A hybrid systems framework for data-based adaptive control of linear time-varying systems (Prof. Andrea Ianelli, Institute for Systems Theory and Automatic Control, University of Stuttgart)

Systems & Control Seminar

• Tuesday 18.06.2024, 16:00 
• Room A145, Building 3403, Appelstr. 11

Abstract

In this talk, we consider the data-driven stabilization of discrete-time linear time-varying systems without model knowledge. The controller is defined as a linear state-feedback that adapts to the plant's changes in an event-triggered fashion using a data-based condition. The resulting closed-loop dynamics hence exhibits both physical jumps, due to the system dynamics, and episodic jumps, due to the feedback update, which leads to a hybrid discrete-time system description. This representation provides a natural setting for designing events which trigger the controller update as well as analyzing the interconnection closed-loop properties. The inherent robustness of the feedback gain synthesised based on the last collected data is leveraged to show a Lyapunov-like property that holds along any closed-loop trajectory. We provide general conditions under which various stability notions can be established for the overall closed-loop system, and discuss two notable cases where these conditions are satisfied. We conclude by providing numerical results that illustrate the relevance of the proposed approach.

Biographical information

Andrea Iannelli is a tenure-track assistant professor in the Institute for Systems Theory and Automatic Control at the University of Stuttgart. He completed his B.Sc. and M.Sc. degrees in Aerospace Engineering at the University of Pisa (Italy) and received his PhD from the University of Bristol (United Kingdom), where he worked on robust control and dynamical systems theory. He was a postdoctoral researcher in the Automatic Control Laboratory at ETH Zürich (Switzerland). His main 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, and sequential decision-making problems. He serves the community as Associated Editor for the International Journal of Robust and Nonlinear Control and as IPC member of international conferences in the areas of control, optimization, and learning.