Bi-level temporally-distributed MPC 

Model predictive control (MPC) has been widely used for large-scale systems, e.g., for traffic networks. One of the common applications of MPC for traffic management involves minimizing the total time spent (via reducing the congestion) and the total emissions of vehicles.

When long-term green urban mobility is considered, e.g., when an upper bound is allowed for the total yearly emissions, the optimization horizon (1 year) of the MPC problem is significantly larger than the control sampling time (a few seconds or minutes), and thus the number of the variables that should be optimized per control time step becomes very large.

For systems with dynamics that involve nonlinear, non-convex, and non-smooth functions, including urban traffic networks, this results in optimization problems that are computationally intractable in real time. 

In this project, we propose a novel bi-level temporal distribution of such complex MPC optimization problems, and we develop two mathematically linked short-term and long-term MPC formulations with small and large control sampling times that will be solved together instead of the original complex optimization problem. 

The resulting bi-level control architecture is used to solve the two MPC formulations online for real-time control of urban traffic networks with the objective of long-term green mobility. In order to assess the performance of the bi-level control architecture, we perform a case study where a rough version of the model of the urban traffic flow, S-model, is used by the long-term MPC level to estimate the states of the urban traffic networks, and a detailed version of the model is used by the short-term MPC level. The results of the simulations prove the effectiveness (with respect to the objective of control, as well as computational efficiency) of the proposed bi-level MPC approach, compared to state-of-the-art control approaches.