本文選題：城市軌道列車 + 自動(dòng)運(yùn)行��； 參考：《山東科技大學(xué)》2017年碩士論文

【摘要】：城市軌道列車作為重要的城際間交通工具,在近幾年來(lái)發(fā)展迅猛。在軌道列車迅猛進(jìn)步之際,為了使得列車能夠安全行駛、準(zhǔn)點(diǎn)到站,運(yùn)輸效率更高,滿足旅客舒適度和環(huán)保節(jié)能的需求,亟待對(duì)列車的運(yùn)行速度曲線進(jìn)行優(yōu)化處理研究。但是,在過(guò)去幾年內(nèi)國(guó)內(nèi)外在列車自動(dòng)運(yùn)行(Automatic Train Operation,ATO)控制策略的研究和優(yōu)化大多都是針對(duì)特定指標(biāo)來(lái)進(jìn)行的,在考慮多目標(biāo)優(yōu)化情形時(shí)的效果并不理想。為了提高列車的運(yùn)輸效率和改善自動(dòng)運(yùn)行策略,各國(guó)公司和學(xué)者紛紛著手研究列車自動(dòng)運(yùn)行速度曲線優(yōu)化,但在列車運(yùn)行過(guò)程中的列車控制策略很難做到實(shí)時(shí)的在線優(yōu)化;而列車運(yùn)行過(guò)程的多目標(biāo)性、復(fù)雜性和非線性使得研究者很難做到確定精確模型和最優(yōu)速度曲線。然而,人工智能技術(shù)和混合優(yōu)化理論的迅速發(fā)展為列車自動(dòng)運(yùn)行控制策略的研究和創(chuàng)新提供了新的契機(jī)。本論文主要針對(duì)列車自動(dòng)運(yùn)行速度曲線優(yōu)化這一問(wèn)題,首先分析了列車運(yùn)行控制策略和列車運(yùn)行時(shí)刻的各項(xiàng)優(yōu)化原則,結(jié)合自動(dòng)運(yùn)行速度曲線的各性能指標(biāo)評(píng)價(jià)體系,建立各項(xiàng)指標(biāo)的評(píng)價(jià)指標(biāo)模型;其次,結(jié)合層次分析法(Analytic Hierarchy Process, AHP )和熵(Entropy )權(quán)法綜合的方法計(jì)算出各項(xiàng)性能指標(biāo)的權(quán)重分配大小;而后結(jié)合列車的自身參數(shù)和運(yùn)行線路工況,在建立的評(píng)價(jià)指標(biāo)模型的基礎(chǔ)上,設(shè)計(jì)了基于遺傳算法和基于GA-SPSO算法的優(yōu)化策略對(duì)列車自動(dòng)運(yùn)行速度曲線進(jìn)行優(yōu)化;最后,采用MATLAB軟件進(jìn)行仿真測(cè)試,結(jié)果表明基于GA-SPSO算法的優(yōu)化策略效果好于基于遺傳算法的優(yōu)化策略,實(shí)現(xiàn)了列車自動(dòng)運(yùn)行控制,確定了更適合給定運(yùn)行環(huán)境和實(shí)際運(yùn)行路線的速度曲線,在確保了列車安全運(yùn)行的條件下,保證了列車的準(zhǔn)點(diǎn)、精準(zhǔn)停車、減少能耗以及乘客的舒適性等各性能指標(biāo)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.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U284.481;TP18

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