Research On Vehicle Platoon Control Parameter Optimization And Smooth Switching Method Under The Condition Of Communication Delay

With the development of technologies such as the Internet of Vehicles,multi-sensing,and wireless communication,Intelligent Transportation Systems(ITS)have become important means to solve traffic safety and congestion issues.As a typical application of ITS,Cooperative Adaptive Cruise Control(CACC)system utilizes vehicle-mounted sensors and inter-vehicle wireless communication to achieve stable,safe,and efficient automatic driving vehicle queue control.Compared to the commercially available Adaptive Cruise Control(ACC)system,the CACC system obtains richer neighboring vehicle information through inter-vehicle wireless communication,which improves the stability and following performance of the queue system,shortens the vehicle headway distance,and improves the traffic efficiency.However,in complex road traffic environments,the CACC system is affected by various external factors,which leads to the objective existence of communication delays.Communication delay has a serious impact on system stability and following performance.Therefore,this paper follows the four-element technology system for vehicle platooning to construct a system mathematical model and focuses on the numerical analysis of the impact of communication delay on system performance,especially the numerical solution of the communication delay boundary.Based on this,methods for optimizing vehicle platoon control parameters and switching controllers are proposed,effectively mitigating the impact of communication delay on the stability of the platoon system.The main research work of this paper is as follows:Firstly,this paper proposes a fuzzy logic-based controller parameter optimization method,referred to as the FL-CACC method.This method designs fuzzy sets and membership functions to fuzzify the quantitative indicators for current communication delay and system stability.Then,by setting fuzzy rules for fuzzy operations and using the center of gravity method for defuzzification,real-time optimization adjustment of the queue system controller parameters is achieved.Simulation experiments show that under the typical working condition of acceleration-cruise-deceleration,compared with the original linear controller,the FL-CACC method can improve the system stability by 8.6%;compared with the controller parameters before optimization adjustment,the FL-CACC method can improve the system stability by 13%.However,further experiments have revealed that the adaptability of this method to complex working conditions involving continuous dynamic changes in the lead vehicle is somewhat lacking.Considering that ACC controllers are not affected by communication delay,this paper attempts to switch the CACC controller to the ACC controller after the communication delay reaches the maximum boundary.However,the transient mutation of the system caused by the controller switch becomes a new technical challenge.Therefore,this paper proposes a controller smooth switching method based on genetic algorithm,abbreviated as GA-CACC method.This method first uses fuzzy logic to determine the start time of the controller switch and designs a buffer time domain for smooth switching.In this time domain,the problem of smooth switching of the controller will be transformed into a parameter optimization problem and solved using a genetic algorithm.This design aims to ensure the system performance while achieving smooth switching between CACC and ACC controllers.Through simulation experiments,this paper compares and analyzes the performance of four methods: direct switching,FL-CACC,SRCACC,and GA-CACC.The simulation results show that the GA-CACC method can not only achieve smooth switching between the two controllers but also further alleviate the transient mutation problem caused by the controller switch and improve the system stability.Compared with the direct switching,FL-CACC,and SR-CACC methods,GA-CACC respectively improves the system stability by the highest percentage of 18.1%,8.2%,and 7.2%.The simulation comparison results further verify the feasibility and effectiveness of the proposed smooth switching method in this paper.