High-speed Train Operation Tracking Based on Dual Closed-loop Adaptive Sliding Mode Control
An adaptive sliding mode control algorithm of RBF neural network based on double loop structure is pro-posed to solve the problem of high-speed train tracking control.Firstly,the system structure is divided into displace-ment and speed control subsystems to prevent the train from losing control due to the failure of a single controller.On this basis,integral sliding mode control is used to strengthen the robustness of the controller.At the same time,parameter adaptive algorithm and RBF neural network adaptive algorithm are introduced into the speed subsystem to reduce the impact of basic resistance,additional resistance and uncertain resistance.Finally,the stability of the system is proved by Lyapunov stability analysis.Through simulation experiments,the results show that the displacement error is within the[-3.3×10-4,1.9×10-4]range and the speed error is within the[-2.1×10-2,3.1×10-2]range,and the algo-rithm can accurately track the speed and displacement of the train.
high-speed trainsliding mode controldouble closed-loop control systemadaptive controlRBF neural net-work
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