Abstract

While recent years have shown rapid progress of learning-based and data-driven methods to effectively utilize data for control tasks, providing rigorous theoretical guarantees for such methods is challenging and an active field of research.  This talk will give an overview of the state of the art of the recently developed framework for data-driven model predictive control (MPC) of unknown systems. In this framework no mathematical model is required for the MPC controller and only input-output data is needed. As a big advantageous feature, this framework admits rigorous theoretical guarantees for the closed loop.

The proposed approach relies on the Fundamental Lemma of Willems et al. which parametrizes trajectories of unknown linear systems using data. First, we cover MPC schemes for linear systems with a focus on theoretical guarantees for the closed loop, which can be derived even if the data are noisy. Building on these results, we then move towards the general, nonlinear case. Specifically, we present a data-driven MPC approach which updates the data used for prediction online at every time step and, thereby, stabilizes unknown nonlinear systems using only input-output data. In addition to introducing the framework and the theoretical results, we also discuss successful applications of the proposed framework in simulation and real-world experiments.

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 has served the scientific community through various roles including Vice-President of the German Research Foundation (2012-2020), President of the International Federation of Automatic Control (2017-2020), Editor for the journal Automatica (2001-2015) and Series Editor for the Springer Lecture Notes in Control and Information Science (since 2008). He has published over 500 scientific articles and received several awards for his research including the 2022 George S. Axelby Award for the data-based MPC scheme presented in this talk.