Keynote Lectures





Andres Rantzer
Lund University, Sweden

Quadratic Inequalities in Learning for Control

Time:
TBD

Location:
TBD

Abstract: The intersection of learning and control has a long history, but has recently gained a tremendous growth due to a large number of new applications. This lecture is focused on the role of quadratic inequalities for design and analysis of learning based control systems. Three main aspects will be discussed: 1) Exploration/exploitation trade-offs in adaptive control, 2) Distributed adaptation in networked control, and 3) Stability and robustness guarantees for feedback systems involving machine learning components. In all three cases, classical control concepts based on quadratic inequalities, such as dissipativity and H-infinity control, will be connected to recent progress in learning theory

Biography: Anders Rantzer was appointed professor of Automatic Control at Lund University, Sweden, after a PhD at KTH Stockholm in 1991 and a postdoc at IMA, University of Minnesota. The academic year of 2004/05 he was visiting associate faculty member at Caltech and 2015/16 he was Taylor Family Distinguished Visiting Professor at University of Minnesota. Rantzer is a Fellow of IEEE, member of the Royal Swedish Academy of Engineering Sciences, Royal Physiographic Society in Lund and former chairman of the Swedish Scientific Council for Natural and Engineering Sciences. His research interests are in modeling, analysis and synthesis of control systems, with particular attention to applications in energy networks.




Bruno SICILIANO
Università degli Studi di Napoli Federico II, Italy

Robot Manipulation and Control: Scenarios and Challenges

Time:
TBD

Location:
TBD

Abstract: The PRISMA Lab <www.prisma.unina.it> has been engaged in robotics research at University of Naples Federico II for 30 years. The team has a track record of successful projects, mainly at a European level, for a total funding of 18 million € in the last 15 years. This talk will survey our most remarkable achievements in seven research areas where robot manipulation and control challenges are found: grasping and manipulation, handling and manipulation, dynamic manipulation, aerial manipulation, interaction with deformable objects, human–robot interaction, and haptic shared control. Several scenarios are highlighted for the application of the developed technologies. Bio sketch

Biography: Bruno Siciliano is Professor of Robotics and Director of ICAROS Center at University of Naples Federico II, and Past President of IEEE Robotics & Automation Society, one cannot overlook what is his background: the city of Naples and the reckless passion for Napoli soccer team. It then happens that the background comes to the fore and becomes for the robotics expert at an international level a turning point. Like when, having earned his PhD degree, he decided to build his academic future in his city and for his city, declining a faculty position at a prestigious American university, (also) because of the football faith that has always accompanied him in the team's highs (those were the times of Maradona) and lows. His book Robotics is among the most adopted texts in universities around the world, even though when he talks about his academic achievements there is the impressive Springer Handbook of Robotics, the reference manual for robotics at international level, edited with Professor Oussama Khatib, which he defines as "the most exciting professional experience of my life". A work of coordination of over 200 renowned researchers, with the goal (fulfilled) of offering a unique tool to the scientific community of robotics and beyond. His research team at PRISMA Lab has had more than 20 projects funded by the European Union, including an Advanced Grant from the RoDyMan, an acronym for "Robotic Dynamic Manipulation", a robot capable of replicating the movements of a pizza maker. In terms of scientific research, it constituted the challenge of creating an automaton capable of manipulating deformable, elastic, non-solid objects, such as water and flour dough. "Keep the gradient" is the motto that Siciliano invented, meaning a constant search for new ideas and new solutions. A hymn to complexity to capture challenges and opportunities always in the name of the art of work & play, as stated in his passionate TEDx Talk. More details are available at http://wpage.unina.it/sicilian/




Wei Kang, USA
Naval Postgraduate School & University of California - Santa Cruz, USA

Topics at the Intersection of Deep Learning, Dynamical Systems and Control Theory

Time:
TBD

Location:
TBD

Abstract: Over the past several decades there has been tremendous progress in the development of systems and control theory. However, applications of these ideas have lagged behind for many problems that have ever-growing scale and complexity, such as large-scale smart grids and data assimilation for numerical weather prediction. On the other hand, advancements in machine learning had significant breakthroughs over the last few years in solving high dimensional and complicated nonlinear problems that cannot otherwise be solved using conventional methodologies in systems and control theory. In this talk, I will review some selected topics at the intersection of deep Learning, dynamical systems and control theory such as using deep learning to overcome some unmet challenges in control theory; the interconnection between deep learning and mean-field optimal control; and the complexity and error upper bound of deep learning for the analysis and control of high dimensional systems.

Biography: Wei Kang received B.S. and M.S. degrees from Nankai University and a Ph.D. degree from the University of California at Davis, all in mathematics. In 1991-1994, he held a faculty position at Washington University in St. Louis. Since 1994, he has been with the faculty of Applied Mathematics at Naval Postgraduate School, where he has served as the department chair during 2018-2021. He was the Director of American Institute of Mathematics for Business and International Collaborations (2008-2011). He was a visiting scientist at Intel (2005) and a consultant of EPRI (2011-2012). He was elected a Fellow of IEEE in 2008. Dr. Kang’s research interests include control theory, computational mathematics, deep learning, data assimilation, bifurcations and normal forms, and their applications to power systems, space systems, robotics, and manufacturing process control. He served as an Associate Editor for several journals, including the IEEE Transactions on Automatic Control, Automatica, and Control Theory and Applications. He was a plenary speaker in several international conferences of SIAM and IFAC. He was the recipient of the Best Paper Award of the 6th ICCARV, 2000. He served SIAM as the Program Director of Activity Group on Control and System Theory (2016-2017). He is currently the Vice Chair of SIAM Activity Group on Control and System Theory.