Research on PSO-RBF sliding mode control method for idling of metro trains
Metro trains are prone to wheel idling during frequent traction acceleration,especially in the acceleration phase.To improve the control performance of adhesion recovery after wheel idling and increase the adhesion utilization of the train wheels and tracks,this article conducts in-depth research on the control of wheel idling and re-adhesion.Firstly,by building a single-axle dynamic simulation model of the train and estimating the train's state through a full-dimensional state observer and extremum search algorithm.Secondly,a sliding mode controller based on the POS-RBF neural network is proposed.Finally,the simulation is used to validate the control performance of wheel idling and re-adhesion of the train.Simulation results show that,compared to a fixed switch gain sliding mode controller,the PSO-RBF neural network-based sliding mode controller with real-time adjustment of the switch gain has the advantages of faster adhesion recovery speed and smaller post-stabilization oscillations.This improvement effectively enhances the adhesion control performance of metro trains during wheel idling,providing strong technical support for the safe and efficient operation of trains.
metro trainsoptimal adhesion controlparticle swarm algorithmradial basis function neural networksliding mode control
NETL
NSTL
万方数据
