Real-time optimal train regulation design for metro lines with energy-saving. (January 2019)

• Record Type: Journal Article
• Authors: Zhang, Huimin; Li, Shukai; Yang, Lixing
• Is Part Of: Computers & industrial engineering. Volume 127(2019)
• Page Start: 1282
• Page End: 1296
• Publication Date: 2019-01
• Subjects: Metro line -- Train regulation -- Real-time -- Model predictive control -- Energy-saving; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.02.019
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A traffic model is proposed for a metro single line with train traffic and passenger flow dynamics. A nonlinear optimal control model is developed to reduce energy consumptions. A model predictive control algorithm is designed to solve the formulated optimal control model. The optimal train regulation strategy is generated in real-time under the proposed algorithm. Abstract: This paper investigates the real-time optimal train regulation design for metro lines with energy-saving based on a model predictive control method. A traffic model is proposed for a metro single line with including both train traffic dynamic and passenger flow dynamic. Then, to simultaneously improve operational efficiency and reduce energy consumptions in the train regulation process after disturbances, a nonlinear optimal control model is further developed by considering the safety and control constraints. For the real-time requirement of train regulation, a model predictive control (MPC) algorithm is particularly designed to solve the formulated optimal control model, which uses the real-time updated delay feedback information of the trains and generates the optimal train regulation strategy in real-time. Numerical examples are given to illustrate the effectiveness of the proposed methods. With the proposed control algorithm, the timetable and the headway deviations for metro lines are effective reduced and meanwhile the goal of the energy-saving is also realized.