Paper Info
Title
A Data-Driven Method for Computing Polyhedral Invariant Sets of Black-Box Switched Linear Systems
Abstract
In this letter, 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 letter 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
10.1109/LCSYS.2020.3044838
IEEE Control Systems Letters
Keywords
DocType
Volume
Randomized algorithms,polyhedral invariant sets,switched linear systems,black-box systems
Journal
5
Issue
ISSN
Citations 
5
2475-1456
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Zheming Wang1308.12
Raphaël M. Jungers222239.39