Reference
U. Todorović,
J. R. D.
Frejo, and B. De Schutter, "Distributed MPC for large freeway networks using
alternating optimization,"
IEEE Transactions on Intelligent
Transportation Systems, vol. 23, no. 3, pp. 1875-1884, Mar. 2022.
Abstract
The Model Predictive Control (MPC) framework has shown great potential for the
control of Variable Speed Limits (VSLs) and Ramp Metering (RM) installations.
However, the implementation to large freeway networks remains challenging. One
major reason is that, by considering the VSLs to be discrete decision
variables, an extremely difficult Mixed Integer Nonlinear Programming (MINLP)
optimization problem has to be solved within every controller sampling
interval. Consequently, many related papers relax the MINLP problems by
considering the VSLs to be continuous variables. This paper proposes two novel
MPC algorithms for coordinated control of discrete VSLs and continuous RM rates
that do not make this relaxation. The proposed algorithms use a distributed
control architecture and an alternating optimization scheme to relax the MINLP
optimization problems but still consider the VSLs as discrete variables and,
hence, offer a trade-off between computational complexity and system
performance. The performance of the proposed algorithms is evaluated in a case
study. The case study shows that relaxing the VSLs to be continuous variables
with a distributed architecture results in a significant performance loss.
Furthermore, both proposed algorithms have a lower computational complexity
than the more conventional centralized approach and, as a result, they do
manage to solve all optimization problems within the sampling intervals.
Moreover, one of the proposed algorithms has a system performance that is
remarkably similar to the optimal performance of the centralized approach.
Publisher page
Downloads
BibTeX
@article{TodFre:21-007,
author = {Todorovi{\'{c}}, Uglje{\v{s}}a and Frejo, Jos{\'{e}} Ram{\'{o}}n
D. and De Schutter, Bart},
title = {Distributed {MPC} for Large Freeway Networks Using Alternating
Optimization},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {23},
number = {3},
pages = {1875--1884},
month = mar,
year = {2022}
}