A method and control system for all-wheel steering of a vehicle

Abstract

This invention provides a method and control system for all-wheel steering of multi-axle vehicles, addressing the technical problem of poor performance in existing all-wheel steering control methods. The method includes: forming a dynamic model of the vehicle's all-wheel steering system, an ideal reference model of the vehicle's center of mass motion during high-speed and low-speed steering, and error dynamic models of the vehicle's lateral and yaw motions; constructing lateral sliding mode control laws and yaw sliding mode control laws based on the error dynamic models; and decoupling the lateral and yaw sliding mode control laws to each axle to form the wheel angles of each axle. This invention divides the virtual lateral control force and virtual yaw control torque of the steering system into a nominal control law that compensates for a disturbance-free system and a robust control law that overcomes uncertain disturbances. It utilizes the robust sliding mode control law to address numerous nonlinear and uncertain factors in the steering system, effectively suppressing system chattering; it exhibits finite-time convergence characteristics, effectively improving the steering maneuverability and stability of multi-axle vehicles.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, specifically to a method and control system for all-wheel steering of multi-axle vehicles. Background Technology

[0002] Multi-axle vehicles offer greater load-bearing capacity and higher transportation efficiency compared to common two-axle vehicles, making them highly valuable in logistics, engineering operations, and special-purpose transport. However, due to their larger number of axles, longer body, and higher center of gravity, multi-axle vehicles suffer from a larger turning radius and poorer maneuverability at low speeds; at high speeds, they exhibit poor stability and are prone to fishtailing, increasing the probability of collisions. All-wheel steering technology is an effective means of improving the steering agility and handling stability of multi-axle vehicles. Its control strategy is crucial to the all-wheel steering system, and different control methods will significantly impact the vehicle's steering performance.

[0003] Currently, common all-wheel steering control methods employ zero-centimeter sideslip angle proportional control. However, this method is based on a linear deterministic model of the steering system, making it highly dependent on the model and unable to handle nonlinearities and uncertainties in the system. These include coupling nonlinearities between the steering and suspension systems, tire nonlinearities, vehicle parameter uncertainties, unmodeled system dynamics, and external disturbances, resulting in unsatisfactory control performance. To address system nonlinearity, some scholars have proposed sliding mode control. While this method offers some capability in handling nonlinear dynamics and uncertainties, the controller design typically uses a sign function to approximate the sliding surface. Discontinuous switching of the control law can easily induce system jitter, thus worsening the control performance. Furthermore, in terms of convergence speed, most current all-wheel steering controls prioritize asymptotic stability of the steering system and cannot quickly reach a stable state within a finite time. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a multi-axle vehicle all-wheel steering control method and control system to solve the technical problem of poor performance of existing all-wheel steering control.

[0005] The vehicle all-wheel steering control method of this invention includes:

[0006] A dynamic model of the all-wheel steering system of a multi-axle vehicle is established, taking into account the nonlinearity and uncertainties of the all-wheel steering system.

[0007] Based on the dynamic model of the steering system and the steering characteristics, an ideal reference model of the center of mass motion of the vehicle during high-speed and low-speed steering is formed to generate an ideal reference trajectory for all-wheel steering.

[0008] Error dynamic models for vehicle lateral and yaw motions are formed based on the motion differences between the steering system dynamic model and the ideal reference model of the center of mass motion.

[0009] A lateral super-twisting sliding mode control law is constructed based on the error dynamics model of lateral motion, forming a lateral finite-time stable process;

[0010] Based on the error dynamics model of the yaw motion, a super-twisting sliding mode control law for the yaw motion is constructed to form a finite-time stable process of the yaw motion.

[0011] The control data generated by the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate are decoupled to each axle to form the wheel angle of each axle.

[0012] In one embodiment of the present invention, the formation of the steering system dynamics model includes:

[0013] A dynamic model of the steering system is formed based on the vehicle's all-wheel steering structure and suspension structure.

[0014] In one embodiment of the present invention, the formation of the ideal reference model for the center of mass motion includes:

[0015] An ideal linear two-degree-of-freedom model is used to form the dynamic model of the steering system;

[0016] The transfer functions between the vehicle's center of gravity sideslip angle and yaw rate and the front wheel steering angle input are obtained based on an ideal linear two-degree-of-freedom model.

[0017] An ideal reference model for the center of mass motion of a vehicle during high-speed cornering is formed based on the transfer function.

[0018] An ideal reference model for the center of mass motion of a vehicle during low-speed steering is formed based on the transfer function.

[0019] In one embodiment of the present invention, the formation of the error dynamics model includes:

[0020] Based on a reasonable approximation of the steering system dynamics model, the lateral error and rate of change, and the yaw error and rate of change of the ideal reference model of the center of mass motion are determined.

[0021] Error dynamics models for lateral and yaw motions are formed based on lateral error and its rate of change, and yaw error and its rate of change.

[0022] In one embodiment of the present invention, the formation of the lateral super-twisting sliding mode control rate includes:

[0023] The lateral super-twisting sliding mode control law is decomposed into the lateral nominal control law and the lateral robust control law;

[0024] A lateral nominal control law is formed to perform nominal control on the dynamic model of undisturbed lateral motion error, thereby achieving finite-time stable convergence of the centroid side slip angle error;

[0025] A robust control law is formed, and the uncertain disturbances in the lateral motion error dynamic model are effectively compensated by designing a reasonable sliding surface and control law.

[0026] In one embodiment of the present invention, the formation of the yaw super-twisting sliding mode control rate includes:

[0027] The yaw super-twisting sliding mode control rate is decomposed into the yaw nominal control rate and the yaw robust control rate;

[0028] A nominal yaw control law is formed to perform nominal control on the dynamic model of undisturbed yaw motion error, thereby achieving stable convergence of yaw angular velocity error in finite time.

[0029] A robust control law for yaw motion is established, and the uncertain disturbances in the dynamic model of yaw motion error are effectively compensated by designing a reasonable sliding surface and control law.

[0030] In one embodiment of the present invention, the formation of the wheel rotation angle decoupling includes:

[0031] Based on the control data output from the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate, as well as the lateral stiffness of each axle and the distance between each axle and the vehicle's center of gravity, control decoupling is performed to form the wheel angle corresponding to the tires of each axle.

[0032] In one embodiment of the present invention, it further includes:

[0033] The control performance of the all-wheel steering system is optimized by adjusting the control parameters of the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate.

[0034] In one embodiment of the present invention, the optimization of the control parameters includes:

[0035] The sideslip angle control parameter and yaw rate control parameter in the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate are iteratively optimized based on the vehicle speed.

[0036] The multi-axle vehicle all-wheel steering control system of this invention includes:

[0037] The dynamic model definition module is used to establish a dynamic model of the all-wheel steering system of a multi-axle vehicle. The model takes into account the nonlinearity and uncertainty of the all-wheel steering system.

[0038] The reference model definition module is used to form an ideal reference model of the vehicle's center of gravity motion during high-speed and low-speed steering based on the steering system dynamics model and the system's steering characteristics, thus forming an ideal reference trajectory for all-wheel steering.

[0039] The error model definition module is used to form error dynamic models of vehicle lateral motion and yaw motion based on the motion differences between the steering system dynamic model and the ideal reference model of the center of mass motion.

[0040] The lateral sliding mode control module is used to construct a lateral super-twisting sliding mode control law based on the error dynamics model of lateral motion, thereby forming a lateral finite-time stable process.

[0041] The yaw sliding mode control module is used to construct the yaw super-twisting sliding mode control law based on the error dynamics model of the yaw motion, thereby forming a finite-time stable process of the yaw.

[0042] The wheel angle decoupling module is used to decouple the control data formed by the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate to each axle to form the wheel angle of each axle.

[0043] The multi-axle vehicle all-wheel steering control method and control system of this invention divides the virtual lateral control force and virtual yaw control torque of the steering system into a nominal control law that compensates for the disturbance-free system and a robust control law that overcomes uncertain disturbances. It has a good ability to handle nonlinearity and uncertainty in the steering system. The super-twisting sliding mode control proposed in this invention uses a continuous robust control law to replace the discontinuous switching control law composed of the sign function in the traditional sliding mode control. It can effectively suppress the chattering phenomenon of the steering system, thereby improving the steering control effect. The nominal control law proposed in this invention can make the nominal system of lateral motion error dynamics and yaw motion error dynamics reach a stable state within a finite time. The proposed robust control law can compensate for the system disturbances of lateral motion error dynamics and yaw motion error dynamics within a finite time, so that the steering system state can quickly and accurately track the ideal reference trajectory, thereby improving the steering maneuverability and stability of multi-axle vehicles. Attached Figure Description

[0044] Figure 1 The diagram shown is a flowchart of a multi-axle vehicle all-wheel steering control method according to an embodiment of the present invention.

[0045] Figure 2 The diagram shown is a schematic diagram of an all-wheel steering structure for a three-axle vehicle (top view) in an embodiment of the multi-axle vehicle all-wheel steering control method of the present invention.

[0046] Figure 3 The diagram shown is a schematic diagram of the suspension structure of a three-axle vehicle (rear view) in an embodiment of the multi-axle vehicle all-wheel steering control method of the present invention.

[0047] Figure 4 The diagram shown illustrates the application process of a multi-axle vehicle all-wheel steering control method according to an embodiment of the present invention.

[0048] Figure 5 The diagram shown is a schematic representation of the vehicle's center of gravity sideslip angle response during low-speed turning in a multi-axle vehicle all-wheel steering control method according to an embodiment of the present invention.

[0049] Figure 6 This is a schematic diagram of the yaw rate response of a vehicle during low-speed turning in a multi-axle vehicle all-wheel steering control method according to an embodiment of the present invention.

[0050] Figure 7 This is a schematic diagram of the vehicle's center of gravity sideslip angle response during high-speed cornering in a multi-axle vehicle all-wheel steering control method according to an embodiment of the present invention.

[0051] Figure 8 This is a schematic diagram of the yaw rate response of a vehicle during high-speed turning in a multi-axle vehicle all-wheel steering control method according to an embodiment of the present invention.

[0052] Figure 9 This is a schematic diagram of the architecture of a multi-axle vehicle all-wheel steering control system according to an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer and more understandable, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0054] An embodiment of the present invention provides a multi-axle vehicle all-wheel steering control method as follows: Figure 1 As shown. In Figure 1 In this embodiment, the following are included:

[0055] Step 100: Establish a dynamic model of the all-wheel steering system of a multi-axle vehicle, taking into account the nonlinearity and uncertainties of the all-wheel steering system.

[0056] The all-wheel steering system of a vehicle includes, but is not limited to, the structural and physical parameters of the tires, steering mechanism, and suspension. The dynamic model reflects the vehicle's center of gravity slip angle, yaw rate, and other dynamic characteristics, and can also be specifically used to provide feedback on the nonlinearity and uncertainties of the all-wheel steering system. For example, it can reflect uncertainties in the vehicle's physical parameters, unmodeled tire forces, the dynamic coupling of the suspension and steering system, and external disturbances that cause uncertainties in the model's dynamics.

[0057] Step 200: Based on the steering system dynamics model and steering characteristics, form an ideal reference model of the vehicle's center of gravity motion during high-speed and low-speed steering, which is used to form an ideal reference trajectory for all-wheel steering.

[0058] The ideal reference model of the center of gravity motion reflects the ideal steering state of the vehicle when the tires on each axle are turning, forming an ideal reference trajectory for tracking the center of gravity sideslip angle and yaw rate. During high-speed steering, front axle steering allows the tires to operate within a linear range, ensuring vehicle stability. During low-speed steering, the wheel angles satisfying the Ackerman steering relationship help reduce tire wear. Based on the vehicle's steering characteristics at high and low speeds, an ideal reference model of the center of gravity motion is formed for high-speed and low-speed steering regarding the center of gravity sideslip angle and yaw rate.

[0059] Step 300: Based on the motion differences between the steering system dynamics model and the ideal reference model of the center of mass motion, form an error dynamics model for the vehicle's lateral motion and yaw motion.

[0060] The error dynamics model reflects the actual tracking error and rate of change of the center of mass sideslip angle and yaw rate in tracking the ideal reference trajectory, characterized by the difference in motion between the actual vehicle dynamics model (including vehicle parameter uncertainties, unmodeled tire friction, external disturbances, lateral forces, and angular approximations) and the ideal reference model of the center of mass motion. It also reflects the tracking error of the straight-line sideslip angle and yaw rate based on the correlation between the actual vehicle dynamics model and the ideal reference model.

[0061] Step 400: Construct a lateral super-twisting sliding mode control law based on the error dynamics model of lateral motion to form a lateral finite-time stable process.

[0062] Based on the error dynamics model of lateral motion, a finite-time stable process is formed through the super-twisting (ST) sliding mode control law for lateral motion, ensuring that the sideslip angle of the center of mass converges to the reference trajectory of the ideal reference model of the center of mass motion within a finite time during vehicle steering. According to the finite-time separation principle, the lateral super-twisting sliding mode control law during the stable process can be decomposed into a lateral nominal control law and a lateral robust control law. The lateral nominal control law is used for nominal control of the unperturbed lateral motion error dynamics model, while the lateral robust control law is used to resist uncertain disturbances in the lateral error dynamics model.

[0063] Step 500: Construct the super-twisting sliding mode control law of the yaw motion based on the error dynamics model of the yaw motion, and form the finite-time stable process of the yaw motion.

[0064] Based on the error dynamics model of yaw motion, a finite-time stable process is formed through the super-twisting sliding mode control law for yaw motion, ensuring that the yaw angular velocity converges to the yaw angular velocity reference trajectory of the ideal reference model of the center of mass motion within a finite time during vehicle turning. According to the finite-time separation principle, the yaw super-twisting sliding mode control law during the stable process can be decomposed into a nominal yaw control law and a robust yaw control law. The nominal yaw control law is used for nominal control of the undisturbed yaw motion error dynamics model, while the robust yaw control law is used to resist uncertain disturbances in the yaw error dynamics model.

[0065] Step 600: Decouple the control data formed by the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate to each axle to form the wheel angle of each axle.

[0066] The sideslip angle and yaw rate of the center of mass are generated based on the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate to track the ideal reference trajectory of the mass. The wheel rotation angle of each axle is then generated by decoupling based on the sideslip stiffness of each axle and the distance between each axle and the center of mass.

[0067] Step 700: Optimize the control performance of the all-wheel steering system by adjusting the control parameters of the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate.

[0068] The control parameters include, but are not limited to, the sideslip angle control parameters and yaw rate control parameters in each nominal control law and robust control law for the lateral motion error dynamics and yaw motion error dynamics models.

[0069] The multi-axle vehicle all-wheel steering control method of this invention divides the virtual lateral control force and virtual yaw control torque of the steering system into a nominal control law that compensates for the disturbance-free system and a robust control law that overcomes uncertain disturbances. It has a good ability to handle many nonlinear and uncertain factors in the steering system. The super-twisting sliding mode control proposed in this invention uses a continuous robust control law to replace the discontinuous switching control law composed of the sign function in the traditional sliding mode control. This can effectively suppress the chattering phenomenon of the steering system, thereby improving the steering control effect. The nominal control law proposed in this invention can make the nominal system of lateral motion error dynamics and yaw motion error dynamics reach a stable state within a finite time. The proposed robust control law can compensate for the system disturbances of lateral motion error dynamics and yaw motion error dynamics within a finite time, so that the steering system state can quickly and accurately track the ideal reference trajectory, thereby improving the steering maneuverability and stability of multi-axle vehicles.

[0070] like Figure 1 As shown, in one embodiment of the present invention, the formation of the steering system dynamics model in step 100 includes:

[0071] Step 110: Develop a dynamic model of the steering system based on the vehicle's all-wheel steering structure and suspension structure.

[0072] Taking a three-axle vehicle as an example, the steering structure of a three-axle vehicle is as follows: Figure 2 As shown. The suspension structure of a three-axle vehicle is as follows. Figure 3 As shown. Combined with Figure 2 and Figure 3 As shown, the lateral and yaw motion dynamics models of the vehicle are formed based on the vehicle's steering structure as follows:

[0073]

[0074]

[0075] A rectangular coordinate system is established at the vehicle's center of mass, with the longitudinal direction pointing towards the front of the vehicle as the positive x-axis and the direction pointing towards the left side of the vehicle as the positive y-axis. In the diagram, β represents the sideslip angle at the center of mass; γ represents the vehicle's yaw rate. v is the roll angle of the vehicle body about the x-axis; x 'a' represents the vehicle's speed; 'a' represents the track width between the left and right wheels of the vehicle; 'l' represents the vehicle's speed. a l b and l c These are the longitudinal distances from the front axle, center axle, and rear axle to the center of mass, respectively; h s F is the distance from the roll center to the vehicle's center of gravity. yi and δ i These represent the lateral tire forces and tire swerve angles for the front, middle, and rear axles, respectively. m is the vehicle mass; m sFor vehicle body weight; I z Let I be the moment of inertia of the entire vehicle about the z-axis; xz The product of inertia of the vehicle body about the x-axis and z-axis; ΔF x ΔM γ The lateral and yaw dynamics models are respectively the uncertainties caused by factors such as vehicle physical parameter uncertainties, unmodeled tire forces, dynamic coupling of suspension and steering systems, and external disturbances. The nonlinearity and cosine function of the tire lateral force in (1) and (2) reflect the nonlinearity of the steering system model to a certain extent. Therefore, the lateral and yaw dynamics models have nonlinear and uncertain characteristics.

[0076] like Figure 1 As shown, in one embodiment of the present invention, the formation of the ideal reference model for the center of mass motion in step 200 includes:

[0077] Step 210: Form an ideal linear two-degree-of-freedom model of the steering system dynamics model.

[0078] Taking the aforementioned three-axle vehicle as an example, an ideal linear two-degree-of-freedom model of the three-axle vehicle steering system is established:

[0079]

[0080] Where, k f k m and k r The lateral stiffnesses α are the front axle, center axle, and rear axle, respectively. f α m and α r The tire slip angle satisfies Define the state vector X = [β γ] T Control vector U = [δ m δ r ] T The output vector is Y = [β γ] T Then (3) can be written in the following state-space equation form:

[0081]

[0082] in,

[0083]

[0084] Step 220: Obtain the vehicle's center of gravity sideslip angle β, yaw rate γ, and front wheel steering angle input δ based on the ideal linear two-degree-of-freedom model. f The transfer function between them.

[0085] Taking the above three-axle vehicle as an example, by performing a Laplace transform on the state-space equation shown in (3), we can obtain the sideslip angle β of the center of mass and the yaw rate γ and the front wheel steering angle input δ. f Transfer function between:

[0086]

[0087] Assume δ m and δ r Satisfy δ m =K 12 δ f δ r =K 13 δ f ,but

[0088]

[0089] During high-speed cornering, front axle steering keeps the tires operating within a linear range, ensuring vehicle stability. Therefore, let K... 12 =0,K 13 =0, s=0, the steady-state gain of the vehicle's yaw rate can be obtained in steady state.

[0090]

[0091] When the ideal vehicle model is stable, it must also meet the following requirements.

[0092] From the inverse solution of (6) and (7), we can obtain

[0093]

[0094] Substituting (8) into (6) yields the transfer function of the ideal reference model for the center of mass motion during high-speed turning.

[0095]

[0096] Considering tire wear during low-speed steering, the steering angles of each wheel should theoretically satisfy the Ackerman steering relationship. According to the Ackerman principle for multi-axle steering, the steering angles of the center axle and rear axle during pure rolling are as follows:

[0097]

[0098] In the formula, L 12 and L 13 These are the longitudinal distances between the front axle and the center axle, and between the front axle and the rear axle, respectively, denoted by L. 23 This represents the longitudinal distance between the center axle and the rear axle, with D1 being the longitudinal projection distance from the vehicle's steering center to the front axle axis. Ideally, the vehicle's sideslip angle is zero, i.e., β = 0, and the vehicle satisfies the following in steady state. Then, based on (3) and (12), we can obtain

[0099]

[0100] Substituting into (6), we can obtain the steady-state gains of the centroid sideslip angle and yaw rate.

[0101]

[0102] Step 230: Based on the transfer function, form an ideal reference model of the center of mass motion of the vehicle during high-speed steering.

[0103] Taking the aforementioned three-axle vehicle as an example, the second-order system is converted into a first-order system:

[0104]

[0105] Where, β dhigh and γ dhigh k represents the ideal values ​​for the sideslip angle and yaw rate during high-speed cornering. βhigh =0, t βhigh and t γhigh The time constant of the inertial element,

[0106] Converting (10) into state-space equation form, the ideal reference model for the center of mass motion during high-speed turning is:

[0107]

[0108] in,

[0109] Step 240: Based on the transfer function, form an ideal reference model of the center of mass motion of the vehicle when turning at low speed.

[0110] Taking the aforementioned three-axle vehicle as an example, the ideal reference model for the center of gravity motion during low-speed turning is as follows:

[0111]

[0112] Where, β dlow and γ dlow k represents the ideal values ​​for the sideslip angle and yaw rate during low-speed cornering. βlow =0, t βlow and t γlow The time constant of the inertial element is typically within the range of 0.1 to 0.25.

[0113] like Figure 1 As shown, in one embodiment of the present invention, the formation of the error dynamics model in step 300 includes:

[0114] Step 310: Determine the lateral error and rate of change, and the yaw error and rate of change of the ideal reference model of the center of mass motion based on the reasonable approximation of the steering system dynamics model.

[0115] Taking a three-axle vehicle as an example, by making reasonable approximations to the lateral dynamics and yaw dynamics of a three-axle vehicle, we can obtain...

[0116]

[0117]

[0118] Where, Δf y (t) represents the error between the lateral dynamics (1) and its reasonable approximation (7), Δg z (t) represents the error between the yaw dynamics (2) and its reasonable approximation (8). Δf y (t) and Δg z (t) is mainly caused by vehicle parameter uncertainties, unmodeled dynamics, external disturbances, and approximations of lateral forces and angles. Assume Δf y (t) and Δg z Both Δf and its rate of change are bounded, satisfying |Δf|. y (t)|≤M β0 , |Δg z (t)|≤M γ0 ,

[0119] Lateral and yaw motion control rates u in (16) and (17) β and u γ satisfy

[0120]

[0121] During vehicle cornering, the sideslip angle and yaw rate track the ideal reference trajectory (β during high-speed cornering). d =β dhigh γ d =γ dhigh When turning at low speed, β d =β dlow γ d =γ dlow ).

[0122] Step 320: Based on the lateral error and its rate of change and the yaw error and its rate of change, form the error dynamics model of the lateral motion and the yaw motion.

[0123] Taking a three-axle vehicle as an example, the tracking error e of the center of gravity sideslip angle is defined. β =β-β d Yaw rate tracking error eγ =-γ-γ d Therefore, the dynamic model for the lateral and yaw motion errors of a three-axle vehicle is as follows:

[0124]

[0125]

[0126] like Figure 1 As shown, in one embodiment of the present invention, the formation of the lateral super-twisting sliding mode control rate in step 400 includes:

[0127] Step 410: Decompose the lateral super-twisting sliding mode control rate into the lateral nominal control rate and the lateral robust control rate.

[0128] Lateral super-twisting sliding mode control rate u β This ensures that the sideslip angle β of the vehicle's center of gravity converges to the sideslip angle of the ideal reference model of the center of gravity's motion within a finite time during the vehicle's turning process (β during high-speed turning). d =β dhigh When turning at low speed, β d =β dlow ).

[0129] Based on the finite-time separation principle, the lateral nominal control law used to compensate for error in the dynamic model without disturbances and the lateral robust control law used to resist uncertain disturbances are designed separately. The lateral super-twisting sliding mode control law u β Represented in the following form

[0130] u β =u βn +u βs (twenty one)

[0131] Lateral super-twisting sliding mode control rate u β Using the nominal control rate u βn and robust control rate u βs It enables the vehicle's actual center of gravity sideslip angle to track its ideal reference trajectory when turning, and the entire center of gravity sideslip angle tracking process is stable for a finite time.

[0132] Step 420: Form a lateral nominal control law and perform nominal control on the dynamic model of the undisturbed lateral motion error to achieve finite-time stable convergence of the centroid side slip angle error.

[0133] Assume Δf in (19) y If (t) = 0, then the design nominal control law u βn for

[0134]

[0135] In the formula, k β1 >0, 0<α β <1.

[0136] Define the Lyapunov function under this nominal control law.

[0137]

[0138] Substituting (22) into (19) yields

[0139]

[0140] For V β1 Differentiation yields

[0141]

[0142] Equation (25) shows that the Lyapunov function V β1 The derivative of is negative definite. According to LaSalle's invariance theorem, for any initial value e, β (0), when t→∞, e β (t)→0, i.e., e β It is globally asymptotically convergent.

[0143] Here are some additional definitions and lemmas:

[0144] Definition 1: Vector field F: R n →R n Relative to weight A d-degree weighted homogeneous vector field refers to a vector field for λ>0 and (z1,z2,…,z…). n )∈R n ,have

[0145]

[0146] Among them, F i It is the i-th component of the vector field F.

[0147] Lemma 1: Assume a vector field f: R n →R n It is relative to the weight. A homogeneous vector field of degree d for the system If f(0) = 0, and the origin of the system is asymptotically stable and d < 0, then the origin of the system is globally finite-time stable.

[0148] Lemma 2: For the system Given f(0) = 0, assume there exists a continuously differentiable positive definite function V(x) defined in the neighborhood of the equilibrium point and a constant α∈(0,1), C>0, such that...

[0149] L f V(x)≤-CV α (x) (27)

[0150] The origin of the system is stable in finite time, and the upper time limit satisfies the following condition:

[0151]

[0152] According to definition 1, the nominal system (19) of the centroid sideslip angle error dynamics is homogeneous, and its dynamics relative to the weight λ is... β The homogeneity of a number equal to 1 is α. β1 -1. Meanwhile, the equilibrium point of system (19) is e. β = 0. According to Lemma 1, since 0 < α β <1, α β1 -1 < 0, then when Δf y When (t) = 0, e β It is stable over a finite time. That is, the nominal control rate u. βn This allows the nominal system of the centroid sideslip angle error dynamics to converge to a steady state within a finite time.

[0153] Step 430: Form a lateral robust control law, and effectively compensate for the uncertain disturbances in the lateral motion error dynamic model by designing a reasonable sliding surface and control law.

[0154] Actual error term Δf y The existence of (t) is unavoidable; a robust control law u must be designed. βs Effective compensation is provided for uncertain disturbances in the dynamic model of centroid sideslip angle error.

[0155] Based on the nominal control rate u βn Define the following lateral sliding surface:

[0156]

[0157] For s β1 Differentiation yields

[0158]

[0159] robust control rate u βs The design is as follows:

[0160]

[0161] Where, k β2 and k β3All are constants greater than zero.

[0162] Substitute (31) into (30) and define s β2 =v β +Δf y (t) can be obtained

[0163]

[0164] Define vector Choose Lyapunov functions

[0165]

[0166] Among them, P β It is a positive definite matrix. For η β Differentiation yields

[0167]

[0168] in, Select parameter k β2 >0, k β3 >M β Then matrix A β It is a Hurwitz matrix. Therefore, there exists a positive definite matrix Q. β ,satisfy

[0169]

[0170] For Lyapunov functions V β2 Taking the derivative, we get

[0171]

[0172] Depend on and achievable

[0173]

[0174] In the formula, According to Lemma 2, V β2 Can be done within a limited time (any) The convergence to 0 means that s β1 and s β2 All of them converge to 0 within a finite time. Therefore, from formula (30), the designed robust control law u can be obtained. βs The perturbation Δf in the centroid sideslip angle error dynamic model (19) can be processed within a finite time. y Compensation will be provided.

[0175] like Figure 1As shown, in one embodiment of the present invention, the formation of the yaw super-twisting sliding mode control rate in step 500 includes:

[0176] Step 510: Decompose the yaw super-twisting sliding mode control rate into the yaw nominal control rate and the yaw robust control rate.

[0177] Super-twisting sliding mode control rate u γ This ensures that the yaw rate γ during vehicle cornering converges to the yaw rate of the ideal reference model of the center of mass motion within a finite time (γ during high-speed cornering). d =γ dhigh When turning at low speed, γ d =γ dlow ).

[0178] Based on the finite-time separation principle, the nominal yaw control law used to compensate for error in the dynamic model without disturbance and the robust yaw control law used to resist uncertain disturbances are designed separately. The yaw super-twisting sliding mode control law u γ Represented in the following form

[0179] u γ =u γn +u γs (38)

[0180] Lateral super-twisting sliding mode control rate u γ Using the nominal control rate u γn and robust control rate u γs It enables the vehicle's actual yaw rate to track its ideal reference trajectory when turning, and the entire yaw tracking process is stable for a finite time.

[0181] Step 520: Form a nominal yaw control law, perform nominal control on the dynamic model of undisturbed yaw motion error, and achieve stable convergence of yaw angular velocity error in finite time.

[0182] Assume Δg in (20) z If (t) = 0, then the nominal control rate u γn Can be designed as

[0183]

[0184] In the formula, k γ1 >0, 0<α γ <1.

[0185] Define the Lyapunov function under this nominal control law.

[0186]

[0187] Substituting (39) into (20) yields

[0188]

[0189] For V γ1 Differentiation yields

[0190]

[0191] Equation (42) shows that the derivative V of the Lyapunov function γ1 It is negative definite. According to LaSalle's invariance theorem, for any initial value e... γ (0), when t→∞, e γ →0, i.e., e γ It is globally asymptotically convergent. The equilibrium point of the yaw rate error dynamic model (20) is e. γ =0, its value relative to the weight λ γ The homogeneity of a number equal to 1 is α. γ -1. Because 0 < α γ If Δg < 1, then by Lemma 1, when Δg z When (t)×0, e γ It is stable over a finite time. That is, the nominal control rate u. γn This allows the nominal system of yaw rate error dynamics to converge to a steady state within a finite time.

[0192] Step 530: Develop a robust control law for yaw motion and effectively compensate for uncertain disturbances in the dynamic model of yaw motion error by designing a reasonable sliding surface and control law.

[0193] Actual error term Δg z The existence of (t) is unavoidable; a robust control law u must be designed. γs Effective compensation is provided for uncertain disturbances in the dynamic model of yaw rate error.

[0194] Based on the nominal control rate u γn Define the following yaw sliding surface:

[0195]

[0196] For s γ1 Differentiation yields

[0197]

[0198] robust control rate u γs The design is as follows:

[0199]

[0200] Where, k γ2 and k γ3 All are constants greater than zero.

[0201] Next, we will prove the robust control law u. γs It can compensate for disturbances in the dynamics of yaw error within a finite time.

[0202] Substitute (45) into (44) and define s γ2 =v γ +Δg z (t) can be obtained

[0203]

[0204] Define vector Choose Lyapunov functions

[0205]

[0206] Among them, P γ It is a positive definite matrix. The stability proof of the reference system (33) can be obtained directly if the parameter k γ2 >0, k γ3 >M γ Then the function V γ2 Can be any It converges within a finite time, where Positive definite matrix P γ and Q γ Satisfying the following Riccati equation:

[0207]

[0208] in,

[0209] Lyapunov function V γ2 The finite-time convergence shows that s γ1 and s γ2 All of them converge to 0 within a finite time. According to (44), the robust control law u γs The disturbance Δg in the yaw rate error dynamic model (20) can be controlled within a finite time. z (t) to provide effective compensation.

[0210] Combined with nominal control rate u γn The design process yields the designed nominal control rate u. γn and robust control rate u γs It enables the vehicle's actual yaw rate to track its ideal reference trajectory when turning, and the entire yaw rate tracking system is stable for a finite time.

[0211] like Figure 1 As shown, in one embodiment of the present invention, the formation of decoupling in step 600 includes:

[0212] Step 610: Based on the control data output by the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate, the lateral stiffness of each axle, and the distance between each axle and the vehicle's center of gravity, control decoupling is performed to form the wheel angle of the tires corresponding to each axle.

[0213] Taking a three-axle vehicle as an example, the wheel rotation angles of the middle axle and rear axle of a three-axle vehicle...

[0214]

[0215] like Figure 1 As shown, in one embodiment of the present invention, the optimization of control parameters in step 700 includes:

[0216] Step 710: Iteratively optimize the sideslip angle control parameters and yaw rate control parameters in the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate based on the vehicle speed.

[0217] The application process of the multi-axle vehicle all-wheel steering control method of this invention is shown in Figure 4. Figure 4 Taking a three-axle vehicle as an example, the dynamic model of the three-axle vehicle's all-wheel steering system encompasses factors such as parameter uncertainties, unmodeled dynamics, and external disturbances. The inputs are the front wheel angle, the middle axle wheel angle, and the rear axle wheel angle. The output states include the center of gravity sideslip angle and yaw rate. The ideal center of gravity sideslip angle and ideal yaw rate during vehicle steering are obtained from the ideal reference model of the center of gravity motion. Based on the outputs of the three-axle vehicle's all-wheel steering system model and the ideal reference model of the center of gravity motion, and their characteristics, the errors and rates of change between the vehicle's center of gravity sideslip angle and yaw rate and their ideal values ​​are obtained in real time. Based on the front wheel angle, center of gravity sideslip angle error, the vehicle's center of gravity sideslip angle, and yaw rate state outputs and their ideal values, the nominal lateral motion control law, lateral sliding surface, and robust lateral motion control law are constructed. Based on the front wheel angle, yaw rate error, the vehicle's center of gravity sideslip angle, and yaw rate state outputs and their ideal values, the nominal yaw motion control law, yaw sliding surface, and robust yaw motion control law are constructed. The control data of the lateral super-twisting sliding control rate and the yaw super-twisting sliding control rate are decoupled by a decoupler to form the steering angle of the center axle tire and the rear axle tire.

[0218] The simulation results of the vehicle's center of gravity sideslip angle response during low-speed cornering in an embodiment of the multi-axle vehicle all-wheel steering control method of the present invention are as follows: Figure 5 As shown in the figure. A simulation of the yaw rate response of a vehicle during low-speed cornering in an embodiment of the multi-axle vehicle all-wheel steering control method of the present invention is shown below. Figure 6 As shown. Combined with Figure 5 and Figure 6 This invention's multi-axle vehicle all-wheel steering control method uses a three-axle vehicle as an example. Through simulation, the low-speed condition is set as follows: vehicle speed 30 km / h, front wheel steering angle input is a 5° step signal. The controller parameters are adjusted to k. β1 =1,k β2 =3,k β3 =0.01, α β =0.5, k γ1 =10,k γ2 =5 and k γ3 =0.02, α γ =0.5. The super-twisting sliding mode all-wheel steering controller is compared with front axle steering, zero-center-of-gravity sideslip proportional control, and discontinuous switching sliding mode control. As shown in the figure, when the vehicle performs a step turn at low speeds, the sideslip angle of the front axle steering deviates significantly from zero at the initial moment, resulting in a large change in vehicle attitude. Simultaneously, the yaw rate of the front axle steering is significantly lower than the ideal value, thus exhibiting poor steering agility. While the zero-center-of-gravity sideslip proportional control theoretically yields zero, simulation results show that, due to its linear model derivation, it cannot account for the nonlinearities and uncertainties in the actual vehicle steering system, leading to significant fluctuations in the sideslip angle and yaw rate near the ideal values. Discontinuous switching sliding mode control and super-twisting sliding mode control employ finite-time control laws, combined with... Figure 5 and Figure 6 Both control methods converge quickly and reach a stable state in a short time. However, the robust control rate of discontinuous switching sliding mode control is discontinuous, resulting in noticeable chattering in the response curve. The comparison results show that super-twisting sliding mode control can track the ideal reference trajectory with higher performance, improving vehicle maneuverability and handling stability.

[0219] A simulation of the vehicle's center of gravity sideslip angle response during high-speed cornering in an embodiment of the multi-axle vehicle all-wheel steering control method of the present invention is as follows: Figure 7 As shown in the figure. A simulation of the yaw rate response of a vehicle during high-speed cornering in an embodiment of the multi-axle vehicle all-wheel steering control method of the present invention is shown below. Figure 8 As shown. This embodiment of the multi-axle vehicle all-wheel steering control method uses a three-axle vehicle as an example. Through simulation, the high-speed operating condition is set as follows: vehicle speed is 70 km / h, and the front wheel steering angle input is a step signal of 5°. The controller parameters are adjusted to k. β1 =5,k β2 =3,k β3 =0.01, α β =0.5, k γ1 =20,kγ2 =10 and k γ3 =0.02, α γ =0.5. Simulation results of the center of gravity sideslip angle and yaw rate under the action of super-twisting sliding mode all-wheel steering controller, front axle steering, zero center of gravity sideslip angle proportional control, and discontinuous switching sliding mode control are as follows: Figure 7 and Figure 8 As shown in the figure, when the vehicle performs a step turn at high speed, the front axle steering has a large negative sideslip angle, resulting in poor road tracking performance. Simultaneously, the yaw rate is higher than the ideal reference curve, reducing understeer and safety margin, and increasing the risk of tail-wagging and rollover. Zero sideslip proportional control, with the participation of the center and rear axles in the steering, stabilizes the sideslip angle near zero after a certain adjustment period, improving vehicle stability. However, the yaw rate is far below the ideal value, significantly reducing steering agility and maneuverability. In contrast, super-twisting sliding mode control and discontinuous switching sliding mode control not only converge the sideslip angle to zero in a shorter time but also exhibit good tracking performance of the yaw rate to the ideal curve, demonstrating superior control performance compared to front axle steering and zero sideslip proportional control. However, because the control rate of discontinuous switching sliding mode control is discontinuous, its control curve exhibits more noticeable jitter compared to super-twisting sliding mode control, which is highly detrimental to high-speed vehicles. Based on the above analysis, it can be concluded that super-twisting sliding mode control has significant advantages when the vehicle is performing step steering at medium and high speeds.

[0220] The vehicle all-wheel steering control system of this invention includes:

[0221] The memory is used to store the program code in the processing of the vehicle all-wheel steering control method in the above embodiments;

[0222] The processor is used to execute program code in the processing of the vehicle all-wheel steering control method described in the above embodiments.

[0223] The processor can be a DSP (Digital Signal Processor), an FPGA (Field-Programmable Gate Array), an MCU (Microcontroller Unit) system board, a SoC (System on a Chip) system board, or a PLC (Programmable Logic Controller) minimum system including I / O.

[0224] The multi-axle vehicle all-wheel steering control system of the present invention is as follows: Figure 9As shown. In Figure 9 In this embodiment, the following are included:

[0225] Dynamic model definition module 10 is used to establish a dynamic model of the all-wheel steering system of a multi-axle vehicle. The model takes into account the nonlinearity and uncertainty of the all-wheel steering system.

[0226] Reference model definition module 20 is used to form an ideal reference model of the center of gravity motion of the vehicle during high-speed and low-speed steering based on the dynamic model and steering characteristics, and to form an ideal reference trajectory for all-wheel steering.

[0227] Error model definition module 30 is used to form error dynamic models of vehicle lateral motion and yaw motion based on the motion differences between the dynamic model and the ideal reference model of the center of mass motion;

[0228] The lateral sliding mode control module 40 is used to construct a lateral super-twisting sliding mode control law based on the error dynamics model of lateral motion, thereby forming a lateral finite-time stable process.

[0229] The yaw sliding mode control module 50 is used to construct the yaw super-twisting sliding mode control law based on the error dynamics model of the yaw motion, thereby forming a finite-time stable process of the yaw.

[0230] The wheel angle decoupling module 60 is used to decouple the control data formed by the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate to each axle to form the wheel angle of each axle;

[0231] The control model optimization module 70 is used to optimize the control performance of the all-wheel steering system by adjusting the control parameters of the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate.

[0232] like Figure 9 As shown, in another embodiment of the present invention, the dynamic model definition module 10 includes:

[0233] The dynamic modeling unit 11 is used to form a dynamic model of the steering system based on the vehicle's all-wheel steering structure and suspension structure.

[0234] like Figure 9 As shown, in another embodiment of the present invention, the reference model definition module 20 includes:

[0235] Ideal modeling unit 21 is used to form an ideal linear two-degree-of-freedom model for the dynamic model of the steering system;

[0236] Transfer function construction unit 22 is used to obtain the vehicle's center of gravity sideslip angle β and yaw rate γ and the front wheel steering angle input δ based on the ideal linear two-degree-of-freedom model. f Transfer function between them;

[0237] High-speed steering modeling unit 23 is used to form an ideal reference model of the center of mass motion of the vehicle during high-speed steering based on the transfer function.

[0238] The low-speed steering modeling unit 24 is used to form an ideal reference model of the center of mass motion of the vehicle when it is turning at low speed, based on the transfer function.

[0239] like Figure 9 As shown, in another embodiment of the present invention, the error model definition module 30 includes:

[0240] The model approximation processing unit 31 is used to determine the lateral error and rate of change and the yaw error and rate of change of the ideal reference model of the center of mass motion based on a reasonable approximation of the steering system dynamics model.

[0241] Error dynamics modeling unit 32 is used to determine the error dynamics model of lateral motion and yaw motion based on lateral error and rate of change and yaw error and rate of change.

[0242] like Figure 9 As shown, in another embodiment of the present invention, the lateral sliding mode control module 40 includes:

[0243] Lateral control decomposition unit 41 is used to decompose the lateral super-twisting sliding mode control law into the lateral nominal control law and the lateral robust control law;

[0244] The lateral nominal control unit 42 is used to form the lateral nominal control law, perform nominal control on the dynamic model of lateral motion error without disturbance, and achieve stable convergence of the centroid side slip angle error;

[0245] The lateral robust control unit 43 is used to form a lateral robust control law, which effectively compensates for uncertain disturbances in the lateral motion error dynamic model by designing a reasonable sliding surface and control law.

[0246] like Figure 9 As shown, in another embodiment of the present invention, the yaw sliding mode control module 50 includes:

[0247] Yaw control decomposition unit 51 is used to decompose the yaw super-twisting sliding mode control rate into the yaw nominal control rate and the yaw robust control rate.

[0248] The yaw nominal control unit 52 is used to form the yaw nominal control law, perform nominal control on the yaw motion error dynamic model without disturbance, and achieve finite-time stable convergence of the yaw angular velocity error.

[0249] The yaw robust control unit 53 is used to form the yaw robust control law, and to effectively compensate for the uncertain disturbances in the yaw motion error dynamic model by designing a reasonable sliding surface and control law.

[0250] like Figure 9 As shown, in another embodiment of the present invention, the wheel angle decoupling module 60 includes:

[0251] The wheel angle decoupling unit 61 decouples the control based on the control data output by the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate, as well as the lateral stiffness of each axle and the distance between each axle and the vehicle's center of gravity, to form the wheel angle corresponding to the tires of each axle.

[0252] like Figure 9 As shown, in another embodiment of the present invention, the control model optimization module 70 includes:

[0253] The control parameter optimization unit 71 is used to iteratively optimize the sideslip angle control parameter and yaw rate control parameter in the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate according to the vehicle speed.

[0254] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for all-wheel steering control of a multi-axle vehicle, characterized in that, include: A dynamic model of the all-wheel steering system of a multi-axle vehicle is established, taking into account the nonlinearity and uncertainty of the steering system. Based on the dynamic model of the steering system and the steering characteristics, an ideal reference model of the center of mass motion of the vehicle during high-speed and low-speed steering is formed to generate an ideal reference trajectory for all-wheel steering. Error dynamic models for vehicle lateral and yaw motions are formed based on the motion differences between the steering system dynamic model and the ideal reference model of the center of mass motion. The formation of the error dynamics model includes: Based on a reasonable approximation of the steering system dynamics model, the lateral error and rate of change, and the yaw error and rate of change of the ideal reference model of the center of mass motion are determined. Error dynamics models for lateral and yaw motions are formed based on lateral error and its rate of change, and yaw error and its rate of change. A lateral super-twisting sliding mode control law is constructed based on the error dynamics model of lateral motion, forming a lateral finite-time stable process; the formation of the lateral super-twisting sliding mode control law includes: The lateral super-twisting sliding mode control law is decomposed into the lateral nominal control law and the lateral robust control law; A lateral nominal control law is formed to perform nominal control on the dynamic model of undisturbed lateral motion error, thereby achieving finite-time stable convergence of the centroid side slip angle error; A laterally robust control law is formed, and the uncertain disturbances in the lateral motion error dynamic model are effectively compensated by designing a reasonable sliding surface and control law; Based on the error dynamics model of yaw motion, a super-twisting sliding mode control law for yaw motion is constructed to form a finite-time stable process of yaw motion; the formation of the super-twisting sliding mode control law for yaw motion includes: The yaw super-twisting sliding mode control rate is decomposed into the yaw nominal control rate and the yaw robust control rate; A nominal yaw control law is formed to perform nominal control on the dynamic model of undisturbed yaw motion error, thereby achieving stable convergence of yaw angular velocity error in finite time. A robust control law for yaw motion is formed, and the uncertain disturbances in the dynamic model of yaw motion error are effectively compensated by designing a reasonable sliding surface and control law. The control data generated from the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate are used to decouple the wheel angles of each axle and form the wheel angles of each axle; the formation of the decoupling includes: Based on the control data output from the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate, as well as the lateral stiffness of each axle and the distance between each axle and the vehicle's center of gravity, control decoupling is performed to form the wheel angle corresponding to the tires of each axle.

2. The multi-axle vehicle all-wheel steering control method as described in claim 1, characterized in that, The formation of the steering system dynamics model includes: A dynamic model of the steering system is formed based on the vehicle's all-wheel steering structure and suspension structure.

3. The multi-axle vehicle all-wheel steering control method as described in claim 1, characterized in that, The formation of the ideal reference model for the center of mass motion includes: An ideal linear two-degree-of-freedom model is used to form the dynamic model of the steering system; The transfer functions between the vehicle's center of gravity sideslip angle and yaw rate and the front wheel steering angle input are obtained based on an ideal linear two-degree-of-freedom model. An ideal reference model for the center of mass motion of a vehicle during high-speed cornering is formed based on the transfer function. An ideal reference model for the center of mass motion of a vehicle during low-speed steering is formed based on the transfer function.

4. The multi-axle vehicle all-wheel steering control method as described in claim 1, characterized in that, Also includes: The control performance of the all-wheel steering system is optimized by adjusting the control parameters of the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate.

5. The multi-axle vehicle all-wheel steering control method as described in claim 4, characterized in that, The optimization of the control parameters includes: The sideslip angle control parameter and yaw rate control parameter in the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate are iteratively optimized based on the vehicle speed.

6. A multi-axle vehicle all-wheel steering control system, characterized in that, include: The dynamic model definition module is used to establish a dynamic model of the all-wheel steering system of a multi-axle vehicle. The model takes into account the nonlinearity and uncertainty of the all-wheel steering system. The reference model definition module is used to form an ideal reference model of the vehicle's center of gravity motion during high-speed and low-speed steering based on the steering system dynamics model and steering characteristics, thus forming an ideal reference trajectory for all-wheel steering. The error model definition module is used to form error dynamic models of vehicle lateral motion and yaw motion based on the motion differences between the steering system dynamic model and the ideal reference model of the center of mass motion. The error model definition module includes: The model approximation processing unit is used to determine the lateral error and rate of change, and the yaw error and rate of change of the ideal reference model of the center of mass motion based on a reasonable approximation of the steering system dynamics model. Error dynamics modeling unit, used to determine the error dynamics model of lateral motion and yaw motion based on lateral error and rate of change and yaw error and rate of change; A lateral sliding mode control module is used to construct a lateral super-twisting sliding mode control law based on the error dynamics model of lateral motion, forming a lateral finite-time stable process; the lateral sliding mode control module includes: The lateral control decomposition unit is used to decompose the lateral super-twisting sliding mode control law into the lateral nominal control law and the lateral robust control law; The lateral nominal control unit is used to form the lateral nominal control law, perform nominal control on the dynamic model of undisturbed lateral motion error, and achieve stable convergence of the centroid side slip angle error; Laterally robust control unit is used to form a laterally robust control law, which effectively compensates for uncertain disturbances in the lateral motion error dynamic model by designing a reasonable sliding surface and control law; A yaw sliding mode control module is used to construct a yaw super-twisting sliding mode control law based on the error dynamics model of the yaw motion, forming a finite-time stable process of the yaw; the yaw sliding mode control module includes: The yaw control decomposition unit is used to decompose the yaw super-twisting sliding mode control rate into the yaw nominal control rate and the yaw robust control rate. The yaw nominal control unit is used to generate the yaw nominal control law, perform nominal control on the dynamic model of yaw motion error without disturbance, and achieve finite-time stable convergence of yaw angular velocity error. A yaw robust control unit is used to generate a yaw robust control law, which effectively compensates for uncertain disturbances in the yaw motion error dynamic model by designing a reasonable sliding surface and control law. A wheel angle decoupling module is used to decouple the control data formed by the lateral super-twisting sliding mode control rate and the yaw super-twisting sliding mode control rate to each axle to form the wheel angle of each axle; the wheel angle decoupling module includes: The wheel angle decoupling unit decouples control based on the control data output by the lateral nominal control rate, lateral robust control rate, yaw nominal control rate, and yaw robust control rate, as well as the lateral stiffness of each axle and the distance between each axle and the vehicle's center of gravity, to form the wheel angle corresponding to the tires of each axle.

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