  Model predictive control (MPC) is a popular strategy for urban traffic
management that is able to incorporate physical and user defined constraints.
However, the current MPC methods rely on finite horizon predictions that are
unable to guarantee desirable behaviors over long periods of time. In this
paper we design an MPC strategy that is guaranteed to keep the evolution of a
network in a desirable yet arbitrary -safe- set, while optimizing a finite
horizon cost function. Our approach relies on finding a robust controlled
invariant set inside the safe set that provides an appropriate terminal
constraint for the MPC optimization problem. An illustrative example is
included.
