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Reference:
X. Liu,
A. Dabiri,
Y. Wang,
J. Xun, and
B. De Schutter,
"Distributed model predictive control for virtually coupled
heterogeneous trains: Comparison and assessment," IEEE
Transactions on Intelligent Transportation Systems, 2024. To
appear.
Abstract:
Virtual coupling is regarded as an efficient way to improve the line
capacity of rail transportation systems by reducing the spacing
between consecutive trains. This paper is the first to compare and
assess different distributed model predictive control (MPC)
approaches, i.e., cooperative distributed MPC, serial distributed MPC,
and decentralized MPC, for virtually coupled trains with a nonlinear
train dynamic model. To make a balanced trade-off between
computational complexity and efficiency, we also propose and assess
convex approximations of the above control approaches. Furthermore, we
are the first to introduce the relaxed dynamic programming approach to
analyze the stability of the MPC-based nonlinear train control
problem. By using the relaxed dynamic programming approach, a
distributed stopping criterion with a stability guarantee is developed
for the cooperative distributed MPC approach. In real life, masses of
trains are different and can change at stations due to changes in
passenger loads. This change in mass can significantly affect the
dynamics and control of the virtually coupled trains when not taken
into account in the control design. Therefore, we explicitly consider
heterogeneous train masses when designing MPC approaches. We evaluate
the different distributed MPC approaches through case studies based on
the data of the Beijing Yizhuang Line. Simulation results indicate
that the cooperative distributed MPC approach has the best tracking
performance, while the serial distributed MPC approach can reduce
communication requirements and computation capabilities with
sacrifices of tracking performance.
Bibtex entry:
@article{LiuDab:24-021,