To eliminate the superimposed effects of network delays and actuator failures on control performance in the context of high-speed train wireless network control,a fault-tolerant control study is conducted.First,a train wireless network control testbed is built to collect delay data,a Convolutional Neural Network(CNN)is used to extract the spatial features from the delay data,and an Improved Particle Swarm Optimization(IPSO)is employed to optimize the Gated Recurrent Unit(GRU)for enhanced prediction accuracy.Second,the train traction/brake actuator health diagnosis is performed by learning the train parameters under fault conditions through a Back Propagation Neural Network(BPNN).Finally,an adaptive sliding mode fault-tolerant controller is designed to compensate for time delays and actuator faults.The results show that compared to the PSO-LSTM prediction model,the proposed model has higher prediction accuracy,with reductions of 94.15％,17.24％,and 74.39％in maximum,minimum,and average prediction relative errors,respectively.Under conditions of network delay and actuator failure,the proposed model reduces the relative errors of mean absolute error,mean square error,and standard deviation of speed tracking by nearly 95％when comparing with the RBF neural network and inverse control.The model accurately predicts network latency,thereby ensuring smooth train operation under various operating conditions.
维普
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