ECC25 will feature plenary and semi-plenary lectures with the following speakers:

Miroslav Krstic , Tobias Geyer, Timm Faulwasser,
Yongduan Song,  Antonis Papachristodoulou, Luca Zaccarian, Frank Allgöwer, Stefano Stramigioli, Jing Zhou

Plenary Speakers

Miroslav Krstic-Robust Adaptive Control and the Greeks who Made it Possible

Abstract

As a feedback designer, I know no harder problem than adaptive control: simultaneous stabilization and identification, under unlimited parametric uncertainty. With his sigma-modification (1983), Petros Ioannou secured the survival of adaptive control in the face of non-parametric uncertainties. And his students Tsakalis, Polycarpou, as well as Tao, Sun, Datta, and others, took robust adaptive control, through the rest of the 1980s and 1990s, to the limits of possibilities. For time-varying parameters, nonlinear systems, and unmodeled dynamics. Among various “robustification” tools, sigma-modification remained the best option for 40 years.
Alas, sigma-modification’s regulation bias is unknown and irreducible. Decades of unsuccessful attempts to improve robust adaptive control, rather than a hard mathematical evidence of fundamental limitations for adaptive control, created a belief that adaptive control is pointless and somehow “cursed,” since under nonparametric uncertainties only properties that are not much more precise than boundedness are achieved.
Then, seemingly out of nowhere, an NTUA mathematician appears, Iasson Karafyllis, with no prior history in adaptive control. He cracks the code for how to move robust adaptive control forward after 40 years: adaptation with a deadzone, plus control with nonlinear damping under a dynamic gain. At last, asymptotic performance is arbitrarily close to perfect, under unlimited disturbances and parametric uncertainty. How exactly? Please join me for the lecture and find out.

Speaker Bio:

Miroslav Krstic is Distinguished Professor and Senior Associate Vice Chancellor for Research at University of California, San Diego. He is Fellow of IEEE, IFAC, ASME, SIAM, AAAS, AIAA (Assoc. Fellow), IET (UK), and member of the Serbian Academy of Sciences and Arts. Krstic is a recipient of the Bellman Control Heritage Award, Bode Lecture Prize, SIAM Reid Prize, ASME Oldenburger Medal, Nyquist Lecture Prize, Paynter Award, Ragazzini Education Award, IFAC Nonlinear Control Systems Award, IFAC Ruth Curtain Distributed Parameter Systems Award, IFAC Adaptive and Learning Systems Award, Chestnut textbook prize, CSS Distinguished Member Award, the PECASE, NSF Career, and ONR Young Investigator awards, the Schuck (’96 and ’19) and Axelby paper prizes, and the first UCSD Research Award given to an engineer. Krstic is also the inaugural recipient of the A.V. Balakrishnan Award for the Mathematics of Systems. He is due to transition from Editor-in-Chief of Systems & Control Letters to EiC of IEEE Trans. Automatic Control in January 2026. Krstic has coauthored 18 books and about 490 journal papers.

Miroslav Krstic

Distinguished Professor
University of California, San Diego, USA

Tobias Geyer-Control of power conversion systems: challenges, theory and applications

Abstract:

Power converters pose fascinating control challenges due to their switched linear system characteristic and the need to operate them in the microsecond time scale. Converters are omnipresent and vital to decarbonize our planet with renewable energy systems and the electrification of the transportation sector and industry. Model predictive control maximizes the performance of such converters by boosting their power, minimizing their cost and increasing their resilience to disturbances and faults. This plenary provides an introduction to the intriguing world of power conversion control, its research challenges and showcases some promising results that are already revolutionizing industry.

Speaker Bio:

Tobias Geyer is a Corporate Executive Engineer at ABB System Drives in Switzerland and R&D platform manager of the ACS6000 and ACS6080. His research interest are high-power converters and drives, optimized pulse patterns and model predictive control. Dr. Geyer received the M.Sc. in electrical engineering, the Ph.D. in control theory and the Habilitation degree in power electronics from ETH Zurich in 2000, 2005 and 2017, respectively. He was appointed as an extraordinary professor at Stellenbosch University in 2017 and has been teaching a course at ETH Zurich since 2016. He has received five IEEE prize paper awards, filed about 90 patents and co-authored more than 170 peer-reviewed publications. He has co-supervised more than 25 students, among them 8 Ph.D. students. He is a former distinguished lecturer of the IEEE Power Electronics Society and a former associate editor of the IEEE Transactions on Power Electronics. Dr. Geyer is a Fellow of the IEEE.

Tobias Geyer

Corporate executive engineer
ABB System Drives, Switzerland

Timm Faulwasser-Dissipativity, Optimal Control, and Turnpikes – How to get from Willems to Wasserstein and Deep Learning?

Abstract:

The dissipativity notion for open dynamic systems conceived by Jan Willems as well as the optimal control twin breakthroughs, i.e. the maximum principle by Lev Pontryagin et al. and Richard Bellman’s dynamic programming, are fundamental pillars of systems and control. At first glance, it may appear as if dissipation inequalities are only loosely related to optimal control. Yet, in this talk we explore the fundamental relations between both through the turnpike phenomenon in optimal control.

The turnpike phenomenon refers to situations where for different initial conditions and varying horizons, the optimal state and input trajectories spend increasing amounts of time close to the optimal steady state. The first observations of such behavior can be traced back to macroeconomics and the works of John von Neumann and Frank P. Ramsey in the 1930s and 1920s. Here, we discuss the equivalence of strict dissipativity and turnpike properties of optimal control problems.

Closing the loop with MPC, we explore how dissipativity helps to analyze the properties of receding-horizon approximations to infinite-horizon problems. Considering port-Hamiltonian structures of thermodynamic systems, we show how to exploit physics in optimal control problems with respect to energy, entropy, and exergy. This leads to turnpikes on the manifold of thermodynamic equilibria. Exploring dissipativity in stochastic optimal control then brings forth turnpikes in the Wasserstein metric.

At last, we enter the turnpike once more to analyze the training of neural networks through the lens of optimal control. Specifically, we discuss the dissipativity properties of cross-entropy loss functions – thus we conclude by leveraging optimal control and dissipativity concepts for deep learning.

Speaker Bio:

Timm Faulwasser is a Professor in the School of Electrical Engineering, Computer Science and Mathematics at Hamburg University of Technology, while before he held a professorship at TU Dortmund University. He has studied Engineering Cybernetics with minor in philosophy at the University of Stuttgart. After doctoral studies in the International Max Planck Research School for Analysis, Design and Optimization in Chemical and Biochemical Process Engineering Magdeburg he obtained his PhD from the Otto-von-Guericke-University Magdeburg, Germany in 2012. He has been postdoctoral researcher at École Polytechnique Fédérale de Lausanne (2013-2016) and senior researcher at Karlsruhe Institute of Technology (2015-2019). Previously, Timm was a member of the IEEE-CSS Conference Editorial Board and associate editor of the European Journal of Control. Currently, he serves as associate editor for the IEEE Transactions on Automatic Control, the IEEE Control System Letters, and Mathematics of Control Systems and Signals. Timm received the 2021-2023 Automatica Paper Prize.

Timm Faulwasser

Professor
School of Electrical Engineering, Computer Science and Mathematics
Hamburg University of Technology

Semi-Plenary Speakers

Yongduan Song- NN Learning Driven Automatic Control & Automatic Control for Machine Learning

Abstract:

In contemporary engineering and scientific research, the interplay between automatic control and machine learning has become increasingly significant. This report explores two key aspects of this relationship: the application of machine learning techniques to enhance automatic control systems and the use of automatic control principles to improve machine learning algorithms. Firstly, we discuss how machine learning can be leveraged to optimize control strategies in complex systems, enabling adaptive and intelligent responses to dynamic environments. Techniques such as reinforcement learning and neural networks are examined for their ability to learn from data, resulting in more efficient control mechanisms that can handle uncertainty and nonlinearity.
Secondly, we investigate how principles of automatic control can be applied to refine machine learning processes. Concepts such as feedback control can be utilized to stabilize learning algorithms, reduce overfitting, and ensure convergence in various machine learning applications. This dual perspective highlights the mutual benefits and synergies that arise from integrating these two fields.
Through case studies and examples, we demonstrate the transformative potential of combining machine learning and automatic control, paving the way for advances in robotics, autonomous systems, and smart technologies. Ultimately, this report aims to provide insights into the future directions of research and the practical implications of merging these two domains.

Speaker Bio:

Yongduan Song (Fellow, IEEE; AAIA, CAA, CAI) earned his Ph.D. in Electrical and Computer Engineering in 1992 and is a tenured Full Professor in the United States. From 2005 to 2008, he served as one of six Langley Distinguished Professors at the National Institute of Aerospace (NIA), where he was also the Founding Director of the Center for Cooperative Systems.

Professor Song previously held the position of Dean of the School of Automation at Chongqing University and is currently the Director of the Chongqing University Artificial Intelligence Research Institute. Additionally, he has a dual appointment as the Dean of the School of Artificial Intelligence at Anhui University.

His research interests are broad and include intelligent systems, guidance, navigation, and control, as well as bio-inspired adaptive and cooperative systems, with a particular focus on data-driven control and machine learning methodologies. He has published 12 books and over 400 scientific papers, and holds 100 patents issued by China, the USA, and Japan. He has made significant contributions to the academic community through his role as an Associate Editor for several prestigious international journals, including the IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Systems, Man, and Cybernetics. Currently, he serves as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems.

Since 2021, Professor Song has been recognized as one of the World’s Top 2% Scientists by Stanford University and has been listed as one of the most highly cited researchers by Clarivate since 2019.

Yongduan Song

Fellow of IEEE,
Fellow of International Eurasian Academy of Sciences,
Fellow of Chinese Automation Association, China

Antonis Papachristodoulou-Analysis and Design of Safe Control Laws

Abstract:

Intelligent, autonomous systems are increasingly prevalent in today’s society and will continue to proliferate. Many of these systems are critical, necessitating rigorous safety guarantees. However, designing and verifying safe controllers remains challenging, even for systems with linear dynamics.
This talk will focus on the design of safe control systems using a Control Barrier Function (CBF) approach, which ensure forward invariance of safe sets. I will first present a new convex design method for linear systems, enabling the co-design of the controller and the barrier function. I will then extend the analysis to more complex scenarios, including systems with nonlinear dynamics, constraints, high relative degree, and model uncertainty.
Finally, I will consider the case of multi-agent systems, proposing a novel distributed control algorithm with parallel computation for enhanced safety. Recognizing that computational limitations may necessitate early termination, I will present a probabilistic result for guaranteeing safety.
This work is a collaborative effort with Dr. Han Wang and Professors Kostas Margellos and Claudio de Persis.

Speaker Bio:

Antonis joined the University of Oxford in 2006, where he is currently the University’s Statutory Professor in Control Engineering and a fellow of Kellogg College, Oxford. Previously, he was a tutorial fellow at Worcester College, as well as an EPSRC fellow and the director of the EPSRC & BBSRC Centre for Doctoral Training in synthetic biology.

Antonis obtained an MA/MEng degree in Electrical and Information Sciences from the University of Cambridge, before completing a PhD in Control and Dynamical Systems at the California Institute of Technology, with a PhD minor in Aeronautics. In 2015 he was awarded the European Control Award for his contributions to robustness analysis and applications to networked control systems and systems biology, as well as the O. Hugo Schuck Best Paper Award.

Antonis is an IEEE Fellow for his contributions to the analysis and design of networked control systems. He serves regularly on Technical Programme Committees for conferences and was associate editor for Automatica and IEEE Transactions on Automatic Control. In 2024 he served as Programme co-chair for the European Control Conference and co-chair for the conference on Learning for Decision and Control.

Antonis Papachristodoulou

Professor of Control Engineering,
University of Oxford, UK

Luca Zaccarian-Uncovering Reset-endowed LTI Flow Dynamics in Hybrid Control of Mechatronic Systems

Abstract:

Control-oriented models of mechatronic systems often reveal, after suitable coordinate transformations, an underlying linear time-invariant (LTI) continuous-time structure. This LTI behavior is typically interrupted by resetting actions, which may be intrinsic to the system or available as design elements for the control engineer. These resets introduce nonlinear effects that create powerful degrees of freedom in the resulting hybrid motion.

This talk will explore how a variety of such systems — despite their apparent differences — share this common LTI flow combined with reset-induced discontinuities. We will illustrate how hybrid Lyapunov theory, combined with timers and logical states, provides a systematic approach to analyzing stability and convergence. The discussion will emphasize how recognizing and exploiting the LTI structure within the hybrid dynamics can open up effective leads for feedback design.

Speaker Bio:

Luca Zaccarian received the Ph.D. degree in 2000 from the University of Roma Tor Vergata (Italy), where he has been Associate Professor until 2011. Since 2011 he is Directeur de Recherche at the LAAS-CNRS, Toulouse (France) and since 2013 he holds a part-time professor position at the University of Trento, Italy. Luca Zaccarian’s main research interests include analysis and design of nonlinear and hybrid control systems, modeling and control of mechatronic systems. He has served in the organizing committee and TPC of several IEEE and IFAC conferences. He was a member of the IEEE-CSS Conference Editorial Board and an AE for Systems and Control Letters and IEEE Transactions on Automatic Control. He is currently senior editor for nonlinear systems and control for the IFAC journal Automatica, a member of the EUCA-CEB and an associate editor for the European Journal of Control. He was a nominated member (2014) and an elected member (2017-19) of the Board of Governors of the IEEE-CSS. He was a recipient of the 2001 O. Hugo Schuck Best Paper Award given by the American Automatic Control Council. He is a fellow of the IEEE, class of 2016.

Luca Zaccarian

Directeur de Recherche and Professor
LAAS-CNRS and University of Trento, Italy

Frank Allgöwer-Event-based control: When to Act, When to Wait, and Why It Matters

Abstract:

Why should a controller act only when the clock says so, rather than when it actually matters? Event-triggered control and its cousin self-triggered control challenge the traditional approach of periodic control by closing feedback loops only when needed. These smart, resource-aware approaches have attracted great interest in areas like networked systems or real-time computing – promising efficiency without compromising control objectives.

But with great flexibility comes great theoretical challenges. Which design frameworks can we use to find performant triggering conditions? How do we rigorously analyze sampling behavior? What trade-offs emerge between control performance and trigger frequency?

In this talk, we will take a tour through research trends in event-based control, highlighting both breakthroughs and unresolved challenges in the field. We will give a perspective on its connections to areas like neuromorphic control and event-based vision, examining its origins and future prospects.

Speaker Bio:

Frank Allgöwer is director of the Institute for Systems Theory and Automatic Control at the University of Stuttgart in Germany and professor in the mechanical engineering department there. His current research interests are to develop new methods for data-based control, optimization-based control and networked control. Frank has published over 500 papers and received several recognitions for his work including the IFAC Outstanding Service Award, the IEEE CSS Distinguished Member Award, the State Teaching Award of the German state of Baden-Württemberg, and the Leibniz Prize of the Deutsche Forschungsgemeinschaft.
Frank has been the President of the International Federation of Automatic Control (IFAC) for the years 2017-2020. He was Editor for the journal Automatica from 2001 to 2015 and is editor for the Springer Lecture Notes in Control and Information Science book series. He was Vice-president for Technical Activities of the IEEE Control Systems Society for the years 2013/14 and was on the EUCA Council for 2001-2004. From 2012 until 2020, Frank also served as Vice-President of Germany’s most important research funding agency, the German Research Foundation (DFG).

Frank Allgöwer

Director of the Institute for Systems Theory and Automatic Control
Institute for Systems Theory and Automatic Control
University of Stuttgart, Germany

Stefano Stramigioli-Port-Based thinking in modeling and control of all physical systems

Abstract:

Stemming from the work of Henry M. Paynter introducing bond graphs in the late 1950s, the system theory of port-Hamiltonian systems has quickly developed thanks the pionirring work of Bernhard Maschke and Arjan van der Schaft. And yet, such powerful ideas are not as widely spread as they should even if they give solutions to many problems in modeling and control of complex physical systems. In this presentation I will introduce the basics of such ideas and show the power in its application in modeling the complex interaction of a fluid and the deformable elasticity of the wings of a robotics bird.

Speaker Bio:

Stefano Stramigioli received the M.Sc. with honors (cum laude) in 1992 and the Ph.D with honors (cum laude) in 1998. He is currently full professor in Advanced Robotics. He is an IEEE Fellow, an ERC AdG and ERC PoC laureate and member of the Royal Holland Society of Science and Humanities (KHMW). He is currently leading a growing group of about 60 people. He has been among other Editor in chief of the IEEE Robotics and Automation Magazine, AdCom member of the IEEE Robotics and Automation Society, founder and chair of the Electronic Products and Services of the IEEE Robotics and Automation Society and he has been serving as Vice President for Membership of the same society for two consecutive terms also in the past.  His scientific interest is in the covariant and port-based physical modeling and control of complex physical systems.  He has been teaching Modeling, Control and Robotics for under and post-graduates for many years. He has around 450 publications including 4 books.

Stefano Stramigioli

Chair of the RAM LAB,
University of Twente, Netherlands

Jing Zhou-Adaptive Control with Quantization

Abstract:

In control engineering, information and sensing technologies are essential components of real-time measurement systems, providing variable resolution quantitative measurements in control systems, such as in electric power grid systems, intelligent transportation systems, vehicle coordination systems, and offshore mechatronics. Typically, these control systems rely on communication channels. An important aspect is to use quantization schemes with sufficient precision while maintaining low communication rates.This talk explores the challenges and advancements in quantized control, focusing on the theoretical foundations of stability analysis and the interplay between quantization and control system design. Specifically, it addresses the adaptive control of dynamic systems in the presence of uncertainties, nonlinear dynamics, and quantization. The discussion highlights how adaptive design and Lyapunov-based analysis can be applied systematically to mitigate, offering a unified and systematic framework for tackling these challenges.

Speaker Bio:

Jing Zhou is a professor and research director of the Priority Research Center of Mechatronics in the Faculty of Engineering and Science, University of Agder, Norway. She received the Ph.D.degree from the Nanyang Technological University, Singapore, in 2006. Dr. Zhou has holds positions as a senior research scientist at the International Research Center of Stavanger, Norway, and as a Post-Doctoral Fellow at the Norwegian University of Science and Technology, Norway. Her research interests include adaptive control, nonlinear control, networked control, learning-based control, and control applications to mechatronics systems including cranes, marine vessels, oil drilling systems, robotics.

Dr. Zhou is a Fellow of Norwegian Academy of Technological Sciences. She serves as an associate editor of several journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Industrial Electronics, Systems & Control Letter. She has contributed to
numerous conferences, serving as general chair and technical program chair and is an AdCom member-at large for IEEE IES.

Website: https://www.uia.no/english/about-uia/employees/jingz/index.html

Jing Zhou

Professor
Department of Engineering Sciences
University of Agder, Norway