Work

A Data-Driven Method for Computing Polyhedral Invariant Sets of Black-Box Switched Linear Systems

Year: 2020

Type: article

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.

Source: IEEE Control Systems Letters

DOI: 10.1109/lcsys.2020.3044838

Authors: Zheming Wang, Raphael M. Jungers

Institution: UCLouvain

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Fonds De La Recherche Scientifique - FNRS, Innoviris, European Research Council

Award: G1029153808