Program

The Benelux 2018 will host the following renowned invited speakers:

Murat Arcak

Professor, Electrical Engineering and Computer Sciences
Berkeley University California, USA


Professor Arcak will hold a mini-course.

Title: Networks of Dissipative Systems: Compositional Certification of Stability, Performance, and Safety

Abstract: Standard computational tools for control synthesis and verification do not scale well to large-scale, networked systems in emerging applications.  This mini-course presents a compositional methodology suitable when the subsystems are amenable to analytical and computational methods but the interconnection, taken as a whole, is beyond the reach of these methods. The main idea is to break up the task of certifying stability, performance, or safety for the network into subproblems of manageable size using dissipativity properties of the subsystems. Along the way we will introduce the notions of equilibrium-independent dissipativity as well as dissipativity with dynamic supply rates, and point to computational tools for verifying these properties.  We will illustrate the compositional approach with case studies in multi-agent systems and biological networks.

Short Biography: Murat Arcak received the B.S. degree from the Bogazici University, Istanbul, Turkey (1996) and the M.S. and Ph.D. degrees from the University of California, Santa Barbara (1997 and 2000). His research is in dynamical systems and control theory with applications to synthetic biology, multi-agent systems, and transportation. Prior to joining Berkeley in 2008, he was a faculty member at the Rensselaer Polytechnic Institute. He received a CAREER Award from the National Science Foundation in 2003, the Donald P. Eckman Award from the American Automatic Control Council in 2006, the Control and Systems Theory Prize from the Society for Industrial and Applied Mathematics (SIAM) in 2007, and the Antonio Ruberti Young Researcher Prize from the IEEE Control Systems Society in 2014. He is a member of SIAM and a fellow of IEEE.

 

Dongheui Lee

Associate Professor, Human-Centered Assistive Robotics, Electrical and Computer Engineering
Technical University of Munich, Germany

Professor Lee will give a plenary lecture.

Title: Robot learning through physical interaction and human guidance

Abstract: As a fundamental cornerstone in the development of intelligent robotic assistants, the research community on robot learning has addressed autonomous motor skill learning and control in complex task scenarios by working on a variety of fundamental sub-problems: movement primitive representation, reaction and adaptation, the link between perception and action, learning under supervision, and learning from self-practice. Imitation learning provides an efficient way to learn new skills through human guidance, which can reduce time and cost to program the robot. Robot learning architectures can provide a comprehensive framework for learning, recognition and reproduction of whole body motions. Also, the architecture can be integrated with different types of teaching modalities and be applied even in situations with incomplete measurement data. The inference mechanism can support not only to learn the robot's free body motion but also to learn physical interaction tasks, including human robot interaction. I will discuss incremental learning in different problem domains including the refinement of learned skills via heterogeneous learning modalities, enhancement of human-robot cooperation tasks over time, and improvement of stability in bipedal walking by iterative learning control. Empirical evaluation on several robotic systems will illustrate the effectiveness and applicability to learn control of high-dimensional anthropomorphic robots.

Short Biography: Professor Dongheui Lee is Associate Professor of Human-centered Assistive Robotics at the TUM Department of Electrical and Computer Engineering. She is also director of a Human-centered assistive robotics group at the German Aerospace Center (DLR). Her research interests include human motion understanding, human robot interaction, machine learning in robotics, and assistive robotics. Prior to her appointment as Associate Professor, she was an Assistant Professor at TUM (2009-2017), Project Assistant Professor at the University of Tokyo (2007-2009), and a research scientist at the Korea Institute of Science and Technology (KIST) (2001-2004). After completing her B.S. (2001) and M.S. (2003) degrees in mechanical engineering at Kyung Hee University, Korea, she went on to obtain a PhD degree from the department of Mechano-Informatics, University of Tokyo, Japan in 2007. She was awarded a Carl von Linde Fellowship at the TUM Institute for Advanced Study (2011) and a Helmholtz professorship prize (2015). She is coordinator of both the euRobotics Topic Group on physical Human Robot Interaction and of the TUM Center of Competence Robotics, Autonomy and Interaction.

 

Tamas Keviczky

Associate Professor, Delft Center for Systems and Control
Delft University of Technology, The Netherlands

Professor Keviczky will give a plenary lecture.

Title: Distributed stochastic model predictive control for large-scale smart energy systems

Abstract: Large-scale energy systems (such as heat, gas, electricity networks) play a crucial role in society and represent important challenges for researchers working on new solutions for the energy transition. These include data-driven modeling, prediction, and quantification of uncertainty and their incorporation in prescriptive models for distributed decision-making problems. In this talk, I will describe our research on distributed stochastic model predictive control (SMPC) for large-scale linear systems with additive disturbances and multiplicative uncertainties. Typical SMPC approaches for such problems involve formulating a large-scale finite-horizon chance-constrained optimization problem at each sampling time, which is in general non-convex and difficult to solve. Large-scale scenario programs instead use an approximation that allows to quantify the robustness of the obtained solution. I will show how such problems can be decomposed into distributed scenario programs that exchange a certain number of scenarios with each other in order to compute local decisions and address computational tractability issues with a-priori probabilistic guarantees for the desired level of constraint fulfillment. Finally, I will present an overview of open problems and our related research efforts.

Short Biography: Dr. Tamas Keviczky is an Associate Professor in Networked Cyber-Physical Systems at the Delft Center for Systems and Control, TU Delft. He was a Postdoctoral Scholar at the California Institute of Technology and received his PhD in Control Science and Dynamical Systems from the University of Minnesota. He was awarded the AACC O. Hugo Schuck Best Paper Award for Practice. He has served as an Associate Editor of Automatica since 2011 and has published over 100 scientific articles. His main research interests include distributed optimization and optimal control, model predictive control, embedded optimization-based control and estimation of large-scale systems with applications in aerospace, automotive and mobile robotics, industrial processes, and infrastructure systems such as water, heat, and electricity networks.