重载列车运行过程的建模与RBFNN滑模控制

Modeling and RBFNN Sliding Mode Control for the OperationProcess of Heavy Haul Trains

【目的】为解决重载列车在复杂线路条件下难以实现高精度轨迹跟踪控制的问题,提出了一种重载列车多质点模型和径向基函数神经网络滑模控制(RBFNNSMC)方法。【方法】首先,考虑空气制动和钩缓装置约束,建立重载列车多质点模型,并对人为测量误差和车辆参数差异等导致的模型不确定性问题,利用RBFNN对其进行估计。其次,设计一种非线性干扰观测器(NDO)对列车运行中受强风、雨雪等外界快时变干扰进行实时估计。然后,设计Lyapunov函数对整个系统进行稳定性证明。【结果】基于大秦线的实际线路数据,进行RBFNNSMC方法、PID方法和SMC方法的速度跟踪对比实验。仿真结果表明,RBFNNSMC方法的速度误差在±0.15 km/h以内,优于其他两种方法。加入NDO后,RBFNNSMC方法的抗干扰能力也更强。【结论】基于NDO的RBFNNSMC方法的跟踪精度相较于SMC方法在无干扰和受干扰情况下分别提升27.3%和28.9%,鲁棒性有所提升。

【Objective】To address the challenge of achieving high-precision trajectory tracking control for heavy haul trains under complex track conditions,this paper proposes a multi-mass model for heavy haul trains and a radial basis function neural network sliding mode control(RBFNNSMC)method.【Method】First,considering the constraints of air brakes and coupler devices,a multi-mass model of the heavy haul trains was established,and the model uncertainty problems caused by human measurement errors and vehicle parameter differences were estimated by using RBFNN.Second,a nonlinear disturbance observer(NDO)was designed to be utilized for re‐al-time estimation of strong wind,rain,snow,and other external fast time-varying disturbances during the opera‐tion of trains.Then,a Lyapunov function was designed to prove the stability of the entire system.【Result】Based on actual track data from the Daqin Railway,speed tracking comparison experiments were conducted using the RBFNNSMC method,PID method,and SMC method.Simulation results show that the speed error of the RBF‐NNSMC method is within±0.15 km/h,which is superior to the other two methods.Furthermore,the inclusion of the NDO significantly enhances the RBFNNSMC method's disturbance rejection capability.【Conclusion】The tracking accuracy of the RBFNNSMC method based on NDO is improved by 27.3%and 28.9%respectively compared to the SMC method in the absence and presence of disturbances,with enhanced robustness as well.