Title
Online learning of data-driven controllers for unknown switched linear systems
Abstract
Motivated by the goal of learning controllers for complex systems whose dynamics change over time, we consider the problem of designing control laws for systems that switch among a finite set of unknown discrete-time linear subsystems under unknown switching signals. To this end, we propose a method that uses data to directly design a control mechanism without any explicit identification step. Our approach is online, meaning that the data are collected over time while the system is evolving in closed-loop, and are directly used to iteratively update the controller. A major benefit of the proposed online implementation is therefore the ability of the controller to automatically adjust to changes in the operating mode of the system. We show that the proposed control mechanism guarantees stability of the closed-loop switched linear system provided that the switching is slow enough. Effectiveness of the proposed design technique is illustrated for two aerospace applications.
Year
DOI
Venue
2022
10.1016/j.automatica.2022.110519
Automatica
Keywords
DocType
Volume
Data-driven control,Switched linear systems,Semidefinite programming
Journal
145
Issue
ISSN
Citations 
1
0005-1098
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Monica Rotulo100.68
de persis2108779.28
Pietro Tesi345232.00