Abstract
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.
Highlights
Since the urban rail train can carry more passengers and has higher comfort and safety, it has become the key development object of rail transit [1,2]
In order to verify the effectiveness of improved genetic algorithm (IGA) and improved dynamic matrix control (IDMC) for automatic train operation (ATO) system of urban rail train, the train of light rail line 12 in Dalian, China is selected as the research object
For the upper-layer control, the multi-objective optimization model of urban rail train is built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the weight coefficient of each index is solved by entropy weight method
Summary
Since the urban rail train can carry more passengers and has higher comfort and safety, it has become the key development object of rail transit [1,2]. At this stage, the urban rail train mainly uses manual driving mode in the world [3]. The traditional manual driving mode has its own advantages, the development of automatic train operation (ATO) is an inevitable trend. For the upper-layer control comprehensively considering the stopping accuracy index, punctuality index, comfort index, Energies 2019, 12, 3842; doi:10.3390/en12203842 www.mdpi.com/journal/energies
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