  We consider stability analysis of constrained switching linear systems in
which the dynamics is unknown and whose switching signal is constrained by an
automaton. We propose a data-driven Lyapunov framework for providing
probabilistic stability guarantees based on data harvested from observations of
the system. By generalizing previous results on arbitrary switching linear
systems, we show that, by sampling a finite number of observations, we are able
to construct an approximate Lyapunov function for the underlying system.
Moreover, we show that the entropy of the language accepted by the automaton
allows to bound the number of samples needed in order to reach some
pre-specified accuracy.
