The aim is to provide data-based conditions for checking stability of strongly autonomous nD systems. It is known that because of the lack of natural ordering of the domain, the problem of defining and studying stability of nD systems is not straightforward. We focus on the following two notions of stability: (i) stability with respect to a given direction, and more generally, (ii) conic stability, that is, stability with respect to subsets of the domain having the structure of a cone. For a strongly autonomous nD system, these two notions of stability have been studied in the literature from a model based perspective. Here, we analyze their data-based counterparts.We assume that the data is noise-free, and that it is measured from a linear system of partial difference equations, with constant real coefficients, having n independent variables. We further assume that the data-generating system is a strongly autonomous nD system, that is, the solution trajectories can be uniquely determined using finite “initial conditions”. Given a finite set of data, that is, a finite set of multi-indexed sequences, we first define a notion of informativity of nD trajectories. Using informative nD trajectories, we provide data-based tests for checking stability of nD systems with respect to a given direction, and, more generally, for conic stability.
Read full abstract