重载列车驾驶过程建模与运行优化控制方法

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 or train brake pipe, which results in nonlinearity and hysteresis quality. The existing HHT provided with manual control mode has large power consumption and is easy to lead to coupler broken and derailment. To address this issue, this project conducts effective modeling and optimizing control of heavy haul train to improve the operational performance of HHT in terms of safety, punctuality, smoothness and energy saving. First, with analyzing the mechanical principle of coupler buffer device, the coupler forces can be figure out based on the characteristic curves. Secondly, combining mechanism analysis and data-driven modeling, a dynamic multi-hybrid model is built under complex railway route environment including ramps, tunnels and curves. Next, a desired velocity versus position curve (V-S curve) of HHT is programmed by considering various constraint conditions including speed limit, railway environment and air damping and excellent driver’s operating experience. Then, an operation optimizing controller is designed to implement the speed tracking control of the desired V-S curve on the basis of the multi-hybrid model, which ensures the safe, smooth and energy-efficient operation of HHT. Finally, Simulation experiments with real HHT running data are carried out on an HHT analog cab to verify and improve the theoretical results. It is believed that this project will lay the theoretical foundations for realizing the automatic train operation of HHT and explore new ways for complex system modeling and optimization control, which has certain theoretical significance and application value.

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/制动力需通过车钩或列车管相继传递给车厢，存在明显的非线性和大滞后性。现有的人工操纵模式存在耗电大，易引起车钩断裂和脱轨等问题。本课题针对此关键问题，以实现重载列车安全、正点、平稳及节能运行为目标，开展其驾驶过程建模与优化控制研究。分析列车钩缓系统受力原理，采用其特性曲线求解车钩力；考虑坡道、曲线率、隧道等环境条件，结合机理分析和数据驱动建模策略建立重载列车多质点动力学混合模型；根据重载列车限速要求、线路环境、空气制动等约束条件及金牌司机的驾驶经验，设置理想的运行目标曲线。据此，研究重载列车运行优化控制方法，跟踪理想目标曲线安全、平稳、低能耗运行；并采集重载列车实际运行数据通过模拟驾驶平台对理论研究结果进行验证和完善。课题为重载列车的自动驾驶实施奠定基础，为复杂系统建模与优化控制探索新途径，具有一定的理论意义和应用价值。

目前，发展重载运输已成为国家发展战略的一部分。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/制动力需通过车钩或列车管相继传递给车厢，存在明显的非线性和大滞后性。现有的人工操纵模式存在耗电大，易引起车钩断裂和脱轨等问题。本课题针对此关键问题，以实现重载列车安全、正点、平稳及节能运行为目标，开展其驾驶过程建模与优化控制研究。分析列车钩缓系统受力原理，采用其特性曲线求解车钩力；考虑坡道、曲线率、隧道等环境条件，结合机理分析和数据驱动建模策略建立重载列车多质点动力学混合模型；根据重载列车限速要求、线路环境、空气制动等约束条件及金牌司机的驾驶经验，设置理想的运行目标曲线。据此，研究重载列车运行优化控制方法，跟踪理想目标曲线安全、平稳、低能耗运行；并采集重载列车实际运行数据通过模拟驾驶平台对理论研究结果进行验证和完善。课题为重载列车的自动驾驶实施奠定基础，具有一定的应用价值。.项目研究过程中，获江西省技术发明一等奖、吴文俊人工智能技术发明一等奖各1项；授权国家发明专利7项，其中美国专利1项，澳大利亚革新专利2项；在国内外权威期刊和重要学术会议上发表研究论文9篇，其中SCI/EI收录5篇。项目执行期间，带领项目组成员和研究生参加国际、国内学术会议交流9人次；协助培养已毕业硕士研究生5人，自主培养在读硕士11人。较好地完成了预期目标。