Program

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

Kristin Pettersen

Professor, Department of Engineering Cybernetics, NTNU, Trondheim, Norway

Title: Modeling, analysis and control of snake robots (Part I: Motivation, Modeling and Analysis, Part II: Control and Industrialization)

Abstract: Snake robots are motivated by the long, slender and flexible body of biological snakes, which allows them to move in virtually any environment on land and in water. Since the snake robot is essentially a manipulator arm that can move by itself, it has a number of interesting applications including firefighting applications and search and rescue operations. In water, the robot is a highly flexible and dexterous manipulator arm that can swim by itself like a snake. This highly flexible snake-like mechanism has excellent accessibility properties; it can for instance access virtually any location on a subsea oil & gas installation, move into the confined areas of ship wrecks, or be used for observation of biological systems. Furthermore, not only can the swimming manipulator access narrow openings and confined areas, but it can also carry out highly complex manipulation tasks at this location since manipulation is an inherent capability of the system.

In the first part, I will present the motivation for our research on snake robots, the models and some analysis results showing inherent properties of snakes. In the second part, I will present results on model-based control of snake robots, including both theoretical and experimental results, and the ongoing efforts for bringing the results from university research towards industrial use.

Short biography: Kristin Y. Pettersen is a Professor in the Department of Engineering Cybernetics, NTNU where she has been a faculty member since 1996. She was Head of Department 2011-2013, Vice-Head of Department 2009-2011, and Director of the NTNU ICT Program of Robotics 2010-2013. She is Adjunct Professor at the Norwegian Defence Research Establishment (FFI). In the period 2013 – 2022 she is also Key Scientist at the CoE Centre for Autonomous Marine Operations and Systems (NTNU AMOS). She is a co-founder of the NTNU spin-off company Eelume AS, where she was CEO 2015-2016.

She received the MSc and PhD degrees in Engineering Cybernetics at NTNU, Trondheim, Norway, in 1992 and 1996, respectively. She has published four books and more than 250 papers in international journals and refereed conferences. Her research interests focus on nonlinear control of mechanical systems with applications to robotics, with a special emphasis on marine robotics and snake robotics. She was awarded the IEEE Transactions on Control Systems Technology Outstanding Paper Award in 2006 and in 2017.

She was a nominated and elected member of the Board of Governors of IEEE Control Systems Society 2012 – 2014 and is currently a member of both the IFAC Council and the EUCA Council. She has also held and holds several board positions in industrial and research companies. She was Program Chair of the IEEE Conference on Control Technology and Applications in 2018, and has served as Associate Editor for several international conferences. She has served as Associate Editor of IEEE Transactions on Control Systems Technology and IEEE Control Systems Magazine, and is currently Senior Editor of Transactions on Control Systems Technology. She is IEEE CSS Distinguished Lecturer 2019-2021. She is recipient of the Hendrik W. Bode Lecture Prize 2020, IEEE Fellow, member of the Norwegian Academy of Technological Sciences, and member of the Academy of the Royal Norwegian Society of Sciences and Letters.

 https://www.ntnu.edu/employees/kristin.y.pettersen



 

Thomas Schön

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

 Title: Learning nonlinear dynamics using sequential Monte Carlo

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: http://user.it.uu.se/~thosc112.


 

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.