Plenary and Semi-Plenary Lectures

Frédéric Mazenc

DR2 Inria at Inria Saclay, France

Plenary Lecture Title:

Two New Stability Analysis Techniques

Time and Location:

14:05-15:00 UTC, September 15, 2021 (Online)

Focus of Lecture:

Control

Speaker Bio:

Frédéric Mazenc received his Ph.D. in Automatic Control and Mathematics from the CAS at Ecole des Mines de Paris in 1996. He was a Postdoctoral Fellow at CESAME at the University of Louvain in 1997. From 1998 to 1999, he was a Postdoctoral Fellow at the Centre for Process Systems Engineering at Imperial College. He was a CR at INRIA Lorraine from October 1999 to January 2004. From 2004 to 2009, he was a CR1 at INRIA Sophia-Antipolis. Since 2010, he has been a first CR1 and next DR2 at INRIA Saclay. He received a best paper award from the IEEE Transactions on Control Systems Technology at the 2006 IEEE Conference on Decision and Control. His current research interests include nonlinear control theory, differential equations with delay, robust control, and microbial ecology. He has more than 200 peer reviewed publications. Together with Michael Malisoff, he authored a research monograph entitled Constructions of Strict Lyapunov Functions in the Springer Communications and Control Engineering Series.

Abstract:

We present two recent stability analysis techniques for wide families of nonlinear dynamical. The first one is called ‘trajectory based approach’ and, in contrast with Lyapunov based approaches, it involves verifying certain inequalities along solutions of auxiliary systems. It is especially useful when systems with delays and discontinuities are studied. The second technique involves inequalities of Halanay's type. It is especially efficient for time-varying dynamical systems. We show in particular how this technique can be extended to the case where vectorial inequalities are satisfied. We give necessary and sufficient stability conditions in the time-invariant case and, in the time-varying case, a sufficient condition, which can be easily checked in practice. Both of the techniques apply to a wide range of systems, notably time-varying systems with time-varying delay, ODE coupled with difference equations, and networked control systems with delay.

Jacquelien M.A. Scherpen

Professor at University of Groningen, the Netherlands

Plenary Lecture Title:

Analytical and Data-based Model Reduction for Nonlinear Systems Based on Differential Balancing

Time and Location:

14:05-15:00 UTC, September 16, 2021 (Online)

Focus of Lecture:

Modelling

Speaker Bio:

Jacquelien M. A. Scherpen received her MSc and PhD degrees in 1990 and 1994 from the University of Twente, the Netherlands. She then joined Delft University of Technology and moved in 2006 to the University of Groningen as a professor in Systems and Control Engineering at the Engineering and Technology institute Groningen (ENTEG), fac. Science and Engineering at the University of Groningen, the Netherlands. From 2013 til 2019 she was scientific director of ENTEG. She is currently director of the Groningen Engineering Center, and Captain of Science of the Dutch top sector High Tech Systems and Materials (HTSM).
Her current research interests include model reduction methods for networks, nonlinear model reduction methods, nonlinear control methods, modeling and control of physical systems with applications to electrical circuits, electromechanical systems, mechanical systems, smart energy networks and distributed optimal control applications to smart grids.
Jacquelien has held various visiting research positions, such as at the University of Tokyo, and Kyoto University, Japan, Université de Compiegne, and SUPÉLEC, Gif-sur-Yvette, France, and Old Dominion University, VA, USA.
Jacquelien has been and is at the editorial board of a few international journals among which the IEEE Transactions on Automatic Control, and the International Journal of Robust and Nonlinear Control. She received the 2017-2020 Automatica Best Paper Prize. In 2019 she received a royal distinction and is appointed Knight in the Order of the Netherlands Lion, and she is a fellow of IEEE.
She has been active at the International Federation of Automatic Control (IFAC), and is currently member of the IFAC council. She is a member of the Board of Governors of the IEEE Control Systems Society, and was chair of the IEEE CSS standing committee on Women in Control in 2020. From 2020 to 2021 she is president of the European Control Association (EUCA).

Abstract:

We present a balancing theory for nonlinear systems in the contraction framework. We use prolonged systems to define the controllability and observability functions which can be used for a simultaneous diagonalization procedure, providing a measure for importance of the states. We show that differential balancing has close relationships with the Frechet derivative of the nonlinear Hankel operator. Furthermore, we take a generalized balancing approach in order to have a computationally more feasible method. Error bounds for model reduction by generalized balancing are provided.
In addition, we propose an empirical balancing method for nonlinear systems whose input vector fields are constants by utilizing its variational system. For a fixed state trajectory, it is possible to compute the values of the differential Gramians by using impulse and initial state responses of the variational system. Therefore, balanced truncation is doable along state trajectories without solving nonlinear partial differential equations. We further develop an approximation method, which only requires trajectories of the original nonlinear system. The method is validated on a nonlinear system of order 100.
The work presented is joint work with Yu Kawano.

Hyungbo Shim

Professor at Seoul National University, Korea

Plenary Lecture Title:

Design of Heterogeneous Multi-Agent System for Distributed Computation and Control

Time and Location:

14:05-15:00 UTC, September 17, 2021 (Online)

Focus of Lecture:

Modelling, Control

Speaker Bio:

Hyungbo Shim received the B.S., M.S., and Ph.D. degrees from Seoul National University, Korea, in 1993, 1995, and 2000, respectively, and held the post-doc position at University of California, Santa Barbara till 2001. He joined Hanyang University, Seoul, in 2002. Since 2003 he has been with Seoul National University, and he is now the director of Automation and Systems Research Institute. He has served as an associate editor for Automatica, IEEE Trans. on Automatic Control, Int. Journal of Robust and Nonlinear Control, and European Journal of Control, and as an editor for Int. Journal of Control, Automation, and Systems. He has been a member of the organizing committee of many conferences including IEEE CDC 2020. He is the IPC chair of IFAC World Congress 2026. His research interest includes stability analysis of nonlinear systems, observer design, disturbance observer technique, secure control systems, and synchronization for multi-agent systems.

Abstract:

This talk presents a way to design networked dynamical systems that perform a desired computation or control as a whole. The key is that the group behavior can be estimated by an average of individual node dynamics participating in the network as long as the averaged dynamics is stable---which is termed as blended dynamics theorem. We first discuss how to use it for designing distributed optimization algorithms. Since stability is asked for the averaged dynamics but not for individual node dynamics, the individual cost functions need not be convex as long as their sum is convex. We also discuss a distributed feedback control towards decentralized construction of individual agents. An agent can leave or join the network during the operation because the proposed design does not require initialization. A discrete-time version and a hybrid system version of the blended dynamics theorem are also briefly presented.

Gianluigi Pillonetto

Professor at University of Padova, Italy

Semi-Plenary Lecture Title:

Kernel-Based Methods in Non-Structured Nonlinear System Identification

Time and Location:

12:00-12:45 UTC, September 16, 2021 (Online)

Focus of Lecture:

Identification

Speaker Bio:

Gianluigi Pillonetto was born on January 21, 1975 in Montebelluna (TV), Italy. He received the Doctoral degree in Computer Science Engineering cum laude from the University of Padova in 1998 and the PhD degree in Bioengineering from the Polytechnic of Milan in 2002. In 2000 and 2002 he was visiting scholar and visiting scientist, respectively, at the Applied Physics Laboratory, University of Washington, Seattle. From 2002 to 2005 he was Research Associate at the Department of Information Engineering, University of Padova, becoming an Assistant Professor in 2005. He is currently a Professor of Control and Dynamic Systems at the Department of Information Engineering, University of Padova. His research interests are in the field of system identification, estimation and machine learning. From 2014 to 2016 he has been Associate Editor of Systems & Control Letters. He currently serves as Associate Editor for Automatica and IEEE Transactions on Automatic Control. In 2003 he received the Paolo Durst award for the best Italian Ph.D. thesis in Bioengineering, he was the 2017 recipient of the Automatica Prize, assigned every three years for outstanding contributions to control theory by the International Federation of Automatic Control (IFAC) and Automatica (Elsevier), and he was Plenary Speaker at System Identification IFAC Symposium in 2018. He has been elevated to IEEE Fellow in 2020 for contributions to System Identification.

Abstract:

Classical system identification relies on parametric estimation paradigms coming from mathematical statistics. Within this paradigm, a key point is the selection of the most adequate model structure which is typically performed via complexity measures such as the Akaike's criterion. Starting from the linear scenario, then moving to the nonlinear one, this talk will describe how the model selection problem can be successfully faced by a different approach. In particular, I will discuss the use of Bayesian kernel-based methods where the unknown system is seen as a Gaussian process whose covariance (kernel) includes information on system stability and/or fading memory. Here, tuning of model complexity gets a whole new dimension and richness in the choice of (continuous) regularization parameters compared to the choice of (discrete) model orders.

Tiago Roux Oliveira

Associate Professor at State University of Rio de Janeiro, Brazil

Semi-Plenary Lecture Title:

Extremum Seeking through Partial Differential Equations

Time and Location:

12:00-12:45 UTC, September 16, 2021 (Online)

Focus of Lecture:

Adaptation

Speaker Bio:

Dr. Tiago Roux Oliveira was born in Rio de Janeiro, Brazil, 1981. He received the B.Sc. degree in Electrical Engineering from the State University of Rio de Janeiro (UERJ) in 2004, the M.Sc. and Ph.D. degrees both in Electrical Engineering from the Graduate School and Research in Engineering, Federal University of Rio de Janeiro (COPPE/UFRJ), in 2006 and 2010, respectively. In 2014, he was a Visiting Scholar with the University of California - San Diego (UCSD), CA, USA. He is currently an Associate Professor with the Department of Electronics and Telecommunication Engineering (DETEL), UERJ. His current research interests include nonlinear control theory, extremum seeking, sliding mode control/observers, time delays and boundary control for partial differential equations. He published about 200 refereed journal articles, conference papers and book chapters. Dr. Oliveira has served as a member of the IFAC Technical Committees: Adaptive and Learning Systems (TC 1.2) and Control Design (TC 2.1), and the Technical Committee on Variable Structure and Sliding Mode Control of the IEEE Control Systems Society (CSS). He was a recipient of the Bolsa Nota 10 (Highest Rank Scholarship prize) sponsored by the Brazilian Agency FAPERJ, the CAPES National Award of Best Thesis in Electrical Engineering, in 2011, and the FAPERJ Young Researcher Award, in 2012, 2015, and 2018. Prof. Tiago was the Guest Editor of the International Journal of Adaptive Control and Signal Processing in the Special Issue "From Adaptive Control to Variable Structure Systems - Seeking Harmony". He has served also as an Associate Editor on the Editorial Board of the Journal of the Franklin Institute, the Journal of Control, Automation, and Electrical Systems, the IEEE Latin America Transactions, Systems & Control Letters, and the International Journal of Robust and Nonlinear Control. In 2017, he was nominated as an Affiliate Member of the Brazilian Academy of Sciences (ABC). In 2018, he was elevated to the grade of IEEE Senior Member of the CSS. Since 2019, he is an Associate Editor for the European Control Association Conference Editorial Board (EUCA-CEB). In 2020, he was elected and nominated Chair of the Technical Committee 1.2 (Adaptive and Learning Systems) of the IFAC for the triennium 2020-2023.

Abstract:

In almost one century since its first applications and more than two decades since its formal convergence proof, the extremum seeking algorithm has been recognized as one of the most important model-free real-time optimization tools. However, until recently extremum seeking has been restricted to dynamic systems represented by connections of Ordinary Differential Equations (ODEs) and non-linear convex maps with unknown extremum points. This plenary presents the first results on the theory and design of extremum seeking strategies for systems governed by Partial Differential Equations (PDEs). The main ideas for the design of the Gradient-Newton methods and the stability analysis for infinite-dimensional systems will be discussed considering a wide class of parabolic and hyperbolic PDEs: delay equations, wave equation and reaction-advection-diffusion models. Moreover, engineering applications are presented, including problems of noncooperative games, neuromuscular electrical stimulation, biological reactors, oil-drilling systems and flow-traffic control for urban mobility. This is joint work with Prof. Miroslav Krstic from University of California, San Diego.

The information on this page is as of June 21, 2021.