Distributed model predictive control for real-time train regulation of metro line based on Dantzig-Wolfe decomposition

This paper aims to propose a novel distributed model predictive control (MPC) scheme for real-time train regulation in urban metro transportation. Particularly, a nonlinear real-time train regulation model is put forward to minimize the timetable deviations and the control strategies for each train-under the uncertain disturbances, which is then reformulated into a linear optimization model for easy to solve. By regarding each train as a subsystem, we design the distributed MPC algorithm based on the Dantzig-Wolfe decomposition for the train regulation problem, which decomposes the original optimization problem into numerous smaller and less complicated optimization control problems that can be solved independently. Under the distributed mechanism, we regard each train as a local subsystem, which only interacts with the coordinator, ensuring the flexibility and modularity of the control structure. Numerical cases are provided to demonstrate the effectiveness and robustness of the proposed distributed MPC method.

Automated summary

This paper proposes a novel distributed model predictive control scheme for real-time train regulation in urban metro transportation. By decomposing the original optimization problem into smaller and less complicated optimization control problems, the flexibility and modularity of the control structure are ensured.