ECC23 is featuring three plenary lectures, four semi-plenary lectures and the European Control Award plenary lecture.

1. Frank Allgöwer – MPC goes data: A framework for data-based Model Predictive Control with system theoretical guarantees
2. Alessandro Astolfi – Pontryagin meets Bellman: on combining Pontryagin’s Principle and Dynamic Programming
3. Antonella Ferrara – Multi-Scale Vehicular Traffic Control
4. Melanie Zeilinger – Safety and Efficiency in Learning-based Control
5. Lucian Bușoniu – Reinforcement learning and planning with applications to robotics
6. Mircea Lazăr – Physics-based neural networks for precision motion control
7. Sorin Olaru – Intermittent satisfaction of constraints and other relaxed notions of set invariance

Frank Allgöwer – MPC goes data: A framework for data-based Model Predictive Control with system theoretical guarantees

Abstract:

While recent years have shown rapid progress of learning-based and data-driven methods to effectively utilize data for control tasks, providing rigorous theoretical guarantees for such methods is challenging and an active field of research.  This talk will give an overview of the state of the art of the recently developed framework for data-driven model predictive control (MPC) of unknown systems. In this framework no mathematical model is required for the MPC controller and only input-output data is needed. As a big advantageous feature, this framework admits rigorous theoretical guarantees for the closed loop. The proposed approach relies on the Fundamental Lemma of Willems et al. which parametrizes trajectories of unknown linear systems using data. First, we cover MPC schemes for linear systems with a focus on theoretical guarantees for the closed loop, which can be derived even if the data are noisy. Building on these results, we then move towards the general, nonlinear case. Specifically, we present a data-driven MPC approach which updates the data used for prediction online at every time step and, thereby, stabilizes unknown nonlinear systems using only input-output data. In addition to introducing the framework and the theoretical results, we also discuss successful applications of the proposed framework in simulation and real-world experiments.

Speaker Bio:

Frank Allgöwer is director of the Institute for Systems Theory and Automatic Control at the University of Stuttgart in Germany. His current research interests are to develop new methods for data-based control, optimization-based control and networked control. Frank 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 and has published over 500 scientific articles. 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
Institute for Systems Theory and Automatic Control
University of Stuttgart,Germany

Alessandro Astolfi – Pontryagin meets Bellman: on combining Pontryagin’s Principle and Dynamic Programming

Abstract:

Speaker Bio:

Alessandro Astolfi was born in Rome, Italy, in 1967. He graduated in electrical engineering from the University of Rome in 1991. In 1992 he joined ETH-Zurich where he obtained an M.Sc. in Information Theory in 1995 and the Ph.D. degree with Medal of Honor in 1995 with a thesis on discontinuous stabilisation of nonholonomic systems. In 1996 he was awarded a Ph.D. from the University of Rome “La Sapienza” for his work on nonlinear robust control. Since 1996 he has been with the Electrical and Electronic Engineering Department of Imperial College London, London (UK), where he is currently Professor of Nonlinear Control Theory and College Consul. From 1998 to 2003 he was also an Associate Professor at the Dept. of Electronics and Information of the Politecnico of Milano. Since 2005 he has also been a Professor at Dipartimento di Ingegneria Civile e Ingegneria Informatica, University of Rome Tor Vergata. His research interests are focussed on mathematical control theory and control applications, with special emphasis for the problems of discontinuous stabilisation, robust and adaptive control, observer design and model reduction.

Alessandro Astolfi

Professor
Dept. of Electrical and Electronic Engineering
Imperial College London, UK

Antonella Ferrara – Multi-Scale Vehicular Traffic Control

Abstract:

The scientific, technological, social and economic impact of successful research in road traffic control is very significant, with immediate effects on safety, quality of life, environment, use of energy resources, and transportation costs. Yet, the development of effective methods and algorithms for road traffic management has to face notable methodological challenges. In addition, the type of traffic control strategies developed so far, the “classical approaches”, need now to be updated and adapted to consider the fast development in automotive technologies, traffic sensors, data processing, and communication. This lecture will address these aspects, starting from an overview of classical traffic control concepts to arrive at encompassing emerging research trends at different scales: from the microscopic scale of individual connected and autonomous vehicle control, to the macroscopic scale of road traffic control, also illustrating how the different scales can efficiently coexist in an advanced vehicular traffic control system. This will be done paying a particular attention to electric mobility, since it seems destined to become the dominant mobility paradigm in the next decades.

Speaker Bio:

Antonella Ferrara received the M.Sc. degree (Cum Laude and printing honors) in electronic engineering and the Ph.D. degree in computer science and electronics from the University of Genova, Italy, in 1987 and 1992, respectively. Since 2005, she has been Full Professor of automatic control at the University of Pavia, Italy, where she is the Head of the Intelligent Robotics Laboratory, as well as the President of the Research Standing Committee of the Department of Electrical, Computer and Biomedical Engineering. Her research activities are mainly in the area of nonlinear control of complex systems, with application to road traffic, automotive systems, robotics and power systems. She is author and co-author of more than 450 publications including more than 150 journal papers, 2 monographs and one edited book. She is/was Senior Editor and Associate Editor of the major scientific journals in the field of Automatic Control. She served as a member of the EUCA Council and, since 2018, she has been the EUCA Conference Editorial Board Chair. She is a member of the IEEE Technical Committee (TC) on Automotive Control, IEEE TC on Smart Cities, IEEE TC on Variable Structure Systems, IFAC Technical Committee on Nonlinear Control Systems, IFAC TC on Transportation Systems, and IFAC Technical Committee on Intelligent Autonomous Vehicles. Among several awards, she was a co- recipient of the 2020 IEEE Transactions on Control Systems Technology Outstanding Paper Award. She is IEEE Fellow and IFAC Fellow.

Antonella Ferrara

Professor
Dept. of  Electrical, Computer and Biomedical Engineering
University of Pavia, Italy

Melanie Zeilinger – Safety and Efficiency in Learning-based Control

Abstract:

Over the last decade, the enormous potential of learning-based control has been shown in many application domains. Despite the significant progress, however, a number of challenges still limit its widespread success in practice. This talk focuses on the two key aspects of safety and data-efficiency. We rely on model-based techniques and demonstrate how optimization-based control can provide a powerful framework for safe and efficient learning. Safety filters as a general and modular safety concept for any learning-based controller will be presented, as well as the integration of predictive control with learning mechanisms to facilitate controller design and to enhance performance despite limited computational resources and information. Throughout the talk, the results will be highlighted using examples from robotics.

Speaker Bio:

Melanie Zeilinger

Professor
Dept. of Mechanical and Process Engineering
ETH Zurich, Switzerland

 Lucian Busoniu – Reinforcement learning and planning with applications to robotics

Abstract:

Powered by advances in deep neural networks, reinforcement learning (RL) has made great strides in the past decade, and its impact in control is increasingly being felt. This talk will begin by introducing the RL framework and some key algorithms. Afterwards, some recent applications of RL in active robotic mapping will be presented, in which the robot is controlled so as to obtain information about the map as quickly as possible. The map is represented either as an occupancy grid or a function defined over the robot’s operating area. A simpler variation of such a technique that only aims to find the maximum of the function will allow analysis of the convergence rate to the optimum. Time permitting, the talk will conclude with a model-based, planning framework counterpart for RL, with strong converge nce guarantees to the optimal control of general nonlinear systems with general costs.

Speaker Bio:

Lucian Bușoniu received his Ph.D. degree cum laude from the Delft University of Technology, the Netherlands, in 2009. He is a full professor with the Department of Automation at the Technical University of Cluj-Napoca, where he leads the group on Robotics and Nonlinear Control. He has previously held research positions in the Netherlands and in France. His research interests include nonlinear optimal control using artificial intelligence and reinforcement learning techniques, robotics, and multiagent systems. His publications include among others a book on reinforcement learning and several influential review articles, one of which is among the top 1% cited papers in ISI Web of Knowledge. He serves on the editorial board of the Elsevier journal Engineering Applications of Artificial Intelligence, and was the recipient of the 2009 Andrew P. Sage Award for the Best Paper in the IEEE Transactions on Systems, Man, and Cybernetics. He has competitively obtained research funding in excess of 1.5 million EUR and was invited to present research on prime-time national TV and in other media.

Lucian Bușoniu

Professor
Dept. of Automation
Technical University of Cluj-Napoca, Romania

Mircea Lazar – Physics-based neural networks for precision motion control

Abstract:

Currently, there is a growing interest in merging physics–based models and artificial intelligence to increase control performance for systems with complex dynamics. In this talk we will present a systematic method for embedding a known (or identified) physical model within a neural network and we will provide regularized training cost functions that address two key issues: competition among black-box and physics-based neural network layers, and robustness to non-training data. We will show that the developed physics-based neural networks (PNNs) model class is able to achieve improved accuracy with the same reliability as physical models. The relation of PNNs with physics-informed neural networks will also be discussed. To demonstrate the impact of PNNs on control performance, we will consider the problem of feedforward control design for precision motion control. Experimental results will be shown for two real-life applications: a coreless linear motor used in lithography machines and a hybrid stepper motor used in industrial printing machines. Such applications are indeed characterized by known physics-based motion dynamics and complex, partially unknown dynamics, e.g., due to electromagnetic forces or nonlinear friction. The PNN-based feedforward controllers achieve a factor 2 improvement with respect to conventional industrial feedforward controllers, while their design and tunning can be largely automated.

Speaker Bio:

Dr. Mircea Lazăr is an Associate Professor in Constrained control of complex systems at the Electrical Engineering Department, Eindhoven University of Technology, The Netherlands. Lazar received the European Embedded Control Institute Ph.D. Award in 2007 for his PhD dissertation and a Veni personal grant from the Dutch Research Council (NWO) in 2008. He supervised 10 PhD researchers (2 received the Cum laude distinction) that received the PhD title. Lazar chaired the 4th IFAC Conference on Nonlinear Model Predictive Control, Noordwijkerhout, The Netherlands, in 2012. His research interests cover physics-based neural networks, nonlinear and data-driven predictive control, non-monotone Lyapunov functions, compositional stability certificates and distributed control. His research is driven by control problems in high-precision mechatronics, power electronics, power networks, water networks, automotive and biological systems. Lazar published 13 papers in IEEE Transactions on Automatic Control and 15 papers in Automatica. He is an Active Member of the IFAC Technical Committees 1.3 Discrete Event and Hybrid Systems, 2.3 Nonlinear Control Systems and an Associate Editor of IEEE Transactions on Automatic Control.

Mircea Lazăr

Associate Professor
Dept. of Electrical Engineering
Eindhoven University of Technology,  Netherland

Sorin Olaru- Intermittent satisfaction of constraints and other relaxed notions of set invariance

Abstract:

Constraints handling and uncertainty characterization for dynamical systems represent a continuous interest in the control literature. In the first part of this talk, classic definitions of constraint satisfaction and set invariance will be reviewed, pointing to the latest developments with respect to some specific classes of dynamics as for example the time-delay ones. In the second part of the talk, the aim is to go beyond the rigid concepts by characterizing the intermittent constraint satisfaction along the evolution of the trajectories of a dynamical system. Two relaxed notions are introduced in this sense, one characterizing the validation of constraints within a given finite window and the other imposing the validation after a fixed number of time-steps following a violation. The constraint satisfaction with respect to a trajectory will then be extended to a set of constraints and tubes of trajectories. It is shown that all these notions can be accordingly anchored to the well-known positive set invariance thus offering a generalized framework for the analysis of dynamical systems in a set-theoretic framework.

Speaker Bio:

Sorin Olaru graduated in electrical engineering from the University “Politehnica” Bucharest (UPB) in 2001 where he also obtained the M.Sc. in 2002, being awarded the EU’s Archimedes Prize. He obtained the PhD from the University Paris XI in Orsay, France in 2005, and the PhD Cum Laude from UPB in 2010. Since 2012 he is Habilitated Professor of Control Engineering at CentraleSupélec, within the University Paris-Saclay. Currently he is leading the RTE Chair on “The Digital Transformation of Electricity Networks”. His research interests encompass the optimization-based control design, set-theoretic characterization of constrained dynamical systems and the resilience of networked control systems. He was the chair of the IFAC Workshop on Control Applications of Optimization held jointly with the International Conference on Discrete Equations and Applications in 2022. He will be the general chair of the Power Systems Computation Conference to be held in 2024 in Paris-Saclay.

Sorin Olaru

Professor
RTE Chair at CentraleSupelec
University Paris-Saclay, France