Research on Precision Parking Control Method for EMU Inbound Process

doi:10.16182/j.issn1004731x.joss.19-0165

Abstract

Abstract: By analyzing the relationship between braking force and speed during the stop-stop process of high-speed EMU(Electric Multiple Units), the multi-point dynamics model of EMU is constructed. The Smith predictor is introduced due to the delay effect on the system caused by the braking force generated during the late braking, and the RBF(Radial Basis Function) neural network-based PID control strategy is combined with the Smith predictor to achieve tracking control of a given speed during braking. Simulation analysis shows that the error of train speed and set speed controlled by RBF neural network PID-Smith controller is less than ±1 km/h, and the parking error is less than ±0.3 m, which meets the inbound braking and parking requirement.

Key words: Electric Multiple Units(EMU), multi-particle modeling, RBF neural network, self-tuning PID control, predictive compensation control

CLC Number:

TP273

Li Zhongqi, Xing Yueshuang. Research on Precision Parking Control Method for EMU Inbound Process[J]. Journal of System Simulation, 2021, 33(1): 149-158.

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