English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present an algorithmic framework for the identification of candidateinvariant subspaces for switched linear systems. Namely, the framework allowsto compute an orthonormal basis in which the matrices of the system are closeto block-triangular matrices, based on a finite set of observed one-steptrajectories and with a priori confidence level. The link between the existenceof an invariant subspace and a common block-triangularization of the systemmatrices is well known. Under some assumptions on the system, one can alsoinfer the existence of an invariant subspace when the matrices are close to beblock-triangular. Our approach relies on quadratic Lyapunov analysis and recenttools in scenario optimization. We present two applications of our results forproblems of consensus and opinion dynamics; the first one allows to identifythe disconnected components in a switching hidden network, while the second oneidentifies the stationary opinion vector of a switching gossip process withantagonistic interactions.

Data-driven invariant subspace identification for black-box switched linear systems