Plenary Lectures

ECC18 will feature the following six plenary speakers:

A Distributed Algorithm for Finding a Common Fixed Point of a Family of Paracontractions and Some of Its Applications

Wednesday Morning Plenary Lecture (13/06, 08:30-09:30)

A. Stephen Morse
Yale University
USA

Abstract: “Paracontractions” are nonlinear maps which arise in certain optimization and estimation problems and in various types of numerical calculations. In this talk a distributed algorithm is described for finding a common fixed point of a family of $m>1$ paracontraction $M_i :\mathbb{R}^n \rightarrow \mathbb{R}^n$ assuming that such a common fixed point exists. The common fixed point is simultaneously computed by $m$ agents assuming each agent $i$ knows only $M_i$, the current estimates of the fixed point generated by its neighbors, and nothing more. Each agent recursively updates its estimate of the fixed point by utilizing the current estimates generated by each of its neighbors. Neighbor relations are characterized by a time-dependent directed graph $\mathbb{N}(t)$ whose vertices correspond to agents and whose arcs depict neighbor relations. For any family of paracontractions $M_i, i \in \{1,2,...,m\}$ whose members have at least one common fixed point, and any sequence of strongly connected neighbor graphs $\mathbb{N}(t), t=1,2,3,...$, the algorithm causes all agent estimates to converge to a common fixed point. Generalizations of this result are also discussed. Finally, as an application of these results, a provably correct and resilient distributed algorithm is described for estimating the state of an $m$ channel linear system of the form $\dot{x} = Ax, y_i = C_ix, i \in \{1,2,...,m \}$.

Biography: A. Stephen Morse was born in Mt. Vernon, New York. He received a BSEE degree from Cornell University, MS degree from the University of Arizona, and a Ph.D. degree from Purdue University. He was associated with the Office of Control Theory and Application {OCTA} at the NASA Electronics Research Center in Cambridge, Mass. Since 1970 he has been with Yale University where he is presently the Dudley Professor of Engineering. His main interest is in system theory and he has done research in network synthesis, optimal control, multivariable control, adaptive control, urban transportation, vision-based control, hybrid and nonlinear systems, sensor networks, and coordination and control of large groupings of mobile autonomous agents. He is a Life Fellow of the IEEE, an IFAC Fellow, a past Distinguished Lecturer of the IEEE Control System Society, and a co-recipient of the Society’s 1993 and 2005 George S. Axelby Outstanding Paper Awards. He has twice received the American Automatic Control Council’s Best Paper Award and is a co-recipient of the Automatica Theory/Methodology Prize. He is the 1999 recipient of the IEEE Technical Field Award for Control Systems. He is the 2013 recipient of the American Automatic Control Council’s Richard E. Bellman Control Heritage Award. He is a member of the National Academy of Engineering and the Connecticut Academy of Science and Engineering.

Transfer learning and learning with concept drift

Wednesday Noon Plenary Lecture (13/06, 12:15-13:15)

Barbara Hammer
Bielefeld University
Germany

Abstract: One of the main assumptions of classical machine learning is that data are generated by a stationary concept. This, however, is violated in practical applications e.g. in the context of life long learning, for the task of system personalisation, or whenever sensor degradation or non-stationary environments cause a fundamental change of the observed signals. Within the talk, we will give an overview about recent developments in the field of learning with concept drift, and we will address two particular challenges in more detail: (1) How to cope with a fundamental change of the data representation which is caused e.g. by a misplacement or exchange of sensors? (2) How to deal with heterogeneous concept drift, i.e. mixed rapid or smooth, virtual or real drift, e.g. caused by a real-life non-stationary environment? We will present novel intuitive distance-based classification approaches which can tackle such settings by means of suitable metric learning and brain-inspired adaptive memory concepts, respectively, and we will demonstrate their performance in different application domains ranging from computer vision to the control of protheses.

Biography: Barbara Hammer chairs the research group Machine Learning at the CITEC centre of excellence at Bielefeld University. She received her Ph.D. and venia legendi (permission to teach) in Computer Science in 1999 and 2004, respectively, from University of Osnabrück, before joining Clausthal University of Technology in 2004 and an Bielefeld University in 2010. Several research stays have taken her to Pisa, Padova, Groningen, Paris, Bangalore, and Birmingham. Barbara is chair of the section Neural Networks of the German Computer Science Society and vice chair of the German Neural Networks Society. She has been chairing the IEEE CIS Data Mining and Big Data Analytics Technical Committee in 2013/2014, and the IEEE CIS distinguished lecturer program in 2016/2017. She co-organized several symposia in the field of Computational Intelligence including CIDM’13, CIDM’14, IJCNN’17 and the yearly workshop series NC^2, which she initiated. Her fields of interest cover recurrent and recursive networks for structures, self-organizing maps, data visualization, learning interpretable models, and incremental learning, clustering as well as applications in bioinformatics, industrial process monitoring, and cognitive science.

Control theory of switches and clocks

Thursday Morning Plenary Lecture (14/06, 08:30-09:30)

Rodolphe Sepulchre
University of Cambridge
UK

Abstract: This talk will present a novel approach for the analysis and design of systems that switch and oscillate. While such nonlinear behaviors abound in control engineering of electrical, mechanical, and biological circuits, it is often considered that they largely fall outside the scope of control theory. In contrast, the proposed approach closely mimics linear-quadratic dissipativity theory, a very foundation of modern control theory.
In its classical formulation, dissipativity theory formulates system properties as dissipation inequalities to be satisfied by the storage, an abstraction of the system internal energy. Linear systems admit quadratic storages. When the storage is positive definite, it serves as a Lyapunov function for stability analysis of equilibria. Our generalization rests on two distinct ingredients. First, we apply dissipativity theory differentially: instead of studying the nonlinear system via the nonlinear theory, we apply the linear theory to a family of linearized systems. Second, we relax the positivity constraint of the quadratic storage to a fixed inertia constraint. We allow for one negative eigenvalue in the analysis of switches and two negative eigenvalues in the analysis of clocks.
The talk will illustrate the theory in classical models of switches and clocks and discuss the potential of dissipativity theory for the analysis and design of interconnected systems away from equilibrium.

Biography: Rodolphe Sepulchre is Professor of Engineering at Cambridge University and a fellow of Sidney Sussex College. His research interests are in dynamics, control and optimization of nonlinear problems. He is currently Editor-in-Chief of Systems and Control Letters and has been an Associate Editor for SIAM Journal of Control and Optimization, the Journal of Nonlinear Science, and Mathematics for Control, Signals, and Systems. In 2008, he was awarded the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize. He is a fellow of IEEE and SIAM. He has been IEEE CSS distinguished lecturer between 2010 and 2015. A focus of his current research is the ERC advanced grant “Switchlets”, motivated by neurophysiological questions and aiming at a multiresolution control theory of excitable systems. He co-authored the monographs “Constructive Nonlinear Control” (Springer-Verlag, 1997) and “Optimization on Matrix Manifolds” (Princeton University Press, 2008).

Robots: Body, Intelligence, and Control

Thursday Noon Plenary Lecture (14/06, 12:15-13:15)

Antonio Bicchi
University of Pisa
Italy

Abstract: Modern approaches to the design of robots are changing the way robots are built, often inspired by a philosophy of embodied intelligence, by which many natural behaviours are deeply rooted in the way our bodies are built. Accordingly, the physical structure of robots is evolving from traditional rigid, heavy industrial machines into soft bodies exhibiting new levels of versatility, adaptability, safety, elasticity, dynamism and energy efficiency. New challenges and opportunities arise for the control of soft robots: for instance, carefully planning for collision avoidance may no longer be a dominating concern, being on the contrary physical interaction with the environment not only allowed, but even desirable to solve complex tasks. Similarly, the traditional use of high-authority feedback loops to control robot motion is challenged by the desire to maintain adaptability to the environment. In this talk I will discuss how these challenges can be addressed, at least partially, by looking at how humans use their own bodies in similar tasks.

Biography: Antonio Bicchi is a scientist interested in Automatic Control (the science and engineering of Systems), in Haptics (the science and technology for the sense of Touch), and in Robotics (i.e., the Machines that are not here yet). After graduating from the University of Bologna, he has been with the MIT AI Lab in Cambridge, USA, and is now Professor of Robotics at the University of Pisa. Since 2009 he leads the Soft Robotics Lab at the Italian Institute of Technology in Genoa, and from 2013 he is Adjunct Professor at Arizona State University in Tempe, Arizona. His 2012-2017 ERC Advanced Grant “SoftHands” established the basis for the theory of soft synergies in human manipulation, which led to the design of a new generation of robotic and prosthetic hands. In 2016 he was the founding Editor in Chief of the IEEE Robotics and Automation Letters, which in about two years became the largest Journal in the field.

Self-Driving Cars, Connectivity and Traffic Flow Control

Friday Morning Plenary Lecture (15/06, 08:30-09:30)

Petros Ioannou
University of Southern California
USA

Abstract: Self- Driving cars are attracting a lot of attention and excitement as they will impact driving comfort and safety as well as modify the current modes of transporting people and goods. Getting rid of the driver however will not necessarily reduce congestion whose main cause is the high volume of vehicles competing in space and time to reach destinations. Connectivity however and compliance to traffic management commands and traffic rules by vehicle autopilots will open the way for far better traffic flow control approaches with strong potential to improve capacity, manage congestion and incidents in a much more effective way. In this talk we present some of the main challenges self-driving vehicles will be facing and how connectivity with the infrastructure is far more crucial than vehicle automation when it comes to traffic flow control. We will present several designs of traffic flow control and load balancing which can bring significant benefits to traffic flow characteristics and efficiency with positive impact on the environment.

Biography: Petros A. Ioannou received the B.Sc. degree with First Class Honors from University College London, in 1978 and the M.S. and Ph.D. degrees from the University of Illinois, Urbana, Illinois, in 1980 and 1982, respectively. In 1982, he joined the Department of Electrical Engineering-Systems, University of Southern California, where he is currently a Professor and holder of the A.V. ‘Bal’ Balakrishnan Chair. He is the Director of the Center of Advanced Transportation Technologies, the Associate Director for Research of METRANS, a University Transportation Center. Dr. Ioannou received many research awards with the most recent ones been the 2012 IEEE Intelligent Transportation System Society Research Award, the 2016 IEEE Transportation Technologies Field Award and the IEEE Control System Society Transition to Practice Award for his work on the design and commercialization of Adaptive Cruise Control Systems. He is a Fellow of IEEE, IFAC, IET and AAAS and the author/co-author of 8 books and over 300 research papers.

Symbolic control: from discrete synthesis to certified continuous controllers

Friday Noon Plenary Lecture (15/06, 12:15-13:15)

Antoine Girard
CNRS
France
(EUCA Award Plenary Lecture)

Abstract: Symbolic control aims at designing “correct by construction” controllers for continuous dynamical systems, by using algorithmic discrete synthesis techniques. The key concept in symbolic control is that of symbolic model, which is a finite-state dynamical system, obtained by abstracting continuous trajectories over a finite set of symbols. When the symbolic and the continuous dynamics are formally related by some behavioral relationship (e.g. simulation or bisimulation relations), controllers synthesized for the symbolic model using discrete synthesis techniques can be refined to certified controllers for the original continuous system. In the first part of this talk, I will present some fundamental results on symbolic control from symbolic model computation, to discrete synthesis and controller refinement. Then, I will report some recent progress on scalability and robustness by means of compositional and quantitative synthesis techniques.

Biography: Antoine Girard is a Senior Researcher at CNRS and a member of the Laboratory of Signals and Systems (L2S). He received the Ph.D. degree in applied mathematics from Grenoble Institute of Technology, in 2004. From 2004 to 2006, he held postdoctoral positions at University of Pennsylvania and Verimag Laboratory. From 2006 to 2015, he was an Assistant/Associate Professor at the Université Grenoble-Alpes. His research interests deal with analysis and control of hybrid systems with an emphasis on computational approaches, formal methods and applications to cyber-physical systems. He is also interested in multi-agent and distributed parameter systems. He received the George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society in 2009. In 2014, he was awarded the CNRS Bronze Medal. In 2015, he was appointed as a junior member of the Institut Universitaire de France (IUF). In 2016, he was awarded an ERC Consolidator Grant.