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Distributed Cooperative Control Of Multi-locomotive Traction Heavy Haul Train

Posted on:2018-10-08Degree:MasterType:Thesis
Country:ChinaCandidate:J L ZhangFull Text:PDF
GTID:2322330512475589Subject:Safety science and engineering
Abstract/Summary:PDF Full Text Request
Significant development of heavy rail transport can effectively improve the transport capacity,which has become the inevitable trend of China's railway transport development.Due to the heavy haul train's force situation is far more complex than the ordinary train,heavy haul train's broken hooks and decoupling conditions will become a potential risk during the operation of the heavy haul train.The energy-saving operation of heavy haul train is of great importance for rail transport.This paper will study the distributed cooperative control of multi-locomotive traction heavy haul train from the aspects of reducing the coupler force and the energy consumption during the operation of the heavy haul train,Which is of great significance to ensure the safe and stable operation and energy-saving operation of heavy haul train.In this paper,we use simulink to establish a multi-locomotive multi-particle heavy haul train dynamic model,initial output coupler force and energy consumption.Then,based on the ant colony algorithm(ACO)and particle swarm optimization(PSO)to establish the heavy haul train speed running curve optimization system,get the optimal speed curve.Finally,based on the dynamic matrix control(DMC)algorithm to establish multi-locomotive multi-particle heavy haul train dynamic matrix control system to track the velocity curve and get the coupler force,displacement and energy consumption,compare the simulation results with the results of the PID control system.The research of this paper mainly includes the following aspects:(1)Analysis the forces on the locomotives and the wagons of the multi-locomotive multi-particle heavy haul train and establish a state space model for each locomotive and each wagon.Use simulink to establish a multi-locomotive multi-particle heavy haul train dynamic model,determine the formation of the heavy haul train,select the train model and the actual running line,simulation on the model,get the energy consumption and the coupler force of the heavy haul train.(2)Based on the ant colony algorithm and particle swarm optimization to establish the heavy haul train speed running curve optimization system,take the heavy train model and the actual running line into the system for simulation,get the optimal speed running curve of multi-locomotive traction heavy haul train,the energy saving effect of the optimal speed running curve is good.(3)Based on the dynamic matrix control algorithm to establish multi-locomotive multi-particle heavy haul train dynamic matrix control system,take the multi-locomotive multi-particle heavy haul train dynamic model into the control system to track and control the energy-saving operation curve of heavy haul train,output the coupler force,speed tracking curve,displacement and energy consumption and compare the simulation results with the results of the PID control system,the results show that the results of the multi-locomotive multi-particle heavy haul train dynamic matrix control system which include the control of the multi-locomotive traction heavy haul train's speed,the reduction of the coupler force,the tracking of the displacement and the energy saving effects are better than the results of the PID control system.
Keywords/Search Tags:Heavy Haul Train, Multi-locomotive Multi-particle Model, ACO, PSO, DMC, Simulink
PDF Full Text Request
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