PROJECT TITLE :

Robust Model Predictive Control for Train Regulation in Underground Railway Transportation

ABSTRACT:

This brief investigates the robust model predictive control (MPC) for train regulation in underground railway transportation. By considering the unsure passenger arrival flow, a constrained state-space model for the train traffic of a metro loop line is developed. The goal of this brief is to design a state feedback management law at every decision step to optimize a metro system cost operate subject to safety constraints on the management input. Primarily based on Lyapunov function theory, the problem of optimizing an higher certain on the system value operate subject to input constraints is reduced to a convex optimization downside involving linear matrix inequalities. Moreover, for the inevitable disturbances leading to the delays, the sturdy MPC strategy of train regulation is designed for a metro loop line such that it ensures the minimization of an higher certain on metro system value function, and meanwhile guarantees a disturbance attenuation level with respect to the disturbances. Numerical examples are given to illustrate the effectiveness of the proposed strategies.