Frank ALLGÖWER (Germany)
Biography
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. For their work on data-based MPC, Frank and his co-workers received the 2022 IEEE CSS George S. Axelby Outstanding Paper Award for the best paper published in the IEEE Transactions on Automatic Control.
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).
Plenary: Innovations in MPC: The Promise of Model-Based and Data-Driven Methods
Recent years have shown rapid progress of learning-based and data-driven methods, significantly impacting the field of control, including model predictive control (MPC). In addition to numerous methodological and computational advancements, a substantial number of application studies featuring data- and learning-based MPC are currently being published. In this talk, we will compare model-based and data-based MPC to explore which holds more potential for future impact. Highlighting recent developments, we will focus on two different data-based MPC schemes: one based on the Fundamental Lemma of Willems et al., and the other on the data-informativity paradigm. By providing an overview and introduction to these methods, we will discuss their theoretical properties, suitability for nonlinear systems, and demonstrate their advantages and limitations compared to model-based MPC through various application examples. This critical analysis and comparison aim to offer insights and recommendations for future research directions in the evolving domain of MPC.