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

In this paper, we consider the problem of invariant set computation for black-box switched linear systems using merely a finite observations system trajectories. particular, paper focuses on polyhedral sets. We propose data-driven method based one step forward reachable set. For formal verification proposed method, introduce concepts $\lambda$-contractive sets and almost-invariant systems. The convexity-preserving property allows us to conduct contraction analysis computed derive probabilistic property. spirit non-convex scenario optimization, also establish chance-constrained guarantee invariance. performance our is then illustrated by numerical examples.