This talk will present the stakes of flow management in the health domain, as well as the specificities of healthcare production systems compared to manufacturing systems.
We will see some examples of work, carried out in the G-SCOP laboratory, showing the contribution of engineering sciences, and more particularly of industrial engineering, for the study and the improvement of the organization of various healthcare production systems. This talk will thus show how modeling, performance evaluation, mathematical optimization, and risk analysis can contribute to making the management of healthcare production systems more efficient.
Maria Di Mascolo
French National Center for Scientific Research
Maria Di Mascolo, is Senior Researcher at the CNRS (French National Center for Scientific Research), and a member of the G-SCOP (Grenoble-Sciences for Design, Optimization and Production) laboratory. Her main scientific interests are related to the modelling, analysis and optimization of systems for the production of goods and services, and in particular healthcare production systems.
Her objective is to develop methods and tools to assist in making decisions that guarantee a good performance to production systems, despite uncertainties. The decisions to be made concern the design of production systems (including the control or maintenance policies to be implemented), as well as the planning of activities to be carried out, or the management of flows, with a special interest in the sustainability and human aspects.
She co-leads the GISEH (Hospital Systems Management and Engineering) technical committee of the SAGIP (French Society of Automatic Control, Industrial and Systems Engineering), and is editor-in-chief of the ISTE OpenScience journal “Industrial and Systems Engineering“. She is Deputy Head of Sustainable Industrial Engineering Master’s degree at Grenoble Institute of Technology.
Digital transformation is a cultural, organizational and operational change of an organization, industry or ecosystem through a smart integration of digital technologies, processes and competencies across all levels and functions. The keynote speech covers the main streams of digital transformation in industry and business. Detailed parts will be devoted to Artificial Intelligence, Machine Learning, Deep learning, Big Data, Internet of Things, System of Systems, Cyber Physical Systems, and Blockchains.
The next part of the presentation summarizes our research and development results and ideas of a newly developed software engine, called MAXWHERE as a good example of how do we move to a digital world. MAXWHERE provides effective working environments with spatial (Virtual Reality) multimedia arrangement and Intelligent System of Systems connectivity.
The last part of the lecture will discuss the questions: How far do we go? What are the expectations and the question of Singularity?
Prof. Imre J. Rudas
Óbuda University, Hungary
Imre J. Rudas graduated in Budapest in 1971 in Mechanical Engineering, received the Master Degree in Mathematics from the Eötvös Loránd University, Budapest, the Ph.D. in Robotics from the Hungarian Academy of Sciences in 1987, while the Doctor of Science degree from the Hungarian Academy of Sciences in 2004.
He is Rudolf Kalman Distinguished Professor, Rector Emeritus and Professor Emeritus of Óbuda University.
Her objective is to develop methods and tools to assist in making decisions that guarantee a good performance to production systems, despite uncertainties. The decisions to be made concern the design of production systems (including the control or maintenance policies to be implemented), as well as the planning of activities to be carried out, or the management of flows, with a special interest in the sustainability and human aspects.
He received Doctor Honoris Causa degree from the Technical University of Košice, Slovakia, from “Polytechnica” University of Timisoara, Romania, from Óbuda University, and from Slovak University of Technology in Bratislava. He was awarded by the Honorary Professor title in 2013 and Ambassador Title by Wroclaw University of Science and Technology.
He is a Life Fellow of IEEE and the Junior Past President of IEEE Systems, Man, and Cybernetics Society. He is a Fellow of International Fuzzy systems Association.
His present areas of research activities are Computational Cybernetics, Cyber Physical Control, Robotics, Systems of Systems. He has edited and/or published 22 three books, published more than 850 papers in international scientific journal, conference proceedings and book chapters, and received more than 7000 citations.
Set-valued sliding-mode control and differentiation, usually suffer from the well-known chattering phenomenon, which deteriorates their performance and can even sometimes prevent their use. It is known that one important source of chattering (at both the output and the input, which takes a bang-bang-like shape) is an inappropriate discretization, which yields the so-called digital, or numerical chattering.
The explicit Euler discretization, which is widely employed, is known to be the source of numerical chattering (see the works of Galias et al). Recently it has been shown that the implicit Euler discretization yields very efficient algorithms to suppress the numerical chattering, while keeping all the nice and powerful properties of the continuous-time counterparts: rigorous definition of a discrete sliding surface, finite-time convergence, robustness to matched (and some unmatched) disturbances, Lyapunov stability, insensitivity to the control gain during the sliding-motion. Several experimental results have validated the theoretical findings. In this talk we will introduce the implicit Euler method on several kinds of systems (linear, Lagrange mechanical, with matched or unmatched disturbances) and several kinds of SMC controllers (first-order, twisting, super-twisting, high-order) as well as differentiators. Most importantly it will be shown the deep link between the implicit discretization and maximal monotone operators, the Yosida approximations of Convex Analysis and the so-called proximal algorithms, which shows that the implicit discretization is not an implementation trick, but a discretization method.
Bernard Brogliato
Inria Grenoble-Rhône-Alpes Research Centre
Bernard Brogliato was born in Saint- Symphorien-de-Lay, France, in 1963. He received the Agrégation de mécanique from the Ecole Normale Supérieure de Cachan, Cachan, France, in 1986, and the Ph.D. and Habilitation à Diriger des Recherches degrees in automatic control from Grenoble INP, Grenoble, France, in 1991 and 1995, respectively. From 1991 to 2001, he was with Centre national de la recherche scientifique, Grenoble, France. Since 2001, he has been with Inria Grenoble Rhône-Alpes, Montbonnot-Saint-Martin, France. His main research interests include nonsmooth dynamical systems analysis, control and modeling, and dissipative dynamical systems..
The development of inexpensive and fast computers, coupled with the discovery of efficient algorithms for dealing with polynomial equations, gave rise to some exciting new applications of algebraic geometry and commutative algebra. One of the main goals of this talk is to show how some tools borrowed from these two fields can be efficiently employed to solve relevant control problem.
After a brief introduction to some algebraic objects and techniques, it is shown how such tools and methodologies can be applied to a wide variety of topics concerning control theory and its application, such as the design of observers for nonlinear plants, the motion planning of mobile robots, the inverse kinematics of robot manipulators, the characterization of invariant sets for nonlinear systems, and the decomposition of a polynomial in sum of squares.
Corrado Possieri
Department of Civil Engineering and Computer Engineering of the University of Rome “Tor Vergata”
Corrado Possieri received the bachelor’s and master’s degrees in medical engineering from Università di Roma “Tor Vergata” in 2011 and 2013, respectively. He received the Ph.D. degree in Computer Science, Control, and Geoinformation from Università di Roma “Tor Vergata” in 2016. During his Ph.D., he visited the University of California, Santa Barbara as a Research Scholar. In 2018, he joined the Dipartimento di Elettronica e Telecomunicazioni of the Politecnico di Torino where he was an Assistant Professor. In 2019, he joined the Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” of the National Research Council of Italy, where he was a Researcher. In 2022, he joined the Dipartimento di Ingegneria Civile e Ingegneria Informatica of the Universita` di Roma “Tor Vergata”, where he is currently an Assistant Professor.
He is the author of more than 110 scientific papers published in peer reviewed journals and conference proceedings, of a book, and of an international patent. He served as Vice-Chair on Social Media of the Technical Committee on Control Design of the International Federation of Automatic Control, as a member of the organizing committee of several international conferences, and as Associate Editor for PLoS One.
His research interests include the design of reinforcement learning algorithm, the application of symbolic computation to control theory, and the design of observers for nonlinear systems.
Differential games have been studied for decades and have played, and continue to play,
an important role in control engineering and decision processes. In this talk some basics on differential games, their relation to optimal control and their relevance to “classical” control engineering problems are briefly recalled. Then, starting with linear quadratic differential games, some of the “peculiarities” of differential games (or dynamic games, as they are referred to in the discrete-time setting) are discussed and various strategies to obtain their solution – via model-based or data-driven algorithms – are presented.
Turning then to a more general class of nonlinear differential games, strategies to obtain their solutions (or approximations thereof) are discussed. Various case studies are considered, with a particular emphasis on applications to robotic systems including, for instance, the multi-agent collision avoidance problem. Finally, the relevance of differential games to current challenges in distributed control are briefly discussed.
Thulasi Mylvaganam
Department of Civil Engineering and Computer Engineering of the University of Rome “Tor Vergata”
Thulasi Mylvaganam was born in Bergen, Norway, in 1988. She received the M.Eng. degree in Electrical and Electronic Engineering and the Ph.D. degree in nonlinear control and differential games from the Department of Electrical and Electronic Engineering, Imperial College London, U.K., in 2010 and 2014, respectively. From 2014 to 2016, she was a Research Associate in the same department. From 2016 to 2017, she was a Research Fellow with the Department of Aeronautics, Imperial College London, UK, where she is currently a Senior Lecturer (Associate Professor). Her research interests include nonlinear control, dynamic optimisation, distributed control and data-driven control. While her research is mainly focused about fundamental aspects of control engineering, since joining the Department of Aeronautics, she has gained a special interest in its applications to robotics. She a Senior Member of the IEEE, a Member of the IEEE CSS Conference Editorial Board, of the EUCA Conference Editorial Board and a Member of the IFAC Technical Committee 2.4 (Design Methods – Optimal Control). She has served as Associate Editor for several conferences, including the IEEE Conference on Decision and Control, the IFAC World Congress, the American Control Conference, the European Control Conference and the IFAC Workshop on Control Applications of Optimization. She was Financial Chair for the 2022 European Control Conference.
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