Various control signals of high-speed trains (HSTs) are transmitted through the train communication network. However, the time delay generated during the transmission will cause a significant threat to the stability and safe operation of the train. To overcome the effect of time delay on the train control system, based on empirical mode decomposition (EMD) and adaptive quantum particle swarm optimization (AQPSO) algorithms, a least squares support vector machine (LS-SVM) time delay prediction model is proposed in this paper. The EMD algorithm is used to decompose the time delay sequence into several subsequences, which emphasizes the different local characteristics of the time delay sequence. By improving the calculation method about the successful value of particle iteration, an AQPSO algorithm with adaptive contraction-expansion coefficient is designed to optimize the parameters of different LS-SVM models for predicting each time delay component, which improves the prediction accuracy of network delay. Further, based on actor-critic reinforcement learning algorithm, an improved generalized predictive control method is proposed for the train network system. The actor-critic network is used to predict the future output of the system, and the recursive least squares identification algorithm with the variable forgetting factor is adopted to identify the future system model parameters. Combined with the time delay predicted accurately, the control quantity is sent in advance according to the properly arranged time series, which compensates efficiently the influence of the time delay on the control system. Simulation results show that compared with other control methods, the proposed method has better robustness and stability, which ensures the safe operation of high-speed trains under various working conditions.
Train Time Delay Prediction for High-Speed Train Dispatching Based on Spatio-Temporal Graph Convolutional Network
