Reference
S. Li, B. De Schutter, L. Yang, and Z. Gao, "Robust model predictive control
for train regulation in underground railway transportation,"
IEEE Transactions on Control Systems Technology, vol. 24, no.
3, pp. 1075-1083, May 2016.
Abstract
This paper investigates the robust model predictive control for train
regulation in underground railway transportation. By considering the uncertain
passenger arrival flow, a constrained state-space model for the train traffic
of a metro loop line is developed. The goal of the paper is to design a
state-feedback control law at each decision step to optimize a metro system
cost function subject to safety constraints on the control input. Based on
Lyapunov function theory, the problem of optimizing an upper bound on the
system cost function subject to input constraints is reduced to a convex
optimization problem involving linear matrix inequalities (LMIs). Moreover, for
the inevitable disturbances leading to the delays, the robust model predictive
control strategy of train regulation is designed for a metro loop line such
that it ensures the minimization of an upper bound on metro system cost
function, and meanwhile guarantees a disturbance attenuation level with respect
to the disturbances. Numerical examples are given to illustrate the
effectiveness of the proposed methods.
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BibTeX
@article{LiDeS:15-046,
author = {Li, Shukai and De Schutter, Bart and Yang, Lixing and Gao,
Ziyou},
title = {Robust Model Predictive Control for Train Regulation in
Underground Railway Transportation},
journal = {IEEE Transactions on Control Systems Technology},
volume = {24},
number = {3},
pages = {1075--1083},
month = may,
year = {2016}
}