摘要
由于重载列车空气制动具有强非线性、反馈减压量误差较大的特点,且充风、排风时间与减压过程之间存在耦合关系,使得重载列车循环空气制动的操纵精确度难以保证,进而影响其操纵安全。为提高重载列车循环空气制动的控制精确度,文章提出一种基于模糊神经网络(fuzzy logic-based neural network,FLNN)的空气制动力预测方法。其首先采用径向基神经网络(radial basis function neural network,RBF-NN)训练空气制动离线数据,得到模糊逻辑形式的空气制动力离线预测规则;然后,计算当前数据与空气制动力离线预测规则的匹配度,得到相应的预测规则;最后,根据当前数据和相应的预测规则,输出空气制动力预测值。该预测方法通过数据处理的方式摆脱了对传统空气制动模型的依赖,避免了充、排风时间与减压过程之间的耦合分析,能够较准确地得到空气制动力预测值。试验结果显示,本文提出的基于FLNN的空气制动力预测方法将重载列车空气制动力在100 kN内的预测精度提高至99%,这验证了该方法在不同的工况下能有效实现空气制动力预测。
Due to the strong nonlinearity of air braking of heavy load train, the large error of feedback decompression amount,and the coupling relationship between charging and exhaust time and decompression process, it is difficult to guarantee the operation accuracy of circulating air braking of heavy load train, which affects its operation safety. In order to improve the control accuracy of circulating air braking of heavy load train, a prediction method of air braking force based on fuzzy logic-based neural network(FLNN) is proposed in this paper. Firstly, radial basis function neural network(RBF-NN) is used to train off-line data of air braking,and off-line prediction rules of air braking force in the form of fuzzy logic are obtained. Then, matching degree between current data and off-line prediction rules of air braking force is calculated, and corresponding prediction rules are obtained. Finally, according to the current data and the corresponding prediction rules, predicted value of air braking force is output. By processing the data, this method can get rid of the dependence on traditional air braking model, avoid coupling analysis between charging and discharging time and decompression process, and can get air braking force prediction value more accurately. Test results show that air braking force prediction method based on FLNN can improve the prediction accuracy of heavy load train air braking force to 99% within100 kN, which verifies that the method can effectively realize air braking force prediction under different working conditions.
作者
史可
张征方
白金磊
蒋杰
SHI Ke;ZHANG Zhengfang;BAI Jinglei;JIANG Jie(CRRC Zhuzhou Institute Co.,Ltd.,Zhuzhou,Hunan 412001,China;Zhuzhou CRRC Times Electric Co.,Ltd.,Zhuzhou,Hunan 412001,China)
出处
《控制与信息技术》
2022年第1期1-6,共6页
CONTROL AND INFORMATION TECHNOLOGY
关键词
重载列车
空气制动力预测
模糊神经网络
径向基神经网络
预测规则
heavy load train
prediction of air braking force
fuzzy logic-based neural network(FLNN)
radial basis function neural network(RBF-NN)
prediction rules
