| In recent years,China's rail transit industry has made great achievement.It is mainly reflected in the substantial growth of passenger and freight transport volume,the railway construction,the significant enhancement of independent innovation,and the rapid development of railway economic belt.At the same time,with the rise of artificial intelligence,big data,5g and other emerging technologies,people expect to further ensure the safety of railway operation,improve the efficiency of railway operation,promote the energy conservation and emission reduction of railway,and finally realize the coordinated development of economy and society.The optimization of energy-saving operation and control technology is the key factor to ensure the safety of railway operation,improving the efficiency of railway operation and promoting the energy-saving and emission reduction of railway.In view of this,the train energy consumption model,energy-saving optimization method,and speed tracking control algorithm is summarized.For the optimization of train target speed profile and the design of speed tracking control algorithm,the main work is as follows:(1)In this paper,based on the optimization of train dynamics model and traction calculation model,the simulation experiments and comparative analysis are carried out around the optimization of target speed profile based on adaptive iterative algorithm,heuristic genetic algorithm,and mixed integer linear programming respectively.(2)Based on the existing research results and research experience,in order to make up for the defects of the existing train control algorithm,such as complex structure design,high control energy loss,low tracking accuracy of complex line section,poor adaptability of parameters and object model,and inability to fundamentally solve the problem of time delay,this paper proposes a system simulation model of tracking and control of the train speed based on nonlinear active disturbance rejects on controller.The simulation model aims to solve the problems of nonlinear,large time-delay,multi disturbance,difficult modeling and strong coupling by using NLADRC's advantages of high tracking accuracy,wide range of processing uncertainty,strong anti-interference ability,small energy loss and low model dependence.Firstly,the time-delay control model of the train is established,and the adaptive simulation model of the train speed tracking control system is built according to the time-delay control model of the train;secondly,the speed tracking controller based on the NLADRC control algorithm is built,and the existing improved bee colony algorithm is used to solve the problems caused by NLADRC,which includes having many parameters,strong contradiction,wide range and complex internal laws.Thirdly,aiming at the problem that the traditional ADRC has limited ability to deal with the delay disturbance,the NLESO(Nonlinear Extended State Observer)is improved to solve the time-delay problem of the train control.Finally,the NLADRC control effect is simulated and verified with the freight train as the application.To make a fair comparison among NLADRC,NPID controller and MM-FPID,the simulation optimization conditions are set to the same.Under the same simulation optimization conditions,the optimal parameters of each controller are optimized by intelligent algorithm,and the control effect of each controller is verified by comparison.According to the above research content,through the simulation experiment and comparative analysis,we can draw the following conclusions: firstly,the lightweight optimization methods such as adaptive iterative algorithm and mixed integer linear programming algorithm have fast solution speed and are suitable for online optimization.It can be used to build train assistant driving software,provide real-time driving advice to drivers or update target speed profile in real time according to train operation requirements;secondly,intelligent algorithms such as genetic algorithm are often different,for specific problems,parameters need to be adjusted,learning strategies need to be improved,and hybrid algorithms need to learn from each other.Finally,the simulation model of train speed tracking control system based on NLADRC is constructed in this paper.Not only it has the characteristics of relatively simple control structure,low control energy loss and low model dependence,but also the improved NLADRC control algorithm based on the optimization of intelligent algorithm can solve the problem of large lag and difficult modeling of train,which is very effective.It can accurately track the speed data of the target under the condition of different system delay and complex interference.Compared with NPID control algorithm and MM-FPID control algorithm,it has high tracking accuracy,strong anti-interference ability and better robustness.This paper has important academic significance and practical application value for the research of intelligent train operation control and efficiency improvement. |
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