Due to the structural characteristics of high-speed trains linked by multiple carriages,it is difficult to effec-tively control the high-speed trains during high speed operation under changing high-speed railway line conditions.In re-sponse to the above problems,this paper proposed a high-speed train longitudinal dynamics model and an adaptive radial basis function neural network(RBFNN)control strategy.Firstly,considering the train coupler force and complex line conditions,based on the analysis of the different forces in the front and rear of the whole train,the train longitudinal dy-namics model was established.Secondly,an ideal feedback control law was designed for the model without external inter-ferences,and RBFNN was introduced to fit the ideal control output.Then,the adaptive law of design parameter estima-tion was used to replace the adjustment of the weights of the neural network under the condition of considering the influ-ence of the interference term,and the Lyapunov stability of the model was proved.Finally,the real line running data of the CRH380B high-speed train from the section between the Beijingxi Railway Station and Zhengzhoudong Railway Sta-tion was used for simulation,and the simulation results were compared with the backstepping sliding mode(BSSM)con-troller under the same conditions.The simulation results show that the proposed controller can more effectively deal with complex high-speed railway condition changes and external disturbances,has better control effect on high-speed trains,and improves the stability and efficiency of their operation.
关键词
高速列车/纵向动力学模型/径向基函数神经网络/自适应算法/Lyapunov理论
Key words
high-speed train/longitudinal dynamics model/radial basis function neural network/adaptive algorithm/Lya-punov theory
维普
万方数据