基于RBFNN的高速列车分数阶滑模速度跟踪控制OA

A speed tracking control strategy,which combines an adaptive RBF neural network and fractional-order sliding mode control,was investigated to address the tracking control problem of high-speed trains subjected to unknown model parameters,resistance uncertainty,and external disturbances.On the basis of constructing a high-speed train dynamics model,a fractional-order nonsingular terminal sliding surface was introduced,and an improved approaching law consisting of both exponential and power convergence terms was designed.Subsequently,a speed tracking strategy for high-speed trains based on the enhanced approaching law was proposed,which can ensure the system status reaches the sliding mode surface within a finite time,improve the system's convergence speed,and suppress chattering.Further,an adaptive control algorithm was used to perform online estimation of unknown parameters such as the basic resistance coefficient and train mass,and the RBF neural network was employed to estimate and compensate for additional resistance and external disturbances,based on which the train speed tracking control strategy was proposed.This enhanced the adaptability and robustness of the train facing time-varying parameter uncertainties,changes in track conditions,and external disturbances.The system's stability was proven based on the Lyapunov stability theory.Simulation verification was performed using CRH380A train parameters.The simulation results show that the speed and displacement tracking errors are minor,convergence is fast,and rapid and precise tracking of the desired speed and displacement is achieved.Compared to the speed-tracking control strategies based on traditional linear sliding mode and integer-order nonsingular terminal sliding mode,the proposed control strategy improves tracking accuracy and enhances convergence speed.