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Keynote Lectures

Control of Road Traffic Systems: A Multi-Scale Perspective
Antonella Ferrara, Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Italy

Personal Data Privacy: Especially Location
John Krumm, Microsoft Research, United States

Youla-Kučera Parameterization: Theory and Applications
Vladimír Kučera, Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Czech Republic


Control of Road Traffic Systems: A Multi-Scale Perspective

Antonella Ferrara
Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia

Brief Bio
Antonella Ferrara received the M.Sc. degree (Cum Laude and printing honours) in Electronic Engineering and the Ph.D. degree in Electronic Engineering and Computer Science from the University of Genoa, Italy, in 1987 and 1992, respectively. Since 2005, she has been Full Professor of Automatic Control at the University of Pavia, Italy. Her research activities are mainly in the area of nonlinear control, with a special emphasis on control of uncertain systems via sliding modes generation, and application to road traffic, automotive systems, electro-mobility, robotics, and power systems. She is author and co-author of more than 450 publications including more than 160 journal papers, 2 monographs (published by Springer Nature and SIAM, respectively) and one edited book (IET). She was/is Principal Investigator and National Coordinator in several projects funded by the European Union and by the Italian Ministry for University and Research. She is currently serving as Associate Editor of Automatica, and Senior Editor of the IEEE Open Journal of Intelligent Transportation Systems. She served as Senior Editor of the IEEE Transactions on Intelligent Vehicles, as well as Associate Editor of the IEEE Transactions on Control Systems Technology, IEEE Transactions on Automatic Control, IEEE Control Systems Magazine and International Journal of Robust and Nonlinear Control. Antonella Ferrara is the Chair of the EUCA Conference Editorial Board and the Director of Operations of the IEEE Control Systems Society. She is a member of the IEEE TC on Automotive Control, IEEE TC on Smart Cities, IEEE TC on Variable Structure Systems, IFAC TC on Nonlinear Control Systems, IFAC TC on Transportation Systems, and IFAC Technical Committee on Intelligent Autonomous Vehicles. She is also serving as the Vice-Chair for Industry of the IFAC TC on Nonlinear Control Systems (2024-2026) and is a member of the IFAC Industry Board. She is also a member of the IFAC Conference Board, by virtue of her appointment as one of the two Program Chairs of the 24th IFAC Word Congress to be held in Amsterdam, The Netherlands, in 2029. Among several awards, she was a co-recipient of the 2020 IEEE Transactions on Control Systems Technology Outstanding Paper Award. She is a Fellow of IEEE and Fellow of IFAC.

The impact of successful research in road traffic control spans across various domains, including the scientific, technological, social, and economic spheres. Its significance is profound, as it directly influences safety, quality of life, climate neutrality, energy resource utilization, and transportation costs. However, the development of effective methods and algorithms for road traffic management encounters notable methodological challenges. Traditionally, traffic control strategies have relied on infrastructure-based approaches. Yet, the rapid advancements in automotive technologies, traffic sensors, data processing, and communication have created unprecedented opportunities for the exploitation of connected and automated vehicles (CAVs), offering innovative solutions to longstanding traffic control challenges. This talk will address these challenges and advancements, beginning with an overview of classical traffic control concepts. It will then focus on emerging research trends that exploit the multi-scale nature of traffic systems, from the microscopic scale of the individual CAV to the macroscopic scale of the traffic flow. Furthermore, it will illustrate how these aspects can efficiently coexist within an advanced vehicular traffic control system that optimizes the traffic throughput and mitigates the environmental impact.



Personal Data Privacy: Especially Location

John Krumm
Microsoft Research
United States

Brief Bio
John Krumm graduated from the School of Computer Science at Carnegie Mellon University in 1993 with a PhD in robotics and a thesis on texture analysis in images. He worked at the Robotics Center of Sandia National Laboratories in Albuquerque, New Mexico for the next four years. His main projects there were computer vision for object recognition for use in robots and vehicles. He was at Microsoft Research in Redmond, Washington, USA for 25 years, starting in 1997. He is currently an associate director of the Integrated Media Systems Center in the Viterbi School of Engineering at the University of Southern California. His research focuses on understanding peoples' location and personal data privacy. In 2017 he received a 10-year impact award for a paper on location privacy from the ACM UbiComp conference, and another from the same conference in 2021. He received the best paper award at the ACM SIGSPATIAL conference in 2022 and at the Mobile Data Management conference in the same year. His h-index on Google Scholar is 75. He is an inventor on 82 U.S. patents. Dr. Krumm was a PC chair for UbiComp 2007, ACM SIGSPATIAL 2013, and ACM SIGSPATIAL 2014. He is a past coeditor in chief of the Journal of Location Based Services and past associate editor for ACM Transactions on Spatial Algorithms and Systems. He currently serves on the editorial board of IEEE Pervasive Computing Magazine. He is the chair of the executive committee of ACM SIGSPATIAL and part of the Science Advisory Committee of the Geospatial Science and Human Security Division at Oak Ridge National Laboratory. He is an editorial fellow for the Paris Institute for Advanced Study.

We generally give away our personal for free on the web. While technologies like differential privacy can protect our aggregated data, the holders of our data can still use it to make individual inferences about us that might be alarming. This talk will demonstrate some of these inferences for location data (GPS tracks) and for more general personal data as a first step to understanding and reducing the privacy risks. This leads to a study to see if people become more concerned about the privacy of their personal data if they know what could be inferred from it. Finally, I’ll discuss how we can compute a specific price for individual location points so people could sell their location data rather than give it away.



Youla-Kučera Parameterization: Theory and Applications

Vladimír Kučera
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague
Czech Republic

Brief Bio

Vladimír Kucera was born in Prague, Czech Republic, in 1943. He received the graduate degree summa cum laude in electrical engineering from Czech Technical University in Prague in 1966 and the CSc. and DrSc. research degrees in control engineering from the Czechoslovak Academy of Sciences, Prague, in 1970 and 1979, respectively.

From 1967 to 2017, he was a Researcher and, from 1990 to 1998, the Director of the Institute of Information Theory and Automation, one of the research institutes of the Academy of Sciences of the Czech Republic. He is currently an Emeritus Scientist of the Academy of Sciences. Since 1996, he has been a Professor of Control Engineering at the Czech Technical University in Prague. He served the university as the Head of the Control Engineering Department, the Dean of Electrical Engineering, and the Masaryk Institute of Advanced Studies Director. He is currently a Distinguished Researcher and Vice Director of the Czech Institute of Informatics, Robotics and Cybernetics. 

He held many visiting positions at prestigious European, American, Asian, and Australian universities. His research interests include linear systems and control theory. His well-known result is the parameterization of all controllers that stabilize a given system, known as the Youla-Kucera parameterization. Recently, he has resolved a long-standing open problem of linear control theory, the decoupling by static-state feedback.

Prof. Kucera is a Life Advisor, Fellow, and former President of IFAC. He is a Life Fellow of IEEE. He received many prizes, including the Czechoslovak Academy of Sciences Prize in 1972, the National Prize of the Czech Republic in 1989, the Automatica Prize Paper Award in 1990, and the IFAC System Structure and Control Lifetime Achievement Award in 2022. He is the 2021 laureate of the National Prize Ceská hlava (Czech Mind), the most prestigious Czech award that scientists in the Czech Republic can achieve.

He is an Honorary Professor at Northeastern University in Shenyang, China, and has received honorary doctorates from Université Paul Sabatier in Toulouse, France, and Université Henri Poincaré in Nancy, France. He was awarded Chevalier dans l’ordre des palmes académiques, a national order of France for distinguished academics. In 2023, he was elected to membership of the American Philosophical Society.

Youla-Kučera parameterization is the parameterization of all linear controllers that stabilize a given linear plant. D.C. Youla (Polytechnic Institute of New York University) [1], [2] and V. Kučera [3], [4] independently discovered the parameterization formula in the late seventies. A comprehensive account of the result was provided ten years later by M. Vidyasagar [5]. A. Quadrat [6] generalized the parameterization results from lumped-parameter systems to a class of distributed-parameter linear systems. The survey paper by Anderson [7] summarized the first twenty years of theoretical developments. In contrast, the recent survey paper by I. Mahtout, F. Navas, V. Milanes, and F. Nashashibi [8] collects the latest developments and industrial applications; it also provides an impressive list of references.

Parameterization is essential when control systems are designed to be stable and meet additional performance specifications. The specifications beyond stability are achieved by selecting an appropriate parameter. There is a one-to-one correspondence between the set of stabilizing controllers and the set of parameters. Furthermore, the parameter appears linearly in the closed-loop system transfer function, whereas the controller appears nonlinearly. Selecting the parameter instead of the controller thus simplifies the design significantly. The system is made stable first, and then the additional specifications can be accommodated, one at a time. 

Performance specifications, such as optimality and robustness, are often conflicting and challenging to achieve using a single controller. In such a case, parameterization allows the designer to reconfigure the controller to reach satisfactory performance while guaranteeing overall system stability.

The Youla-Kučera parameterization is a fundamental result that launched an entirely new area of research and has been used to solve many control problems, ranging from optimal control, robust control, disturbance and noise rejection, and vibration control to stable controller switching and fault-tolerant control.
There is a dual parameterization, which describes all linear systems stabilized by a given linear controller. The parameter can then describe plant variations. This is useful for solving the problem of closed-loop plant identification. Open-loop identification is more straightforward, but it is often prohibitive to disconnect the plant. Identifying the dual parameter instead of the plant is a linear problem like open-loop identification.

This keynote presentation is a guided tour through the theory and applications of the Youla-Kučera parameterization. It explains the origins of the result, the derivation of the parameterization formula using the transfer functions, and the state-space representation of all stabilizing controllers. It also explains how to select the parameter to satisfy specific design requirements. New and exciting applications of the Youla–Kučera parameterization are then discussed: stabilization subject to input constraints, output overshoot reduction, and fixed-order stabilizing controller design. A selection of applications in different control fields is presented showing the efficiency of this approach in controlling complex systems.

[1] D.C. Youla, J.J. Bongiorno, and H.A. Jabr, “Modern Wiener-Hopf design of optimal controllers, Part I: The single-input case,” IEEE Transactions on Automatic Control, vol. 21, 1976, pp. 3-14.
[2] D.C. Youla, H.A. Jabr, and J.J. Bongiorno, “Modern Wiener-Hopf design of optimal controllers, Part II: The multivariable case,” IEEE Transactions on Automatic Control, vol. 21, 1976, pp. 319-338.
[3] V. Kučera, “Stability of discrete linear control systems,” IFAC Proceedings Volumes, vol. 8, no. 1, part 1, 1975, pp. 573-578.
[4] V. Kučera, Discrete Linear Control: The Polynomial Equation Approach. Chichester, UK: Wiley, 1979.
[5] M. Vidyasagar, Control System Synthesis: A Factorization Approach. Cambridge, MA: MIT Press, 1985.
[6] A. Quadrat, “On a generalization of the Youla-Kučera parametrization. Part I: The fractional ideal approach to SISO systems,” Systems & Control Letters, vol. 50, 2003, pp. 135-148.
[7] B.D.O. Anderson, “From Youla-Kucera to identification, adaptive and nonlinear control,” Automatica, vol. 34, 1988, pp. 1485-1506.
[8] I. Mahtout, F. Navas, V. Milanes, F. Nashashibi, “Advances in Youla-Kucera parametrization: A Review,” Annual Reviews in Control, vol. 49, 2020, pp. 81-94.

This work was co-funded by the European Union under Project Robotics and Advanced Industrial Production CZ.02.01.01/00/22_008/0004590.