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弓网滑动电接触过程中,电、力、速多个物理域的复杂耦合影响列车的高速、重载、稳定运行。为了提高载荷最优控制,使摩擦副摩擦磨损与受流稳定性达到相对最佳,利用量子遗传算法优化支持向量机的相关参数,建立了受电弓滑板磨损率的预测模型。经过MATLAB仿真结果表明,量子遗传算法比遗传算法有更好的优化性能,建立的模型能够稳定预测滑板磨损率,对选取最优载荷、研究载流摩擦副材料具有重要意义。
In the process of sliding electrical contact, the complicated coupling of multiple physical domains of electricity, force and velocity affects the high speed, heavy load and stable operation of the train. In order to improve the optimal load control, the relative friction and wear of the friction pair and the flow stability are relatively optimal. The genetic algorithm is used to optimize the parameters of the support vector machine, and the prediction model of the pantograph sliding rate is established. The results of MATLAB simulation show that the quantum genetic algorithm has better performance than the genetic algorithm. The established model can predict the sliding wear rate steadily, which is of great significance for selecting the optimal load and studying the load-bearing friction material.