Reference
S. Lin, B. De Schutter, Y. Xi, and H. Hellendoorn, "Model predictive control
for urban traffic networks via MILP,"
Proceedings of the 2010
American Control Conference, Baltimore, Maryland, pp. 2272-2277,
June-July 2010.
Abstract
Model Predictive Control (MPC) is an advanced control strategy that can easily
coordinate urban traffic networks. But, due to the nonlinearity of the traffic
model, the optimization problem of the MPC controller will become intractable
in practice when the scale of the controlled traffic network grows larger. To
solve this problem, the nonlinear traffic model is reformulated into a model
with only linear equations and inequalities. Mixed-Integer Linear Programming
(MILP) algorithms can efficiently solve the reformulated optimization problem,
and guarantee the global optimum at the same time. Moreover, the MILP
optimization problem is further relaxed by model reduction and adding upper
bound constraints.
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BibTeX
@inproceedings{LinDeS:10-007,
author = {Lin, Shu and De Schutter, Bart and Xi, Yugeng and Hellendoorn,
Hans},
title = {Model Predictive Control for Urban Traffic Networks via
{MILP}},
booktitle = {Proceedings of the 2010 American Control Conference},
address = {Baltimore, Maryland},
pages = {2272--2277},
month = jun # {--} # jul,
year = {2010}
}