Systems Theory in Data and Optimization
Description
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.
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Persons
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.
Content
Part I. Data-Driven and Learning-Based Control.- Chapter 1. PACSBO: Probably Approximately Correct Safe Bayesian Optimization.- Chapter 2. Value of Communication: Data-Driven Topology Optimization for Distributed Linear Cyber-Physical Systems.- Chapter 3. Variance-Informed Model Reference Gaussian Process Regression: Utilizing Variance Information for Control in Nonlinear Systems.- Chapter 4. Data-Driven Dynamic Model and Model Reference Control of Inverter Based Resources.- Chapter 5. Adaptive Tracking MPC for Nonlinear Systems via Online Linear System Identification.- Part II: Machine Learning: Theory and Applications.- Chapter 6. Investigation of the Influence of Training Data and Methods on the Control Performance of MPC Utilizing Gaussian Processes.- Chapter 7. Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty.- Chapter 8. A Universal Reproducing Kernel Hilbert Space for Learning Nonlinear Systems Operators.- Chapter 9. On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks.- Chapter 10. Solving Partial Differential Equations with Equivariant Extreme Learning Machines.- Chapter 11. Adaptive Robust L2 Loss Function using Fractional Calculus.- Chapter 12. Sparse Reconstruction of Forces, Torques and Velocity Signals for a Swimmer in a Wake.- Chapter 13. Control Theoretic Approach to Fine-Tuning and Transfer Learning.- Part III. Model Predictive Control.- Chapter 14. Accelerating Multi-Objective Model Predictive Control Using High-Order Sensitivity Information.- Chapter 15. On Discount Functions for Economic Model Predictive Control without Terminal Conditions.- Chapter 16. Multi-Parametric Programming with Constraint Telaxation for the Optimal Operation of Micro-Grids Integrating Renewables.- Chapter 17. Multi-Objective Learning Model Predictive Control.- Chapter 18. Terminal Set of Nonlinear Model Predictive Control with Koopman Operators.- Part IV: Optimization.- Chapter 19. Optimal Dynamic Pricing in Energy Markets: A Stackelberg Game Approach.- Chapter 20. Distributed Newton Optimization with ADMM-Based Consensus.- Chapter 21. Inexactness in Bilevel Nonlinear Optimization: A Gradient-free Newton's Method Approach.
