Moving Horizon Optimization of Train Speed Profile Based on Sequential Quadratic Programming

Abstract

Recommended speed profile is a complementary function of the automatic train operation (ATO). Most studies focus on the off-line optimization of recommended speed profile. In this paper, we design a moving horizon optimization algorithm which can get the speed profile online. We use the velocity and operation time as state variables of the nonlinear train operation model and take the energy consumption and punctuality as objectives. Then we apply the sequential quadratic programming (SQP) to such multi-objective optimization problem with several nonlinear constraints. The simulation results based on the Yizhuang Line of Beijing Subway indicate that, the sequential quadratic programming based moving horizon optimization algorithm has high computational efficiency and can make a trade-off between the energy consumption and punctuality.