摘要
为研究RBF神经网络控制对高速列车悬挂系统控制的有效性,基于Matlab/Simulink搭建出高速列车磁流变半主动悬挂系统模型,研究高速列车在不加控制的磁流变半主动悬挂系统下的振动响应;采用Matlab/Simulink搭建出RBF神经网络控制模块,与磁流变模型进行整合得到RBF神经网络半主动悬挂系统控制器,并将其导入到动力学软件UM的整车模型中进行数值模拟。结果表明,在RBF神经网络控制下车辆的振动响应减弱;基于RBF神经网络控制的半主动悬挂系统提高了高速列车在行驶过程中的安全性和稳定性,验证了半主动悬挂的优越性。
In order to investigate the effectiveness of RBF neural network control to the suspension system of high-speed trains,we established a high-speed train magneto-rheological semi-active suspension system model based on the Matlab/Simulink to study the vibration response of high-speed trains under uncontrolled magneto-rheological semi-active suspension system.We build an RBF neural network control module using Matlab/Simulink,and integrate it with the magneto-rheological model to obtain a RBF neural network semiactive suspension system controller which is imported into the vehicle model in the dynamics software UM for numerical simulation.The results show that the vibration response of vehicles is weakened under the control of RBF neural network;the semi-active suspension system based on RBF neural network control improves the safety and stability of high-speed trains during operation,verifying the superiority of semi-active suspension.
作者
陶富文
郑金玉
TAO Fuwen;ZHENG Jinyu(Nanjing CRRC Puzhen Haitai Brake Equipment Co.,Ltd.,Nanjing 211800,China;Beijing Boke Test System Co.,Ltd.,Beijing 101100,China)
出处
《铁道机车与动车》
2024年第4期19-23,43,I0001,I0002,共8页
Railway Locomotive and Motor Car
关键词
悬挂系统
磁流变
RBF神经网络控制
suspension system
magneto-rheological technology
RBF neural network control
