The Monitor Of Snaking Motion And Research On Semi-Active Control Of High Speed Train

In the running process of high-speed train,it is not only affected by the track stationary random irregularity,but also disturbed by the track non-stationary random irregularity at the special position.The generation of the track non-stationary irregularity will cause the transient impact between the wheel and rail,which will produce a violent dynamic effect and increase instability probability of snaking motion,not only to make the wheel rail force increased,but also to affect the comfort of passengers.So it's very necessary to monitor snaking motion in real-time.In addition,there are many curve negotiation conditions in the actual running of the train,and curve negotiation performance is also an important index to evaluate the train dynamic performance.In order to improve the critical speed of the train operation and suppress the snaking motion of vehicle system,the yaw damper is longitudinally arranged between the body and the bogie of the train to provide gyro damping,but the damping value of yaw damper is constant in the running process,and cannot be adjusted according to the actual operating conditions,and is poor adaptability to the line condition.Aimed at snaking motion under the track non-stationary irregularity and curve negotiation condition,the semi active variable damping control system of high speed train yaw damper was established,and to analyze the control effect of the control system on train snaking motion and dynamic performance by combining multi body dynamics software with simulation software of control system.In details,the main research work is as follows:1.Introduce the mechanism of snaking motion and the reason of snaking motion instability,and established the test system to monitor snaking motion for high speed train in real-time,then analyzed normal vibration and abnormal vibration of bogie frame by Hilbert-Huang transform.2.Established multi-body dynamics model of high speed train by using multi-body dynamics software UM,and UM/SIMULINK simulation control model is established through combining the simulation interface and control simulation software SIMULINK.The semi active variable damping control system of high speed train yaw damper is established by the iterative optimization control algorithm or iterative learning control algorithm.3.Research and analysis on the yaw damper on the dynamic performance,and establish the foundation that using semi active control of yaw damper to optimise train snaking motion and dynamic performance.4.Research and analysis on the effect of the train snaking motion and dynamic performance under the track non-stationary irregularity.And analyze the control effect by the variable-step iterative optimization control system.5.Research and analysis on the effect of different curve radius and different damping value and working state of yaw damper on the train dynamic performance in curve negotiation condition.And analyze the control effect on train dynamic performance by the two order PD iterative learning control algorithm system.Research shows,the frequency spectrum of the frame abnormal vibration data is mainly distributed in the vicinity of 5Hz,and the amplitude is larger by the analysis of Hilbert-Huang transform.Under the track non-stationary irregularity in linear condition,the train dynamic performance becomes obvious with the increase of train speed,and yaw damper damping value has great influence on the train dynamic performance,but not the bigger the better.Each bogie is only allowed a failure working state of yaw damper when the train speed is higher than 150km/h,and when the failure number is more than one,the influence on the dynamic performance is especially serious.The track non-stationary random irregularity exacerbates snaking motion and seriously affects the train dynamic performance,after the variable-step iterative optimization semi active control,the train dynamic performance can be improved obviously.With the increase of damping value of yaw damper,the performance of the curve negotiation performance is decreased firstly and then increased,and two order PD iterative learning semi-active control is able to optimize the curve negotiation performance.