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CN-121995922-A - Self-adaptive track tracking control method for automatic driving vehicle

CN121995922ACN 121995922 ACN121995922 ACN 121995922ACN-121995922-A

Abstract

The invention relates to the technical field of automatic driving, in particular to a self-adaptive track tracking control method for an automatic driving vehicle, which comprises the following steps of establishing a vehicle dynamics track tracking model, estimating lateral force of a tire by adopting a high-order strong tracking volume Kalman filtering algorithm based on the vehicle dynamics track tracking model, wherein in the Gao Jiejiang tracking volume Kalman filtering algorithm, singular value decomposition is adopted to replace Cholesky decomposition, a fading factor is introduced to dynamically adjust filtering covariance, calculating a cornering stiffness correction factor according to the estimated tire lateral force and the tire lateral force calculated based on a linear tire model, and utilizing the obtained cornering stiffness correction factor to correct cornering stiffness parameters of a tire in a model predictive controller in real time to construct the self-adaptive model predictive controller. The invention can improve the path tracking performance of the automatic driving vehicle under uncertain working conditions.

Inventors

  • GAO ZHIWEI
  • ZHAO JIAYI

Assignees

  • 东北石油大学

Dates

Publication Date
20260508
Application Date
20260212

Claims (8)

  1. 1. An adaptive trajectory tracking control method for an autonomous vehicle, comprising the steps of: (1) Establishing a vehicle dynamics track tracking model; (2) Estimating the tire lateral force by adopting a high-order strong tracking volume Kalman filtering algorithm based on the vehicle dynamics track tracking model in the step (1), wherein in the Gao Jiejiang tracking volume Kalman filtering algorithm, singular value decomposition is adopted to replace Cholesky decomposition, and an fading factor is introduced to dynamically adjust filtering covariance; (3) Calculating a cornering stiffness correction factor according to the tire lateral force estimated in the step (2) and the tire lateral force calculated based on the linear tire model; (4) And (3) correcting the cornering stiffness parameters of the tires in the model predictive controller in real time by using the cornering stiffness correction factors obtained in the step (3), and constructing an adaptive model predictive controller so as to realize track tracking control of the automatic driving vehicle.
  2. 2. The adaptive trajectory tracking control method for an autonomous vehicle according to claim 1, wherein said introducing an fading factor in step (2) specifically comprises: Calculating a residual sequence according to the system state predicted value, the observed value and the system noise; calculating a residual covariance matrix through a recursive algorithm with forgetting factors based on the residual sequence; And calculating the fading factor according to the residual covariance matrix, the system noise covariance matrix, the observation noise covariance matrix and the state prediction covariance matrix, wherein the calculation formula of the fading factor lambda k+1 is lambda k+1 = max(1, tr(N k+1 )/tr(M k+1 ), and N k+1 and M k+1 are intermediate calculation matrices.
  3. 3. The adaptive trajectory tracking control method for an autonomous vehicle according to claim 1 or 2, wherein the calculating of the cornering stiffness correction factor in step (3) is specifically that the cornering stiffness correction factors λ cf and λ cr are calculated by the following formula: ; F cf and F cr are front and rear wheel side forces calculated based on a linear tire model.
  4. 4. The adaptive trajectory tracking control method for an autonomous vehicle according to claim 3, wherein the real-time corrected tire cornering stiffness parameter correction value in step (4) is: ; Wherein C cf and C cr are nominal front and rear wheel cornering stiffness.
  5. 5. The adaptive trajectory tracking control method for an autonomous vehicle according to claim 4, wherein the vehicle dynamics trajectory tracking model in step (1) is a three-degree-of-freedom vehicle dynamics model, and state variables of the model include a longitudinal speed, a lateral speed, a yaw angle, a yaw rate, a longitudinal position and a lateral position in a ground coordinate system of the vehicle.
  6. 6. The adaptive trajectory tracking control method for an automatically driven vehicle according to claim 5, wherein the constraint of the model predictive controller dynamic constraint condition front wheel angle control amount in step (4) can be expressed as: 。
  7. 7. an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-6 when the program is executed by the processor.
  8. 8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-6.

Description

Self-adaptive track tracking control method for automatic driving vehicle Technical Field The invention relates to the technical field of automatic driving, in particular to a self-adaptive track tracking control method for an automatic driving vehicle. Background Lateral motion control of an autonomous vehicle is one of the key technologies for achieving vehicle trajectory tracking and driving stability. In the prior art, aiming at the track tracking problem of an automatic driving vehicle, a model predictive control (Model Predictive Control, MPC) method based on a vehicle monorail model is commonly adopted by the publications and patents for transverse control. For example, domina et al in 2023 issued MPC path tracking methods, calculate lateral forces using brush tire models, and verify the effect in simulation and real vehicle experiments. The method is characterized in that the vehicle mass m and the yaw inertia Iz are measured and determined, and the cornering stiffness Cf and Cr of the front wheels and the rear wheels are identified offline through a ramp steering test. It is pointed out that the linear tire characteristics are only established in the small slip angle range of 0-3.5 ° for the front wheel and 0-2 ° for the rear wheel, and when the linear tire characteristics are beyond this range, the prediction model generates significant errors due to neglecting tire nonlinearities. This indicates that the MPC controller can not update the cornering stiffness parameter in real time, and has limited applicability to the working condition of large cornering angle. The traditional MPC controller based on the fixed cornering stiffness has limitations on model precision and track tracking performance, most methods are calibrated offline through experiments or assume constant cornering stiffness, tire nonlinear characteristics cannot be reflected in real time, and few strategies adopting online estimation are based on linear models, and uncertainty of estimation is not fully considered. Therefore, under complex or extreme working conditions, the difference between the prediction model and the real dynamics of the vehicle can be increased remarkably, so that the transverse displacement error and the course angle error are increased, and the track tracking and the stability are difficult to ensure. This background is highlighting the need to develop model predictive controllers with cornering stiffness adaptation capabilities. Disclosure of Invention The invention aims to overcome the defects of the prior art and provide an adaptive track tracking control method for an automatic driving vehicle. The method can improve the path tracking performance of the automatic driving vehicle under uncertain working conditions. The invention also provides a device and a storage medium for realizing the self-adaptive track tracking control method for the automatic driving vehicle. In order to solve the technical problems, the invention is realized as follows: An adaptive trajectory tracking control method for an autonomous vehicle, comprising the steps of: (1) Establishing a vehicle dynamics track tracking model; (2) Estimating the lateral force of the tire by adopting a high-order strong tracking volume Kalman filtering algorithm based on the vehicle dynamics track tracking model, wherein in the Gao Jiejiang tracking volume Kalman filtering algorithm, singular value decomposition is adopted to replace Cholesky decomposition, and a fading factor is introduced to dynamically adjust filtering covariance; (3) Calculating a cornering stiffness correction factor according to the tire lateral force estimated in the step (2) and the tire lateral force calculated based on the linear tire model; (4) And (3) correcting the cornering stiffness parameters of the tires in the model predictive controller in real time by using the cornering stiffness correction factors obtained in the step (3), and constructing an adaptive model predictive controller so as to realize track tracking control of the automatic driving vehicle. Further, the introducing the fading factor in the step (2) specifically includes: Calculating a residual sequence according to the system state predicted value, the observed value and the system noise; calculating a residual covariance matrix through a recursive algorithm with forgetting factors based on the residual sequence; And calculating the fading factor according to the residual covariance matrix, the system noise covariance matrix, the observation noise covariance matrix and the state prediction covariance matrix, wherein the calculation formula of the fading factor lambda k+1 is lambda k+1 = max(1, tr(Nk+1)/tr(Mk+1), and N k+1 and M k+1 are intermediate calculation matrices. Further, the calculating of the cornering stiffness correction factor in the step (3) specifically includes that the cornering stiffness correction factors lambda cf and lambda cr are calculated by the following formula: F cf and F cr a