城轨列车多目标优化控制算法研究与仿真

doi:10.16182/j.issn1004731x.joss.201703016

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (3): 581-588.doi: 10.16182/j.issn1004731x.joss.201703016

城轨列车多目标优化控制算法研究与仿真

孟建军^1,2,3, 裴明高¹, 武福¹, 韦腾舟¹, 郝帅¹

1.兰州交通大学机电工程学院,兰州 730070;
2.兰州交通大学机电技术研究所,兰州 730070;
3.甘肃省物流及运输装备信息化工程技术研究中心,兰州 730070

收稿日期:2015-05-29 修回日期:2015-08-11 出版日期:2017-03-08 发布日期:2020-06-02
作者简介:孟建军(1966-),男,河南淮阳,博士,教授,博导,研究方向为轨道交通装备检测与监控技术等。
基金资助:
国家自然科学基金(61563027),兰州交通大学优秀科研团队资助计划(201604)

Research and Simulation on Control Algorithm for Multi-objective Optimization of Urban Rail Train

Meng Jianjun^1,2,3, Pei Minggao¹, Wu Fu¹, Wei Tengzhou¹, Hao Shuai¹

1. School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Mechatronics T & R Institute, Lanzhou Jiaotong University, Lanzhou 730070, China;
3. Engineering Technology Center for Informatization of Logistics & Transport Equipment, Lanzhou 730070, China

Received:2015-05-29 Revised:2015-08-11 Online:2017-03-08 Published:2020-06-02

摘要/Abstract

摘要： 针对城轨列车运行多目标的特点,以能耗、准时性、精确停车以及舒适度为指标建立城轨列车运行多目标模型,利用遗传算法对其进行优化,根据列车牵引计算以及计算机仿真得到列车运行目标曲线。将模糊控制和PID控制算法应用到城轨列车运行系统中,建立自适应模糊PID控制器和PID控制器,对目标曲线进行跟踪。仿真结果表明自适应模糊PID控制与PID控制相比,其能更好的使列车跟随目标曲线,从而保证列车安全、平稳、准时地运行,确保列车进站停车的精确性。

关键词: 遗传算法, 多目标, 牵引计算, 目标曲线, 模糊PID

Abstract: According to the characteristics of urban rail train running multiple objective, the multi-objective operation model for urban rail train was established with the energy consumption, punctuality, accurate parking and comfort level as the optimization indexes. Genetic algorithms was used to optimize running multi-objective model of urban rail train, and according to train traction calculation and computer simulation, the train running target curve was obtained. The fuzzy control and PID control algorithm were applied to urban rail train system to establish adaptive fuzzy PID controller and PID control in order to track the target curve. Simulation results show that adaptive fuzzy PID control compared with PID control, the former can better make the train follow the target carve operation, so as to ensure train safety, smooth, punctual operation, at the same time also to ensure the accuracy of a train stopping.

Key words: genetic algorithms, multi-target, traction calculation, target curve, fuzzy PID

中图分类号:

U239.5

孟建军, 裴明高, 武福, 韦腾舟, 郝帅. 城轨列车多目标优化控制算法研究与仿真[J]. 系统仿真学报, 2017, 29(3): 581-588.

Meng Jianjun, Pei Minggao, Wu Fu, Wei Tengzhou, Hao Shuai. Research and Simulation on Control Algorithm for Multi-objective Optimization of Urban Rail Train[J]. Journal of System Simulation, 2017, 29(3): 581-588.

参考文献

[1] 余进, 何正友, 钱清泉. 基于微粒群算法的多目标列车运行过程优化[J]. 西南交通大学学报, 2010, 45(1): 70-75.
(Yu Jin, He Zhengyou, Qian Qingquan.Multi-objective Train Operation Optimization Based on Particle Swarm Algorithm[J]. Journal of Southwest Jiaotong University (S0258-2724), 2010, 45(1): 70-75.)
[2] 陈德旺. 城轨列车智能驾驶的优化模型及算法 [M]. 北京: 北京交通大学出版社, 2014: 116-117.
(Chen Dewang.Optimization Models and Algorithms for Intelligent Train Operation of Urban Rail Transit [M]. Beijing,China: Beijing Jiaotong University Press, 2014: 116-117.)
[3] Eugene Khmelnitsky.On an Optimal Control Problem of Train Operation[J]. IEEE Transactions on Automatic Control (S0018-9286), 2000, 45(7): 1257-1266.
[4] Chebbi O, Chaouachi J.An elitist multi-objective genetic algorithm for minimizing vehicle numbers and energy consumption in the context of Personal Rapid Transit[C]// Advanced Logistics and Transport (ICALT), 2014 International Conference on. USA: IEEE, 2014: 270-274.
[5] Garcia-Najera A, Bullinaria J A.An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows[J]. Computers & Operations Research (S0305-0548), 2011, 38(1): 287-300.
[6] 石红国, 彭其渊, 郭寒英. MRT列车运行模拟模型的多目标改进遗传算法[J]. 西南交通大学学报, 2006, 41(5): 658-662.
(Shi Hongguo, Peng Qiyuan, Guo Hanying.Improved Multi-objective GA for MRT Train Operation Simulation Model[J]. Journal of Southwest Jiaotong University (S0258-2724), 2006, 41(5): 658-662.)
[7] 中华人民共和国铁道部. 列车牵引计算规程 [M]. 北京: 中国铁道出版社, 1999: 20-28.
(Ministry of Railways of the People's Republic of China. Code for Calculation of Train Traction [M]. Beijing, China: China Railway Publishing House, 1999: 20-28.)
[8] 许立. 基于遗传算法的ATO速度曲线优化 [D]. 成都: 西南交通大学, 2013.
(Xu Li.Optimization of ATO Speed Curve Based on Genetic Algorithm [D]. Chengdu, China: Southwest Jiaotong University, 2013.)
[9] 耿晨歌, 赵睿昕. 基于遗传算法的地铁列车自动驾驶控制算法研究[J]. 武汉理工大学学报, 2013, 37(6): 1193-1197.
(Geng Chenge, Zhao Ruixin.Research on the GA-based ATO Control Algorithm[J]. Journal of Wuhan University of Technology (S2095-3844), 2013, 37(6): 1193-1197.)
[10] 李瑞敏, 陆化普. 基于遗传算法的交通信号控制多目标优化[J]. 长安大学学报(自然科学版), 2009, 29(3): 85-88.
(Li Ruimin, Lu Huapu.Traffic signal control multi-object optimization based on genetic algorithm[J]. Journal of Chang’an University (Natural Science Edition)(S1671-8879), 2009, 29(3): 85-88.)
[11] 雷英杰, 张善文, 李续武, 等. MATLAB遗传算法工具箱及应用 [M]. 西安: 西安电子科技大学出版社, 2014: 3-10.
(Lei Yingjie, Zhang Shanwen, Li Xuwu.MATLAB genetic algorithm toolbox and Application [M]. Xi’an, China: Xidian University Press, 2014: 3-10.)
[12] 许立, 王长林. 基于遗传算法列车自动运行速度曲线的优化[J]. 铁路计算机应用, 2013, 22(10): 46-49.
(Xu Li, Wang Changlin.Optimization of automatic train operation speed curve based on Genetic Algorithm[J]. Railway Computer Application (S1005-8451), 2013, 22(10): 46-49.)
[13] 李欢. 地铁列车运行仿真及策略优化 [D]. 成都: 西南交通大学, 2012.
(Li Huan.Simulation of Subway Train Operation and Optimization Strategy [D]. Chengdu, China: Southwest Jiaotong University, 2012.)
[14] 张茜. 城市轨道交通列车自动运行(ATO)多目标控制策略研究 [D]. 北京交通大学, 2014.
(Zhang Xi.Study on Multi-Objective Control Strategies of Automatic Train Operation in Urban Rail Transit [D]. Beijing, China: Jiaotong University, 2014.)
[15] 程军. 自适应模糊PID控制器在可调式射流泵供水系统中的应用[J]. 武汉大学学报(工学版), 2011, 44(5): 668-670.
(Cheng Jun.Adaptive fuzzy PID controller's application to technical water supply system based on adjustable jet pump[J]. Engineering Journal of Wuhan University( S1671-8836), 2011, 44(5): 668-670.)
[16] 石辛民, 郝整清. 模糊控制及其MATLAB仿真 [M]. 北京: 清华大学出版社/北京交通大学出版社, 2008: 94-101.
(Shi Xinmin, Hao Zhengqing.Fuzzy control and MATLAB simulation [M]. Beijing, China: Tsinghua university press/Beijing Jiaotong University press, 2008: 94-101.)

城轨列车多目标优化控制算法研究与仿真

Research and Simulation on Control Algorithm for Multi-objective Optimization of Urban Rail Train

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