Plenary Lectures


ROCOND2022 will feature the following plenary speakers:

More details about the dates and format of the plenary lectures will be announced on this page in due course.


Toshiharu Sugie

Kyoto University, Japan

Plenary Lecture Title:

Continuous-time system identification in uncertain closed loops

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Speaker Bio:

Toshiharu Sugie is a Professor Emeritus at Kyoto University. He received the B.E., M.E., and Ph.D. degrees in engineering from Kyoto University, Japan, in 1976, 1978, and 1985, respectively. From 1978 to 1980, he was a Research Member of the Musashino Electric Communication Laboratory, NTT, Musashino, Japan. From 1984 to 1988, he was a Research Associate with the Department of Mechanical Engineering, University of Osaka Prefecture, Osaka. From 1988 to 2019, he worked at Kyoto University, where he was a Professor at the Department of Systems Science from 1998 to 2019. In 2019, he joined Osaka University, where he is a specially appointed Professor at the Graduate School of Engineering. His research interests include robust control, identification for control, and control application to mechanical systems. He was as an Associate Editor of several international journals, and served as an Editor for Automatica (2008-2017). He is a Fellow of IEEE and IFAC.

Abstract:

This talk is concerned with system identification, in which a continuous-time system model is constructed from input-output data only. Continuous-time models are suitable for representing many physical systems and are highly consistent with control system design. Among various identification methods, identification in a closed-loop environment may be necessary in practice. In fact, closed-loop identification is inevitable to identify subsystems in large-scale networked systems or open loop unstable systems. Some difficulties of closed-loop identification are shown first, and then a new identification framework to overcome such difficulties will be introduced. In the framework, any information on controllers or excitation signals is not required, and the system identification can be performed in a unified way for stable/unstable systems in closed loop environments. Numerical examples show that it is effective in the presence of heavy measurement noises.


Bénédicte Girouart

European Space Agency, The Netherlands

Plenary Lecture Title:

Robust Control for European Space missions: overview and perspectives

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Speaker Bio:

Bénédicte Girouart received her Aerospace Engineer diploma in 1997 from the Ecole Nationale Supérieure de l'Aéronautique et de l'Espace (ISAE/Supaéro), with a specialisation in Control Systems. She worked almost ten years in this field at EADS Astrium, mainly on Attitude and Orbit Control Sub-systems (AOCS) and Guidance, Navigation, and Control (GNC), being involved in R&D activities (advanced control and estimation theory, re-entry, navigation techniques), and satellites Phase-A studies before moving towards AOCS development and validation for Earth Observation and Scientific programmes (Pléiades, Venus Express, and THEOS). Bénédicte joined the European Space Agency in 2006 as Control Systems Engineer. She provided AOCS support for Science missions (Gaia, Solar Orbiter, Euclid), Earth Observation missions and Telecommunications missions, together with being technical officer for AOCS exploratory activities and advanced studies and being involved in ECSS standard preparation. She became Head of the AOCS and Pointing Systems section in 2016 and Head of the GNC, AOCS and Pointing Division in the Systems Department of the ESA Directorate of Technology, Engineering and Quality in 2020.

Abstract:

AOCS (Attitude and Orbit Control Systems) and GNC (Guidance Navigation and Control) systems for European Space Missions are developed with robustness as critical need: robustness to varying platform physical properties, robustness to uncertain/unknown space environment conditions, robustness and adaptation to enable on board real-time autonomy. Verification being a demanding process for Space missions AOCS and GNC systems, the integration of robustness in the design process is a key asset. The talk presents the challenges of Control design for European Space Agency missions and Robust Control achievements on several past and flying missions and opens on the evolution of AOCS and GNC systems process and design techniques currently studied or foreseen in our technical roadmaps to cope with new challenges on enhanced autonomy, process optimisation for product lines production, New Space platforms,...


Anders Rantzer

Lund University, Sweden

Plenary Lecture Title:

Towards Optimal and Adaptive Control for Large-scale Systems

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Speaker Bio:

Anders Rantzer was appointed professor of Automatic Control at Lund University, Sweden, after a PhD at KTH Stockholm in 1991 and a postdoc 1992/93 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 scalability, adaptation and applications in energy networks.

Abstract:

Classical control theory does not scale well for large systems like traffic networks, power networks and chemical reaction networks. To change this situation, new approaches need to be developed, not only for analysis and synthesis of controllers, but also for modelling and verification. In this lecture we will present some classes of networked control problems for which scalable distributed controllers can be optimised with respect to H2- and H-infinity type performance objectives. Moreover, we will discuss how the lack of accurate models can be addressed using new methods for minimax adaptive control with provable robustness bounds for the closed loop system, including the nonlinear learning procedure.


Laurent Lessard

Northeastern University, Boston, USA

Plenary Lecture Title:

The Speed-Robustness Trade-Off for Iterative Optimization Algorithms

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Speaker Bio:

Laurent Lessard is an Associate Professor of Mechanical and Industrial Engineering at Northeastern University, Boston, USA, and a core faculty member of the Experiential Institute for AI. He received a B.A.Sc. in Engineering Science from the University of Toronto, and the M.S. and Ph.D. in Aeronautics and Astronautics at Stanford University. His research interests include: decentralized control, robust control, optimization, and machine learning. Before joining Northeastern, he was a Charles Ringrose Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison. Prior to that, he was an LCCC Postdoc in the Department of Automatic Control at Lund University, Sweden, and a postdoctoral researcher in the Berkeley Center for Control and Identification at the University of California, Berkeley. Laurent is a recipient of the Hugo Schuck best paper award and the NSF CAREER award. He is also a Senior Member of IEEE.

Abstract:

Most complicated optimization problems, in particular those involving a large number of variables, are solved in practice using iterative algorithms. A popular way to characterize the performance of such algorithms is the worst-case convergence rate. However, practical use cases often involve noisy data, so robustness can also be important. These two objectives are often competing. For example, with ordinary gradient descent, the choice of stepsize directly mediates the trade-off between speed and robustness. But for more complicated algorithms such as Polyak's Heavy Ball method or Nesterov's Accelerated method that have several tuning parameters, it is not clear how they should be tuned if more speed or more robustness is needed. In this talk, we will present a tractable way to compute convergence rate and sensitivity to additive gradient noise for a broad family of first-order methods, using tools from robust control. Specifically, we will solve small semidefinite programs in order to certify suitable dissipation inequalities. We will also present near-Pareto-optimal algorithm designs. Each design consists of a simple analytic update rule with two states of memory, similar to existing accelerated methods. Moreover, each design has a scalar tuning parameter that explicitly trades off convergence rate and robustness to noise. Finally, we validate the performance and near-optimality of our designs through numerous numerical simulations.


Giulia Giordano

University of Trento, Italy

Plenary Lecture Title:

Robustness in nature: a journey across biology and epidemiology

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Speaker Bio:

Giulia Giordano is currently an Assistant Professor with the Department of Industrial Engineering, University of Trento, Italy. She received the B.Sc. and M.Sc. degrees summa cum laude in electrical engineering and the Ph.D. degree (Hons.) in systems and control theory from the University of Udine, Italy, in 2010, 2012, and 2016, respectively. She visited the Control and Dynamical Systems Group, California Institute of Technology, Pasadena, CA, USA, in 2012, and the Institute of Systems Theory and Automatic Control, University of Stuttgart, Germany, in 2015. She was a Research Fellow with the LCCC Linnaeus Center and the Department of Automatic Control, Lund University, Sweden, from 2016 to 2017, and an Assistant Professor with the Delft Center for Systems and Control, Delft University of Technology, The Netherlands, from 2017 to 2019. She has been an Associate Editor for the IEEE Control Systems Letters since 2020 and for Automatica since 2022. She was recognised with the Outstanding Reviewer Letter from the IEEE Transactions on Automatic Control in 2016 and from the Annals of Internal Medicine in 2020, and chosen as Outstanding Associate Editor of the IEEE Control Systems Letters for the year 2021. She received the EECI Ph.D. Award 2016 from the European Embedded Control Institute for her thesis "Structural Analysis and Control of Dynamical Networks", the NAHS Best Paper Prize 2017, as the coauthor of the article "A Switched System Approach to Dynamic Race Modelling", Nonlinear Analysis: Hybrid Systems, 2016, and the SIAM Activity Group on Control and Systems Theory Prize 2021, for "significant contributions to the development of innovative methodologies for the structural analysis of networked control systems and their applications to biological networks". Her main research interests include the analysis and the control of dynamical networks, with applications especially to biology and epidemiology.

Abstract:

Natural systems across biology, ecology and epidemiology can be seen as dynamical networks, namely dynamical systems that are naturally endowed with an underlying network structure, because they are composed of several subsystems that interact according to an interconnection topology. Despite their large scale and complexity, most systems in nature exhibit an extraordinary robustness, which can be found at all scales, from protein interactions to gene regulatory networks, from single cells to whole organisms and species: fundamental properties and qualitative behaviours that are crucial for survival are preserved even in the presence of huge parameter variations and environmental fluctuations.
With a special focus on biochemical reaction networks, the first part of the talk discusses systems-and-control approaches to unravel the key features of these systems and identify the roots of the amazing robustness that often characterises them, by identifying properties and emerging behaviours that exclusively depend on the system structure (the graph topology along with qualitative information), regardless of parameter values. The BDC-decomposition is introduced to capture the system structure and enable the parameter-free assessment of important properties, including the stability of equilibria and the sign of steady-state input-output influences, thus allowing structural model falsification and structural comparison of alternative mechanisms proposed to explain the same phenomenon.
The second part of the talk is inspired by the COVID-19 pandemic and the observation that compartmental models for epidemics can be seen as a special class of chemical reaction networks. Epidemiological systems describing the spread of infectious diseases within a population are considered along with control approaches to curb the contagion. Strategies relying on optimal and robust control theory are proposed to cope with the deep uncertainty affecting parameter values and optimally control the epidemic.