  We present the observation that the process of stochastic model predictive
control can be formulated in the framework of iterated function systems. The
latter has a rich ergodic theory that can be applied to study the system's
long-run behavior. We show how such a framework can be realized for specific
problems and illustrate the required conditions for the application of relevant
theoretical guarantees.
