[1]冯江华,黎向宇,胡云卿,等.地铁列车舒适度最优的自动驾驶过程建模及求解[J].控制与信息技术(原大功率变流技术),2018,(06):32-37.[doi:10.13889/j.issn.2096-5427.2018.06.006]
 FENG Jianghua,LI Xiangyu,HU Yunqing,et al.Automatic Driving Process Modeling and Solving of the Riding Comfort Optimal Problem for Metro Train[J].High Power Converter Technology,2018,(06):32-37.[doi:10.13889/j.issn.2096-5427.2018.06.006]
点击复制

地铁列车舒适度最优的自动驾驶过程建模及求解()
分享到:

《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2018年06期
页码:
32-37
栏目:
控制理论与应用
出版日期:
2018-12-05

文章信息/Info

Title:
Automatic Driving Process Modeling and Solving of the Riding Comfort Optimal Problem for Metro Train
文章编号:
2096-5427(2018)06-0032-06
作者:
冯江华黎向宇胡云卿王 斌
(中车株洲电力机车研究所有限公司,湖南 株洲 412001)
Author(s):
FENG Jianghua LI Xiangyu HU Yunqing WANG Bin
( CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Hunan 412001, China )
关键词:
乘坐舒适度最优自动驾驶控制变量离散状态变量离散凸二次规划原-对偶非可行路径跟踪法预测校正
Keywords:
riding comfort optimal automatic driving control variable discretization state variable discretization convex quadratic programming primal-dual infeasible path-following method prediction and correction
分类号:
U231.6;TP13
DOI:
10.13889/j.issn.2096-5427.2018.06.006
文献标志码:
A
摘要:
乘坐舒适度最优是地铁列车自动驾驶研究中的典型问题。文章在考虑地铁列车的牵引/制动特性、运行准点和精确停车等要求的基础上,建立了列车在站间行驶的乘坐舒适度最优驾驶模型;并进一步提出控制变量和状态变量的同步离散化策略,将决策变量无限维的最优驾驶问题转化为一个决策变量为有限维的凸二次规划问题;采用带预测校正的原-对偶非可行路径跟踪法求解,最终获得乘坐舒适度最优的驾驶策略。最后,文章采用一个地铁列车在站间行驶的实际案例进行了仿真计算,结果表明本文所提出的方法可以有效求解乘坐舒适度最优驾驶问题。
Abstract:
Riding comfort optimal driving problem is a typical one in automatic driving system for metro trains. An optimal riding comfort driving model for a metro train traveling between stations was established, which takes the traction and brake characteristics into account and ensures the requirements of safe operation, on-time scheduling, and accurate parking, etc. In order to solve the continuous problem, a synchronous control variable and state variable discretization strategy was proposed. After that, the optimal driving problem with infinite dimensional control variables is transformed into a convex quadratic programming problem with finite dimensional decision variables. Primal-dual infeasible path-following algorithm with prediction and correction method was adopted to solve the quadratic programming problem, which can obtain the riding comfort optimal driving strategy by iterative way to approach the optimal solution from an initial guessed point. To verify the effectiveness of the established model and proposed algorithm, a practical example was calculated and simulation results show that the solving strategy and method proposed in this paper can effectively handle the established riding comfort optimal driving problem.

参考文献/References:

[1]HOWLETT P G, PUDNEY , XUAN VU. Local energy minimization in optimal train control[J]. Automatica, 2009(45):2692-2698.
[2]荀径,杨欣,宁滨,等. 列车节能操纵优化求解方法综述[J]. 铁道学报,2014,36(4):14-20.
XUN J, YANG X, NING B, et al. Survey on Trajectory Optimization for Train Operation[J]. Journal of the China Railway Society,2014, 36(4):14-20.
[3]冯晓云,何鸿云,朱金陵. 列车优化操纵原则及其优化操纵策略的数学描述[J]. 机车电传动,2001(4):13-16.
[4]朱金陵,李会超,王青元,等. 列车节能控制的优化分析[J].中国铁道科学,2008,29(2):104-108.
ZHU J L, LI H C, WANG Q Y, et al. Optimization Analysis on the Energy Saving Control for Trains[J]. China Railway Science, 2008,29(2):104-108.
[5]唐海川,王青元,冯晓云. 地铁列车追踪运行的节能控制与分析[J]. 铁道学报,2015,37(1):37-43.
TANG H C, WANG Q Y, FENG X Y. Energy Saving Control of Metro Train Tracing Operation[J]. Journal of the China Railway Society, 2015, 37(1): 37-43.
[6]BU B, YU F R, TANG T, et al. Performance improvements of communication-based train control (CBTC) systems with unreliable wireless networks[J]. Wireless Networks, 2014, 20(1):53-71.
[7]KUTTANNAIR K, WOLFGANG D. Trip optimization system and method for a train:073547 A2[P]. 2008-06-19.
[8]杨静. 西门子信号系统在中国城轨交通的应用[J]. 现代城市轨道交通,2007(3):52-53.
YANG J. Application of Siemens Signaling System in Urban Rail Transit System of China[J]. Modern Urban Transit, 2007(3):52-53.
[9]余进,何正友,钱清泉. 基于混合微粒群优化的多目标列车控制研究[J]. 铁道学报,2010,32(1):38-42.
YU J, HE Z Y, QIAN Q Q. Study on Multi-objecive Train Control Based on Hybrid Particle Swarm Optimization[J]. Journal of the China Railway Society, 2010, 32(1):38-42.
[10]姚理. 基于智能控制算法的列车自动驾驶系统的优化研究[D]. 北京:北京交通大学. 2009. [11]林佳. 城际列车自动驾驶系统(ATO)的研究[D]. 杭州:浙江大学. 2012.
[12]李庆扬,王能超,易大义. 数值分析[M]. 北京:清华大学出版社,2008.
[13]刘兴高,胡云卿. 应用最优化方法及MATLAB实现[M]. 北京:科学出版社,2014.

相似文献/References:

[1]李 勋,石阳阳,彭 勤.一种城际高速磁浮列车运行调整方法[J].控制与信息技术(原大功率变流技术),2018,(02):61.[doi:10.13889/j.issn.2096-5427.2018.02.014]
 LI Xun,SHI Yangyang,PENG Qin.An Operation Adjustment Method for Intercity High Speed Maglev Train[J].High Power Converter Technology,2018,(06):61.[doi:10.13889/j.issn.2096-5427.2018.02.014]
[2]李 柏,张友民,邵之江. 自动驾驶车辆运动规划方法综述[J].控制与信息技术(原大功率变流技术),2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.06.100]
 LI Bai,ZHANG Youmin,SHAO Zhijiang. Motion Planning Methodologies for Automated Vehicles: A Critical Review[J].High Power Converter Technology,2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.06.100]
[3]李 柏,张友民,邵之江.自动驾驶车辆运动规划方法综述[J].控制与信息技术(原大功率变流技术),2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.06.100]
 LI Bai,ZHANG Youmin,SHAO Zhijiang.Motion Planning Methodologies for Automated Vehicles: A Critical Review[J].High Power Converter Technology,2018,(06):1.[doi:10.13889/j.issn.2096-5427.2018.06.100]

备注/Memo

备注/Memo:
收稿日期:2018-09-11
作者简介:冯江华(1964—),男,博士,教授级高级工程师,长期从事牵引传动与控制等方面的研究工作。
基金项目:国家重点研发计划(2018YFB1201600)
更新日期/Last Update: 2018-12-25