基于GA-SPSO算法的列车速度曲线优化算法研究

发布时间：2018-05-16 10:24

本文选题：城市轨道列车 + 自动运行　；参考：《山东科技大学》2017年硕士论文

【摘要】：城市轨道列车作为重要的城际间交通工具,在近几年来发展迅猛。在轨道列车迅猛进步之际,为了使得列车能够安全行驶、准点到站,运输效率更高,满足旅客舒适度和环保节能的需求,亟待对列车的运行速度曲线进行优化处理研究。但是,在过去几年内国内外在列车自动运行(Automatic Train Operation,ATO)控制策略的研究和优化大多都是针对特定指标来进行的,在考虑多目标优化情形时的效果并不理想。为了提高列车的运输效率和改善自动运行策略,各国公司和学者纷纷着手研究列车自动运行速度曲线优化,但在列车运行过程中的列车控制策略很难做到实时的在线优化;而列车运行过程的多目标性、复杂性和非线性使得研究者很难做到确定精确模型和最优速度曲线。然而,人工智能技术和混合优化理论的迅速发展为列车自动运行控制策略的研究和创新提供了新的契机。本论文主要针对列车自动运行速度曲线优化这一问题,首先分析了列车运行控制策略和列车运行时刻的各项优化原则,结合自动运行速度曲线的各性能指标评价体系,建立各项指标的评价指标模型;其次,结合层次分析法(Analytic Hierarchy Process, AHP )和熵(Entropy )权法综合的方法计算出各项性能指标的权重分配大小;而后结合列车的自身参数和运行线路工况,在建立的评价指标模型的基础上,设计了基于遗传算法和基于GA-SPSO算法的优化策略对列车自动运行速度曲线进行优化;最后,采用MATLAB软件进行仿真测试,结果表明基于GA-SPSO算法的优化策略效果好于基于遗传算法的优化策略,实现了列车自动运行控制,确定了更适合给定运行环境和实际运行路线的速度曲线,在确保了列车安全运行的条件下,保证了列车的准点、精准停车、减少能耗以及乘客的舒适性等各性能指标的基本要求。
[Abstract]:As an important intercity vehicle, urban rail train has developed rapidly in recent years. With the rapid progress of rail trains, in order to make the train run safely, arrive on time, improve the transportation efficiency and meet the needs of passenger comfort and environmental protection and energy saving, it is urgent to study the optimal processing of train speed curve. However, in the past few years, the research and optimization of automatic Train operation (ATO) control strategy at home and abroad are mostly carried out according to specific targets, and the results are not satisfactory when considering the multi-objective optimization situation. In order to improve the efficiency of train transportation and improve the automatic operation strategy, companies and scholars all over the world have begun to study the optimization of train automatic speed curve, but it is difficult to achieve real-time on-line optimization of train control strategy in the course of train operation. The multi-objective complexity and nonlinearity of train operation make it difficult for researchers to determine the exact model and the optimal velocity curve. However, the rapid development of artificial intelligence technology and hybrid optimization theory provides a new opportunity for the research and innovation of automatic train operation control strategy. This paper mainly aims at the optimization of automatic train running speed curve. Firstly, this paper analyzes the train operation control strategy and the optimization principle of train running time, and combines with the evaluation system of each performance index of automatic running speed curve. The evaluation index model of each index is established. Secondly, the weight distribution of each performance index is calculated by combining the Analytic Hierarchy Process, AHP) and Entropy weight method of Analytic hierarchy process (AHP), and then combined with the train's own parameters and the working conditions of the running line. Based on the established evaluation index model, the optimization strategy based on genetic algorithm and GA-SPSO algorithm is designed to optimize the automatic train running speed curve. Finally, the simulation test is carried out by using MATLAB software. The results show that the optimization strategy based on GA-SPSO algorithm is better than that based on genetic algorithm, the automatic train operation control is realized, and the speed curve which is more suitable for the given running environment and actual running route is determined. Under the condition of ensuring the safe operation of the train, the basic requirements of the performance indexes such as train punctuality, accurate parking, reducing energy consumption and passenger comfort are ensured.
【学位授予单位】：山东科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U284.481;TP18

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