何德峰,周龙,余世明.基于收缩块的高速列车多目标滚动时域速度规划[J].高技术通讯(中文),2021,31(6):615~627 |
基于收缩块的高速列车多目标滚动时域速度规划 |
Shrink blocking based multi-objective receding horizon speed planning of high-speed train |
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DOI:10.3772/j.issn.1002-0470.2021.06.006 |
中文关键词: 高速列车; 模型预测控制(MPC); 收缩块; 速度规划 |
英文关键词: high speed train, model predictive control (MPC), shrink blocking, speed planning |
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中文摘要: |
随着轨道交通产业的发展,列车最优速度轨迹规划对节能环保事业发展和提升人们出行质量有着越来越重要的作用。针对优化高速列车运行速度轨迹以保证高速列车运行的能效性、安全性、正点性和舒适性的多目标问题,本文提出了一种利用实时交通信息规划高速列车最优运行轨迹的新方法——基于收缩块的高速列车多目标滚动时域速度规划算法。高速列车模型采用非线性纵向动力学模型,考虑了变化的道路斜坡和速度限制。根据高速列车的实时位置和运行时间,设计了一种包含多目标性能函数的收缩块滚动时域最优速度规划方法。通过在线求解一系列的最优速度规划问题,得到高速列车运行的最优速度-时间曲线。仿真结果表明,该方法能满足高速列车运行节能、准点、安全和舒适的要求。 |
英文摘要: |
With the development of rail transportation industry, the optimal speed trajectory planning of trains is playing a significant role in the enhancement of energy efficiency, environmental protection and passengers’ traveling quality. The optimization of a high-speed train’s speed trajectory to ensure the efficiency, safety, punctuality and comfort belongs to a multi-objective optimization problem. To solve this problem, a new optimal trajectory planning method for high-speed trains is proposed based on real-time traffic information, which is named as shrink blocking-based multi-objective receding horizon speed planning algorithm of high-speed train. The nonlinear longitudinal dynamics of high-speed trains are used to predict the future behaviors of the train and describe variable slopes and variable speed limitations based on real-time traffic information. According to the real-time position and running time of the train, a shrink blocking based receding horizon optimal trajectory planning method with a multi-objective performance function is designed. The optimal velocity-time graph of the high-speed train can be obtained by online solving a series of optimal trajectory planning problems. Simulation results demonstrate that the proposed method can satisfy the requirements of energy efficiency, punctuality, safety and comfort of the high-speed train. |
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