Informativity for Identification for $2D$ State-Representable Autonomous Systems, with Applications to Data-Driven Simulation

We define persistency of excitation and informativity for system identification for the class of
$2D$
state-representable autonomous systems. We characterize informativity for system identification in terms of properties of a matrix constructed from the restrictions of a system trajectory on successive consecutive lines. We state a procedure to compute arbitrary trajectories from a “sufficiently rich” one.