Paper Info
Title
A data-driven method for computing polyhedral invariant sets of black-box switched linear systems
Abstract
In this paper, we consider the problem of invariant set computation for black-box switched linear systems using merely a finite set of observations of system trajectories. In particular, this paper focuses on polyhedral invariant sets. We propose a data-driven method based on the one step forward reachable set. For formal verification of the proposed method, we introduce the concepts of lambda-contractive sets and almost-invariant sets for switched linear systems. The convexity-preserving property of switched linear systems allows us to conduct contraction analysis on the computed set and derive a probabilistic contraction property. In the spirit of non-convex scenario optimization, we also establish a chance-constrained guarantee on set invariance. The performance of our method is then illustrated by numerical examples.
Year
DOI
Venue
2021
10.23919/ACC50511.2021.9483010
2021 AMERICAN CONTROL CONFERENCE (ACC)
DocType
ISSN
Citations 
Conference
0743-1619
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Zheming Wang1308.12
Raphaël M. Jungers201.35