Qiao, Xiaoyun (Southwest Jiaotong University), YAN, Fei (Southwest Jiaotong University)

An Adaptive MPC-Based Optimal Control for Subway Regulation Considering Stranded Passengers

Scheduled for presentation during the Regular Session "Rail Traffic Management II" (FrBT7), Friday, September 27, 2024, 15:10−15:30, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

This paper investigates dynamic models of trains and passenger flows within subway systems and explores an optimal joint strategy for regulating trains and controlling passenger flow. A dynamic model is developed to describe the departure times of subway trains, train loads, and the number of stranded passengers. To address heavy passenger flow, mitigate subway train delays and overloading, and reduce passenger stranding, a quadratic programming (QP) optimization problem is solved in the model predictive control (MPC) framework. Additionally, given the uncertainty and time-varying nature of parameters in subway system models, a gradient descent adaptive updating law is employed to adjust the estimated parameters of the system. The theoretical underpinning of the gradient descent method ensures the asymptotic stability of state errors in closed-loop systems. Finally, the effectiveness of the proposed estimated model and adaptive MPC approach is validated through numerical simulations.