CN-121615378-B - Ground vehicle four-wheel cornering stiffness estimation method considering cross-country environment disturbance

CN121615378BCN 121615378 BCN121615378 BCN 121615378BCN-121615378-B

Abstract

The invention belongs to the technical field of automobiles, and particularly relates to a ground vehicle four-wheel cornering stiffness estimation method considering cross-country environment disturbance. Firstly, a vehicle dynamics model and a kinematic model which take coupling gradient into consideration are established through coordinate transformation and dynamics analysis, so that the accuracy of the established unmanned ground vehicle model in a complex off-road environment is guaranteed, secondly, an unmanned ground vehicle path tracking system is established, and state space equation discretization is carried out, so that the unmanned ground vehicle path tracking system can be used for four-wheel independent cornering stiffness estimation, and finally, the four-wheel cornering stiffness estimation method based on self-adaptive forgetting least square is provided, so that the accurate and reliable estimation of the tire cornering stiffness in the off-road environment can be guaranteed.

Inventors

ZHAO JIAN
ZHOU WENBO
CHEN ZHICHENG
XIAO FENG
HAN JIAYI
SONG DONGJIAN
ZHANG PEIXING

Assignees

吉林大学

Dates

Publication Date: 20260505
Application Date: 20260202

Claims (3)

1. The method for estimating the four-wheel cornering stiffness of the ground vehicle by considering the disturbance of the off-road environment is characterized by comprising the following steps of: The method comprises the steps of establishing a vehicle dynamics model and a vehicle kinematics model which consider the coupling gradient, enabling an unmanned ground vehicle to dynamically adapt to the influence of a road surface which simultaneously covers a transverse slope and a longitudinal slope on the vehicle dynamics characteristic through vehicle dynamics coordinate conversion; The method comprises the steps of establishing an unmanned ground vehicle system comprising four-wheel independent cornering stiffness, establishing a state space equation of four-wheel cornering stiffness estimation by selecting unmanned ground vehicle system variables, and discretizing; Designing a four-wheel cornering stiffness estimation method based on self-adaptive forgetting least square, and designing a self-adaptive forgetting least square cost function based on a self-adaptive filter so as to obtain a four-wheel independent cornering stiffness estimation law and realize the estimation of the tire cornering stiffness in an off-road environment; the specific method of the first step is as follows: s11, defining three coordinate systems as global coordinate systems Coordinate system of vehicle And a vehicle projection coordinate system The global coordinate system is fixed on the flat ground, the vehicle coordinate system is positioned at the center of gravity of the vehicle, and the origin of the vehicle projection coordinate system is the projection of the center of gravity of the vehicle on the XOY plane; s12, the gravity of the vehicle under the global coordinate system can generate a coordinate system along the vehicle Additional force of (2) And The following is shown: (1) (2) (3) In the formula, Is a longitudinal additional force; Is a transverse additional force; is a vertical additional force; a coordinate transformation matrix of a vehicle coordinate system is used for projecting the coordinate system for the vehicle; a coordinate transformation matrix from the global coordinate system to the vehicle projection coordinate system; is road lateral grade; Is the road longitudinal grade; projecting a heading angle for the vehicle; is the vehicle mass; ; S13, establishing a vehicle dynamics model on a coupling gradient neglecting the roll, pitch and vertical motion of the vehicle, wherein the model is as follows: (4) (5) In the formula, Yaw torque for the vehicle; Is the vehicle slip angle; Yaw rate for the vehicle; Is the longitudinal speed of the vehicle; is the vehicle lateral speed; for the longitudinal forces of the four tires, Is the lateral force of the four tires, Is that And then represents the front wheel of the bicycle, Is that And then represents the rear wheel of the bicycle, Is that And then represents the right wheel of the bicycle, Is that And represents the left wheel; is an ideal front wheel steering angle; Distance from the front axle to the center of gravity of the vehicle; Distance from the center of gravity of the vehicle to the rear axle; Is the wheel track of the vehicle; Is a transverse attachment force; Assuming that the vehicle rotation angle and the longitudinal acceleration are zero, equations (4) and (5) are rewritten as: (6) (7) In the formula, A vehicle yaw moment induced for the coupled grade; An additional vehicle yaw moment due to wheel torque distribution; is the ground friction coefficient; The right vertical force is applied to the vehicle; left vertical force applied to the vehicle; under the influence of coupling gradient and lateral acceleration, the vertical forces of tires on two sides The calculation is as follows: (8) (9) In the formula, Is the wheelbase of the vehicle; is road grade; the height of the mass center of the vehicle from the ground; by linearizing the tire model, the front and rear axle tires are subjected to lateral forces Can be expressed as follows: (10) (11) (12) (13) , (14) In the formula, Is the cornering stiffness of the four tires, Is that And then represents the front wheel of the bicycle, Is that And then represents the rear wheel of the bicycle, Is that And then represents the right wheel of the bicycle, Is that And represents the left wheel; Is the slip angle of the front wheel; is the slip angle of the rear wheel; Is the nominal cornering stiffness of the wheel; the tire cornering stiffness variation range is; For the time-varying stiffness correction factor, Is that And then represents the front wheel of the bicycle, Is that And then represents the rear wheel of the bicycle, Is that And then represents the right wheel of the bicycle, Is that And represents the left wheel; combining equations (1) - (14), building a vehicle dynamics model taking into account the coupling gradient: (15) (16) In the formula, The vehicle front axle lateral deflection rigidity; the vehicle rear axle lateral deflection rigidity; s14, establishing a Frenet coordinate system on the reference path Lateral error Heading error for the distance between the center of gravity of the vehicle and the center of gravity of the reference path For the actual heading angle of the vehicle And (3) with Tangential angle of reference path Is the difference between (1); The vehicle kinematic model is expressed as: (17) In the formula, Is the curvature of the road.
2. The method for estimating the four-wheel cornering stiffness of the ground vehicle taking into account the disturbance of the off-road environment according to claim 1, wherein the specific method of the second step is as follows: S21, combining the vehicle dynamics model and the vehicle kinematics model which consider the coupling gradient, selecting the unmanned ground vehicle path tracking system variable, namely selecting the state variable Input variable Disturbance variable , wherein, Is the actual front wheel steering angle; S22, establishing a state space equation for four-wheel cornering stiffness estimation according to the unmanned ground vehicle path tracking system variable: (18) (19) (20) In the formula, ; ; ; ; ; ; In the formula, Is a state variable; Is an input variable; Is a disturbance variable; is an unmanned ground vehicle system matrix; The system matrix is an unmanned ground vehicle reference system matrix; the left front tire system matrix of the unmanned ground vehicle; the matrix is an unmanned ground vehicle right front tire system matrix; the left rear tire system matrix of the unmanned ground vehicle; The matrix is a right rear tire system matrix of the unmanned ground vehicle; Inputting a matrix for the unmanned ground vehicle; Inputting a matrix for the reference of the unmanned ground vehicle; inputting a matrix for a left front tire of the unmanned ground vehicle; Inputting a matrix for a right front tire of the unmanned ground vehicle; The system is an unmanned ground vehicle state interference matrix; Nominal cornering stiffness for the front wheels; Nominal cornering stiffness for the rear wheels; the change range of the cornering stiffness of the front wheel is; The change range of the cornering stiffness of the rear wheel is; the left front wheel time-varying rigidity correction coefficient; the rigidity correction coefficient is the rigidity correction coefficient of the right front wheel; the left rear wheel time-varying rigidity correction coefficient; The rigidity correction coefficient is the rigidity correction coefficient of the right rear wheel; S23, discretizing a state space equation of a path tracking system, predicting the future state of a controlled object by using a discrete model by model prediction control, obtaining an optimal control quantity through rolling optimization, discretizing the state space equation of the system under continuous time, discretizing a continuous time matrix in (18) by using a forward Euler discretization method, and obtaining the state space equation of the path tracking system after discretization: (21) (22) In the formula, The system matrix is an unmanned ground vehicle reference discrete system matrix; The left front tire discrete system matrix is an unmanned ground vehicle; The matrix is a discrete system matrix of the right front tire of the unmanned ground vehicle; Is a left rear tire discrete system matrix of an unmanned ground vehicle, The matrix is a discrete system matrix of the right rear tire of the unmanned ground vehicle; is an unmanned ground vehicle a reference discrete input matrix; a left front tire discrete input matrix for an unmanned ground vehicle; Is a discrete input matrix for the right front tire of an unmanned ground vehicle, Is a unit matrix; Discrete time intervals for the system; Is the first A state variable for each cycle; Is the first Input variables for each cycle; Is the first A disturbance variable for each cycle; Is the first A state variable for each cycle; the system is an unmanned ground vehicle discrete state interference matrix; The system matrix is a left front, right front, left back and right back tyre discrete system matrix of the unmanned ground vehicle; The system matrix is an unmanned ground vehicle left front, right front, left rear and right rear tire system matrix; the matrix is input for the left front and right front tires of the unmanned ground vehicle in a discrete manner; the matrix is input for the left front and right front tires of the unmanned ground vehicle.
3. The method for estimating the four-wheel cornering stiffness of the ground vehicle taking into account the disturbance of the off-road environment according to claim 2, wherein the specific method of the third step is as follows: s31, building a tire cornering stiffness estimator comprising an adaptive filter: (23) (24) In the formula, Is a regression matrix; is the observation residual; The rigidity correction coefficient of the time-varying tire is obtained; Is the first The left front wheel time-varying rigidity correction coefficient; Is the first The right front wheel time-varying rigidity correction coefficient; Is the first The left rear wheel time-varying rigidity correction coefficient; Is the first The right rear wheel time-varying rigidity correction coefficient; introducing an adaptive filter To ensure the continuity of parameter update, avoid estimating divergence: (25) In the formula, First, the A step of adaptive filter; Stabilizing the gain matrix for Schur; is a regression matrix; ; Is the first A step of adaptive filter; The tire cornering stiffness estimator is designed as follows: (26) In the formula, Is that State variable estimation value of moment; Is that Time-varying tire stiffness correction coefficient estimation values at the moment; Is that Time-varying tire stiffness correction coefficient estimation errors; Is that State variable estimation error of time; Is that State variable estimation value of moment; Is that Time-varying tire stiffness correction coefficient estimation values at the moment; s32, designing a recursive least squares cost function containing forgetting factors, combining the equation (25) and the equation (26) to obtain State variable estimation error for time of day : (27) Building auxiliary variables Will be Break down into effects True parameter error term and parameter independent error term of (2) : (28) (29) In the formula, Is that Error terms independent of parameters at the moment; Is that Real parameter error items of time; Will be By minimizing the time-varying stiffness correction factor estimate as output And fitting a constant error term A forgetting recursive least squares cost function containing bilinear optimization is designed and the nonlinear problem is decoupled as: (30) In the formula, A forgetting recursive least squares cost function; Is a forgetting factor; Fitting a constant error term; the current time domain step number; Is the maximum time domain step number; Is that Real parameter error items of time; First, the A step of adaptive filter; Is that Time-varying tire stiffness correction coefficient estimation values at the moment; Is that Time-varying tire stiffness correction coefficients at time; s33 by minimizing the cost function Obtaining a tire cornering stiffness estimation law: (31) defining an inverse covariance matrix Time-varying tire stiffness correction factor estimation Expressed as follows: (32) (33) In the formula, Is that An inverse covariance matrix of the moment; by combining the formula (32) and the formula (33), the following tire cornering stiffness estimator based on AFRLS method is established through algebraic transformation: (34) In the formula, Is that Time-varying tire stiffness correction coefficient estimation values at the moment; Is that Inverse covariance matrix of time instant.

Description

Ground vehicle four-wheel cornering stiffness estimation method considering cross-country environment disturbance Technical Field The invention belongs to the technical field of automobiles, and particularly relates to a ground vehicle four-wheel cornering stiffness estimation method considering cross-country environment disturbance. Background With the wide application of unmanned ground vehicles in complex off-road environments such as military reconnaissance, field rescue, planetary detection and the like, the requirements on vehicle control stability and path tracking precision are increasingly improved. The tire cornering stiffness is used as a key parameter affecting the lateral dynamics performance of the vehicle, and directly determines the stability boundary and the path tracking effect of the unmanned ground vehicle. In a complex off-road environment, tire cornering stiffness can vary significantly with operating conditions due to ground potholes and road coupling gradients. If the cornering stiffness of the tire cannot be accurately estimated in real time, the performance of the unmanned ground vehicle control system is seriously affected, and even instability is caused. Therefore, achieving high-accuracy tire cornering stiffness estimation has become a key technology for improving the ability of unmanned ground vehicles to travel in off-road environments. At present, the tire cornering stiffness estimation method mainly depends on a vehicle dynamics model and a vehicle kinematics model, and is widely used for path tracking control of unmanned ground vehicles. Current tire cornering stiffness estimation methods remain problematic in some aspects in complex off-road environments. Firstly, the existing method mostly assumes that vehicles run on a horizontal structural road, four-wheel vertical load fluctuation is smaller along with the running fluctuation of the vehicles, nonlinear influences of complex disturbance in off-road environments on tire force and tire cornering stiffness are not fully considered, so that estimation accuracy is insufficient, secondly, the traditional estimation method mostly concentrates on single tires or equivalent stiffness among axles, independent estimation on the four-wheel cornering stiffness cannot be achieved, the method is difficult to adapt to the actual situation of uneven stress of the tires due to ground pits and road coupling gradients in complex off-road environments, and finally, most estimation methods rely on high-accuracy sensors or complex optimization algorithms, are high in cost, large in calculation burden and poor in instantaneity, and cannot be applied in real time under the condition of limited calculation resources. Disclosure of Invention In order to solve the problems, the invention provides a four-wheel cornering stiffness estimation method of a ground vehicle considering disturbance of an off-road environment, which comprises the steps of firstly establishing a vehicle dynamics model and a kinematics model considering coupling gradient through coordinate transformation and dynamics analysis to ensure the accuracy of the established unmanned ground vehicle model in a complex off-road environment, secondly establishing an unmanned ground vehicle path tracking system and discretizing a state space equation to enable the unmanned ground vehicle path tracking system to be used for four-wheel independent cornering stiffness estimation, and finally providing a four-wheel cornering stiffness estimation method based on self-adaptive forgetting least square to ensure the accurate and reliable estimation of the tire cornering stiffness in the off-road environment. The technical scheme of the invention is as follows in combination with the accompanying drawings: the invention provides a ground vehicle four-wheel cornering stiffness estimation method considering cross-country environment disturbance, which comprises the following steps: The method comprises the steps of establishing a vehicle dynamics model and a vehicle kinematics model which consider the coupling gradient, enabling an unmanned ground vehicle to dynamically adapt to the influence of a road surface which simultaneously covers a transverse slope and a longitudinal slope on the vehicle dynamics characteristic through vehicle dynamics coordinate conversion; The method comprises the steps of establishing an unmanned ground vehicle system comprising four-wheel independent cornering stiffness, establishing a state space equation of four-wheel cornering stiffness estimation by selecting unmanned ground vehicle system variables, and discretizing; The method comprises the steps of designing a four-wheel cornering stiffness estimation method based on self-adaptive forgetting least square, and designing a self-adaptive forgetting least square cost function based on a self-adaptive filter so as to obtain a four-wheel independent cornering stiffness estimation law and realize the estimation of the t