CN-121979034-A - Unmanned vehicle queue longitudinal control method considering communication delay problem

CN121979034ACN 121979034 ACN121979034 ACN 121979034ACN-121979034-A

Abstract

The invention relates to the technical field of automatic driving automobile control, in particular to a longitudinal control method for an unmanned vehicle queue considering the problem of communication delay, firstly, based on a longitudinal kinematic model of a vehicle queue, considering the time delay problem of a vehicle execution system, adding a first-order inertia link to establish a discrete state space equation of a longitudinal controller. The Kalman filtering algorithm is improved by introducing Sage-Husa filtering, and the AEKF algorithm is designed to estimate the vehicle state information in the queue. Stability analysis and LMI stability margin deduction through Lyapunov-Krasovskii method verify system stability. And designing an SMC algorithm upper-layer controller based on the estimated vehicle state information, and outputting the expected acceleration. The lower PID controller outputs driving moment and braking pressure based on expected acceleration, so that longitudinal control of the train following vehicle is realized. When the invention is oriented to the problem of communication delay, the control precision can be improved, the performance reduction is reduced, and the longitudinal stability of the unmanned vehicle queue in the unstable communication environment is increased.

Inventors

JING HUI
WANG TAO
ZHAO HONGZHUAN
JIA GUANGYU
ZHANG XIAOYUAN
BAO JIADING
KUANG BING
CHEN HUCHENG
MA JIANPING
TANG RONGJIANG
HE SHUILONG
GAN JIULIANG
LI SIZE

Assignees

桂林电子科技大学

Dates

Publication Date: 20260505
Application Date: 20260108

Claims (6)

1. An unmanned vehicle queue longitudinal control method considering communication delay problem is characterized by comprising the following steps: Step 1, based on a longitudinal kinematic model of a vehicle queue, taking the delay problem of a vehicle execution system into consideration, adding a first-order inertia link to establish a discrete state space equation of a longitudinal controller; step 2, introducing a Sage-Husa filtering improved Kalman filtering algorithm, and designing an AEKF algorithm to estimate the vehicle state information in the queue; step 3, stability analysis and LMI stability margin deduction based on Lyapunov-Krasovskii method prove system stability; Step 4, designing an SMC algorithm upper controller based on the estimated vehicle state information, and outputting expected acceleration; And 5, outputting driving moment and braking pressure by the lower PID controller based on the expected acceleration to realize longitudinal control of the train following vehicle.
2. The unmanned vehicle queue longitudinal control method of claim 1, wherein the communication delay problem is considered, In step 1, a fixed headway strategy is adopted as a vehicle queue spacing strategy, a vehicle queue longitudinal kinematics model is established, and a discrete time domain relation is as follows: ; Wherein, the As the vehicle-to-vehicle distance error, In order to follow the relative speed of the vehicle and the preceding vehicle, In order to follow the actual acceleration of the vehicle, In order for the acceleration to be desired, The gain of the first-order system is 1, Is the time constant of the inertial link, For the moment at which the current system is located, For the next time instant of the system, Sampling time for the system; Selecting a state vector The discrete state space equation of the system is: ; Wherein, the 。
3. The unmanned vehicle queue longitudinal control method of claim 2, wherein the communication delay problem is considered, The state prediction equation in step2 is: ; ; Wherein, the Is that The posterior error covariance matrix of the moment in time, A noise covariance matrix in the state estimation process; the measurement update equation is: ; Wherein, the In the form of a kalman gain matrix, In order to measure the noise covariance matrix, In order to observe the residual error, the residual error is observed, Is a unit matrix; after a Sage-Husa filtering improved Kalman filtering algorithm is introduced, estimating a process noise covariance matrix Q and an observation noise covariance matrix R, and calculating and updating the following modes: ; ; Wherein, the The slower the update is for an exponentially weighted impact factor, approaching 1; is a minute positive number for maintaining a positive determination; is the observation residual; is the prediction covariance matrix.
4. The unmanned vehicle queue longitudinal control method of claim 3, wherein the communication delay problem is considered, In step 3, according to Lyapunov-Krasovskii theorem, introducing: ; ; Such that: wherein 。
5. The unmanned vehicle queue longitudinal control method of claim 4, wherein the communication delay problem is considered, In the step 4, selecting the longitudinal distance between two vehicles as an error variable of a longitudinal controller of a vehicle queue, wherein the expression is as follows: ; In the formula, In order to be in the position of the front vehicle, In order to follow the position of the vehicle, Representing a minimum safe distance between the vehicles and the vehicle, Represents the time interval of the vehicle head, Representing the speed of the preceding vehicle, Is the length of the vehicle; the slip form surface is designed as follows: ; In the formula, As an error proportional gain coefficient, the response speed and the convergence speed of the system are affected; And providing integral compensation for the error integral gain coefficient, and eliminating steady-state error of the system.
6. The unmanned vehicle queue longitudinal control method of claim 5, wherein the communication delay problem is considered, In step 5, in the driving mode, the desired torque of the driving motor is: ; Wherein, the The acceleration of the gravity is that, In order to be a coefficient of rolling resistance, For the angle of the ramp, Is the coefficient of air resistance and is used for the air resistance, In order to be a windward area, For the rolling radius of the wheel, Is the transmission ratio of the main speed reducer, The transmission efficiency of the power transmission system is achieved; In braking mode, the desired master cylinder pressure is: ; Wherein, the Is the ratio of braking force to brake master cylinder pressure.

Description

Unmanned vehicle queue longitudinal control method considering communication delay problem Technical Field The invention relates to the technical field of automatic driving automobile control, in particular to an unmanned vehicle queue longitudinal control method considering the communication delay problem. Background In recent years, with rapid development of automatic driving technology, autonomous control of unmanned vehicles has received extensive attention, and queue control of unmanned vehicles can effectively improve task execution efficiency, enhance stability and safety of a control system, and improve overall collaborative capability. In the queue control, unmanned vehicles in the queue can share the information of the position, the speed and the like of each other through the communication among the vehicles, so that the queue control is realized, and the occurrence of accidents in the driving process is reduced. In the signal transmission process among the vehicles in the queue, the influence of severe weather and signal interference is encountered, the problems of communication delay, data packet loss and external interference sometimes exist, the normal running of unmanned vehicles is interfered, the stability and the safety in the running process of the queue are reduced, and the energy consumption is increased. Many achievements are made at home and abroad for the study of the communication delay problem in the processing queue longitudinal control, but the limitation still exists. Existing studies are typically designed based on fixed frequency communication delays, but in practical applications these problems occur randomly with uncertainty and dynamics. These problems affect the accuracy of vehicle state information, reduce the stability of the queue, and cannot adapt to complex and changeable driving environments. Disclosure of Invention The invention aims to provide an unmanned vehicle queue longitudinal control method considering the communication delay problem, which aims to solve the random communication delay problem existing in automobile queue control under a V2V communication environment. In order to achieve the above object, the present invention provides a method for controlling a queue of unmanned vehicles in consideration of communication delay, comprising the steps of: Step 1, based on a longitudinal kinematic model of a vehicle queue, taking the delay problem of a vehicle execution system into consideration, adding a first-order inertia link to establish a discrete state space equation of a longitudinal controller; step 2, introducing a Sage-Husa filtering improved Kalman filtering algorithm, and designing an AEKF algorithm to estimate the vehicle state information in the queue; step 3, stability analysis and LMI stability margin deduction based on Lyapunov-Krasovskii method prove system stability; Step 4, designing an SMC algorithm upper controller based on the estimated vehicle state information, and outputting expected acceleration; And 5, outputting driving moment and braking pressure by the lower PID controller based on the expected acceleration to realize longitudinal control of the train following vehicle. Optionally, in step 1, a fixed headway strategy is adopted as a vehicle queue spacing strategy, a vehicle queue longitudinal kinematic model is built, and a discrete time domain relation is as follows: Wherein, the As the vehicle-to-vehicle distance error,In order to follow the relative speed of the vehicle and the preceding vehicle,In order to follow the actual acceleration of the vehicle,In order for the acceleration to be desired,The gain of the first-order system is 1,Is the time constant of the inertial link,For the moment at which the current system is located,For the next time instant of the system,Is the system sampling time. Selecting a state vectorThe discrete state space equation of the system is: Wherein, the 。 Optionally, in step 2, the state prediction equation is: Wherein, the Is thatThe posterior error covariance matrix of the moment in time,A noise covariance matrix in the state estimation process; the measurement update equation is: Wherein, the In the form of a kalman gain matrix,In order to measure the noise covariance matrix,In order to observe the residual error, the residual error is observed,Is a unit matrix; after a Sage-Husa filtering improved Kalman filtering algorithm is introduced, estimating a process noise covariance matrix Q and an observation noise covariance matrix R, and calculating and updating the following modes: Wherein, the The slower the update is for an exponentially weighted impact factor, approaching 1; is a minute positive number for maintaining a positive determination; is the observation residual; is the prediction covariance matrix. Optionally, in step 3, according to Lyapunov-Krasovskii theorem, introducing: Such that: wherein 。 Optionally, in step 4, the longitudinal distance between two vehicles is selected