Automatic Driving Research of Cloud Model High-Speed Train based on Genetic Algorithm Optimization

Abstract

High-speed train is a complex nonlinear system with strong coupling, which is easily disturbed by uncertain factors. The traditional model of single-mass point train does not consider the influence of train length and interaction force between trains. Given that high-speed train is vulnerable to time-varying disturbance in the complex and changeable external environment, and the traditional single-mass point train model does not consider the train length and interaction force between vehicles, a genetic algorithm (GA) optimised cloud model proportion integration differentiation (PID) speed controller based on a rigid multi-mass point model was designed. The numerical features of the cloud model are first optimized by the global optimization capability of the GA, then the cloud model reasoner corrects the parameters of the PID controller in real time through the corresponding reasoning rules. Moreover, the PID controller with adjustable parameters completes the control output of the speed controller. The rigid multi-mass point model of the train is established, and CRH3 train is selected to simulate the selected line to prove the feasibility of the cloud model PID control algorithm based on GA optimisation. Under the same conditions, PID and fuzzy PID controllers are set for speed-tracking performance comparison, which verifies that the cloud model PID controller based on GA optimisation has small speed-tracking error and strong robustness. It can more effectively reduce the influence of interference caused by uncertain factors on the automatic driving operation speed controller of high-speed train and has better control effect.