Title :
Fast Model Predictive Control for Urban Road Networks via MILP
Author :
Lin, Shu ; De Schutter, Bart ; Xi, Yugeng ; Hellendoorn, Hans
Author_Institution :
Delft Center for Syst. & Control, Delft Univ. of Technol., Delft, Netherlands
Abstract :
In this paper, an advanced control strategy, i.e., model predictive control (MPC), is applied to control and coordinate urban traffic networks. However, due to the nonlinearity of the prediction model, the optimization of MPC is a nonlinear nonconvex optimization problem. In this case, the online computational complexity becomes a big challenge for the MPC controller if it is implemented in a real-life traffic network. To overcome this problem, the online optimization problem is reformulated into a mixed-integer linear programming (MILP) optimization problem to increase the real-time feasibility of the MPC control strategy. The new optimization problem can be very efficiently solved by existing MILP solvers, and the global optimum of the problem is guaranteed. Moreover, we propose an approach to reduce the complexity of the MILP optimization problem even further. The simulation results show that the MILP-based MPC controllers can reach the same performance, but the time taken to solve the optimization becomes only a few seconds, which is a significant reduction, compared with the time required by the original MPC controller.
Keywords :
integer programming; linear programming; predictive control; road traffic; fast model predictive control; mixed integer linear programming optimization problem; nonlinear nonconvex optimization problem; urban road networks; urban traffic networks; Computational modeling; Mathematical model; Optimization; Predictive control; Predictive models; Real time systems; Vehicles; Model predictive control (MPC); urban traffic modeling; urban traffic network control;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2011.2114652