This book contains the proceedings of the Symposium on Systems Theory in Data and Optimization (SysDO) held in Stuttgart, Germany, from 30th September to 2nd October 2024. It addresses theoretical and practical research questions arising at the intersection of systems and control theory, data, and optimization. The increasing prevalence of cyber-physical systems sparks the need for new methods to integrate measured data and different forms of feedback, especially optimization-based feedback, inside the decision-making mechanism. There are distinctive challenges that arise in this scenario, such as the existence of different time-scales, the need to guarantee sufficient richness of the collected data, and the effect of suboptimal decisions under uncertainty. This book presents new methods and applications addressing these challenges.
This book is a valuable source on latest research findings spanning diverse topics including:
- data-driven and learning-based control;
- theory and applications of machine learning;
- model predictive control; and
- optimization.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
85
12 s/w Abbildungen, 85 farbige Abbildungen
XI, 350 p. 97 illus., 85 illus. in color.
ISBN-13
978-3-031-83191-1 (9783031831911)
Schweitzer Klassifikation
Julian Berberich is a Lecturer (Akademischer Rat) at the Institute for Systems Theory and Automatic Control at the University of Stuttgart, Germany. He received his Ph.D. in Mechanical Engineering in 2022, and a Master's degree in Engineering Cybernetics in 2018, both from the University of Stuttgart, Germany. In 2022, he was a visiting researcher at ETH Zürich, Switzerland. For his Ph.D. thesis, he received the Dr.-Klaus-Körper Prize by the International Association of Applied Mathematics and Mechanics, as well as the Bürkert University Prize by the Foundation of the University of Stuttgart. Furthermore, he is a recipient of the 2022 George S. Axelby Outstanding Paper Award as well as the Outstanding Student Paper Award at the 59th IEEE Conference on Decision and Control in 2020. His research interests include data-driven analysis and control as well as quantum computing.
Andrea Iannelli is an assistant professor in the Institute for Systems Theory and Automatic Control at the University of Stuttgart (Germany). He was born in Ascoli Piceno (Italy), and he completed his B.Sc. and M.Sc. degrees in Aerospace Engineering at the University of Pisa (Italy). He received his PhD in Control and Dynamical Systems from the University of Bristol (United Kingdom), where he worked on robust control and dynamical systems theory for aeroelastic systems. He was a postdoctoral researcher in the Automatic Control Laboratory at ETH Zürich (Switzerland). His research interests are at the intersection of control theory, optimization, and learning, with a particular focus on robust and adaptive optimization-based control, uncertainty quantification for system identification, and sequential decision-making problems. He currently serves the community as Associated Editor for the International Journal of Robust and Nonlinear Control and IPC member of international conferences in the areas of control and learning. He was General Chair of the Symposium on Systems Theory in Data and Optimization (SysDO), held at the University of Stuttgart in September 2024.
Frank Allgöwer studied Engineering Cybernetics and Applied Mathematics in Stuttgart and at the University of California, Los Angeles (UCLA), respectively, and received his Ph.D. degree from the University of Stuttgart in Germany. Since 1999 he is the Director of the Institute for Systems Theory and Automatic Control and professor at the University of Stuttgart. His research interests include networked control, cooperative control, predictive control, and nonlinear control with application to a wide range of fields including systems biology. For the years 2017-2020 Frank served as President of the International Federation of Automatic Control (IFAC) and for the years 2012-2020 as Vice President of the German Research Foundation DFG.
