology in 2017 and the Arnberg prize in 2016, both awarded by the Royal Swedish Academy of Sciences (KVA). He was awarded the Automatica Best Paper Prize in 2014, and in 2013 he received the best PhD thesis award by The European Association for Signal Processing. He received the best teacher award at the Institute of Technology, Linköping University in 2009. He is a Senior member of the IEEE and a fellow of the ELLIS society.

Schön has a broad interest in developing new algorithms and mathematical models capable of learning from data. His  main scientific field is Machine Learning, but he also regularly publishes in other fields such as Statistics, Automatic Control, Signal Processing and Computer Vision. He pursues both basic research and applied research, where the latter is typically carried out in collaboration with industry or applied research groups. More information about his research can be found on his website:


Paul Van den Hof

Professor, Department of Electrical Engineering, Eindhoven University of Technology

Title: Data-driven model learning in linear dynamic networks


Abstract: Interconnected networks of dynamic systems play a growing role in science and technology, leading to decentralized, distributed and multi-agent type of control problems. From the modeling side, this leads to an urge to develop data-driven methods for learning models in/for large-scale interconnected systems.  In this seminar the main developments and challenges in this area will be highlighted for the situation of linear dynamic networks with continuous dynamics. Besides setting up a modelling framework for directed networks, we will address problems of network identifiability and of local identification of a particular part of the network, including the selection of the appropriate signals to be measured. Machine learning tools are incorporated for mitigating the model structure selection problems and graph-based methods are introduced for verifying identifiability, providing conditions for the location of excitation signals in the network graph.

Short biography: Paul van den Hof obtained an MSc from Eindhoven University of Technology (TU/e) in 1982 and a PhD from there in 1989. He started work at Delft University of Technology in 1986, where he was appointed as Full Professor in 1999. He was the founding co-director of the Delft Center for Systems and Control (DCSC), with appointments in the faculty of Mechanical, Maritime, and Materials Engineering, and the faculty of Applied Sciences. In 2011, he was appointed Full Professor at the Electrical Engineering Department of TU/e. From 2005-2015, he was Scientific Director of the National Research and Graduate School Dutch Institute of Systems and Control (DISC), and National Representative of the Dutch NMO in IFAC.

Henk Nijmeijer

Professor, Department of Electrical Engineering, Eindhoven University of Technology

Title: Cooperative and/or Autonomous driving: where are we going?


Abstract: This talk reviews in general terms the status of cooperative and autonomous driving and particularly a discussion regarding the potentials and limitations of this is given. The unlimited believe in full automation seems to loose some of its momemtum in the last years.

Short biography: Henk Nijmeijer obtained his MSc and PhD in Mathematics from the University of Groningen, the Netherlands. He has published a large number of journal and conference papers, and several books, and is or was at the editorial board of numerous journals. Henk is an editor of Communications in Nonlinear Science and Numerical Simulations. He is a fellow of the IEEE since 2000. Henk Nijmeijer is honorary knight of the ‘Golden Feedback Loop’, NTNU, Trondheim. Henk is editor in chief of the Journal of Applied Mathematics, corresponding editor of the SIAM Journal on Control and Optimization, and board member of the International Journal of Control, Automatica, European Journal of Control, Journal of Dynamical Control Systems, SACTA, International Journal of Robust and Nonlinear Control, and the Journal of Applied Mathematics and Computer Science. Since 2017, he has been director of the Graduate Program Automotive Technology of the TU/e.



Ming Cao

Professor, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands 

Title: Strategic decision-making and learning for autonomous agents


Abstract: Game theoretic models have proven to be powerful to gain insight into the processes of strategic decision-making and learning among interacting autonomous agents. I present a review of some fundamental concepts, emerging research, and open problems related to the analysis and control of evolutionary games, with particular emphasis on applications in social, economic, biological and robotic networks. In populations of autonomous  agents, when individuals' self-interested goals conflict with the greater interest of the group,  counter-intuitive outcomes and social dilemmas may arise. Evolutionary game theory has emerged as a vital tool set in the investigation of such network dynamics. In addition, one may explore how agents might learn to choose their strategies over time to adapt with the peers and surroundings; control theorists are particularly interested in knowing whether a small group of agents can manipulate the collective actions at large. Hence, decision-making, learning and control can be discussed in a unified framework, leading to new challenges and research opportinities for control scientists and engineers.

Short biography: Ming Cao is Professor of Networks and Robots at the Faculty of Science and Engineering. His research focuses on multi-agent systems, complex systems and networks, sensor networks and autonomous robots. Cao conducts ground-breaking research in the field of control systems that allow groups of autonomous robots to work together. If autonomous cars and robots are to function effectively and safely, they must be able to take each other’s actions into account. Cao works on this development with colleagues from the fields of sociology, mathematics and biology. The algorithms he develops for robots are partly inspired by the movements of animals, particularly fish and birds, which also act in formation.

Cao obtained a Bachelor's degree in Engineering at Tsinghua University in Peking, China, in 1999. He then graduated as a Master of Engineering at the same university in 2002. In 2003, he also obtained a Master of Science degree at Yale University in New Haven, the United States, and in 2007, he was awarded a PhD by the Department of Electrical Engineering at Yale University.


Marcel Heertjes

Principle engineer and control competence leader at ASML
Professor, Department of Mechanical Engineering, TU Eindhoven, Eindhoven, The Netherlands

Title: Hybrid Integrator-Gain Systems: How to use them in Motion Control of Wafer Scanners?


Abstract: Wafer scanners, whose list prices range from several tens to hundreds of millions of euros, are key tools in the production of microchips. They consist of highly-complex mechatronic systems that combine high throughput with high precision. Modern wafer scanners expose 200-300 wafers per hour, each wafer containing over a hundred fields, where each field possibly contains a large processor or memory chip. The motion systems of wafer scanners are controlled (often with nanometer precision) by a combination of feedforward and feedback controls, the latter mainly referring to PID control. Though PID control is experienced as being intuitive, simple, and effective, its frequency-domain robust control design faces inherent design limitations. Also, in time-domain, associated overshoot and settling times that may negatively impact machine performance measures like overlay and throughput, are only to some extent overcome by feedforward control. To deal with these limitations, recent developments with hybrid integrator-gain system will be discussed. Topics to be addressed are: rigorous robust control design, easy and non-conservative frequency-domain tools for stability analysis, proofs and insights on the possibilities for HIGS to beat linear designs, practical tuning rules, and experimental demonstrations of HIGS control designs on industrial wafer stage systems.

Short biography: Marcel Heertjes is part-time professor on nonlinear industrial control for high-precision mechatronics at the department of mechanical engineering at Eindhoven University of Technology. His primary affiliation is with ASML. He served as guest editor of International Journal of Robust and Nonlinear Control (2011) and IFAC Mechatronics (2014) and is currently associate editor of IFAC Mechatronics (since 2016). He is (co-)recipient of the IEEE Control Systems Technology award (2015) for variable gain control and its applications to wafer scanners. His main research interests are nonlinear control, feedforward and learning control, and data-driven optimization of high-precision systems.