Automatic Railway Catenary Operation Vehicle Research On Control System

As a veritable rail transit power,rail transit is currently undergoing a transition from manual driving to automatic driving.The automatic driving system of rail transit vehicles has been greatly improved in terms of safety,stability,and punctuality.At the same time,rail vehicles that guarantee the safety and traffic order of rail transit also urgently need to be improved and adapted to it.Among them,the rail catenary operation vehicle is a special operation vehicle used to ensure the normal power supply of rail vehicles and the safety of the catenary.It mainly undertakes the tasks of erecting,overhauling,and erecting poles related to the catenary.The operation vehicle is equipped with two drivers to take on driving tasks,and night tasks are the main ones;in order to reduce labor intensity and reduce staff and increase efficiency,the research on the automatic driving system of the catenary operation vehicle has become particularly important.This article has carried out corresponding research on the speed curve optimization and curve tracking control in the automatic driving control system of JW-4G rail catenary operating vehicle,which is of great significance for ensuring the safety and traffic efficiency of rail transit.The main work in this paper is as follows:(1)Establish a single-particle power model for catenary operating vehicles,and establish a multi-objective optimization model based on the energy-saving,punctuality,riding comfort and parking accuracy of the operating vehicles;the model proposes an operation sequence suitable for rail vehicles,which is the same as the traditional Compared with the four-stage operation sequence,the constraints on energy consumption are significantly relaxed,and the switching point is increased,which is conducive to the collaborative optimization of multiple performances and is suitable for modern automatic driving planning and control;(2)Draw lessons from the genetic algorithm widely used in the optimization of train curves and add hill-climbing algorithms,genetic recombination and individual agglomeration judgment methods;improve the local optimization ability of the algorithm,accelerate the algorithm convergence speed,and suppress the phenomenon of population individual agglomeration The occurrence of this paper is conducive to solving the operation sequence optimization problem of multiple switching points and multiple operating conditions proposed by the model in this paper;in addition,in order to enhance the adaptiveness of the algorithm and reduce the optimization search range,this paper adds corresponding constraints based on driving experience to indirectly improve Optimized rate;(3)In order to reduce the dependence on parameter adjustment of the fuzzy controller and fuzzy PID control algorithm used in the current railway speed curve tracking,this paper designs a model predictive controller with overspeed protection function.The tracking ability of the speed curve ensures that the operating vehicle can achieve stable tracking under safe and limited speed conditions.On the other hand,the controller has a certain improvement in its adaptive ability and weakens the parameter adjustment process of the controller;At the end of this paper,the simulation of running curve optimization and speed tracking control based on multi-scenario working conditions is carried out,and the effectiveness and feasibility of the improved genetic algorithm and control algorithm based on this paper are verified.