Data-driven stability analysis of switched linear systems with Sum of Squares guarantees

We present a new data-driven method to provide probabilistic stability guarantees for black-box switched linear systems. By sampling finite number of observations trajectories, we construct approximate Lyapunov functions and deduce the underlying system with user-defined confidence. The required attain this confidence level on guarantee is explicitly characterized. Our contribution twofold: first, propose novel approach common quadratic functions, relying sensitivity analysis quasi-convex optimization program. doing so, improve recently proposed bound. Then, show that our allows extension Sum Squares providing further improvement technique. demonstrate these improvements numerical example.