  In this document, some novel theoretical and computational techniques for
constrained approximation of data-driven systems, are presented. The motivation
for the development of these techniques came from structure-preserving matrix
approximation problems that appear in the fields of system identification and
model predictive control, for data-driven systems and processes. The research
reported in this document is focused on finite-state approximation of
data-driven systems.
  Some numerical implementations of the aforementioned techniques in the
simulation and model predictive control of some generic data-driven systems,
that are related to electrical signal transmission models, are outlined.
