Optimization of Heavy Haul Train Operation Process Based on Coupler Constraints
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摘要: 重载列车是一种由上百甚至几百节车厢组成的动力集中式大载重系统, 其牵引力/制动力需通过车钩相继传递给车厢, 存在明显的非线性和大滞后性. 现有的人工驾驶模式, 司机难以考虑车厢之间的钩缓约束, 易引起车钩断裂和脱轨; 且运行性能与司机的操纵经验密切相关, 存在耗电大, 无法按照列车运行图正点运行等问题. 本文针对此关键问题, 以实现重载列车安全、正点、节能运行为目标, 开展其驾驶过程运行优化研究. 分析列车钩缓系统受力原理, 基于其特性曲线, 采用翟方法构造重载列车钩缓模型及整车纵向动力学模型; 据此, 考虑钩缓约束运用多目标自适应遗传算法, 结合实际运行线路(限速、坡道、曲线率等)约束条件设定列车理想的运行速度目标曲线; 最后, 采用改进广义预测控制器设计重载列车驾驶过程优化控制方法, 跟踪理想速度目标曲线安全、正点、低能耗运行. 基于大秦线上HXD1型重载列车实际数据的仿真结果表明本文所设计的理想目标速度曲线优化方法可以较好地改善列车运行中的安全, 正点和节能等关键性指标, 运行优化控制能保证列车精确跟踪理想速度目标曲线, 实现其驾驶过程优化运行.Abstract: Heavy haul train (HHT) equipped with concentrated power is a complicated heavy-load system that consists of several hundred carriages. The traction/braking force is transferred to carriages with couplers, which results in nonlinearity and hysteresis quality. The existing HHT is provided with manual operation mode and the driver cannot consider coupler constraints between carriages, which may lead to coupler broken and derailment. In addition, the running performance of HHT depends mainly on the drivers′ manipulation experiences, which may result in large power consumption and unpunctuality. To address this issue, this paper conducts optimization research of HHT operation process to improve the operational performance of HHT in terms of safety, punctuality and energy saving. Firstly, with analyzing the mechanical principle and characteristic curves of coupler device, the coupler force model and vehicle longitudinal dynamic model can be figured out based on the " Zhai” numerical integration method. Secondly, considering coupler constraints, the multi-objective adaptive genetic algorithm is used to establish the ideal train speed curve by combining the constraints of actual railway routes (speed limit, ramps, curve rates, etc.). Finally, an operation optimizing controller is designed to implement the safe, punctual and energy-saving operation by tracking the ideal train speed curve based on the improved generalized predictive control method. The simulation results on the actual type-HXD1 HHT of the Daqin line show that the presented ideal train speed curve can better guarantee the safety, punctuality and energy saving of HHT, the operational optimization control method can ensure the excellent tracking performance of ideal train speed curve, which realizes the optimized operation of HHT.1) 1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013 2. Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang 3300132) 收稿日期 2019-03-22 录用日期 2019-06-02 Manuscript received March 22, 2019; accepted June 2, 2019 国家自然科学基金 (61673172, 51565012, 61733005, 61803155, 61663013) 资助 Supported by National Natural Science Foundation of China (61673172, 51565012, 61733005, 61803155, 61663013) 本文责任编委 董海荣 Recommended by Associate Editor DONG Hai-Rong 1. 华东交通大学电气与自动化工程学院 南昌 330013 2. 江西省先进控制与优化重点实验室 南昌 330013
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图 3 重载列车多质点模型运行计算流程图
Fig. 3 Flow charts of multi-particle model operation calculation for heavy haul train
图 4 多目标自适应遗传算法计算流程图
Fig. 4 Computational flow chart of multi-objective adaptive genetic algorithms
图 9 本文仿真的第10号车钩车钩力变化趋势
Fig. 9 Tendency of coupler force change of coupler No. 10 simulated in this paper
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[1] Coleman A. Railroads of North Carolina. Arcadia Publishing, 2008. [2] 翟婉明, 赵春发. 现代轨道交通工程科技前沿与挑战. 西南交通大学学报, 2016, 51(2): 209−226 doi: 10.3969/j.issn.0258-2724.2016.02.0012 Zhai Wan-Ming, Zhao Cun-Fa. Frontiers and challenges of sciences and technologies in modern railway engineering. Journal of Southwest Jiaotong University, 2016, 51(2): 209−226 doi: 10.3969/j.issn.0258-2724.2016.02.001 [3] 3 Lu Q W, He B B, Wu M Z, Zhang Z C, Luo J T, Zhang Y K. Establishment and analysis of energy consumption model of heavy-haul train on large long slope. Energies, 2018, 11(4): 965 doi: 10.3390/en11040965 [4] 4 Wei W, Zhang J, Zhao X B, Zhang Y. Heavy haul train impulse and reduction in train force method. Australian Journal of Mechanical Engineering, 2018, 16(2): 118−125 doi: 10.1080/14484846.2018.1457259 [5] 5 Cherkashin U M, Zakharov S M, Semechkin A E. An overview of rolling stock and track monitoring systems and guidelines to provide safety of heavy and long train operation in the Russian railways. Proceedings of the Institution of Mechanical Engineers, Part F Journal of Rail and Rapid Transit, 2009, 223(2): 199−208 [6] 6 Dong H R, Ning B, Cai B G, Hou Z S. Automatic train control system development and simulation for high-speed railways. IEEE Circuits and System, 2010, 10(2): 6−18 [7] 7 Erofeyev E. Calculation of optimum train control using dynamic programming method. Moscow Railway Engineering Institute: Moscow, Russia, 1967, 811: 16−30 [8] 8 Howlett P. A new look at the rate of change of energy consumption with respect to journey time on an optimal train journey. Transportation Research Part B: Methodological, 2016, 94(12): 387−408 [9] 9 Howlett P G, Pudney P J, Xuan V. Local energy minimization in optimal train control. Automatica, 2009, 45(11): 2692−2698 doi: 10.1016/j.automatica.2009.07.028 [10] 10 Wang P L, Goverde R M P. Multiple-phase train trajectory optimization with signalling and operational constraints. Transportation Research Part C: Emerging Technologies, 2016, 69(8): 255−275 [11] 11 Wang P L, Goverde R M P. Multi-train trajectory optimization for energy-efficient timetabling. European Journal of Operational Research, 2018, 272(2): 621−635 [12] 12 Scown B, Roach D, Wilson P. Freight train driving strategies developed for undulating track through train dynamics research. CORE 2000: Railway Technology for 21st Century, 2000: 236−247 [13] 13 Zhang L, Zhuan X. Optimal operation of heavy-haul trains equipped with electronically controlled pneumatic brake systems using model predictive control methodology. IEEE Transactions on Control Systems Technology, 2013, 22(1): 13−22 [14] 陈荣武, 刘莉, 郭进. 基于遗传算法的列车运行能耗优化算法. 交通运输工程学报, 2012, 12(1): 112−11814 Chen Rong-Wu, Liu Li, Guo Jin. Optimization algorithm of train operation energy consumption based on genetic algorithm. Journal of Transportation Engineering, 2012, 12(1): 112−118 [15] 15 Zou R, Luo S, Ma W. Simulation analysis on the coupler behaviour and its influence on the braking safety of locomotive. Vehicle System Dynamics, 2018, 56(11): 1−21 [16] 16 Wu G, Huang W, Yuan Y. Improvements for the stability of heavy-haul couplers with arc surface contact. Vehicle System Dynamics, 2018, 56(3): 1−15 [17] 翟婉明. 列车 − 轨道耦合动力学. 北京: 科学出版社, 2007.Zhai Wan-Ming. Vehicle-Orbit Coupling Dynamics, Beijing: Science Press, 2007. [18] 金弟, 刘杰, 杨博, 何东晓, 刘大有. 局部搜索与遗传算法结合的大规模复杂网络社区探测. 自动化学报, 2011, 37(7): 873−88218 Jin Di, Liu Jie, Yang Bo, He Dong-Xiao, Liu Da-You. Genetic algorithm with local search for community detection in large-scale complex networks. Acta Automatica Sinica, 2011, 37(7): 873−882 [19] 苏锑, 杨明, 王春香, 唐卫, 王冰. 一种基于分类回归树的无人车汇流决策方法. 自动化学报, 2018, 44(1): 35−4319 Su Ti, Yang Ming, Wang Chun-Xiang, Tang Wei, Wang Bing. Classification and regression tree based traffic merging for method self-driving vehicles. Acta Automatica Sinica, 2018, 44(1): 35−43 [20] 张日东, 王树青, 李平. 基于支持向量机的非线性系统预测控制. 自动化学报, 2007, 33(10): 1066−107320 Zhang Ri-Dong, Wang Shu-Qing, Li Ping. Support vector machine based predictive control for nonlinear systems. Acta Automatica Sinica, 2007, 33(10): 1066−1073 [21] 徐杨, 陆丽萍, 褚端峰, 黄子超. 无人车辆轨迹规划与跟踪控制的统一建模方法. 自动化学报, 2019, 45(4): 799−80621 Xu Yang, Lu Li-Ping, Chu Duan-Feng, Huang Zi-Chao. Unified modeling of trajectory planning and tracking for unmanned vehicle. Acta Automatica Sinica, 2019, 45(4): 799−806 [22] 唐晓铭, 邓梨, 虞继敏, 屈洪春. 基于区间二型 T-S 模糊模型的网络控制系统的输出反馈预测控制. 自动化学报, 2019, 45(3): 604−61622 Tang Xiao-Ming, Deng Li, Yu Ji-Min, Qu Hong-Chun. Output feedback model predictive control for interval type-2 T-S fuzzy networked control systems. Acta Automatica Sinica, 2019, 45(3): 604−616 [23] 23 Spiryagin M, Wu Q, Cole C. International benchmarking of longitudinal train dynamics simulators: benchmarking questions. Vehicle System Dynamics, 2017, 55(4): 450−463 doi: 10.1080/00423114.2016.1270457 [24] 24 Wu Q, Spiryagin M, Cole C, Chang C Y, Guo G, Sakalo A, et al. International benchmarking of longitudinal train dynamics simulators: results. Vehicle System Dynamics, 2018, 56(3): 343−365 doi: 10.1080/00423114.2017.1377840 -
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