Research on Speed Tracking Control Algorithm for Urban Rail Transit Trains Based on Sliding Mode and RBF Neural Network

Abstract

Objective Addressing the issues of low control accuracy and poor disturbance rejection in conventional ATO (automatic train operation) speed control algorithms in urban rail transit train operation control systems, a new speed control algorithm is proposed to improve control accuracy. Method Firstly, a single-mass point dynamic equation for train is established, and a delay compensation module is designed to address the phenomenon of delay in executing commands by the traction and braking systems. Secondly, in the controller design part, speed and position errors are collected to establish a sliding mode switching function, and a sliding mode controller is derived through differential equations. Finally, to suppress the inherent oscillation phenomenon of the sliding mode controller, the switching control output is optimized by training a RBF (radial basis function) neural network. Result & Conclusion Simulation experiments are conducted based on the parameters of the train from the Phase II renovation of Xuzhou Metro Line 3 in Matlab software. The simulation results demonstrate that the proposed algorithm ensures that the controller output speed can more efficiently and accurately track the recommended speed curve during train operation.

Keywords

• urban rail transit train
• rbf neural network
• sliding mode control
• speed tracking