【摘要】：近年來,城市軌道交通領(lǐng)域的發(fā)展引領(lǐng)著城軌列車設(shè)計制造、信號設(shè)備研發(fā)生產(chǎn)等相關(guān)行業(yè)的飛速發(fā)展,越來越多的高校和研究機構(gòu)從事軌道交通領(lǐng)域的基礎(chǔ)研究和應(yīng)用研究。列車動力學(xué)模型作為其研究的基礎(chǔ),對列車的運行控制有著極大的影響。在對城軌列車節(jié)能優(yōu)化研究過程中,由于列車模型不夠精確,仿真環(huán)境下理論節(jié)能效果與現(xiàn)場實測節(jié)能效果有所差異。因此,準確的列車模型對工程和理論研究具有深刻的意義。在總結(jié)了列車模型現(xiàn)有研究的基礎(chǔ)上,根據(jù)列車的運行工況和數(shù)據(jù)特點,本文建立了符合實際列車運行情況的單質(zhì)點動力學(xué)模型,將列車模型劃分為惰行階段模型、牽引階段模型和制動階段模型。將牽引階段分為牽引建立和牽引切除階段,其中,牽引建立階段模型分為低速牽引建立階段模型和高速牽引建立階段模型。將列車模型劃分為若干個模型,能夠更準確的表述實際列車運行過程。針對參數(shù)擬合計算問題,本文提出了果蠅優(yōu)化算法的一種改進型算法CIP-FOA(Fruit Fly Optimization Algorithm with Changing Iteration and Population),該算法采用改變步長半徑的方式,從迭代和種群兩個方面對步長進行改進。通過與其他算法進行了仿真對比分析,驗證了 CIP-FOA在穩(wěn)定性、計算精度和計算效率等方面較其他算法有明顯優(yōu)勢。本文在分析處理的北京地鐵亦莊線的夜間測試數(shù)據(jù)的基礎(chǔ)上,應(yīng)用CIP-FOA對列車基本阻力參數(shù)、低速牽引建立階段、高速牽引建立階段、牽引切除階段共14個模型參數(shù)進行擬合計算。本文在建立的城軌列車動力學(xué)模型和提出的CIP-FOA的基礎(chǔ)上,開發(fā)了"城軌列車動力學(xué)模型仿真軟件",該軟件用于參數(shù)擬合計算和模型的仿真驗證工作�；�"城軌列車動力學(xué)模型仿真軟件",對不同控制策略的列車運行數(shù)據(jù)進行仿真計算,通過仿真結(jié)果的對比和分析,驗證了本模型符合列車的實際運行過程,良好的實驗結(jié)果體現(xiàn)了本研究在工程規(guī)劃設(shè)計和列車動力學(xué)模型性能預(yù)測方面的作用。
[Abstract]:In recent years, the development of urban rail transit has led the rapid development of urban rail train design and manufacture, signal equipment research and production and other related industries. More and more universities and research institutions are engaged in the basic research and application research in the field of rail transit. As the basis of the research, the train dynamics model has a great influence on the train operation control. In the course of energy saving optimization research of urban rail trains, due to the inaccuracy of train model, the effect of theoretical energy saving in simulation environment is different from that of field measurement. Therefore, the accurate train model has profound significance for engineering and theoretical research. On the basis of summarizing the existing research on the train model and according to the operating conditions and data characteristics of the train, this paper establishes a single mass point dynamic model which accords with the actual train operation, and divides the train model into the idling stage model. Traction stage model and braking stage model. The traction stage is divided into traction establishment and traction resection, in which the traction establishment stage model is divided into low speed traction establishment stage model and high speed traction establishment stage model. The train model can be divided into several models, which can more accurately describe the actual train operation process. To solve the problem of parameter fitting, this paper presents an improved algorithm, CIP-FOA (Fruit Fly Optimization Algorithm with Changing Iteration and Population), for the optimization algorithm of Drosophila melanogaster. The algorithm improves the step size in terms of iteration and population by changing the radius of step size. By comparing with other algorithms, it is proved that CIP-FOA has obvious advantages over other algorithms in terms of stability, accuracy and efficiency. On the basis of analyzing the night test data of Beijing Metro Yizhuang Line, this paper applies CIP-FOA to the basic resistance parameters of trains, the low speed traction establishment stage, the high speed traction establishment stage. A total of 14 model parameters were fitted and calculated in the traction resection stage. Based on the established dynamic model of urban rail train and the proposed CIP-FOA, this paper develops a simulation software for the dynamic model of urban rail train, which is used for parameter fitting calculation and simulation verification of the model. Based on the simulation software of urban rail train dynamics model, the train operation data with different control strategies are simulated and calculated. Through the comparison and analysis of the simulation results, it is verified that the model accords with the actual running process of the train. Good experimental results show the function of this study in engineering planning and performance prediction of train dynamics model.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U270.11;TP391.9

【參考文獻】

相關(guān)期刊論文 前10條

1 衷路生;李兵;龔錦紅;張永賢;祝振敏;;高速列車非線性模型的極大似然辨識[J];自動化學(xué)報;2014年12期

2 郭紅戈;謝克明;;動車組列車制動系統(tǒng)的Hammerstein模型及其參數(shù)辨識方法[J];鐵道學(xué)報;2014年04期

3 吳小文;李擎;;果蠅算法和5種群智能算法的尋優(yōu)性能研究[J];火力與指揮控制;2013年04期

4 康熊;曾宇清;張波;;高速列車空氣阻力測量分析方法[J];中國鐵道科學(xué);2012年05期

5 于振宇;陳德旺;;城軌列車制動模型及參數(shù)辨識[J];鐵道學(xué)報;2011年10期

6 郜春海;陳德旺;;基于模型選擇和優(yōu)化技術(shù)的自動駕駛制動模型辨識研究[J];鐵道學(xué)報;2011年10期

7 楊輝;張坤鵬;王昕;衷路生;;高速列車多模型廣義預(yù)測控制方法[J];鐵道學(xué)報;2011年08期

8 李成楠;;一種遺傳算法的改進及其應(yīng)用[J];微計算機信息;2009年27期

9 田紅旗;;風環(huán)境下的列車空氣阻力特性研究[J];中國鐵道科學(xué);2008年05期

10 趙金帥;魯瑞華;;一種用于防止早熟收斂的改進遺傳算法[J];西南大學(xué)學(xué)報(自然科學(xué)版);2008年01期

相關(guān)碩士學(xué)位論文 前3條

1 范禮乾;基于蟻群算法的列車推薦速度曲線優(yōu)化[D];北京交通大學(xué);2016年

2 李兵;高速動車組的模型辨識與狀態(tài)估計[D];華東交通大學(xué);2015年

3 吳亮;牽引計算在高速鐵路閉塞分區(qū)設(shè)計中的應(yīng)用研究[D];西南交通大學(xué);2008年

，

本文編號：2267933