Research On Cooperative Adaptive Cruise Control Strategy Based On Hybrid Potential Function

Advanced assisted driving is one of the important development directions of the current automotive industry.Cooperative adaptive cruise control is a fleet assisted driving system based on vehicle wireless communication technology.On the basis of considering the performance of ACC,we should also comprehensively consider the queue stability of the team.Therefore,CACC system responds more quickly to road emergencies,can better ensure the driving safety of vehicles,shorten the following distance between vehicles and improve the traffic efficiency of roads.This paper focuses on the theoretical and experimental research of CACC fleet control algorithm.Firstly,the vehicle control model is proposed.Firstly,the vehicle error transfer function model is established.Based on the vehicle dynamic performance index of speed acceleration and vehicle spacing,the vehicle dynamics model is established.Through the error dynamics equation,the error state is defined and the error transfer function model is derived.The CACC multi vehicle queue model is calculated by single vehicle,and the stability of the CACC multi vehicle queue model is evaluated.Under the condition of no time delay,the CACC model established in this chapter can be considered to be strictly stable.Secondly,a CACC fleet control strategy based on PID and potential function is proposed.Firstly,this chapter studies the PID control algorithm and potential function control algorithm of CACC controller,deduces the two algorithms respectively,obtains the control equation of the closed-loop control system,and uses Matlab /SIMULINK for simulation verification.In order to expand the disturbance upstream of the fleet,this paper establishes a model fleet of 20 vehicles as the research object,The PID control algorithm and potential function control algorithm are simulated and verified.Three virtual vehicle driving speed conditions are designed: smooth condition,low-speed condition and high-speed condition.The simulation is carried out under these three conditions.It is found that the adjustment time of PID algorithm is shorter than that of potential function algorithm,the control stability of potential function control algorithm is much greater than that of PID control algorithm,the algorithm is more stable and has better collision avoidance ability.Thirdly,by analyzing the advantages and disadvantages of PID and potential function control algorithms,a hybrid controller is proposed.By designing a state indication equation,the team can use the control method most suitable for the current team state in different states.Through the status indicator,define the stable state and adjustment state of the fleet.When the fleet is in the stable state,the fleet uses the potential function control algorithm to adjust the fleet;When the fleet is in the adjustment state,the fleet uses the potential function control algorithm to adjust the gap.The switching process of the two control algorithms is not smooth,so the switching function is established to transition the switching,so that the two algorithms can transition smoothly without jumping in the switching process.Finally,the stability and security of the hybrid control algorithm are analyzed through MATLAB / Simulink joint simulation.Finally,build a miniature vehicle test platform,use the nano robot intelligent vehicle with ROS system to build a miniature vehicle test platform,and test the control algorithm proposed in this paper to verify the effectiveness of the hybrid potential function algorithm.