RBF neural network control of high-speed train suspension system based on UM
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 semi-active 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.
suspension systemmagneto-rheological technologyRBF neural network control
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