A vehicle steering control method, device, medium and product

Abstract

The application discloses a vehicle steering control method, device, medium and product, and relates to the field of four-wheel steering vehicle dynamics control; the method comprises the following steps: acquiring running data of a target vehicle in a current sampling period; determining ideal reference data in the current sampling period according to a preset variable transmission ratio and a steering wheel steering angle in the current sampling period; determining a feedforward wheel steering angle in the current sampling period based on a feedforward control switching strategy; determining an equivalent cornering stiffness in the current sampling period based on a segmented affine tire model; determining a weight coefficient in the current sampling period according to the feedforward wheel steering angle in the current sampling period, the equivalent cornering stiffness in the current sampling period and a yaw rate error in the current sampling period by using an adaptive linear quadratic control algorithm, and then determining a wheel feedback control steering angle in the current sampling period, so as to perform steering feedback control on the target vehicle in a next sampling period; and the application can improve the control precision of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of four-wheel steering vehicle dynamics control, and in particular to a vehicle steering control method, device, medium, and product. Background Technology

[0002] With the deepening electrification and intelligentization of the automotive industry, steer-by-wire systems have become an inevitable development direction for future steering systems due to their enormous potential in improving steering characteristics, enhancing vehicle stability, and improving safety. Vehicle handling stability control based on steer-by-wire systems has also become a research hotspot for improving vehicle road safety among scholars both domestically and internationally. Compared to traditional front-wheel steering systems, four-wheel steering systems, through active steering control of the front and rear wheels, can effectively reduce the steady-state gain of lateral acceleration and yaw rate during steering, improving vehicle stability. Simultaneously, at low speeds, through counter-phase steering of the front and rear wheels, the minimum turning radius of the vehicle is effectively reduced, improving maneuverability and agility. Therefore, four-wheel steering control has become an important approach to improving vehicle handling stability.

[0003] Existing Option 1: such as Figure 1 As shown, a model-predictive-based stability control method for four-wheel steering vehicles is proposed. Steps: ① Calculate the ideal reference state and solve for the combined reference value β of the sideslip angle and yaw rate. ref γ ref ② Estimate the sideslip angle of the center of mass. Based on the principle of combining kinematic and dynamic estimation methods, the final estimated value β of the sideslip angle of the center of mass is obtained. est ; ③ A rear wheel steering angle controller based on model predictive control with variable weight coefficients was designed, which takes into account the nonlinear characteristics of the tire and incorporates rear wheel steering angle limit constraints, rear wheel steering angle increment constraints and center of gravity sideslip angle limit constraints.

[0004] Scheme 1 uses a model predictive control method based on variable weight coefficients to control four-wheel steering vehicles. However, it only designs the controller weight coefficients based on the nonlinear characteristics of the tires and does not consider the dynamic changes in the driver's control requirements and the vehicle's response hysteresis characteristics. This results in the vehicle's dynamic response lagging behind the driver's commands, reduced vehicle control accuracy, and poor vehicle stability under extreme conditions.

[0005] Existing Option 2: such as Figure 2 As shown, a steer-by-wire four-wheel steering vehicle stability control method is described. The steps are as follows: ① The acquired vehicle yaw rate signal and ideal yaw rate are used as input signals to the active disturbance rejection controller. With the goal of tracking the ideal yaw rate, the additional front wheel angle required to ensure yaw rate tracking is calculated and used to compensate for the front wheel angle. ② The acquired center-of-gravity sideslip angle deviation is used as input signal to the rear wheel angle controller. With the goal of zeroing the center-of-gravity sideslip angle, the rear wheel angle is calculated and output.

[0006] Scheme 2 uses an active disturbance rejection controller and a PID controller to control the front and rear wheel angles respectively. It does not consider the differences in the nonlinear characteristics of the tires under different working conditions, and also ignores the differences in the impact of the front and rear wheel angles on the vehicle's control performance. As a result, under extreme working conditions, the control model does not match the actual motion state of the vehicle, the vehicle control accuracy is low, and the ultimate performance of the vehicle is not fully explored. Summary of the Invention

[0007] The purpose of this invention is to provide a vehicle steering control method, device, medium, and product that can improve the control accuracy of vehicles.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A vehicle steering control method, the method comprising:

[0010] Acquire the target vehicle's operating data during the current sampling period; the operating data includes: steering wheel angle and yaw rate;

[0011] Based on the preset variable gear ratio and the steering wheel angle of the current sampling period, the ideal reference data for the current sampling period is determined; the ideal reference data includes: the desired front wheel steering angle, the ideal yaw rate, the reference center of gravity offset angle, and the reference yaw rate.

[0012] Based on the ideal reference data of the current sampling period, the feedforward wheel angle of the current sampling period is determined according to the feedforward control switching strategy; the feedforward wheel angle includes: feedforward front wheel angle and feedforward rear wheel angle; the feedforward control switching strategy includes: quasi-steady-state control, dynamic compensation control and swerving compensation control;

[0013] Based on the feedforward wheel angle and the yaw rate error of the current sampling period, the equivalent lateral stiffness of the current sampling period is determined using a segmented affine tire model. The segmented affine tire model is a simulation model constructed based on the nonlinear relationship between tire lateral force and tire slip angle. The tire lateral force is determined by the tire slip angle, vertical load, and road adhesion coefficient. The equivalent lateral stiffness includes front axle equivalent lateral stiffness and rear axle equivalent lateral stiffness. The yaw rate error is determined based on the yaw rate and the ideal yaw rate.

[0014] An adaptive linear quadratic control algorithm is adopted. Based on the feedforward wheel angle, the equivalent lateral stiffness, and the yaw rate error of the current sampling period, the weighting coefficients for the current sampling period are determined. The weighting coefficients include: control quantity weighting coefficients and state error weighting coefficients.

[0015] Based on the weighting coefficients and ideal reference data of the current sampling period, the wheel feedback control angle for the current sampling period is determined; the wheel feedback control angle for the current sampling period is used to perform steering feedback control on the target vehicle in the next sampling period.

[0016] Optionally, based on the preset variable gear ratio and the steering wheel angle of the current sampling period, the ideal reference data for the current sampling period is determined, specifically including:

[0017] Based on the steering wheel angle and the preset variable gear ratio of the current sampling period, determine the expected front wheel angle for the current sampling period;

[0018] Based on the expected front wheel steering angle of the current sampling period, and using the vehicle model with the center of mass sideslip angle as zero and the steady-state yaw rate under the zero center of mass sideslip angle as reference states, the reference center of mass sideslip angle and the ideal yaw rate of the current sampling period are determined; the vehicle model is a two-degree-of-freedom vehicle physical model constructed based on the front and rear wheel steering and lateral-yaw motion.

[0019] The reference yaw rate for the current sampling period is determined based on the road surface adhesion coefficient and the ideal yaw rate for the current sampling period.

[0020] Optionally, based on the ideal reference data of the current sampling period and the feedforward control switching strategy, the feedforward wheel angle of the current sampling period is determined, specifically including:

[0021] Based on the vehicle model, the steady-state feedforward wheel angle for the current sampling period is determined according to the expected front wheel angle for the current sampling period; the steady-state feedforward wheel angle includes: the front wheel steady-state feedforward wheel angle and the rear wheel steady-state feedforward wheel angle;

[0022] The rate of change of angular velocity corresponding to the reference yaw rate in the current sampling period is determined based on the expected front wheel steering angle in the current sampling period.

[0023] Based on the rate of change of angular velocity, the dynamic compensation wheel angle for the current sampling period is determined by dynamic feedforward control; the dynamic compensation wheel angle includes: the dynamic compensation wheel angle of the front wheel and the dynamic compensation wheel angle of the rear wheel;

[0024] The oblique steering principle and road surface adhesion limit relationship are adopted. The oblique steering compensation wheel angle for the current sampling period is determined according to the expected front wheel steering angle of the current sampling period. The oblique steering compensation wheel angle includes: the oblique steering compensation wheel angle of the front wheel and the oblique steering compensation wheel angle of the rear wheel.

[0025] The feedforward wheel angle for the current sampling period is determined based on the ideal yaw rate, the steady-state feedforward wheel angle, the dynamic compensation wheel angle, and the swerving steering compensation wheel angle.

[0026] Optionally, the preset expression for the variable transmission ratio is:

[0027]

[0028] Among them, i w The preset variable transmission ratio; For yaw rate gain; i fixed For a fixed transmission ratio; i max i is the maximum transmission ratio; min is the minimum gear ratio; m is the vehicle's curb weight; a is the distance from the vehicle's center of gravity to the front axle; b is the distance from the vehicle's center of gravity to the rear axle; L is the wheelbase between the front and rear axles; v x k represents the longitudinal velocity of the vehicle. f The ideal front axle lateral stiffness; χ is the transmission ratio weighting coefficient; v1 and v2 are the vehicle speeds at the two endpoints of the variable transmission ratio transition zone, respectively; i ωr It is based on a fixed yaw rate gain and variable transmission ratio.

[0029] Optionally, the feedforward wheel angle for the current sampling period is expressed as:

[0030]

[0031] Where, δ f1 The feedforward front wheel angle is the current sampling period; η1 is the hybrid weighting coefficient; δ f_dc For the current sampling period, the front wheel dynamic compensation wheel angle; δ f_oc For the front wheel oblique steering compensation wheel angle in the current sampling period; δ f_s δ represents the front wheel steady-state feedforward wheel angle during the current sampling period. r1 The feedforward rear wheel rotation angle for the current sampling period; δ r_dc The rear wheel dynamic compensation wheel angle for the current sampling period; δ r_oc The rear wheel angle for oblique steering compensation during the current sampling period; δ r_s ω is the steady-state feedforward wheel angle of the rear wheel during the current sampling period. r_tra ρ is the control boundary for yaw rate; ρ is the correction coefficient for control weight allocation; ω r_d is the ideal yaw rate for the current sampling period; abs() is the absolute value function.

[0032] Optionally, the expression for the vehicle model is:

[0033]

[0034] Where, k r β is the ideal rear axle sideslip stiffness; β is the sideslip angle of the center of mass; ω r I is the yaw rate of the vehicle; z δ is the yaw moment of inertia of the vehicle. f The desired front wheel steering angle; δ r The desired rear wheel steering angle; k f denoted as ideal front axle lateral stiffness; 'a' is the distance from the vehicle's center of gravity to the front axle; 'b' is the distance from the vehicle's center of gravity to the rear axle; 'm' is the vehicle's curb weight; 'v' is the distance from the vehicle's center of gravity to the rear axle. x The longitudinal speed of the vehicle; To find the first derivative with respect to the centroid sideslip angle β; For the yaw rate ω of the vehicle r The first derivative.

[0035] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the vehicle steering control method described above.

[0036] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle steering control method described above.

[0037] A computer program product includes a computer program that, when executed by a processor, implements the vehicle steering control method described above.

[0038] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0039] This invention discloses a vehicle steering control method, device, medium, and product. It establishes a hybrid feedforward control strategy integrating steady-state control, dynamic compensation, and yaw rate compensation, outputting feedforward control variables. Considering the influence of factors such as vertical load, road surface adhesion coefficient, and yaw rate tracking error, a linear quadratic feedback control (LQR) based on a segmented affine tire model is proposed. Based on the steady-state gain of the front and rear wheel steering angles on the vehicle's yaw rate, an adaptive adjustment mechanism for the LQR weight coefficients is designed, balancing the vehicle's dynamic response characteristics under normal operating conditions and its stability under extreme operating conditions. This improves the vehicle's control accuracy. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of the method framework corresponding to Solution 1 in the prior art;

[0042] Figure 2 This is a schematic diagram of the method framework corresponding to Scheme 2 in the prior art;

[0043] Figure 3 This is a flowchart illustrating the vehicle steering control method provided in an embodiment of the present invention.

[0044] Figure 4 This is a diagram of a four-wheel steering control architecture in practical applications provided by an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of a hybrid feedforward control switching strategy;

[0046] Figure 6 This is a simulation diagram corresponding to the segmented affine tire model;

[0047] Figure 7 This diagram illustrates the relationship between equivalent cornering stiffness and tire slip angle under different yaw rate errors. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] To address the challenges of response hysteresis and tire nonlinear control in steer-by-wire systems, this paper first establishes a variable gear ratio design method for four-wheel steering to improve driver workload and vehicle stability. Second, a hybrid feedforward control strategy integrating steady-state control, dynamic compensation, and yaw rate compensation is developed to enhance the vehicle's dynamic response to driver input. Furthermore, considering the effects of vertical load, road adhesion coefficient, and yaw rate tracking error, a linear quadratic feedback (LQR) controller based on a piecewise affine tire model is proposed to improve vehicle stability under extreme conditions. Finally, based on the steady-state gain of the front and rear wheel steering angles on the vehicle's yaw rate, an adaptive adjustment mechanism for the LQR weight coefficients is designed to ensure the applicability of the control strategy under different conditions, balancing the vehicle's dynamic response characteristics under normal conditions and its stability under extreme conditions.

[0050] The purpose of this invention is to provide a vehicle steering control method, device, medium, and product, which aims to improve the control accuracy of vehicles.

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Example 1

[0053] like Figure 3 As shown in this embodiment, a vehicle steering control method includes:

[0054] Step 100: Obtain the target vehicle's operating data for the current sampling period. Operating data includes: steering wheel angle and yaw rate.

[0055] Step 200: Determine the ideal reference data for the current sampling period based on the preset variable gear ratio and the steering wheel angle of the current sampling period. The ideal reference data includes: desired front wheel steering angle, ideal yaw rate, reference center of gravity offset angle, and reference yaw rate.

[0056] In one embodiment, the ideal reference data for the current sampling period is determined based on a preset variable gear ratio and the steering wheel angle of the current sampling period, specifically including:

[0057] Based on the steering wheel angle of the current sampling period and the preset variable gear ratio, determine the desired front wheel angle for the current sampling period.

[0058] Based on the expected front wheel steering angle of the current sampling period, and using the vehicle model with the center of mass sideslip angle as zero and the steady-state yaw rate under the zero center of mass sideslip angle as reference states, the reference center of mass sideslip angle and the ideal yaw rate of the current sampling period are determined. The vehicle model is a two-degree-of-freedom 2DOF vehicle physical model constructed based on the front and rear wheel steering and lateral-yaw motion.

[0059] The reference yaw rate for the current sampling period is determined based on the road surface adhesion coefficient and the ideal yaw rate for the current sampling period.

[0060] Specifically, the expression for the preset variable transmission ratio is:

[0061]

[0062] Among them, i w The preset variable transmission ratio; For yaw rate gain; i fixed For a fixed transmission ratio; i max i is the maximum transmission ratio; min is the minimum gear ratio; m is the vehicle's curb weight; a is the distance from the vehicle's center of gravity to the front axle; b is the distance from the vehicle's center of gravity to the rear axle; L is the wheelbase between the front and rear axles; v x k represents the longitudinal velocity of the vehicle. f The ideal front axle lateral stiffness; χ is the transmission ratio weighting coefficient; v1 and v2 are the vehicle speeds at the two endpoints of the variable transmission ratio transition zone, respectively; i ωr It is based on a fixed yaw rate gain and variable transmission ratio.

[0063] The expression for the vehicle model is:

[0064]

[0065] Where, k r β is the ideal rear axle sideslip stiffness; β is the sideslip angle of the center of mass; ω r I is the yaw rate of the vehicle; z δ is the yaw moment of inertia of the vehicle. f The desired front wheel steering angle; δ r The desired rear wheel steering angle; k f denoted as ideal front axle lateral stiffness; 'a' is the distance from the vehicle's center of gravity to the front axle; 'b' is the distance from the vehicle's center of gravity to the rear axle; 'm' is the vehicle's curb weight; 'v' is the distance from the vehicle's center of gravity to the rear axle. x The longitudinal speed of the vehicle; To find the first derivative with respect to the centroid sideslip angle β; For the yaw rate ω of the vehicle r The first derivative.

[0066] Step 300: Based on the ideal reference data of the current sampling period, determine the feedforward wheel angle for the current sampling period according to the feedforward control switching strategy. The feedforward wheel angle includes: feedforward front wheel angle and feedforward rear wheel angle; the feedforward control switching strategy includes: quasi-steady-state control, dynamic compensation control, and swerving steering compensation control.

[0067] As an optional implementation, based on ideal reference data for the current sampling period and a feedforward control switching strategy, the feedforward wheel angle for the current sampling period is determined, specifically including:

[0068] Based on the vehicle model, the steady-state feedforward wheel angle for the current sampling period is determined according to the expected front wheel angle for the current sampling period; the steady-state feedforward wheel angle includes the front wheel steady-state feedforward wheel angle and the rear wheel steady-state feedforward wheel angle.

[0069] The rate of change of angular velocity corresponding to the reference yaw rate in the current sampling period is determined based on the expected front wheel steering angle in the current sampling period.

[0070] Based on the rate of change of angular velocity, the dynamic compensation wheel angle for the current sampling period is determined by dynamic feedforward control; the dynamic compensation wheel angle includes: the dynamic compensation wheel angle of the front wheel and the dynamic compensation wheel angle of the rear wheel.

[0071] Based on the principle of oblique steering and the road surface adhesion limit relationship, the oblique steering compensation wheel angle for the current sampling period is determined according to the expected front wheel steering angle of the current sampling period. The oblique steering compensation wheel angle includes: the oblique steering compensation wheel angle of the front wheels and the oblique steering compensation wheel angle of the rear wheels.

[0072] The feedforward wheel angle for the current sampling period is determined based on the ideal yaw rate, the steady-state feedforward wheel angle, the dynamic compensation wheel angle, and the swerving steering compensation wheel angle.

[0073] Specifically, the feedforward wheel angle for the current sampling period is expressed as:

[0074]

[0075] Where, δ f1 The feedforward front wheel angle is the current sampling period; η1 is the hybrid weighting coefficient; δ f_dc For the current sampling period, the front wheel dynamic compensation wheel angle; δ f_oc For the front wheel oblique steering compensation wheel angle in the current sampling period; δ f_s δ represents the front wheel steady-state feedforward wheel angle during the current sampling period. r1 The feedforward rear wheel rotation angle for the current sampling period; δ r_dc The rear wheel dynamic compensation wheel angle for the current sampling period; δr_oc The rear wheel angle for oblique steering compensation during the current sampling period; δ r_s ω is the steady-state feedforward wheel angle of the rear wheel during the current sampling period. r_tra ρ is the control boundary for yaw rate; ρ is the correction coefficient for control weight allocation; ω r_d This represents the ideal yaw rate for the current sampling period. abs() represents the absolute value function.

[0076] Step 400: Based on the feedforward wheel angle and the yaw rate error of the current sampling period, determine the equivalent lateral stiffness of the current sampling period using a segmented affine tire model. The segmented affine tire model is a simulation model constructed based on the nonlinear relationship between tire lateral force and tire slip angle; the tire lateral force is determined by the tire slip angle, vertical load, and road adhesion coefficient; the equivalent lateral stiffness includes: front axle equivalent lateral stiffness and rear axle equivalent lateral stiffness. The yaw rate error is determined based on the yaw rate and the ideal yaw rate.

[0077] Step 500: Employ an adaptive linear quadratic control algorithm to determine the weighting coefficients for the current sampling period based on the feedforward wheel angle, the equivalent lateral stiffness, and the yaw rate error. The weighting coefficients include: control quantity weighting coefficients and state error weighting coefficients.

[0078] Step 600: Determine the wheel feedback control angle for the current sampling period based on the weighting coefficients and the ideal reference data for the current sampling period. The wheel feedback control angle for the current sampling period is used to provide steering feedback control for the target vehicle in the next sampling period.

[0079] In practical applications, this invention uses the driver's steering wheel angle as input and the wheel angles of the front and rear wheels as outputs to develop a four-wheel steering vehicle steering control method that takes into account system time delay. Figure 4 As shown, firstly, considering the influence of front and rear wheel angles on vehicle steering characteristics, a variable transmission ratio design method for four-wheel steering is established. Based on this, considering reference yaw rate and road adhesion constraints, a hybrid feedforward control method combining steady-state control, dynamic compensation, and yaw rate compensation is established. Considering the influence of vertical load, road adhesion coefficient, and yaw rate tracking error, a linear quadratic feedback (LQR) controller based on a piecewise affine tire model is proposed. Then, based on the steady-state gain of front and rear wheel steering angles on vehicle yaw rate, an adaptive adjustment mechanism for the LQR weight coefficients is designed to ensure the applicability of the control strategy under different operating conditions. The specific operation process is as follows:

[0080] 1.1 Variable transmission ratio setting and ideal state calculation.

[0081] (1) Variable rotation ratio.

[0082] Variable steering ratio improves vehicle performance by changing the ratio of steering wheel angle to front wheel angle. This invention, based on existing variable steering ratio design methods for front-wheel steering vehicles, develops a variable steering ratio design method for 4WS vehicles to improve handling and stability. The specific variable gear ratio settings are as follows:

[0083]

[0084] In the formula, This represents the yaw rate gain, set to 0.24s. -1 i fixed This represents a fixed transmission ratio, set to 21.5; i max and i min This represents the maximum and minimum transmission ratios at 40km / h and 90km / h, set to 18.5 and 21.5 respectively, with χ being the transmission ratio weighting coefficient; v1 and v2 are the two endpoint vehicle speeds of the third segment (transition zone) of the variable transmission ratio expression, set to 40km / h and 90km / h respectively, and i ωr It is based on a fixed yaw rate gain and variable transmission ratio.

[0085] The variable gear ratio takes the driver's steering wheel angle as input and the front wheel angle as output. The specific calculation formula is as follows:

[0086] δ f =δ w / i w (3)

[0087] In the formula, δ f For the desired front wheel steering angle, δ w The steering wheel angle input by the driver.

[0088] (2) Ideal reference model.

[0089] Using the desired front wheel steering output from the variable gear ratio module as input, the ideal yaw rate and center-of-gravity sideslip angle of the vehicle are calculated based on the reference model, providing reference variables for hybrid feedforward control and adaptive LQR feedback control. Based on the 2DOF vehicle model with front and rear wheel steering, the reference states are defined as zero center-of-gravity sideslip angle and the steady-state yaw rate under zero center-of-gravity sideslip angle.

[0090]

[0091] In the formula, β ref For reference centroid sideslip angle, ω r_d Let m be the ideal yaw rate, a and b be the vehicle's curb weight, a and b be the distances from the vehicle's center of gravity to the front and rear axles respectively, L be the wheelbase between the front and rear axles, and v be the yaw rate. xIt is the longitudinal speed of the vehicle, k f Ideal front axle lateral stiffness.

[0092] Taking into account the influence of road surface adhesion, the reference yaw rate is set as follows:

[0093]

[0094] 1.2 Hybrid feedforward control.

[0095] Considering the limitations of the vehicle's desired yaw rate and road adhesion coefficient, a hybrid feedforward control strategy is proposed, combining steady-state control, dynamic compensation, and yaw steering compensation. To improve vehicle stability and dynamic response speed, a hybrid feedforward control switching strategy is proposed, eliminating the adverse effects of frequent switching between dynamic compensation and yaw steering compensation on vehicle motion. Using the ideal yaw rate and desired front wheel steering rate as inputs, the outputs are feedforward front wheel steering angles and feedforward rear wheel steering angles. It comprises four modules: quasi-steady-state control, dynamic compensation, yaw steering compensation, and the hybrid feedforward control switching strategy. Among these, quasi-steady-state control is always active, while dynamic compensation and yaw steering compensation work collaboratively according to the hybrid feedforward control switching strategy, ultimately outputting the feedforward wheel steering angles (feedforward front wheel steering angle and feedforward rear wheel steering angle) to the adaptive LQR control module and the vehicle steering system.

[0096] (1) Two-degree-of-freedom vehicle model.

[0097] This section forms the model foundation for quasi-steady-state control and LQR control, providing theoretical models for the construction of quasi-steady-state control and adaptive LQR control methods. A two-degree-of-freedom vehicle model is used to describe the lateral and yaw motions of the vehicle, which can be expressed by the following equation:

[0098]

[0099] Where, k r This represents the ideal rear axle sideslip stiffness, where β represents the sideslip angle at the center of mass, and ω represents the ideal rear axle sideslip stiffness. r It is the yaw rate of the vehicle, I z It is the yaw moment of inertia of the vehicle, δ f δ r This refers to the wheel angle between the front and rear axles.

[0100] Then, it is transformed into a state space form to meet the controller design requirements.

[0101]

[0102]

[0103] (2) Quasi-steady-state control.

[0104] Using the ideal vehicle state as input, the output is the steady-state feedforward wheel angle. The purpose of steady-state control is to calculate the feedforward control quantity based on the desired vehicle state. From the vehicle reference model, it can be seen that when the vehicle is in the ideal steady-state condition, i.e.:

[0105] At that time, there exists a control variable u s =[δ f_s ,δ r_s ] T Satisfying the formula:

[0106]

[0107] Using the front wheel angle input as the front wheel angle for steady-state control, the steady-state feedforward control is derived as follows:

[0108]

[0109] (3) Dynamic compensation.

[0110] Using the desired wheel angular velocity as input, the output is a feedforward dynamically compensated wheel angular velocity. To improve the tracking accuracy of the vehicle's time-varying reference state, dynamic compensation control is introduced to improve the vehicle's response speed. Using the front wheel angular velocity as input, the desired yaw rate of change is:

[0111]

[0112] Introducing dynamic feedforward control u f =[δ ff ,δ rf ], to satisfy

[0113]

[0114] In the formula, Let β represent the ideal vehicle state variable. d It is the ideal centroid sideslip angle, and β d It is always zero.

[0115] Based on the desired rate of change of yaw rate, the dynamic compensation of the front and rear wheels is calculated as follows:

[0116]

[0117] (4) Diagonal steering compensation.

[0118] Taking the desired front wheel steering angle as input, the output is the wheel steering angle for feedforward oblique steering compensation. As shown in the reference model, under the premise of zero centroid sideslip angle, the desired yaw rate is determined by the front wheel steering angle. When the desired yaw rate exceeds the maximum allowable value for road surface adhesion, the desired yaw rate will remain unchanged. At this point, if the front wheel steering angle continues to increase, the actual yaw rate will increase, leading to a larger error in the vehicle's yaw rate, which can easily cause vehicle instability under extreme conditions. To address this problem, this invention utilizes the oblique steering principle to compensate for the portion of the front wheel steering angle exceeding the road surface adhesion limit through oblique steering, thereby improving the vehicle's yaw stability.

[0119] Considering the road adhesion limit, the maximum wheel steering angle is:

[0120]

[0121] In oblique steering compensation mode, the front and rear wheel steering angles beyond the road adhesion limit are calculated using the distance from the vehicle's center of gravity to the front and rear axles:

[0122]

[0123] (5) Hybrid feedforward control switching strategy.

[0124] Using ideal yaw rate, steady-state feedforward wheel angle, dynamic compensation wheel angle, and oblique steering compensation wheel angle as inputs, the feedforward wheel angle is output to the adaptive LQR control module and the vehicle steering system by calculating the control weights of dynamic compensation and oblique steering compensation. In feedforward control, steady-state control mainly responds to driver control input and is always in operation; dynamic compensation control compensates for dynamic changes in the vehicle's desired motion state and mainly operates in the vehicle's stable region; oblique steering compensation control aims to improve vehicle stability and mainly works when the vehicle's motion state exceeds the road adhesion limit. To fully utilize the functions of each feedforward control module, this invention designs a hybrid feedforward control method applicable to different operating conditions. Its main principle is based on the vehicle's desired yaw rate, designing a hybrid weight coefficient for dynamic compensation and oblique steering compensation, and then outputting the feedforward wheel angle according to the hybrid weight coefficient. The specific weight calculation formula is as follows:

[0125]

[0126] In the formula, ω r_tra This represents the yaw rate control boundary, and ρ is the correction coefficient for the control weight allocation.

[0127] The formula for calculating the feedforward wheel angle is:

[0128]

[0129] When a vehicle is near its desired yaw rate limit, its motion becomes more sensitive to the driver's maneuvers. For example... Figure 5 As shown, when the steering wheel angle and the angular velocity have the same sign... As the steering wheel angle continues to increase, the vehicle is near the stability boundary, requiring higher stability control. When the steering wheel angle and angular velocity have opposite signs... As the steering wheel angle decreases, the vehicle's stability requirements diminish, while the demand for dynamic response characteristics increases. Therefore, designing a reasonable yaw rate control boundary requires careful consideration of different driving needs.

[0130] Based on the above analysis, a hybrid feedforward control switching strategy that considers different driving needs was developed. At this time, the controller needs to enter the transition zone in advance within the dynamic compensation zone to improve vehicle stability; when To improve the vehicle's response speed to yaw rate, a transition zone is set in the oblique steering compensation mode to enhance the vehicle's dynamic response characteristics. To achieve the best control effect, simulation tests show that the yaw rate control boundary in the hybrid feedforward control strategy is set as follows:

[0131]

[0132] In addition, to avoid the impact of sudden changes in steering wheel angle and its angular velocity, as well as frequent switching between the two hybrid control strategies, on the overall vehicle handling stability, the following rules are set:

[0133] 1) When the hybrid weight coefficient is not 0 / 1, dynamic compensation and yaw steering compensation work simultaneously. To avoid sudden changes in tire angle due to the switching of the hybrid feedforward control strategy, the hybrid control strategy should remain unchanged.

[0134] 2) When the hybrid weight coefficient is 0 / 1, in order to avoid frequent switching and false triggering of the hybrid feedforward control strategy, the controller is set to execute the control switching command after the hybrid feedforward control strategy switching conditions are met for n consecutive sampling periods. In this invention, n is set to 5.

[0135] 1.3 Adaptive LQR feedback control.

[0136] The system takes the feedforward wheel steering angle, yaw rate tracking error, and center of gravity sideslip angle tracking error as inputs and outputs the feedback wheel steering angle. The PWA tire model construction and equivalent sideslip stiffness provide the equivalent sideslip stiffness for the adaptive LQR control algorithm, while the construction and solution of the vehicle control model provide the model foundation for the adaptive LQR feedback controller. The LQR feedback controller calculates and outputs the feedback wheel steering angle.

[0137] (1) PWA tire model construction.

[0138] like Figure 6 As shown, this paper introduces a method for constructing a segmented affine tire model, providing a basic tire model for calculating equivalent lateral stiffness. The nonlinear relationship between tire lateral force and tire slip angle is a core challenge for achieving precise control. To obtain a high-precision, low-computational-cost tire model, this invention establishes a segmented affine tire model based on 225 / 60R18 tire model data. Within each operating condition range, the relationship between tire lateral force and slip angle is fitted using a straight line segment:

[0139]

[0140] Where j∈{1,2,3,4,5,6,7,8} refers to different operating condition intervals. k j F represents the lateral stiffness in different lateral slip angle ranges. 0j The fitting parameters of the affine tire model are represented. These represent two endpoints that indicate different operating conditions.

[0141] To obtain an accurate affine tire model, the optimization objective is to minimize the root mean square of the fitting error. The optimization solution is applied to each interval range, ultimately achieving the model under standard operating conditions (μ = 1F). z The range of tires with a strength of 5884N and their corresponding lateral stiffness are shown in Table 1.

[0142] Table 1. Parameters of the Tire Affine Model

[0143]

[0144] Considering that tire lateral force is affected by vertical load and road adhesion coefficient, firstly, based on the peak coefficient calculation formula in the Magic Tire formula, the vertical load influence factor is set as follows:

[0145]

[0146] Where a0 and a1 are the fitting parameters for the vertical load, by fitting the tire lateral force data under different vertical loads, we obtain a0 = -9.75a1 = 1055, F zn Let n = fl, fr, rl, rr, representing the vertical load on the four tires. The formula for calculating the vertical load on the tires, considering both longitudinal and lateral acceleration, is:

[0147]

[0148] Among them, a x ,a y ,h g These represent the vehicle's longitudinal acceleration, lateral acceleration, and center of gravity height, respectively, with B representing the wheelbase.

[0149] Based on this, considering the influence of the road surface adhesion coefficient μ on the tire lateral force, the final tire lateral force formula is as follows:

[0150] F y =μη2(k j α j +F 0j ),j=1,2,3,4,5,6,7,8 (22)

[0151] (2) Calculation of equivalent lateral stiffness.

[0152] Using wheel steering angle as input and the equivalent lateral stiffness of the front and rear axles as output, a time-varying equivalent lateral stiffness is provided to the adaptive LQR controller. During vehicle operation, the vertical load and lateral angle of the tires cause changes in tire lateral stiffness. Especially when the tire is in the nonlinear / saturation region, there is a significant difference between the traditional fixed lateral stiffness and the actual tire lateral stiffness, leading to a mismatch between the control model and the actual vehicle response, resulting in reduced control accuracy and potentially causing vehicle instability. To address this issue, the lateral stiffness within different segment intervals is extracted based on a piecewise affine tire model. Furthermore, considering the influence of tire lateral angle and yaw rate errors on the equivalent lateral stiffness, and combining the properties of the arctangent function, the piecewise affine tire model is corrected. The specific correction process is as follows:

[0153] Step 1: Using the feedforward wheel rotation angle as the output, calculate the tire slip angle, and calculate the equivalent slip stiffness of a single wheel based on the tire vertical load and adhesion coefficient; the tire slip angles of the front and rear wheels are calculated as follows:

[0154]

[0155] Step 2: Determine the fitting direction: when e ωr When α > 0, the vehicle tends to oversteer, requiring a negative yaw moment. The front wheels generate a negative tire force. If α i >0, the absolute value of the tire slip angle increases, and the tire fits towards the direction of lower slip stiffness. If α i <0, the absolute value of the tire slip angle decreases, and the tire is fitted towards the direction of greater lateral stiffness. The specific fitting results are as follows: Figure 7 As shown.

[0156] Step 3: Fitting: Calculate the tire slip angle α i The common endpoint α of the two segments to be fitted j The difference Δα ij .

[0157] Δα ij =α i -α j (twenty four)

[0158] Step 4: To obtain a reasonable fitting function, combining the properties of the arctangent function and the influence of yaw rate error on lateral stiffness, the fitting weight coefficients for the interval are set based on the distance of the point from the endpoint and the yaw rate error:

[0159]

[0160] In the formula, λ is the scaling factor, set to 30, and α0 is the side deflection angle weighting factor, set to 2.

[0161] Step 5: Output the fitted equivalent lateral stiffness.

[0162] k i =η3k b1 +(1-η3)k b2 (26)

[0163] When a vehicle is turning, the lateral stiffness of the left and right wheels is inconsistent. This invention employs an independent front and rear axle steering system, outputting only the front and rear wheel steering angles. Therefore, this invention utilizes piecewise linear functions of the left and right tires, linearly adding them together to obtain the equivalent lateral stiffness of the front and rear axles.

[0164]

[0165] (3) Construction and solution of vehicle control model.

[0166] This section is part of adaptive LQR control, providing a model foundation for building the adaptive LQR control algorithm (i.e., constructing matrices A and B in the formula). To further reduce the impact of system hysteresis response on vehicle control, communication delay and the response characteristics of the steering actuator are integrated into LQR feedback control to improve the vehicle's tracking accuracy of yaw rate.

[0167] Treating the response hysteresis of the steering mechanism as a first-order response system:

[0168]

[0169] In the formula, δ is the time constant. t =[δ ft ,δ rt ] represents the actual wheel rotation angle, δ l =[δ fl ,δ rl ] T This is the input for steering control.

[0170] In feedback control, yaw rate error and sideslip angle are used as system state variables. Based on this, a vehicle control model incorporating steering mechanism response and communication delay is established by adding these factors to the vehicle control model.

[0171]

[0172] e0 = [β - β] d ,ω r -ω r_d ] T (30)

[0173] In the formula, e0 represents the vehicle state response error, δ l =[δ fl ,δ rl ] T To execute the control signals received by the system.

[0174] make Simplify the original expression to:

[0175]

[0176] Using the Euler method, the vehicle model is discretized with a sampling period of T to obtain the discretized vehicle model as follows:

[0177]

[0178] In the formula,

[0179] Based on this, the signal transmission delay in the system is incorporated into the control model. This invention does not consider the effects of communication-induced delay, packet loss, etc., and sets the signal transmission delay to one sampling period. Therefore, the vehicle control model becomes:

[0180]

[0181] At this time, The vehicle control model then simplifies to:

[0182] e(k+1)=Ae(k)+Bδ b (k) (34)

[0183] (4) Construction of the adaptive LQR controller.

[0184] The system takes steering wheel angular velocity, yaw rate tracking error, center of gravity sideslip angle tracking error, front wheel steering angle from the previous sampling period, and rear wheel steering angle from the previous sampling period as inputs, and uses feedback control of wheel steering angles (front wheel steering angle and rear wheel steering angle) as outputs. First, the system state error is calculated based on the difference between the ideal state and the actual vehicle state. Then, the system state error e and control increment u are used as outputs. b The cost function for constructing LQR includes system state errors such as yaw rate error and centroid sideslip angle error, which are the aforementioned yaw rate tracking error and centroid sideslip angle tracking error; the cost function is:

[0185]

[0186] Where Q and R represent the controller’s sensitivity to system state error and changes in control quantity, respectively.

[0187] Based on the above equation, the state feedback control law is set as follows:

[0188] δ b (k)=-Ke(k) (36)

[0189] Wherein, the optimal control matrix K is

[0190] K = (R + B) T PB) -1 B T PA (37)

[0191] P can be obtained by iteratively solving the Riccati equation:

[0192] P k-1 =Q+A T P k (I+BR -1 B T P k ) -1 A (38)

[0193] The weighting coefficients in the cost function are key factors affecting vehicle control performance. To ensure that the proposed algorithm has good control performance under all operating conditions, adaptive weighting is performed on the control weighting coefficients and the state error weighting coefficients.

[0194] 1) Adaptive adjustment of the control quantity weight coefficient.

[0195] This section is part of adaptive LQR, and it outputs the control quantity weighting coefficient R based on the steering wheel angle and yaw rate errors. Based on a two-degree-of-freedom vehicle model, the gain of yaw rate on the front and rear wheel steering angles is obtained:

[0196]

[0197] As shown in the above equation, the magnitudes of the yaw rate gains from the front and rear wheel steering angles are equal, but their directions are opposite. This invention fully considers the vehicle's control requirements and the difference in yaw rate gains between the front and rear axle wheels, aiming to reduce the absolute value of the wheel steering angles. It establishes front and rear wheel control weights, and the specific control strategy is as follows:

[0198] When the steering wheel angle and angular velocity have the same sign, the actual yaw rate lags behind the desired yaw rate, resulting in understeer. Based on the gain of yaw rate from the front and rear wheel angles, it can be seen that increasing the front wheel angle or decreasing the rear wheel angle can increase the vehicle's yaw rate response. However, increasing the front wheel angle will accelerate the tires into the nonlinear / saturation region, reducing tire force margin and affecting vehicle stability. Therefore, rear wheel angle control should be prioritized. Similarly, when the steering wheel angle and angular velocity have opposite signs, front wheel angle control should be prioritized. The final adjustment rules for the control weight coefficients of the front and rear wheels are as follows:

[0199] η1=(tanh(e ωr δ sw / 2)+1) / 2 (40)

[0200]

[0201] In the formula, R0 represents the preset total weight, R0 = 800, e ωr δ represents the yaw rate error. sw Indicates the steering wheel angle.

[0202] To avoid excessive differences in the control variables for the front and rear wheel steering angles, the weighting coefficients are set to be limited to the range of [0.3, 0.7].

[0203] 2) Adaptive adjustment of state error weighting coefficients.

[0204] This section is part of adaptive LQR, outputting the state error coefficient Q based on vehicle speed and road adhesion coefficient. The state error weighting coefficient controls the deviation of the vehicle's state from the reference state and is significantly affected by vehicle speed and road adhesion coefficient. Based on the traditional adaptive adjustment method, a road adhesion coefficient correction is added to obtain the state error weighting coefficient:

[0205]

[0206] Four-wheel steering control is an important way to improve vehicle handling stability. By properly controlling the steering angles of the front and rear wheels, the dynamic response speed and driving stability of the vehicle can be effectively improved. However, existing research methods have not fully considered the adverse effects of system response hysteresis and tire nonlinearity on the accuracy of vehicle dynamics control, resulting in problems such as low vehicle control accuracy and poor driving stability.

[0207] To address the challenges of dynamic control technology for four-wheel steering vehicles, this invention uses the desired yaw rate as the observed variable for mode switching. Combining road adhesion limitations and driving requirements under different operating conditions, a hybrid feedforward control switching strategy is proposed, considering different driving needs, to achieve smooth switching between dynamic compensation and oblique steering compensation. Considering the influence of tire vertical load and road adhesion coefficient on tire lateral force, a segmented affine tire model is constructed. Based on the tracking error of tire slip angle and yaw rate, a method for calculating equivalent tire slip stiffness is developed to improve the model's control accuracy. Based on the steady-state gain of the front and rear wheel steering angles on the vehicle's yaw rate, an adaptive adjustment mechanism for weighting coefficients is designed to ensure the applicability of the control strategy under different operating conditions.

[0208] Example 2

[0209] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle steering control method of Embodiment 1.

[0210] Example 3

[0211] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the vehicle steering control method of Embodiment 1.

[0212] Example 4

[0213] A computer program product includes a computer program that, when executed by a processor, implements the vehicle steering control method of Embodiment 1.

[0214] Example 5

[0215] A computer device, which may be a database, includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores pending transactions. The I / O interfaces facilitate information exchange between the processor and external devices. The communication interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements the vehicle steering control method described in Embodiment 1.

[0216] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0217] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided by this invention may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided by this invention may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0218] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0219] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A vehicle steering control method, characterized in that, The method includes: Acquire the target vehicle's operating data during the current sampling period; the operating data includes: steering wheel angle and yaw rate; Based on the preset variable gear ratio and the steering wheel angle of the current sampling period, the ideal reference data for the current sampling period is determined; the ideal reference data includes: the desired front wheel steering angle, the ideal yaw rate, the reference center of gravity offset angle, and the reference yaw rate. Based on the ideal reference data of the current sampling period, the feedforward wheel angle of the current sampling period is determined according to the feedforward control switching strategy; the feedforward wheel angle includes: feedforward front wheel angle and feedforward rear wheel angle; the feedforward control switching strategy includes: quasi-steady-state control, dynamic compensation control and swerving compensation control; Based on the feedforward wheel angle and the yaw rate error of the current sampling period, the equivalent lateral stiffness of the current sampling period is determined using a segmented affine tire model. The segmented affine tire model is a simulation model constructed based on the nonlinear relationship between tire lateral force and tire slip angle. The tire lateral force is determined by the tire slip angle, vertical load, and road adhesion coefficient. The equivalent lateral stiffness includes front axle equivalent lateral stiffness and rear axle equivalent lateral stiffness. The yaw rate error is determined based on the yaw rate and the ideal yaw rate. An adaptive linear quadratic control algorithm is adopted. Based on the feedforward wheel angle, the equivalent lateral stiffness, and the yaw rate error of the current sampling period, the weighting coefficients for the current sampling period are determined. The weighting coefficients include: control quantity weighting coefficients and state error weighting coefficients. Based on the weighting coefficients and ideal reference data of the current sampling period, the wheel feedback control angle of the current sampling period is determined; the wheel feedback control angle of the current sampling period is used to perform steering feedback control on the target vehicle in the next sampling period. Based on the ideal reference data of the current sampling period, and using the feedforward control switching strategy, the feedforward wheel angle for the current sampling period is determined, specifically including: Based on the vehicle model, the steady-state feedforward wheel angle for the current sampling period is determined according to the expected front wheel angle for the current sampling period; the steady-state feedforward wheel angle includes: the front wheel steady-state feedforward wheel angle and the rear wheel steady-state feedforward wheel angle; The rate of change of angular velocity corresponding to the reference yaw rate in the current sampling period is determined based on the expected front wheel steering angle in the current sampling period. Based on the rate of change of angular velocity, the dynamic compensation wheel angle for the current sampling period is determined by dynamic feedforward control; the dynamic compensation wheel angle includes: the dynamic compensation wheel angle of the front wheel and the dynamic compensation wheel angle of the rear wheel; The oblique steering principle and road surface adhesion limit relationship are adopted. The oblique steering compensation wheel angle for the current sampling period is determined according to the expected front wheel steering angle of the current sampling period. The oblique steering compensation wheel angle includes: the oblique steering compensation wheel angle of the front wheel and the oblique steering compensation wheel angle of the rear wheel. The feedforward wheel angle for the current sampling period is determined based on the ideal yaw rate, the steady-state feedforward wheel angle, the dynamic compensation wheel angle, and the swerving steering compensation wheel angle for the current sampling period. The feedforward wheel angle for the current sampling period is expressed as: ； ； in, The feedforward front wheel angle for the current sampling period; These are mixed weighting coefficients; For the current sampling period, the front wheel dynamic compensation wheel angle; For the current sampling period, the front wheel angle is used to compensate for oblique steering. The front wheel steady-state feedforward wheel angle for the current sampling period; The feedforward rear wheel rotation angle for the current sampling period; The rear wheel dynamic compensation wheel angle for the current sampling period; The wheel angle for rear wheel oblique steering compensation in the current sampling period; The steady-state feedforward wheel angle of the rear wheel during the current sampling period; As the yaw rate control boundary; Correction coefficients for controlling weight allocation; is the ideal yaw rate for the current sampling period; abs() is the absolute value function.

2. The vehicle steering control method according to claim 1, characterized in that, Based on the preset variable gear ratio and the steering wheel angle of the current sampling period, the ideal reference data for the current sampling period is determined, specifically including: Based on the steering wheel angle and the preset variable gear ratio of the current sampling period, determine the expected front wheel angle for the current sampling period; Based on the expected front wheel steering angle of the current sampling period, and using the vehicle model with the center of mass sideslip angle as zero and the steady-state yaw rate under the zero center of mass sideslip angle as reference states, the reference center of mass sideslip angle and the ideal yaw rate of the current sampling period are determined; the vehicle model is a two-degree-of-freedom vehicle physical model constructed based on the front and rear wheel steering and lateral-yaw motion. The reference yaw rate for the current sampling period is determined based on the road surface adhesion coefficient and the ideal yaw rate for the current sampling period.

3. The vehicle steering control method according to claim 1, characterized in that, The preset expression for the variable transmission ratio is: ； in, The preset variable transmission ratio; Gain for yaw rate; For a fixed transmission ratio; This is the maximum transmission ratio; Minimum transmission ratio; For vehicle curb weight; This is the distance from the vehicle's center of gravity to the front axle. This is the distance from the vehicle's center of gravity to the rear axle. This refers to the wheelbase between the front and rear axles; The longitudinal speed of the vehicle; Ideal front axle lateral stiffness; This is the transmission ratio weighting coefficient; and These are the vehicle speed values ​​at the two endpoints of the variable gear ratio transition zone. It is based on a fixed yaw rate gain and variable transmission ratio.

4. The vehicle steering control method according to claim 2, characterized in that, The expression for the vehicle model is: ； in, Ideal rear axle lateral stiffness; It is the centroid sideslip angle; The yaw rate of the vehicle; Let yaw moment be the vehicle's moment of inertia; The desired front wheel steering angle; The desired rear wheel steering angle; Ideal front axle lateral stiffness; This is the distance from the vehicle's center of gravity to the front axle. This is the distance from the vehicle's center of gravity to the rear axle. For vehicle curb weight; The longitudinal speed of the vehicle; For the centroid side slip angle The first derivative; For the yaw rate of the vehicle The first derivative.

5. A computer device, comprising: The memory and processor contain a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the vehicle steering control method according to any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vehicle steering control method according to any one of claims 1-4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle steering control method according to any one of claims 1-4.

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