Speed tracking control of high-speed trains based on RBF neural network
QIN Shiyu
XU Chuanfang
LI Yunhao
Abstract：This paper proposes an adaptive non-singular fast terminal sliding mode controller based on a Radial Basis Function(RBF)neural network to solve the speed tracking control problem of high-speed trains,considering the effects of unknown model parameters,uncertain additional resistance,unknown inter-car forces,and external disturbances.First,a multi-mass dynamic model of high-speed trains is established,incorporating nonlinear resistance and inter-car coupling forces between ad-jacent carriages.Second,a finite-time speed tracking control strategy for high-speed trains based on a novel saturation function is designed.The non-singular fast terminal sliding mode control method is in-troduced to achieve finite-time convergence of the system state,enhancing both steady-state accuracy and transient performance in speed tracking.Furthermore,an adaptive non-singular fast terminal sliding mode control strategy based on RBF neural network is developed.Adaptive estimation techniques are employed to estimate train model parameters and the upper limit of uncertainty terms,including addi-tional resistance and inter-vehicle forces,in real time.To mitigate chattering caused by discontinuous switching control,an RBF neural network is utilized to remap the switching control term.Additionally,an adaptive update law for weight coefficients is designed to ensure continuous switching,effectively sup-pressing chattering effects.Finally,the stability of the high-speed train speed tracking control system and the finite-time convergence of system states are proven using Lyapunov stability theory.The pro-posed approach is validated through simulations using the CRH380B high-speed train as the control ob-ject.Simulation results demonstrate that the proposed controller enables high-speed trains to converge and track the desired trajectory within a finite time,with a 49％reduction in tracking error and achieving high tracking accuracy.These findings provide valuable insights for high-speed train tracking control.
Keywords：high-speed trainradial basis function neural networkmulti-mass modelspeed trackingadaptive sliding mode control
Publication Date：2025-06-30
Online Publishing Date：2025-08-21(First online date of this platform, not the publication date of the document)
Pages：9( 111-119 )