Research On Intelligent Control Of Formation Cooperative Operation Of Intelligent Networking Monorail Rapid Transit System

At present,the construction and operation costs of urban rail transit systems mainly based on subways are high,which puts significant financial pressure on local operators and affects the planning and construction progress of urban rail transit in some cities.Therefore,many cities are seeking low-cost urban rail transit solutions.Low cost urban rail transit systems are often built on the ground,which can seriously affect road traffic.Therefore,there is an urgent need for a low-cost urban rail transit system that adopts elevated construction to fill the market gap.In this context,this article proposes a new intelligent connected monorail rapid transit system for elevated construction.It solves the problem of high construction,operation,and maintenance costs of traditional urban rail transit systems at the system level by adopting the trinity concept design concept of miniaturized vehicles,intelligent formation operation,and new energy power.Due to significant differences in system architecture,operational scenarios,and vehicle operation control methods compared to traditional urban rail transit systems.Especially in the three key operational scenarios of intelligent formation operation collaborative collision avoidance,intelligent formation operation,and dynamic vehicle cross line grouping,new key scientific problems that urgently need to be solved have emerged.Therefore,this article focuses on the intelligent control problem of coordinated operation of intelligent connected monorail rapid transit systems,and mainly conducts research on the following aspects:(1)Conduct research on the architecture topology and operational scenarios of intelligent connected monorail rapid transit system.Based on the needs of construction and operation,propose an overall architecture of an intelligent connected monorail rapid transit system and its intelligent formation operation control system architecture suitable for future urban construction in China,and analyze the key operational scenarios and corresponding common key scientific issues in system operation.(2)Research on collaborative collision avoidance control method for intelligent formation operation based on collision risk assessment.Starting from the operational scenarios of the intelligent formation of new intelligent connected monorail rapid transit vehicles,this paper analyzes the sources and factors of collision risk during the operation of intelligent connected monorail rapid transit vehicle formation.Then,based on a comprehensive analysis of existing vehicle collision risk assessment methods,an intelligent formation operation collision risk assessment method is proposed,which considers vehicle distance and speed.Next,establish a collaborative collision avoidance control algorithm for intelligent vehicle formation operation based on collision risk assessment.Finally,the effectiveness of the intelligent formation operation collaborative collision avoidance control algorithm based on collision risk assessment was verified through simulation experiments in extreme collision risk scenarios.(3)Research on collaborative control method for heterogeneous vehicle intelligent formation operation based on artificial potential field deep reinforcement learning.Firstly,based on the structural characteristics of intelligent connected monorail rapid transit vehicles and the characteristics of track lines,a dynamic model of heterogeneous vehicle intelligent formation under complex lines is established.Next,based on the theory of deep reinforcement learning algorithms and combined with the artificial potential field algorithm,a heterogeneous vehicle intelligent formation operation collaborative control algorithm based on artificial potential field deep reinforcement learning is constructed.At the same time,construct intelligent agents for formation vehicles and train them.Then,an evaluation index system for intelligent formation operation is proposed,and joint simulation is conducted using Matlab/Simulink and Simpack.Simulation experiments are conducted on heterogeneous vehicle intelligent formations operating on complex routes.Finally,the effectiveness of the heterogeneous vehicle intelligent formation operation collaborative control algorithm based on artificial potential field deep reinforcement learning was verified.(4)Research on intelligent cross line vehicle grouping control method based on cooperative game optimal grouping sequence.Firstly,analyze the characteristics of the intelligent formation’s cross line operation scenario,design a vehicle cross line intelligent grouping control strategy,and establish a mathematical model of the vehicle cross line intelligent grouping optimization decision system.Next,a joint cost optimization function considering efficiency,energy consumption,and comfort is constructed,and an optimal grouping sequence model for dynamic cross line grouping scenarios is established.Then,a vehicle cross line intelligent grouping control algorithm based on cooperative game optimal grouping sequence is constructed,and the analytical solution of the optimal grouping sequence is derived based on the Pontryagin minimum principle.Finally,the effectiveness of the intelligent vehicle cross line grouping control algorithm based on cooperative game optimal grouping sequence was verified through simulation experiments of two typical dynamic cross line grouping scenarios.(5)Conduct research on the design,scenario,and algorithm validation of an intelligent formation operation simulation experimental platform for the intelligent connected monorail rapid transit system.Firstly,based on the requirements of the intelligent formation operation simulation experimental platform for the intelligent connected monorail rapid transit system,a scheme design is carried out for the experimental platform,and the software and hardware system architecture of the simulation experimental platform is completed.Then build a high-precision map that simulates the track operation environment and configure the vehicle to simulate the track operation function.Thus achieving similarity in structure,principles,and functions between the platform and the real system,enabling the simulation experiment platform to have the conditions for conducting system level experiments on all operational scenarios.Finally,experiments were conducted on four typical intelligent formation operation collaborative collision avoidance scenarios,one heterogeneous intelligent formation vehicle operation scenario,and two dynamic cross line grouping scenarios on the simulation experimental platform to verify the effectiveness of the proposed intelligent formation operation collaborative collision avoidance control algorithm based on collision risk assessment,heterogeneous vehicle intelligent formation operation collaborative control algorithm based on artificial potential field deep reinforcement learning,and vehicle cross line intelligent grouping control algorithm based on cooperative game optimal grouping sequence.