3 citations · 3 across the 5 of their papers we have counts for
9 papers
Data-driven invariant subspace identification for black-box switched linear systems
Guillaume O. Berger, Raphaël M. Jungers, Zheming Wang
We present an algorithmic framework for the identification of candidate invariant subspaces for switched linear systems. Namely, the framework allows to compute an orthonormal basi…
Black-box stability analysis of hybrid systems with sample-based multiple Lyapunov functions
Adrien Banse, Zheming Wang, Raphaël M. Jungers
We present a framework based on multiple Lyapunov functions to find probabilistic data-driven guarantees on the stability of unknown constrained switching linear systems (CSLS), wh…
Probabilistic guarantees on the objective value for the scenario approach via sensitivity analysis
Zheming Wang, Raphaël M. Jungers
This paper is concerned with objective value performance of the scenario approach for robust convex optimization. A novel method is proposed to derive probabilistic bounds for the…
Data-driven stability analysis of switched affine systems
Matteo Della Rossa, Zheming Wang, Lucas N. Egidio +1
We consider discrete-time switching systems composed of a finite family of affine sub-dynamics. First, we recall existing results and present further analysis on the stability prob…
Data-driven stability analysis of switched linear systems with Sum of Squares guarantees
Anne Rubbens, Zheming Wang, Raphaël M. Jungers
We present a new data-driven method to provide probabilistic stability guarantees for black-box switched linear systems. By sampling a finite number of observations of trajectories…
Chance-constrained quasi-convex optimization with application to data-driven switched systems control
Guillaume O. Berger, Raphaël M. Jungers, Zheming Wang
We study quasi-convex optimization problems, where only a subset of the constraints can be sampled, and yet one would like a probabilistic guarantee on the obtained solution with r…