Scalable identification of stable positive systems

Positive systems frequently appear in applications, and enjoy substantially simplified analysis and control design compared to the general LTI case. In this paper we construct a polytopic parameterization of all stable positive systems, and a convex upper bound for simulation error (a.k.a. output error) for which the resulting optimization is a linear program. Previous work on analogous methods for both the positive and general LTI case result in semidefinite programs. We exploit the decomposability of the constraints in these linear programs to develop distributed solutions applicable to identification of large-scale networked systems.