弓网接触力长短时记忆网络预测的 受电弓主动控制与仿真

针对列车高速行驶时弓网系统接触力波动剧烈影响受流质量的问题,充分利用长短时记忆网络对时序预测的优势,提出了弓网接触力长短时记忆网络预测的受电弓主动控制方法。首先以二元受电弓模型作为研究对象,建立其动力学方程,并对其模型进行仿真得到接触力波动数据。然后,将仿真得到的接触力数据作为训练样本输入长短时记忆网络中建立预测模型,以预测下一时刻接触力。最后,以接触力预测值和期望值的差值作为目标控制力输出至磁流变阻尼器,由磁流变阻尼器提供控制力作用到受电弓,从而抑制接触力的动态波动以提高提高列车受流质量。通过实验证明,所提方法对弓网接触力控制更加准确,且大幅降低弓网接触力波动标准差,降幅超过70.13%,且规避了弓网系统离线情况的发生,验证了所提方法在改善弓网受流质量上的稳定性和优越性。

Active control and simulation for pantograph based on contact force prediction of long short-term memory network

The contact force fluctuation of the pantograph-catenary system greatly affects the current quality when the train is running at high speed. To address this issue, an active control method of pantograph is proposed, which is based on contact force prediction of long short-term memory network. The advantage of long short-term memory network for time series prediction is fully utilized. Firstly, the pantograph model with two lumped masses is taken as the research object. Its dynamic equation is established, and the contact force fluctuation data is obtained by simulation of the model. Then, the contact force data obtained from simulation are utilized to train the long short-term memory network. In this way, a prediction model is formulated to predict the contact force at the next moment. Finally, the difference between the predicted value and the expected value of the contact force is taken as the target control force output to the magnetorheological damper. The magnetorheological damper provides the control force to act on the pantograph. The dynamic fluctuation of the contact force is suppressed and the current quality of the train is improved. Experimental results show that the proposed method is more accurate in the control of the contact force. The standard deviation of the fluctuation of the contact force is significantly reduced by more than 70. 13% . The offline situation of the pantograph-catenary system is avoided, which verifies the stability and superiority of the proposed method for improving the current quality of the pantograph.