基于弹复力调整的高速列车群动态运行轨迹优化方法

为提高列车控制过程的自主性和智能性，研究了列车群动态运行过程，采用多智能体和图论方法构建了列车群分布式信息交互模型；以节能和准点为优化目标，以安全和乘客舒适度为约束条件，建立了列车群运行轨迹多目标优化模型，利用基于模拟退火思想改进的差分进化算法获取了列车群静态最优运行轨迹；在此基础上，为避免或消解列车运行过程中随机干扰导致的延误传播问题，针对移动闭塞系统，基于弹复力构建了信息交互支撑的列车群动态间隔调整机制，设计了列车群在线协同优化算法，实现了列车群运行轨迹的动态调整，最后采用武广高速铁路实际数据进行了仿真验证。研究结果表明:提出的在线协同优化算法可以有效提升最优解搜索能力，避免Pareto最优解集的频繁更新，在不同干扰场景下算法触发频率平均降低36.7%；在试验设计的一般干扰场景中，优化后的动态调整策略在保证列车群安全平稳运行的同时，将受扰列车的延误度由6.2%降至0，与立即恢复延误策略相比，节能率达4.8%；在试验设计的较大干扰场景中，受扰列车的延误度由13.1%降至1.4%，全局时间偏差恢复为0，节能率达1.8%。可见，提出的方法能够解决运行轨迹静态规划方式无法完全适应外部动态环境变化的问题，有效保障干扰情况下列车运行复合紊态的及时恢复。 

Optimization method of dynamic trajectory for high-speed train group based on resilience adjustment

The dynamic operation process of high-speed train groups was investigated to enhance the autonomy and intelligence of train control, and a distributed information interaction model of high-speed train groups was constructed based on the multi-agent and graph theoretic approaches. A multiobjective optimization model was formulated to optimize the energy saving and punctuality of train groups and ensure the safety and passengers' comfort. The static optimal trajectories of train groups were determined through the differential evolution algorithm modified based on the simulated annealing. On this basis, a resilience-based dynamic interval adjustment mechanism for the train groups supported by the information exchange was specifically established for the moving block system to prevent or eliminate the train delay propagation caused by the stochastic disturbances during the operation. Moreover, an online cooperative optimization algorithm was developed to achieve the dynamic adjustment of the train group trajectories. Finally, simulations were performed based on the actual field data of the Wuhan-Guangzhou High-Speed Railway. Research results show that the proposed online cooperative optimization algorithm can effectively improve the optimal solution searching ability, and avoid excessively frequent updates of the Pareto optimal set. The average algorithm trigger times under different disturbance scenarios decreases by 36.7%. In typical disturbance scenarios, the optimized dynamic adjustment approach decreases the delay degree of the disturbed train from 6.2% to 0, and guarantees the safe and smooth operation of the train group. The optimized approach can save the energy consumption by up to 4.8% compared with the immediate delay recovery approach. Even with more significant disturbance scenarios, the delay degree of the disturbed train decreases from 13.1% to 1.4%, and the global time deviation decreases to 0 with an energy-saving rate of 1.8%. The proposed method can solve the problem that the static trajectory planning is unable to fully adapt to the change in the external dynamic environment, and effectively and timely restore the train operation despite complex disturbances. 7 tabs, 24 figs, 31 refs.