Parametrized Model Predictive Control Approaches for Urban Traffic Networks

Abstract

Model Predictive Control (MPC) has shown promising results in the control of urban traffic networks, but unfortunately it has one major drawback. The, often nonlinear, optimization that has to be performed at every control time step is computationally too complex to use MPC controllers for real-time implementations (i.e. when the online optimization is performed within the control time interval of the controlled network). This paper proposes an effective parametrized MPC approach to lower the computational complexity of the MPC controller. Two parametrized control laws are proposed that can be used in the parametrized MPC framework, one based on the prediction model of the MPC controllers, and another is constructed using Grammatical Evolution (GE). The performance and computational complexity of the parametrized MPC approach is compared to a conventional MPC controller by performing an extensive simulation-based case study. The simulation results show that for the given case study the parametrized MPC approach is real-time implementable while the performance decreases with less than 3% with respect to the conventional MPC controller.