基于改进遗传算法的高速列车受电弓随网压力模糊控制方法研究
Research on Fuzzy Control Method for Pantograph-Catenary Following Contact Force in High-Speed Trains Based on an Improved Genetic Algorithm
摘要: 高速列车是当今社会重要的交通设施之一，但在实际运行中，弓网系统的跟随性能随工况变化而恶化，导致受流质量不佳，影响列车的安全可靠运行。为研究接触网与受电弓的跟随性能，以受电弓随网压力为切入点，以简单链式接触网和三质量块受电弓为研究对象构建弓网耦合动力学模型，通过MATLAB仿真软件搭建相应模型，设计弓网模糊控制器，运用改进遗传算法(IGA)对控制器进行参数优化，将所提优化方法与传统控制方法、模糊主动控制方法作对比，实验结果表明：优化后的控制方法相比被动控制，随网压力标准方差提高了94.11%，相比模糊主动控制，提高了69.00%，有效抑制了随网压力的波动，提高了弓网受流质量，且此方法具有较好的适应性和鲁棒性。
Abstract: High-speed trains are one of the most important transportation facilities in modern society. However, during actual operation, the tracking performance of the pantograph-catenary system deteriorates with varying operating conditions, leading to poor current collection quality and affecting the safe and reliable operation of trains. To investigate the tracking performance between the overhead contact line and pantograph, a coupled dynamics model was constructed using pantograph-catenary following contact force as the entry point, with a simple chain-type overhead contact line and a three-mass-block pantograph as research subjects. The corresponding model was built using MATLAB simulation software, and a fuzzy pantograph-overhead contact line controller was designed. An improved genetic algorithm (IGA) was employed for controller parameter optimization. The proposed optimization method was compared with traditional control methods and fuzzy active control methods. Experimental results demonstrate that the optimized control method achieves a 94.11% improvement in the standard deviation of pantograph-catenary following contact force compared to passive control and a 69.00% improvement compared to fuzzy active control. It effectively suppresses fluctuations in pantograph-catenary following contact force, enhances pantograph-catenary current collection quality, and exhibits excellent adaptability and robustness.
文章引用：张阿龙, 韩聪信. 基于改进遗传算法的高速列车受电弓随网压力模糊控制方法研究[J]. 动力系统与控制, 2026, 15(2): 119-132. https://doi.org/10.12677/dsc.2026.152013

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