Research On Train Speed Control Based On Model Reference Adaptive Control

The train is one of the transportation tools that people use frequently in daily travel.The auto drive system is an important equipment composed of trains,and the automatic train operation(ATO)subsystem is a particularly important control equipment to achieve automatic driving.Its main role is to assist or even replace manual driving to improve the accuracy of train control Reduce the error rate of manual operations and improve the operational efficiency during train operations.With the rapid development of the current society,new requirements have been put forward for the complexity and reliability of the train system functions,especially the controller of the train auto drive system must have good control performance,and the train auto drive system has naturally become a research hotspot for relevant researchers.This article will study train speed control from the aspects of train modeling,control strategies,and tracking performance related to automatic train operation technology.The main research content is as follows:(1)Analysis of uncertain resistance factors of trains.Trains are always affected by various uncertain factors during operation,mainly in the form of resistance,such as basic resistance,tunnel resistance,ramp resistance,and curve resistance.Analyzing the influencing factors of train operation process is the primary key to achieving precise train control,while also preparing for the mathematical expression of resistance factors to be connected to the train dynamics model.(2)Establishment of train dynamics model.The establishment of a train dynamics model is the mathematical expression of the train control model.Based on the criteria of computational complexity and comprehensive consideration of influencing factors,this article compares three traditional train dynamics models,namely single particle model,multi particle model,and rigid multi particle model,and selects the unit shift multi particle model.The unit shift multi particle model not only ensures a small computational burden,but also ensures high experimental authenticity.(3)Design of model reference adaptive control system.This article mainly adopts the theory of Model Reference Adaptive Control(MRAC),and uses the adaptive law of MRAC to adjust the uncertain influencing factors that cause changes in system parameters,that is,speed adjustment is carried out to reduce or eliminate the influence of uncertain resistance factors on speed.In order to ensure fast convergence and stability of the control system,this paper adopts the Lyapunov design method to derive the adaptive law.The simulation results show that the control system has the feasibility of speed control,and can effectively deal with the uncertainty problem of high-speed trains under complex operating line conditions,and also achieve the goal of speed tracking.(4)Performance evaluation of control systems.The speed tracking error effect of the train controller is monitored through the Simulated Annexing algorithm.In longitudinal comparison,it can provide reference for the reliability of the auto drive system of the train,even if it is found that the controller cannot adapt to the new operating environment;In terms of horizontal comparison,it is easy to make corresponding performance comparisons when replacing new control algorithms or comparing other control algorithms,both of which ensure the operational efficiency of train operations.