ECC22 will feature four plenary lectures, four semi-plenary lectures and the European Control Award pleanary lecture.
The latest studies confirm that considerable changes in freshwater resources have been occurring across the globe, indicating a future in which already limited water resources will become even more precious. According to the World Economic Forum, water crises is one of the top global risks in terms of impact. On the other hand, the continuous expansion of urban footprint means that an estimated 70% of the world’s population will live in urban areas by 2050. The dramatic increase in water demands resulting from this unprecedented urbanization, together with increasingly uncertain climate conditions, indicate the need for a holistic, intelligent decision-making framework for managing water infrastructures in the cities of the future. Consequently, there is a need for a new approach for designing the next generation of urban drinking water systems that is applicable not only to planning and management of mature water infrastructure systems such as those found in developed countries, but also to developing countries where the fastest population growth is predicted over the next 50 years.
From a system engineering perspective, urban drinking water networks are complex, large-scale systems designed to supply clean water to industrial and domestic users. Some of the key water challenges include water losses, ensuring water quality, energy efficiency, and safety and security of water resources. Recent advances in information and communication technologies have facilitated the modernization of water systems with the installation of sensors, actuators, data processing units and wireless communications, which enables the collection of far more real-time data related to water systems. The objective of this presentation is to provide an overview of current advances in smart water systems from a systems and control perspective. Several results on monitoring, control and fault tolerance of water distribution networks will be presented and illustrated, and directions for future research will be discussed.
Marios Polycarpou is a Professor of Electrical and Computer Engineering and the Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He is also a Member of the Cyprus Academy of Sciences, Letters, and Arts, and an Honorary Professor of Imperial College London. He received the B.A degree in Computer Science and the B.Sc. in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and learning control systems, fault diagnosis, machine learning, and critical infrastructure systems. Dr. Polycarpou has published more than 400 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 7 books. He is also the holder of 6 patents.
Prof. Polycarpou received the 2016 IEEE Neural Networks Pioneer Award. He is a Fellow of IEEE and IFAC and the recipient of the 2014 Best Paper Award for the journal Building and Environment (Elsevier). He served as the President of the IEEE Computational Intelligence Society (2012-2013), as the President of the European Control Association (2017-2019), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). Prof. Polycarpou currently serves on the Editorial Boards of the Proceedings of the IEEE, the Annual Reviews in Control, and the Foundations and Trends in Systems and Control. His research work has been funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant, the ERC Synergy Grant and the EU Teaming project.
KIOS Centre of Excellence
University of Cyprus, Cyprus
When robots are to be deployed over long time scales, optimality should take a backseat to “survivability”, i.e., it is more important that the robots do not break or completely deplete their energy sources than that they perform certain tasks as effectively as possible. For example, in the context of multi-agent robotics, we have a fairly good understanding of how to design coordinated control strategies for making teams of mobile robots achieve geometric objectives, such as assembling shapes or covering areas. But, what happens when these geometric objectives no longer matter all that much? In this talk, we consider this question of long duration autonomy for teams of robots that are deployed in an environment over a sustained period of time and that can be recruited to perform a number of different tasks in a distributed, safe, and provably correct manner. This development will involve the composition of multiple barrier certificates for encoding tasks and safety constraints through the development of non-smooth barrier functions, as well as a detour into ecology as a way of understanding how persistent environmental monitoring can be achieved by studying animals with low-energy life-styles, such as the three-toed sloth.
Dr. Magnus Egerstedt is the Dean of Engineering and a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Irvine. Prior to joining UCI, Egerstedt was on the faculty at the Georgia Institute of Technology. He received the M.S. degree in Engineering Physics and the Ph.D. degree in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden, the B.A. degree in Philosophy from Stockholm University, and was a Postdoctoral Scholar at Harvard University. Dr. Egerstedt conducts research in the areas of control theory and robotics, with particular focus on control and coordination of multi-robot systems. Magnus Egerstedt is a Fellow of IEEE and IFAC, and is a Foreign member of the Royal Swedish Academy of Engineering Science. He has received a number of teaching and research awards, including the Ragazzini Award, the O. Hugo Schuck Best Paper Award, the Outstanding Doctoral Advisor Award and the HKN Outstanding Teacher Award from Georgia Tech, and the Alumni of the Year Award from the Royal Institute of Technology.
Dept. of Electrical Engineering and Computer Science
University of California, Irvine
Sandra Hirche holds the TUM Liesel Beckmann Distinguished Professorship and heads the Chair of Information-oriented Control in the Faculty of Electrical and Computer Engineering at Technical University of Munich (TUM), Germany (since 2013). She received the diploma engineer degree in Aeronautical and Aerospace Engineering in 2002 from the Technical University Berlin, Germany, and the Doctor of Engineering degree in Electrical and Computer Engineering in 2005 from the Technische Universität München, Munich, Germany. From 2005-2007 she has been a PostDoc Fellow of the Japanese Society for the Promotion of Science at the Fujita Laboratory at Tokyo Institute of Technology, Japan. Prior to her present appointment she has been an Associate Professor at TUM.
Her main research interests include learning, cooperative, and networked control with applications in human-robot interaction, multi-robot systems, and general robotics. She has published more than 200 papers in international journals, books and refereed conferences. She has received multiple awards such as the Rohde & Schwarz Award for her PhD thesis, the IFAC World Congress Best Poster Award in 2005 and – together with students – Best Paper Awards of IEEE Worldhaptics and IFAC Conference of Manoeuvring and Control of Marine Craft in 2009 and the Outstanding Student Paper Award of the IEEE Conference on Decision and Control 2018. In 2013 she has been awarded with an ERC Starting Grant on the “Control based on Human Models” and in 2019 with the ERC Consolidator Grant on “Safe data-driven control for human-centric systems”.
Sandra Hirche is Fellow of the IEEE. She has served as IEEE Control System Society (CSS) Vice-President for Member Activities (2014/15), as Chair for Student Activities in the IEEE CSS (2009-2014), as Chair of the CSS Awards Subcommittee on “CDC Best Student-Paper Award” (2010-2014), and has been elected member of the Board of Governors of IEEE CSS (2010-2013). She has been Co-Chair of the IFAC TC 1.5 “Networked Control Systems” (2010-2017).
Faculty of Electrical and Computer Engineering
Technical University of Munich (TUM), Germany
For mission-critical systems, the availability of a redundant set of actuators is more of a necessity than a desirable trait, as redundancy bears the promise of enhanced performance, fault-tolerance and increased robustness. Input redundancy in a control system is typically approached by means of finite-horizon optimization (MPC) or via (static) control allocation strategies. MPC methods resolve the redundancy via on-line optimization seamlessly with the definition of the actual control policy, but leads to controllers that are difficult to be retrofitted to existing or more desirable (e.g., physically-inspired) control policies. In classic control allocation, standing assumptions prescribe the definition of a virtual control input with the same dimensionality of the regulated output. A control strategy designed on the basis of this virtual input is then “distributed” across the redundant set of actuators via on-line optimization. Reality is, of course, more complex: While it is desirable to devise allocation strategies that can be merged into existing control architectures, the setup of classic control allocation is unduly restrictive and does not hold for many technological systems of interest.
In this talk, we present an approach to the systematic design of dynamic control allocation schemes for general classes of input-redundant systems that results in plug-in modules for existing or desired control architectures. The enabling methodology is a geometric characterization of over-actuated systems that leads to the exploitation of input redundancy in the system inverse, rather than in the plant model itself. In the proposed approach, the steady-state behavior of the system is shaped through adaptation of free parameters stemming from dynamic optimization of selected performance criteria penalizing both the control input and the state trajectory, all while maintaining invariance of the error-zeroing manifold. The method is applied to fault-tolerant control of over-actuated aircraft, where results from flight tests are presented alongside simulation studies.
Andrea Serrani received the Laurea (B.Eng.) degree in Electrical Engineering, summa cum laude, from the University of Ancona, Italy, in 1993, and the Ph.D. degree in Artificial Intelligence Systems from the same institution in 1997. From 1994 to 1999, he was a Fulbright Fellow at Washington University in St. Louis, MO, where he obtained the M.S. and D.Sc. degrees in Systems Science and Mathematics in 1996 and 2000, respectively. Since 2002, he has been with the Department of Electrical and Computer Engineering of The Ohio State University, where he is currently a Professor and Interim Chair.
He has held visiting positions at the Universities of Bologna and Padua, Italy, and multiple summer faculty fellowships at AFRL. The research activity of Prof. Serrani lies at the intersection of methodological aspects of nonlinear, adaptive and geometric control theory with applications in aerospace and marine systems, fluidic systems, robotics and automotive engineering. His work has been supported by AFRL, NSF, Ford Motor Co. and NASA, among others.
Prof. Serrani has authored or co-authored more than 150 articles in journals, proceedings of international conferences and book chapters, and is the co-author of the book Robust Autonomous Guidance: An Internal Model Approach published by Springer-Verlag. Prof. Serrani was a Distinguished Lecturer of the IEEE CSS. Prof. Serrani is the Editor-in-Chief of the IEEE Trans. on Control Systems Technology and a past Associate Editor for the same journal (2010-2016), Automatica (2008-2014) and the Int. Journal of Robust and Nonlinear Control (2006-2014). He serves on the Conference Editorial Boards of IEEE CSS and IFAC, served as Program Chair for the 2019 ACC, and serves as General Co-chair for the 2022 CDC.
Dept. of Electrical and Computer Engineering
The Ohio State University
Though wave energy systems are not yet commercial, control has been identified as an important enabling technology which can reduce the cost of wave energy, allowing it to compete economically with other renewable and conventional energy sources. However, wave energy systems, which are diverse in form and operating principle, represent a challenging control problem, in terms of panchromatic reciprocating energy flux, hydrodynamic modelling complexity, non-causality in the fundamental control solution, and adverse sensitivity properties. In addition, the wave energy control problem is expressed in terms of an energy maximising performance function, rather than being easily reduced to a set-point following problem, while a key system input variable, the wave excitation force, is unmeasurable.
This talk will detail the major control issues faced in dealing with wave energy systems, also providing an overview of wave energy technology and some typical devices, while showing some possibilities in the solution domain. Some experimental control results will also be presented and the talk will conclude with some perspectives on future research directions.
John Ringwood received the HonsDipEE from TU Dublin, the BSc(Eng) in electrical engineering from Trinity College Dublin (both in 1981), and the PhD in control systems from Strathclyde University (1984). He subsequently received an MA in music technology from Maynooth University in 2005. He spent 15 years in Dublin City University as a member of academic staff in the School of Electronic Engineering, with concurrent terms as a visiting academic in Massey University and the University of Auckland. He joined Maynooth University in 2000, as chair professor and founding head of the Dept. of Electronic Engineering and built the Dept. from a greenfield site, also serving as Dean of Engineering from 2001 to 2006. He is currently Professor of Electronic Engineering and Director of the Centre for Ocean Energy Research in Maynooth University. He is Associate Editor for IEEE Trans. on Sustainable Energy and the Journal of Ocean Engineering and Marine Energy, Subject Editor for Energies, and Deputy Subject Editor for IET RPG. John received the 2016 IEEE Control Systems Magazine Outstanding Paper Award and was awarded Chevalier des Palmes Academiques by the French Government in 2017 for his contribution to ocean energy research. In addition to over 400 peer-reviewed publications, he is co-author of the book Hydrodynamic Control of Wave Energy Devices (with Umesh Korde) and holds 3 patents. His commercialization activities, which include the spin-out company Wave Venture, has been recognized by Enterprise Ireland (2008 Industrial Technologies Commercialization Award) and Maynooth University (2013 Commercialisation Award). His research interests are in ocean and renewable energy, control systems, and biomedical engineering.
Dept. of Electronic Engineering
Maynooth University, Ireland
How should a system operator strategically allocate its assets in an adversarial environment? In this talk, we explore this question in the context of the well-studied Colonel Blotto game. Colonel Blotto games model strategic scenarios where two opposing entities are tasked with allocating a given number of assets over a collection of fronts. The classical setting has primarily focused on scenarios where each front is associated with an independent valuation and each entity seeks to maximize the cumulative performance over the fronts. This talk will focus on emerging variants of these Colonel Blotto games encompassing both informational asymmetries and alternative structures for the classic linear sum objectives. One such variant we will focus on is a “networked” Colonel Blotto game, where the valuation of a front now depends on the outcome associated with other fronts in the system.
Jason R. Marden is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason received a BS in Mechanical Engineering in 2001 from UCLA, and a PhD in Mechanical Engineering in 2007, also from UCLA, under the supervision of Jeff S. Shamma, where he was awarded the Outstanding Graduating PhD Student in Mechanical Engineering. After graduating from UCLA, he served as a junior fellow in the Social and Information Sciences Laboratory at the California Institute of Technology until 2010 when he joined the University of Colorado. In 2015, Jason joined the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. Jason is a recipient of the ONR Young Investigator Award (2015), NSF Career Award (2014), the AFOSR Young Investigator Award (2012), the American Automatic Control Council Donald P. Eckman Award (2012), and the SIAM/SGT Best Sicon Paper Award (2015). Furthermore, Jason is also an advisor for the students selected as finalists for the best student paper award at the IEEE Conference on Decision and Control (2011, 2016, 2017) and American Control Conference (2020). Jason’s research interests focus on game theoretic methods for the control of distributed multiagent systems.
Dept. of Electrical and Computer Engineering
University of California, Santa Barbara
There are a range of environments, in, for example, nuclear, offshore and space, which can be considered ‘demanding’ or ‘challenging’ because of the extreme conditions within them. As a result of the conditions, which might include high levels of radioactivity or extreme temperatures or pressures, humans are typically unable to enter them to perform even routine tasks, without subjecting themselves to elevated and sometimes unacceptable levels of risk. In such environments there is a growing desire to develop robotic systems able to eliminate the need for humans to enter such areas. The focus of this presentation will be on some of the robotic challenges that are faced in the nuclear industry and an overview of the robots that have been developed at the University of Manchester to address these challenges.
Many of the robotics challenges in demanding environments can be divided into either inspection and characterisation or remote handling. The characterisation and inspection work typically involves the development of a mobile robotic platform with appropriate sensors integrated on to it, such as LiDARS to provide simultaneous localisation and mapping, and environmental sensors to measure variables of interest, such as radiation and temperature. The presentation will discuss some of the platforms that have been developed and the low-level systems that have been integrated onto the robots. This will include an analysis of the control systems that have been used and the higher-level optimisation systems linked to mission planning. A particular focus of the research is to ensure that these systems are resilient to faults and that they are transparent, so that regulatory approval can be obtained. Many of the robots that have been developed have been deployed into active nuclear facilities at Sellafield and Dounreay in the UK, as well as overseas, including Slovenia, Finland and Japan and details of these deployments will be provided.
To support the remote handling work, we have been working collaboratively with the UK Atomic Energy Agency’s RACE centre to design a remote glovebox capability. This system enables robotic manipulators to be inserted into a glovebox, rather than human arms, allowing humans to be kept a safe distance away from what might be a radioactive environment within the glovebox. Once inserted, the manipulators can be tele-operated, or there is a growing suite of tools that have been developed that allow them to be operated autonomously. For example, objects can be grasped autonomously and the manipulators themselves can be manoeuvred in the highly congested glovebox whilst avoiding obstacles using model predictive control techniques.
Whilst considerable progress has been made in the development of robotic systems for use in demanding environments, there remain many challenges. A summary of some of the immediate, as well as long-term robotic and control challenges, will be discussed.
Barry Lennox is Fellow of the Royal Academy of Engineering and Professor of Applied Control and Nuclear Engineering Decommissioning at The University of Manchester. He holds a Royal Academy Chair in Emerging Technologies and is the Director of the Robotics and Artificial Intelligence for Nuclear (RAIN) Research Hub that received £15M of funding through the ISCF Robotics for a Safer World programme. RAIN has led to many first-of-a-kind robotic deployments, including CARMA, which became the first fully autonomous robot to be deployed into an active facility on the Sellafield site. Professor Lennox also leads the £5M EPSRC Programme Grant: Robotics for Nuclear Environments and the robotics work within various other programmes including the EPSRC ALACANDRA project.
Dept. of Electrical & Electronic Engineering
The University of Manchester, UK
In this talk, several key notions of dynamical systems subject to constraints, and more specifically to isolated nonlinearities, are discussed. These constraints can be due to physical, safety or technological constraints affecting the control actuators and/or sensors and it is clear that neglecting these constraints can be a source of undesirable or even catastrophic behavior for the closed-loop dynamical system. Hence, dealing first with nonlinear actuators, we discuss how to account for the nonlinearities to address the stability/performance analysis or controller synthesis. Leveraging on this, we then illustrate how this framework can be expanded and adapted to handle different kinds of nonlinearities, potentially leading to other analysis and synthesis problems, as for example in the context of mechanical and communication constraints.
This talk will present some recent elements related to the proposed techniques and illustrate their potential on some applications as the control of anesthesia. Furthermore, it will discuss how to take inspiration from the proposed tools in order to handle other kinds of problems as for example the convergence of some optimization algorithms.
Sophie Tarbouriech received the Ph.D. and HDR degrees in automatic control from University Paul Sabatier, Toulouse, France, in 1991 and 1998, respectively. From 1991, she has been full-time CNRS researcher in LAAS-CNRS, Toulouse. She is currently Director of Research. During 2013-2015, she was recipient of a Special Visiting Researcher fellowship, in the context of the Science without Borders Program, in Brazil. Her main research interests include analysis and control of linear and nonlinear systems with constraints (limited information), hybrid dynamical systems, event-based control, control of systems described by PDEs, and applications in flight control, aerospace and more recently control of anesthesia. Her research has benefited from numerous scientific collaborations with a broad range of national and international colleagues but also from industrial grants. Dr. Tarbouriech is currently an Associate Editor for SIAM Journal on Control and Optimization and a Senior Editor for IEEE Control Systems Letters and Automatica. From 2005, she acted as Associate Editor (AE) for several in- ternational journals (IET Control Theory and Applications; IJRNC, European Journal of Control, IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, Automatica). She has also been involved in IFAC and IEEE activities. In particular she is currently member of the IFAC Conference Board, Vice-Chair CC 2 – Design Methods (2020-2023) and a member of the IEEE CSS Board of Governors (2022-2024).
Director of Research
Université de Toulouse, France
In this seminar, we start with a broad class of anomaly detection for large-scale nonlinear dynamical systems. Noting a connection between the diagnosis filter and the so-called behavioral sets of dynamical systems, we leverage tools from the traditional model-based approaches and modern data-driven analytics to address the inherent complexity of the problem. We then shift our attention to the performance guarantees of the proposed solution. In this part, we study this topic in a general context of data-driven decision-making with a particular focus on the distributionally robust optimization framework. We will discuss the role of convexity from the different viewpoints of computational, statistical, and real-time implementation.
Peyman Mohajerin Esfahani is an associate professor at the Delft Center for Systems and Control, and a co-director of the Delft-AI Energy Lab at the Delft University of Technology. He joined TU Delft in October 2016 as an assistant professor. Prior to that, he held several research appointments at EPFL, ETH Zurich, and MIT between 2014 and 2016. He received the BSc and MSc degrees from Sharif University of Technology, Iran, and the PhD degree from ETH Zurich. His research interests include theoretical and practical aspects of decision-making problems in uncertain and dynamic environments, with applications to control and security of large-scale and distributed systems.
He is an associate editor of Operations Research and Open Journal of Mathematical Optimization. He was one of the three finalists for the Young Researcher Prize in Continuous Optimization awarded by the Mathematical Optimization Society in 2016, and a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society. He received the ERC Starting Grant, and the INFORMS Frederick W. Lanchester Prize in 2020.
Delft Center for Systems and Control
Delft University of Technology