A New Control Method of Automatic Train Operation in Urban Rail Transit Based on Improved Generalized Predictive Control Theory

2014 ◽  
pp. 567-573 ◽  
Author(s):  
Wentian Zhao ◽  
Chunhai Gao
Keyword(s):  
Control Theory ◽  
Predictive Control ◽  
Control Method ◽  
Urban Rail Transit ◽  
Generalized Predictive Control ◽  
Rail Transit ◽  
Urban Rail ◽  
Train Operation ◽  
Automatic Train Operation

Research on Methods for Urban Rail Transit Train Operation Regulation

2014 ◽  
Vol 536-537 ◽  
pp. 820-823
Author(s):  
Chao Cai
Keyword(s):  
Mathematical Model ◽  
Urban Rail Transit ◽  
Traffic Density ◽  
Rail Transit ◽  
Key Technology ◽  
Rapid Construction ◽  
Urban Rail ◽  
Train Operation ◽  
Automatic Train Operation ◽  
High Traffic Density

Train operation regulation is the key technology to ensure the orderly operation of trains, its value has been fully realized under the background of rapid construction and high traffic density in urban rail transit. This paper did a deep analysis on different strategies and methods for urban rail transit train operation regulation and the applications of these strategies and methods in different conditions. Furthermore, an automatic train operation regulation mathematical model was established. Then developed a software system in Visual C++ and used it to analysis and test the model by data of a real metro line, the result showed that both the model and the system were available.

Modeling for Subway Station Temperature Control System of Urban Rail Transit Based on Predictive Control

2011 ◽  
Vol 464 ◽  
pp. 119-122 ◽  
Author(s):  
Yu Dong Tian
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Temperature Control ◽  
Control Method ◽  
Urban Rail Transit ◽  
Temperature Control System ◽  
Subway Station ◽  
Rail Transit ◽  
Technology Application ◽  
Urban Rail

Urban rail transit is an important research field in the public traffic, and the subway station temperature control influences the performance of subway ventilation greatly. To aim at the problem, firstly the principle, algorithm and characteristics of predictive control method is analyzed; then a subway station temperature control system of urban rail transit and its characteristics are researched; at last, modeling of subway station temperature control system is advanced by applied the generalized predictive control in the view of technology application in order to resolve the control problems caused by time-change, long-hysteresis, uncertainty and strong-coupling to meet the require of temperature control characteristic.

scholarly journals Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3842 ◽  
Author(s):  
Kai-wei Liu ◽  
Xing-Cheng Wang ◽  
Zhi-hui Qu
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Lower Layer ◽  
Target Speed ◽  
Speed Controller ◽  
Urban Rail ◽  
Train Operation ◽  
Automatic Train Operation ◽  
And Control ◽  
Layer Control

The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

scholarly journals Fare Optimality Analysis of Urban Rail Transit under Various Objective Functions

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Lianbo Deng ◽  
Zhao Zhang ◽  
Kangni Liu ◽  
Wenliang Zhou ◽  
Junfeng Ma
Keyword(s):  
Simulated Annealing Algorithm ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Elastic Demand ◽  
Benefit Analysis ◽  
Objective Functions ◽  
Passenger Flow ◽  
Urban Rail ◽  
Train Operation ◽  
Annealing Algorithm

Urban rail transit fare strategies include fare structures and fare levels. We propose a rail transit line fare decision based on an operating plan that falls under elastic demand. Combined with the train operation plan, considering flat fare and distance-based fare, and based on the benefit analysis of both passenger flow and operating enterprises, we construct the objective functions and build an optimization model in terms of the operators’ interests, the system’s efficiency, system regulation goals, and the system costs. The solving algorithm based on the simulated annealing algorithm is established. Using as an example the Changsha Metro Line 2, we analyzed the optimized results of different models under the two fare structures system. Finally the recommendations of fare strategies are given.

Sign in / Sign up

Export Citation Format

Share Document