A hybrid systems framework for data-based adaptive control of linear time-varying systems

TITLE: A hybrid systems framework for data-based adaptive control of linear time-varying systems

ABSTRACT: We consider the problem of stabilizing discrete-time linear time-varying systems when the model of the plant is unknown. The controller is defined as a linear state-feedback that changes over time by adapting to the plant's changes in an event-triggered fashion. Specifically, measured data are used both to design the new controller and to decide when to update it. As a result, the resulting closed-loop dynamics exhibits physical jumps, due to the system dynamics, and episodic jumps, due to the feedback update, which leads to a hybrid system description. This representation provides a natural setting for designing events which trigger the controller adaptation only when this becomes necessary due to the plant’s variations, as well as providing closed-loop guarantees on this nonlinear adaptive feedback interconnection. We conclude by providing numerical results that illustrate the relevance of the proposed approach.

BIO: Andrea Iannelli is a tenure-track assistant professor in the Institute for Systems Theory and Automatic Control at the University of Stuttgart (Germany). 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 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 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, optimization, and learning.