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Model predictive control (MPC) is a popular strategy for urban trafficmanagement that is able to incorporate physical and user defined constraints.However, the current MPC methods rely on finite horizon predictions that areunable to guarantee desirable behaviors over long periods of time. In thispaper we design an MPC strategy that is guaranteed to keep the evolution of anetwork in a desirable yet arbitrary -safe- set, while optimizing a finitehorizon cost function. Our approach relies on finding a robust controlledinvariant set inside the safe set that provides an appropriate terminalconstraint for the MPC optimization problem. An illustrative example isincluded.

A Provably Correct MPC Approach to Safety Control of Urban Traffic Networks