关键词: 高速列车, 多质点模型, 速度跟踪, 神经网络, 反馈控制

Abstract:

The speed tracking control system for high-speed trains is a complex nonlinear system, and it is hard to achieve high-precision tracking performance. A neural network PID（Proportion-Integration-Differentiation） controller for the high-speed train is designed to reduce the speed tracking error. Firstly, a unit-displacement multi-particle model is built to describe the train running process. In this model, the basic resistance and extra resistance to the train, as well as the interaction between carriages, are considered. Then, PID control regulated by BP（Back Propagation） neural network is formulated, and a speed tracking controller for the train is designed. The traction force and braking force are determined by the neural network PID control based on the speed error. Finally, simulation comparison with a fuzzy control and a conventional PID control is accomplished. Simulation results indicate that the neural network PID control has a very small speed tracking error and has superior speed tracking performance. This method can meet the demand for the accurate running of high-speed trains.

Key words: high-speed train, multi-particle model, speed tracking, neural network, feedback control