摘要
动车组高速高密度地追踪运行是高速铁路运营模式的发展趋势,但其运行过程复杂、运行环境多变,难以同时满足安全、正点、节能、舒适等多目标要求。依据动车组追踪运行及其环境条件特征,建立了动车组追踪运行特征描述模型、线路特征模型以及多目标操纵优化模型,并实时获取动车组运行限速、速度、位置等信息,采用改进的多目标粒子群优化算法,进行动车组追踪运行多目标实时优化。基于CRH380AL运行过程现场数据的仿真结果验证了该方法的有效性。
High-speed and high-density tracking of electric multiple units (EMU) is the developing tendency of high-speed railway. However, because of the complicated operation process and the changeable operating environment, it is difficult to meet the multi-objective operational requirements of security, punctuality, energy saving and ride comfort simultaneously. Consequently, based on the analysis of tracking features and running conditions of EMU, the descriptive model of EMU's tracking process and the characteristics model of running conditions, as well as the multi-objective optimization model are built. Then, the multi-objective real-time optimization of EMU's operation strategies are conducted by a modified multi-objective particle swarm optimization algorithm, while obtaining speed restriction, velocity and position of EMU in real time. The simulation results on the field data of CRH380AL (China Railway High-speed EMU type-380AL) running process verify the effectiveness of the proposed method.
出处
《控制工程》
CSCD
北大核心
2015年第2期257-261,共5页
Control Engineering of China
基金
国家自然科学基金(61164013
51174091
61364013
U1334211)
铁道部重点项目(2011Z00-2D)
江西省研究生创新基金资助项目(YC2013-S153)
关键词
动车组:追踪运行:实时操纵优化
多目标粒子群算法
Electric multiple units, tracking running, real-time operation optimization, multi-objective particle swarm optimization algorithm.