Vehicle control method, device, medium, equipment and vehicle based on automatic driving

Abstract

The application discloses a vehicle control method, device, medium, equipment and vehicle based on automatic driving, and the method comprises the following steps: establishing state quantity including lateral position deviation and yaw angle deviation, parameter quantity including vehicle speed and curvature, and control quantity including expected yaw angular velocity kinematics equation; determining the kinematics outer loop control law by using the first nominal model determined by the kinematics equation and the backstepping method, and calculating the expected yaw angular velocity based on the kinematics outer loop control law; establishing state quantity including lateral velocity and actual yaw angular velocity, parameter quantity including longitudinal velocity, front-rear axle cornering stiffness, distance from the mass center point to the front-rear axle of the vehicle, vehicle body mass and yaw rotational inertia, and control quantity including front wheel steering angle dynamics equation; determining the state feedback control law and the feedforward control law by using the small gain theorem and the Laplace transform final value theorem respectively based on the second nominal model determined by the dynamics equation and the expected yaw angular velocity, and calculating the front wheel steering angle control vehicle steering based on the two kinds of control laws.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and more specifically, to a vehicle control method, device, medium, equipment, and vehicle based on autonomous driving. Background Technology

[0002] Autonomous vehicles achieve real-time closed-loop communication between the vehicle and the external traffic environment through environmental perception, interactive decision-making, and control modules. Path tracking control, characterized by fast convergence, high accuracy, and strong robustness, is fundamental for the accurate response of autonomous vehicles to changes in the external traffic environment. Path tracking control for autonomous vehicles involves vehicle kinematics and dynamics. The inherent coupling and nonlinearity of vehicle kinematics and dynamics result in a high-dimensional and highly nonlinear nominal control model, leading to high complexity and poor stability in path tracking control systems designed based on such high-dimensional and highly nonlinear nominal control models. Furthermore, the lateral stiffness of the front and rear axles in the dynamic characteristics of autonomous vehicles changes in real time with factors such as road adhesion, vehicle load, and driving conditions. Consequently, path tracking control systems designed based on high-dimensional and highly nonlinear nominal control models exhibit low control performance and robustness. Summary of the Invention

[0003] This application provides a vehicle control method, device, medium, equipment, and vehicle based on autonomous driving, which can solve the problems of high complexity, poor stability, and low control performance and robustness of autonomous vehicle path tracking control systems designed based on high-dimensional and strongly nonlinear control nominal models.

[0004] The specific technical solution is as follows:

[0005] In a first aspect, embodiments of this application provide a vehicle control method based on autonomous driving, the method comprising:

[0006] The state variables include the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of gravity. The parameter variables include the vehicle speed and the curvature at the reference point on the target planning path. The control variables include the kinematic equation of the path tracking error for the vehicle's desired yaw rate. The reference point is the point on the target planning path that is the closest to the vehicle's center of gravity.

[0007] A first nominal model is determined based on the kinematic equation of the path tracking error. The outer loop control law of the kinematics is determined using the first nominal model and the backstepping method. The desired yaw rate of the vehicle is calculated based on the outer loop control law of the kinematics.

[0008] The established state variables include the vehicle's lateral velocity and actual yaw rate; the parameter variables include the vehicle's longitudinal velocity, front axle lateral stiffness, rear axle lateral stiffness, distance from the vehicle's center of gravity to its own front axle, distance from the vehicle's center of gravity to its own rear axle, vehicle mass, and vehicle yaw moment of inertia; and the control variables include the linear two-degree-of-freedom dynamic equation for the vehicle's front wheel steering angle.

[0009] A second nominal model is determined based on the linear two-degree-of-freedom dynamic equations and the desired yaw rate of the vehicle. A state feedback control law is determined based on the second nominal model and the small gain theorem. A feedforward control law is determined based on the second nominal model and the Laplace transform final value theorem. The front wheel steering angle of the vehicle is calculated based on the state feedback control law and the feedforward control law, and the vehicle is controlled to steer according to the front wheel steering angle.

[0010] In a first possible implementation of the first aspect, determining the first nominal model based on the path tracking error kinematic equations includes:

[0011] The first nominal model is determined based on the path tracking error kinematic equation, the system bounded disturbance, the first system state quantity, the kinematic outer loop control law, the virtual state quantity, and the virtual control law. The system bounded disturbance is equivalent to the vehicle's lateral velocity. The first system state quantity includes a first component equivalent to the lateral position deviation and a second component equivalent to the yaw angle deviation. The kinematic outer loop control law is equivalent to the vehicle's desired yaw rate. The virtual state quantity includes a virtual state quantity equivalent to the first component of the first system state quantity and a virtual state component determined based on the second component of the first system state quantity and the virtual control law.

[0012] In a second possible implementation of the first aspect, the path tracking error kinematic equations include:

[0013]

[0014] Wherein, 'e' represents the lateral position deviation, and the This indicates the yaw angle deviation, and the v x v y The longitudinal velocity and lateral velocity of the vehicle are respectively represented in the vehicle speed description, ρ represents the curvature at the reference point on the target planned path, and γ represents the curvature at the reference point on the target planned path. * This indicates the desired yaw rate of the vehicle.

[0015] In a third possible implementation of the first aspect, determining the first nominal model based on the path tracking error kinematic equation includes:

[0016] When the system is bounded, d = v y The first system state quantity The kinematic outer loop control law u γ =γ * At that time, the first formula is obtained based on the kinematic equation of the path tracking error;

[0017] Substituting the second formula into the first formula yields the first nominal model;

[0018] The first formula includes:

[0019]

[0020] The second formula includes:

[0021]

[0022] The first nominal model includes:

[0023]

[0024] Wherein, the x γ1 Let x represent the first system state quantity equivalent to e. γ The component of x γ2 Indicates and The equivalent first system state quantity x γ The component of z γ1 The z represents a virtual control quantity. γ2 τ represents the virtual control quantity, and τ represents the virtual control law.

[0025] In the fourth possible implementation of the first aspect, the kinematic outer loop control law includes:

[0026]

[0027] Wherein, κ1, κ2, and κ3 represent the kinematic outer loop control laws, respectively.

[0028] In a fifth possible implementation of the first aspect, determining the second nominal model based on the linear two-degree-of-freedom dynamic equations and the vehicle's desired yaw rate includes:

[0029] The second nominal model is determined based on the linear two-degree-of-freedom dynamic equation, the second system state variables, and the system control input. The second system state variables include a first component of the second system state variables that is equivalent to the vehicle's lateral velocity, and a second component of the second system state variables that is equivalent to the yaw rate difference. The yaw rate difference is the difference between the vehicle's actual yaw rate and the vehicle's desired yaw rate.

[0030] In the sixth possible implementation of the first aspect, the linear two-degree-of-freedom dynamic equations include:

[0031]

[0032] Wherein, the v x v y The longitudinal velocity and lateral velocity of the vehicle are respectively represented, γ represents the actual yaw rate of the vehicle, and C represents the longitudinal velocity and lateral velocity of the vehicle, respectively. f This indicates the lateral stiffness of the front axle of the vehicle, and C... r Indicates the lateral stiffness of the rear axle of the vehicle, l f The distance from the vehicle's center of gravity to its front axle is represented by l. r The distance from the vehicle's center of gravity to its rear axle is represented by m, where m represents the vehicle's mass, and I represents the distance from the center of gravity to the rear axle. z δ represents the yaw moment of inertia of the vehicle. f This indicates the steering angle of the vehicle's front wheels.

[0033] In the seventh possible implementation of the first aspect, determining the second nominal model based on the linear two-degree-of-freedom dynamic equations includes:

[0034] When the second system state variable x δ =[x δ1 x δ2 ] T =[v y γ-γ * ] T System control input u δ =δ f Then, the second nominal model is obtained according to the linear two-degree-of-freedom dynamic equations;

[0035] The second nominal model includes:

[0036]

[0037] Wherein, the x δ1 The second system state quantity x represents the equivalent of the vehicle's lateral velocity. δ The component of x δ2 This represents the actual yaw rate γ of the vehicle and the desired yaw rate γ of the vehicle. * The difference.

[0038] In the eighth possible implementation of the first aspect, the state feedback control law includes:

[0039] u δ2 =W * (X* ) -1 x δ ;

[0040] Wherein, the u δ2 This represents the state feedback control law, where W... * X * These represent matrix variables.

[0041] In the ninth possible implementation of the first aspect, the feedforward control law includes:

[0042]

[0043] Wherein, the u δ1 Denotes the feedforward control law, where ψ des f3 represents the desired heading angle, and f3 represents the components of the preset matrix.

[0044] In a tenth possible implementation of the first aspect, calculating the vehicle's front wheel steering angle based on the state feedback control law and the feedforward control law includes:

[0045] The sum of the state feedback control law and the feedforward control law is determined as the steering angle of the vehicle's front wheels.

[0046] Secondly, embodiments of this application provide a vehicle control device based on autonomous driving, the device comprising:

[0047] The first establishment unit is used to establish state variables including the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of gravity, parameter variables including the vehicle speed and the curvature at the reference point on the target planning path, and control variables including the path tracking error kinematic equation of the vehicle's desired yaw rate, wherein the reference point is the point on the target planning path that is the closest to the vehicle's center of gravity.

[0048] The first determining unit is used to determine a first nominal model based on the kinematic equation of the path tracking error;

[0049] The second determining unit is used to determine the kinematic outer loop control law using the first nominal model and the backstepping method;

[0050] The first calculation unit is used to calculate the vehicle's desired yaw rate based on the kinematic outer loop control law.

[0051] The second establishment unit is used to establish state variables including vehicle lateral velocity and actual vehicle yaw rate, parameter variables including vehicle longitudinal velocity, vehicle front axle lateral stiffness, vehicle rear axle lateral stiffness, distance from the vehicle's center of gravity to its own front axle, distance from the vehicle's center of gravity to its own rear axle, vehicle mass and vehicle yaw moment of inertia, and control variables including the linear two-degree-of-freedom dynamic equation of the vehicle's front wheel steering angle.

[0052] The third determining unit is used to determine the second nominal model based on the linear two-degree-of-freedom dynamic equation and the vehicle's desired yaw rate.

[0053] The fourth determining unit is used to determine the state feedback control law based on the second nominal model and the small gain theorem, and to determine the feedforward control law based on the second nominal model and the Laplace transform final value theorem.

[0054] The second calculation unit is used to calculate the steering angle of the vehicle's front wheels based on the state feedback control law and the feedforward control law;

[0055] The control unit is used to control the vehicle to steer according to the steering angle of the vehicle's front wheels.

[0056] In a first possible implementation of the second aspect, the first determining unit is configured to determine the first nominal model based on the path tracking error kinematic equation, the system bounded disturbance, the first system state quantity, the kinematic outer loop control law, the virtual state quantity, and the virtual control law, wherein the system bounded disturbance is equivalent to the vehicle lateral velocity, the first system state quantity includes a first component of the first system state quantity equivalent to the lateral position deviation and a second component of the first system state quantity equivalent to the yaw angle deviation, the kinematic outer loop control law is equivalent to the vehicle's desired yaw angle velocity, and the virtual state quantity includes a virtual state quantity equivalent to the first component of the first system state quantity and a virtual state component determined based on the second component of the first system state quantity and the virtual control law.

[0057] In a second possible implementation of the second aspect, the path tracking error kinematic equations include:

[0058]

[0059] Wherein, 'e' represents the lateral position deviation, and the This indicates the yaw angle deviation, and the v x v y The longitudinal velocity and lateral velocity of the vehicle are respectively represented in the vehicle speed description, ρ represents the curvature at the reference point on the target planned path, and γ represents the curvature at the reference point on the target planned path. * This indicates the desired yaw rate of the vehicle.

[0060] In a third possible implementation of the second aspect, the first determining unit is configured to:

[0061] When the system is bounded, d = v y The first system state quantity The kinematic outer loop control law u γ =γ * At that time, the first formula is obtained based on the kinematic equation of the path tracking error;

[0062] Substituting the second formula into the first formula yields the first nominal model;

[0063] The first formula includes:

[0064]

[0065] The second formula includes:

[0066]

[0067] The first nominal model includes:

[0068]

[0069] Wherein, the x γ1 Let x represent the first system state quantity equivalent to e. γ The component of x γ2 Indicates and The equivalent first system state quantity x γ The component of z γ1 The z represents a virtual state quantity. γ2 Let τ represent the virtual state quantity, and let τ represent the virtual control law.

[0070] In the fourth possible implementation of the second aspect, the kinematic outer loop control law includes:

[0071]

[0072] Wherein, κ1, κ2, and κ3 represent the adjustable parameters of the kinematic outer loop control law.

[0073] In a fifth possible implementation of the second aspect, the third determining unit is used to determine the second nominal model based on the linear two-degree-of-freedom dynamic equation, the second system state quantity, and the system control input, wherein the second system state quantity includes a first component of the second system state quantity equivalent to the vehicle lateral velocity, and a second component of the second system state quantity equivalent to the yaw rate difference, the yaw rate difference being the difference between the actual yaw rate of the vehicle and the desired yaw rate of the vehicle.

[0074] In the sixth possible implementation of the second aspect, the linear two-degree-of-freedom dynamic equations include:

[0075]

[0076] Wherein, the v x v y The longitudinal velocity and lateral velocity of the vehicle are respectively represented, γ represents the actual yaw rate of the vehicle, and C represents the longitudinal velocity and lateral velocity of the vehicle, respectively. f This indicates the lateral stiffness of the front axle of the vehicle, and C... r Indicates the lateral stiffness of the rear axle of the vehicle, l f The distance from the vehicle's center of gravity to its front axle is represented by l. r The distance from the vehicle's center of gravity to its rear axle is represented by m, where m represents the vehicle's mass, and I represents the distance from the center of gravity to the rear axle. z δ represents the yaw moment of inertia of the vehicle. f This indicates the steering angle of the vehicle's front wheels.

[0077] In the seventh possible implementation of the second aspect, the third determining unit is configured to:

[0078] When the second system state variable x δ =[x δ1 x δ2 ] T =[v y γ-γ * ] T System control input u δ =δ f Then, the second nominal model is obtained according to the linear two-degree-of-freedom dynamic equations;

[0079] The second nominal model includes:

[0080]

[0081] Wherein, the x δ1 The second system state quantity x represents the equivalent of the vehicle's lateral velocity. δ The component of x δ2This represents the actual yaw rate γ of the vehicle and the desired yaw rate γ of the vehicle. * The difference.

[0082] In the eighth possible implementation of the second aspect, the state feedback control law includes:

[0083] u δ2 =W * (X * ) -1 x δ ;

[0084] Wherein, the u δ2 This represents the state feedback control law, where W... * X * These represent matrix variables.

[0085] In the ninth possible implementation of the second aspect, the feedforward control law includes:

[0086]

[0087] Wherein, the u δ1 Denotes the feedforward control law, where ψ des f3 represents the desired heading angle, and f3 represents the components of the preset matrix.

[0088] In a tenth possible implementation of the second aspect, the second calculation unit is used to determine the sum of the state feedback control law and the feedforward control law as the vehicle front wheel steering angle.

[0089] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described in any possible implementation of the first aspect.

[0090] Fourthly, embodiments of this application provide an electronic device, which includes:

[0091] One or more processors;

[0092] The processor is coupled to a storage device for storing one or more programs;

[0093] When one or more programs are executed by one or more processors, the electronic device performs the method as described in any possible implementation of the first aspect.

[0094] Fifthly, embodiments of this application provide a vehicle that includes the means as described in any possible implementation of the second aspect, or includes electronic equipment as described in the fourth aspect.

[0095] In a sixth aspect, embodiments of this application provide a computer program product containing instructions that, when executed on a computer or processor, cause the computer or processor to perform the method described in any possible implementation of the first aspect.

[0096] The vehicle control method, device, medium, equipment, and vehicle based on autonomous driving provided in this application decouple and construct a kinematic control nominal model (i.e., the first nominal model) and a dynamic control nominal model (i.e., the second nominal model). The outer loop control based on the first nominal model calculates and outputs the desired vehicle yaw rate based on the deviation between the current driving path and the target planned path. The inner loop control based on the second nominal model controls the steering angle of the vehicle's front wheels in real time, enabling the vehicle to stably, quickly, and accurately track the desired vehicle yaw rate and minimize the vehicle's lateral speed. As a result, the path tracking control of the autonomous vehicle exhibits high performance with "fast convergence, high accuracy, and strong robustness".

[0097] The dual-loop decoupling design simplifies the nominal control model, thus solving the problems of high complexity and poor stability in the path tracking control system for autonomous vehicles designed based on high-dimensional and strongly nonlinear nominal control models. Furthermore, the state feedback control law and feedforward control law determined based on the small gain theorem and the Laplace transform final value theorem take into account both control performance and robustness requirements, enabling the path tracking control system for autonomous vehicles to better cope with real-time changes in factors such as road surface adhesion, vehicle load, and driving conditions.

[0098] Based on the kinematic equations of path tracking error, the kinematic outer loop control law is designed using the backstepping method. Based on the linear two-degree-of-freedom dynamic equations containing perturbations of the lateral stiffness parameters of the front and rear axles of the vehicle, the state feedback control law is independently determined based on the small gain theorem, so that the state trajectory of the closed-loop system is asymptotically stable when there are no disturbance terms. The feedforward control law is determined using the Laplace transform final value theorem to suppress the influence of disturbance terms caused by the desired vehicle yaw rate on the control performance of the closed-loop system. Attached Figure Description

[0099] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0100] Figure 1 A flowchart illustrating a vehicle control method based on autonomous driving, provided as an embodiment of this application;

[0101] Figure 2A schematic diagram of vehicle path tracking error provided in an embodiment of this application;

[0102] Figure 3 An example diagram of a linear two-degree-of-freedom dynamic model provided in this application embodiment;

[0103] Figure 4 A block diagram illustrating the composition of a vehicle control device based on autonomous driving, provided as an embodiment of this application;

[0104] Figure 5 This is a schematic diagram of the structure of an electronic device or computer device provided in an embodiment of this application. Detailed Implementation

[0105] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0106] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The terms "comprising" and "having," and any variations thereof, in the embodiments and drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0107] Figure 1 This is a flowchart illustrating a vehicle control method based on autonomous driving. This method can be applied to electronic devices or computer equipment, specifically to vehicles or servers, and may include the following steps:

[0108] S110: The state variables include the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of mass. The parameter variables include the vehicle speed and the curvature at the reference point on the target planning path. The control variables include the kinematic equation of the path tracking error for the vehicle's desired yaw rate.

[0109] The reference point is the point on the target planned path that is closest to the vehicle's center of gravity. The target planned path is the path automatically planned by the navigation system from the starting point to the ending point after the user inputs the starting point (which can be the current location by default) and the ending point in the navigation system interface. Yaw angle deviation refers to the yaw angle deviation between the reference point on the target planned path and the vehicle's center of gravity.

[0110] The kinematic equations for path tracking error can be derived from the relationship between the three coordinate systems: the inertial coordinate system, the Serret-Frenet coordinate system, and the vehicle coordinate system, and are ultimately established in the Serret-Frenet coordinate system.

[0111] The kinematic equations for path tracking error include:

[0112]

[0113] Where e represents the lateral positional deviation. Indicates the yaw rate deviation, v x v y Let ρ and γ represent the longitudinal and lateral velocities of the vehicle, respectively, where ρ represents the curvature at the reference point on the target planned path, and γ represents the lateral velocity of the vehicle. * This represents the vehicle's desired yaw rate.

[0114] The process of establishing the kinematic equations for path tracking error is explained in detail below:

[0115] like Figure 2 The diagram illustrates vehicle path tracking error. {N}, {F}, and {B} represent the inertial coordinate system, the Serret-Frenet coordinate system (with its origin at the point on the target planned path, i.e., the target path in the diagram, closest to the vehicle's center of mass), and the vehicle coordinate system (with its origin at the vehicle's center of mass), respectively. The radius vector of the vehicle's center of mass relative to the origin of the inertial coordinate system {N} is known to be r. B / N The position vector of the origin of the Serret-Frenet coordinate system {F} relative to the origin of the inertial coordinate system {N} is r. F / N The radius vector of the vehicle's center of mass relative to the origin of the Serret-Frenet coordinate system {F} is r. B / F Then there is

[0116] r B / N =r F / N +r B / F (1)

[0117] In the Serret-Frenet coordinate system {F}, differentiating equation (1) yields the position vector r. B / N The column matrix of the absolute derivative of in the Serret-Frenet coordinate system {F} is

[0118]

[0119] In the formula, and They are respectively the radius r F / NThe absolute derivative of the vector r in the Serret-Frenet coordinate system {F} is expressed as a column matrix. B / F The relative derivatives in the Serret-Frenet coordinate system {F}, the angular velocity vector in the Serret-Frenet coordinate system {F}, and the position vector r B / F The column matrices in the Serret-Frenet coordinate system {F} can be represented as follows:

[0120]

[0121]

[0122]

[0123]

[0124] In the formula, s is the arc length of the target planned path curve; denoted as , where is the angle between the x-axis of the Serret-Frenet coordinate system {F} and the x-axis of the inertial coordinate system {N}; e is the distance between the vehicle's center of gravity and the origin of the Serret-Frenet coordinate system. If the origin of the Serret-Frenet coordinate system is chosen as the reference point, then e also represents the lateral positional deviation between the vehicle's center of gravity and the reference point on the target planned path.

[0125] If the arrow diameter r B / N The column matrix of the absolute derivative of in the vehicle coordinate system {B} is but and Satisfy the following coordinate transformation relationship

[0126]

[0127] In the formula, The yaw angle deviation between the vehicle's center of gravity and a reference point on the target planned path can be expressed as:

[0128]

[0129] In the formula, This refers to the vehicle's yaw angle.

[0130] Substituting formula (2) into formula (7), we get

[0131]

[0132] According to Frenet's formula,

[0133]

[0134] In the formula, ρ is the curvature at the reference point on the target planning path.

[0135] Substituting equation (10) into equation (9), we can obtain the above kinematic equation for path tracking error.

[0136] S120: Determine the first nominal model based on the kinematic equation of the path tracking error, determine the kinematic outer loop control law using the first nominal model and the backstepping method, and calculate the vehicle's desired yaw rate based on the kinematic outer loop control law.

[0137] A nominal model is simply the model used when designing a controller or performing system analysis. Because a true model is generally difficult to obtain in reality, practical applications use methods such as system identification to obtain a parametric model (such as a transfer function or state-space equation) or a non-parametric model (such as a frequency response) of the controlled object. Strictly speaking, these models are only approximations of the true model, hence the name nominal model.

[0138] After obtaining the kinematic equations of the path tracking error, a first nominal model is determined based on the kinematic equations of the path tracking error, the bounded disturbance of the system, the first system state variables, the kinematic outer loop control law, the virtual state variables, and the virtual control law. Here, the bounded disturbance of the system is equivalent to the lateral velocity of the vehicle, the first system state variables include a first component of the first system state variables equivalent to the lateral position deviation and a second component of the first system state variables equivalent to the yaw angle deviation, the kinematic outer loop control law is equivalent to the desired yaw angle velocity of the vehicle, and the virtual state variables include a virtual state variable equivalent to the first component of the first system state variables and a virtual state component determined based on the second component of the first system state variables and the virtual control law.

[0139] Assuming the vehicle's longitudinal velocity remains constant and its lateral velocity satisfies |v y |≤η1v x In the case of bounded disturbance d = v y The first system state quantity Kinematic outer loop control law u γ =γ * Where η1 represents the adjustable parameter, x γ1 Let x represent the first system state quantity equivalent to e. γ The components, x γ2 Indicates and The equivalent first system state quantity x γ The amount.

[0140] When the system is bounded, d = v y The first system state quantity Kinematic outer loop control law uγ =γ * At that time, the first formula is obtained based on the kinematic equation of the path tracking error;

[0141] Substituting the second formula into the first formula yields the first nominal model;

[0142] The first formula includes:

[0143]

[0144] During the process of a vehicle tracking a target path, it is typically required that the vehicle's front end always points towards the target path, and that the yaw angle deviation between the vehicle's center of gravity and a reference point on the target path depends on the lateral position deviation of these two points, so that the vehicle can smoothly approach the reference point on the target path. Therefore, the second formula includes:

[0145]

[0146] |z γ2 |<η2=(1-λ1)π / 2, where λ1 is a sufficiently small positive number. Based on this, substituting the second formula into the first formula, the resulting first nominal model includes:

[0147]

[0148] Among them, z γ1 z represents a virtual state variable. γ2 Let τ represent the virtual state variable, τ represent the virtual control law, and ε represent the adjustable parameter, where ε > 0.

[0149] In one implementation, the kinematic outer-loop control law u γ include:

[0150]

[0151] Wherein, κ1, κ2, and κ3 represent the adjustable parameters of the kinematic outer loop control law.

[0152] The following describes the specific implementation process of determining the kinematic outer loop control law using the first nominal model and the backstepping method:

[0153] First, construct the Lyapunov candidate function as follows:

[0154]

[0155] In the formula, κ1>0.

[0156] Differentiating equation (11), we get

[0157]

[0158] Depend on and |v y |≤η1v x , can be obtained

[0159]

[0160] By augmenting the Lyapunov candidate function V1, we can obtain

[0161]

[0162] In the formula, κ2>0.

[0163] Differentiating equation (14) and combining it with inequality (13), we can obtain

[0164]

[0165] Define the kinematic outer loop control law as

[0166]

[0167] In the formula, κ3>0.

[0168] Substituting equation (16) into inequality (15), we get

[0169]

[0170] In the formula, 0 < θ < 1, and θ represents an adjustable parameter.

[0171] when or and When, the sum of the second and third terms on the right-hand side of inequality (17) satisfies

[0172]

[0173] Where η2 represents the adjustable parameter.

[0174] From inequalities (17) and (18), we can obtain

[0175]

[0176] In equation (19), z γ =[z γ1 z γ2 ] T σ(η1) is a function of η1, which can be expressed as:

[0177]

[0178] Therefore, the closed-loop system state trajectory formed by the kinematic outer-loop control law defined by equation (16) is uniformly and ultimately bounded. Meanwhile, applying the trigonometric inequalities to equation (14), we can obtain...

[0179]

[0180] From the definition of the infinite norm of a vector, we can obtain

[0181]

[0182]

[0183] Substituting inequalities (22) and (23) into inequality (21), we get

[0184] α1(||z γ || ∞ )≤V2≤α2(||z γ || ∞ ) (twenty four)

[0185] In the formula, α1(||z γ || ∞ ) and α2(||z γ || ∞ Let ) be a class of K functions, which can be represented as follows:

[0186]

[0187]

[0188] Therefore, the final boundary of the closed-loop system state trajectory formed by the kinematic outer-loop control law defined by equation (16) is:

[0189]

[0190] As can be seen from equation (27), if the lateral speed of the vehicle is zero during the process of the vehicle tracking the target planning path, the lateral position deviation and yaw angle deviation between the vehicle's center of mass and the reference point on the target planning path will asymptotically converge to zero.

[0191] By using formulas (11)-(15) and (17)-(27), we can not only obtain the kinematic outer loop control law, but also verify its rationality.

[0192] S130: The state variables include the vehicle's lateral velocity and actual yaw rate. The parameter variables include the vehicle's longitudinal velocity, front axle lateral stiffness, rear axle lateral stiffness, distance from the vehicle's center of gravity to its own front axle, distance from the vehicle's center of gravity to its own rear axle, vehicle mass, and yaw moment of inertia. The control variables include the linear two-degree-of-freedom dynamic equation for the vehicle's front wheel steering angle.

[0193] The linear two-degree-of-freedom dynamic equations include:

[0194]

[0195] Among them, v x v y Representing the vehicle's longitudinal velocity and lateral velocity respectively, γ represents the vehicle's actual yaw rate, and C... f C represents the lateral stiffness of the vehicle's front axle. r Indicates the rear axle lateral stiffness of the vehicle, l f The distance l represents the distance from the vehicle's center of gravity to its front axle. r I represents the distance from the vehicle's center of gravity to its rear axle, where m represents the vehicle's mass. z δ represents the yaw moment of inertia of a vehicle. f This indicates the steering angle of the vehicle's front wheels.

[0196] The process of establishing the linear two-degree-of-freedom dynamic equations is explained in detail below:

[0197] The following will be based on Figure 3 The linear two-degree-of-freedom dynamic model shown establishes the dynamic relationship between the vehicle's yaw rate, lateral velocity, and front wheel steering angle.

[0198] Establish the following equation:

[0199]

[0200] In the formula, F yf and F yr These are the lateral forces on the front and rear axles of the vehicle, respectively, and can be expressed as:

[0201]

[0202] Substituting equation (29) into equation (28), we obtain the linear two-degree-of-freedom dynamic equation.

[0203] S140: Determine the second nominal model based on the linear two-degree-of-freedom dynamic equations and the vehicle's desired yaw rate; determine the state feedback control law based on the second nominal model and the small gain theorem; determine the feedforward control law based on the second nominal model and the Laplace transform final value theorem; calculate the vehicle's front wheel steering angle based on the state feedback control law and the feedforward control law; and control the vehicle to steer according to the vehicle's front wheel steering angle.

[0204] The goal of vehicle dynamics inner-loop control is to enable the vehicle to stably, quickly, and accurately track the desired yaw rate and minimize the vehicle's lateral velocity by controlling the steering angle of the front wheels in real time, thereby reducing the steady-state tracking error of vehicle kinematics outer-loop control.

[0205] After obtaining the linear two-degree-of-freedom dynamic equations, the second nominal model is determined based on the linear two-degree-of-freedom dynamic equations, the second system state variables, and the system control input. The second system state variables include the first component of the second system state variables, which is equivalent to the vehicle's lateral velocity, and the second component of the second system state variables, which is equivalent to the yaw rate difference. The yaw rate difference is the difference between the vehicle's actual yaw rate and the vehicle's desired yaw rate.

[0206] Therefore, a second system state variable x is defined. δ =[x δ1 x δ2 ] T =[v y γ-γ * ] T System control input u δ =δ f Based on this, a second nominal model is obtained according to the linear two-degree-of-freedom dynamic equation;

[0207] The second nominal model includes:

[0208]

[0209] Where, x δ1 The second system state variable x represents the vehicle's lateral velocity. δ The components, x δ2 This represents the difference between the vehicle's actual yaw rate γ and the vehicle's desired yaw rate γ. * The difference.

[0210] In one implementation, the state feedback control law includes:

[0211] u δ2 =W * (X * ) -1 x δ ;

[0212] Among them, u δ2 W represents the state feedback control law. * X * These represent matrix variables.

[0213] In one implementation, the feedforward control law includes:

[0214]

[0215] Among them, u δ1 Denotes the feedforward control law, ψ des f3 represents the desired heading angle, and f3 represents the components of the preset matrix. The preset matrix includes matrix KC, which is the product of matrix K and matrix C as described below.

[0216] The following section elaborates on the specific implementation processes of determining the state feedback control law based on the second nominal model and the small gain theorem, and determining the feedforward control law based on the second nominal model and the Laplace transform final value theorem:

[0217] Considering the perturbation problem of the lateral stiffness parameters of the front and rear axles caused by the strong coupling and nonlinear characteristics exhibited during vehicle operation, the lateral stiffness of the front and rear axles is corrected to...

[0218]

[0219] In the formula, C f0 and C r0 These are the nominal values ​​of the lateral stiffness of the front and rear axles of the vehicle, respectively; C fe and C re These represent the maximum perturbation of the lateral stiffness of the front and rear axles of the vehicle, respectively.

[0220] Substituting equation (30) into the formula for the second nominal model, we can obtain...

[0221]

[0222] In the formula, A, B, and C are the nominal system matrices, and ΔA, ΔB, and ΔC are the uncertainty matrices reflecting the perturbation of the lateral stiffness parameters of the front and rear axles of the vehicle, which can be expressed as follows:

[0223]

[0224]

[0225]

[0226] [ΔA ΔB ΔC]=DF[E1 E2 E3] (35)

[0227] In the formula, To satisfy F T The perturbation matrix is ​​F≤I; D, E1, E2, and E3 are constant matrices characterizing the perturbation structure of the system, which can be expressed as follows:

[0228]

[0229]

[0230]

[0231]

[0232] In this embodiment, the desired vehicle yaw rate in equation (31) is considered as a disturbance term, and a feedforward control law u is designed independently. δ1To suppress the impact of this disturbance term on control performance. Meanwhile, regarding the model uncertainty caused by the perturbation of the vehicle's front and rear axle lateral stiffness parameters included in equation (31), this application embodiment independently designs the state feedback control law u based on the small gain theorem. δ2 This ensures that the closed-loop system's state trajectory is asymptotically stable when there are no disturbance terms. Therefore, when designing the state feedback control law, equation (31) can be simplified to...

[0233]

[0234] Separating the nominal and uncertain parts of equation (40), we can obtain

[0235]

[0236] State feedback control law u δ2 =Kx δ The closed-loop system can be obtained as follows:

[0237]

[0238] According to the small gain theorem, the necessary and sufficient condition for the closed-loop system described by equation (42) to be quadratic stability is that there exists a positive definite matrix P that satisfies the nonlinear matrix inequality (43).

[0239]

[0240] Using a diagonal matrix diag{P -1 Multiplying I and I} on the left and right sides of the left side of the nonlinear matrix inequality (43) respectively, we can obtain

[0241]

[0242] Define X = P -1 And W=KP -1 Then the nonlinear matrix inequality (44) can be transformed into an equivalent linear matrix inequality with respect to matrix variables X and W:

[0243]

[0244] If the feasible solution to the linear matrix inequality (45) is X * and W * Then the state feedback control law is

[0245] u δ2 =Kx δ =W * (X * ) -1 x δ (46)

[0246] Substituting equation (46) into the second nominal model and using the Laplace transform final value theorem, we can obtain the steady-state value of the closed-loop system as x. δ1_ss =0 and The feedforward control law is

[0247]

[0248] In one implementation, after obtaining the state feedback control law and the feedforward control law, the sum of the state feedback control law and the feedforward control law can be determined as the steering angle of the vehicle's front wheels.

[0249] The vehicle control method based on autonomous driving provided in this application decouples and constructs a kinematic control nominal model (i.e., the first nominal model) and a dynamic control nominal model (i.e., the second nominal model). The outer loop control based on the first nominal model calculates and outputs the desired vehicle yaw rate based on the deviation between the current driving path and the target planned path. The inner loop control based on the second nominal model controls the steering angle of the vehicle's front wheels in real time, enabling the vehicle to stably, quickly, and accurately track the desired vehicle yaw rate and minimize the vehicle's lateral speed. As a result, the path tracking control of the autonomous vehicle exhibits high performance with "fast convergence, high accuracy, and strong robustness".

[0250] The dual-loop decoupling design simplifies the nominal control model, thus solving the problems of high complexity and poor stability in the path tracking control system for autonomous vehicles designed based on high-dimensional and strongly nonlinear nominal control models. Furthermore, the state feedback control law and feedforward control law determined based on the small gain theorem and the Laplace transform final value theorem take into account both control performance and robustness requirements, enabling the path tracking control system for autonomous vehicles to better cope with real-time changes in factors such as road surface adhesion, vehicle load, and driving conditions.

[0251] Based on the kinematic equations of path tracking error, the kinematic outer loop control law is designed using the backstepping method. Based on the linear two-degree-of-freedom dynamic equations containing perturbations of the lateral stiffness parameters of the front and rear axles of the vehicle, the state feedback control law is independently determined based on the small gain theorem, so that the state trajectory of the closed-loop system is asymptotically stable when there are no disturbance terms. The feedforward control law is determined using the Laplace transform final value theorem to suppress the influence of disturbance terms caused by the desired vehicle yaw rate on the control performance of the closed-loop system.

[0252] It should be noted that the embodiments of this application can be applied not only to autonomous driving scenarios such as high-speed navigation, urban navigation, and autonomous valet parking for passenger vehicles, but also to autonomous driving scenarios such as high-speed navigation and high-speed platooning for commercial vehicles. Therefore, both passenger vehicle autonomous driving systems and commercial vehicle autonomous driving systems employing this method fall within the protection scope of the embodiments of this application.

[0253] Based on the above method embodiments, another embodiment of this application provides a framework for a vehicle control system based on autonomous driving, which includes a kinematic outer loop control module and a dynamic inner loop control module;

[0254] The kinematic outer loop control module is used to establish state variables including the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of gravity; parameter variables including vehicle speed and curvature at the reference point on the target planning path; and control variables including the path tracking error kinematic equation for the vehicle's desired yaw rate. The reference point is the point on the target planning path that has the smallest distance from the vehicle's center of gravity. Based on the path tracking error kinematic equation, a first nominal model is determined. The kinematic outer loop control law is determined using the first nominal model and the backstepping method. The vehicle's desired yaw rate is then calculated based on the kinematic outer loop control law.

[0255] The dynamic inner-loop control module is used to establish state variables including vehicle lateral velocity and actual yaw rate, and parameter variables including vehicle longitudinal velocity, front axle lateral stiffness, rear axle lateral stiffness, distance from vehicle center of mass to front axle, distance from vehicle center of mass to rear axle, vehicle mass, and yaw moment of inertia. The control variables include the linear two-degree-of-freedom dynamic equations for the vehicle's front wheel steering angle. Based on the linear two-degree-of-freedom dynamic equations and the desired yaw rate, a second nominal model is determined. Based on the second nominal model and the small gain theorem, a state feedback control law is determined, and based on the second nominal model and the Laplace transform final value theorem, a feedforward control law is determined. Based on the state feedback control law and the feedforward control law, the vehicle's front wheel steering angle is calculated, and the vehicle is controlled to steer according to the front wheel steering angle.

[0256] In the framework of the vehicle control system based on autonomous driving provided in this application embodiment, the kinematic outer loop control module calculates and outputs the desired vehicle yaw rate based on the deviation between the vehicle's current driving path and the target planned path, and the dynamic inner loop control module controls the vehicle's front wheel steering angle in real time to enable the vehicle to stably, quickly and accurately track the desired vehicle yaw rate and minimize the vehicle's lateral speed.

[0257] Corresponding to the above method embodiments, another embodiment of this application provides a vehicle control device based on autonomous driving, such as... Figure 4 As shown, the device includes:

[0258] The first establishment unit 210 is used to establish state variables including the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of gravity, parameter variables including the vehicle speed and the curvature at the reference point on the target planning path, and control variables including the path tracking error kinematic equation of the vehicle's desired yaw rate, wherein the reference point is the point on the target planning path that is the closest to the vehicle's center of gravity.

[0259] The first determining unit 220 is used to determine a first nominal model based on the path tracking error kinematic equation;

[0260] The second determining unit 230 is used to determine the kinematic outer loop control law using the first nominal model and the backstepping method;

[0261] The first calculation unit 240 is used to calculate the desired yaw rate of the vehicle based on the kinematic outer loop control law.

[0262] The second establishment unit 250 is used to establish state variables including vehicle lateral velocity and vehicle actual yaw rate, parameter variables including vehicle longitudinal velocity, vehicle front axle lateral stiffness, vehicle rear axle lateral stiffness, distance from the vehicle's center of gravity to its own front axle, distance from the vehicle's center of gravity to its own rear axle, vehicle mass and vehicle yaw moment of inertia, and control variables including the linear two-degree-of-freedom dynamic equation of the vehicle's front wheel steering angle.

[0263] The third determining unit 260 is used to determine the second nominal model based on the linear two-degree-of-freedom dynamic equation and the vehicle's desired yaw rate.

[0264] The fourth determining unit 270 is used to determine the state feedback control law based on the second nominal model and the small gain theorem, and to determine the feedforward control law based on the second nominal model and the Laplace transform final value theorem.

[0265] The second calculation unit 280 is used to calculate the steering angle of the vehicle's front wheels based on the state feedback control law and the feedforward control law;

[0266] Control unit 290 is used to control the vehicle to steer according to the steering angle of the vehicle's front wheels.

[0267] In one possible implementation, the first determining unit is configured to determine the first nominal model based on the path tracking error kinematic equation, the system bounded disturbance, the first system state quantity, the kinematic outer loop control law, the virtual state quantity, and the virtual control law, wherein the system bounded disturbance is equivalent to the vehicle lateral velocity, the first system state quantity includes a first component of the first system state quantity equivalent to the lateral position deviation and a second component of the first system state quantity equivalent to the yaw angle deviation, the kinematic outer loop control law is equivalent to the vehicle's desired yaw angle velocity, and the virtual state quantity includes a virtual state quantity equivalent to the first component of the first system state quantity and a virtual state component determined based on the second component of the first system state quantity and the virtual control law.

[0268] In one possible implementation, the path tracking error kinematic equations include:

[0269]

[0270] Wherein, 'e' represents the lateral position deviation, and the This indicates the yaw angle deviation, and the v x v y The longitudinal velocity and lateral velocity of the vehicle are respectively represented in the vehicle speed description, ρ represents the curvature at the reference point on the target planned path, and γ represents the curvature at the reference point on the target planned path. * This indicates the desired yaw rate of the vehicle.

[0271] In one possible implementation, the first determining unit 220 is configured to:

[0272] When the system is bounded, d = v y The first system state quantity The kinematic outer loop control law u γ =γ * At that time, the first formula is obtained based on the kinematic equation of the path tracking error;

[0273] Substituting the second formula into the first formula yields the first nominal model;

[0274] The first formula includes:

[0275]

[0276] The second formula includes:

[0277]

[0278] The first nominal model includes:

[0279]

[0280] Wherein, the x γ1 Let x represent the first system state quantity equivalent to e. γ The component of x γ2 Indicates and The equivalent first system state quantity x γ The component of z γ1 The z represents a virtual state quantity. γ2 Let τ represent the virtual state quantity, and let τ represent the virtual control law.

[0281] In one possible implementation, the kinematic outer-loop control law includes:

[0282]

[0283] Wherein, κ1, κ2, and κ3 represent the adjustable parameters of the kinematic outer loop control law.

[0284] In one possible implementation, the third determining unit 260 is used to determine the second nominal model based on the linear two-degree-of-freedom dynamic equation, the second system state quantity, and the system control input, wherein the second system state quantity includes a first component of the second system state quantity equivalent to the vehicle's lateral velocity, and a second component of the second system state quantity equivalent to the yaw rate difference, the yaw rate difference being the difference between the vehicle's actual yaw rate and the vehicle's desired yaw rate.

[0285] In one possible implementation, the linear two-degree-of-freedom dynamic equations include:

[0286]

[0287] Wherein, the v x v y The longitudinal velocity and lateral velocity of the vehicle are respectively represented, γ represents the actual yaw rate of the vehicle, and C represents the longitudinal velocity and lateral velocity of the vehicle, respectively. f This indicates the lateral stiffness of the front axle of the vehicle, and C... r Indicates the lateral stiffness of the rear axle of the vehicle, l f The distance from the vehicle's center of gravity to its front axle is represented by l. r The distance from the vehicle's center of gravity to its rear axle is represented by m, where m represents the vehicle's mass, and I represents the distance from the center of gravity to the rear axle. z δ represents the yaw moment of inertia of the vehicle. f This indicates the steering angle of the vehicle's front wheels.

[0288] In one possible implementation, the third determining unit 260 is configured to:

[0289] When the second system state variable x δ =[x δ1 x δ2 ] T =[v y γ-γ * ] T System control input u δ =δ f Then, the second nominal model is obtained according to the linear two-degree-of-freedom dynamic equations;

[0290] The second nominal model includes:

[0291]

[0292] Wherein, the x δ1The second system state quantity x represents the equivalent of the vehicle's lateral velocity. δ The component of x δ2 This represents the actual yaw rate γ of the vehicle and the desired yaw rate γ of the vehicle. * The difference.

[0293] In one possible implementation, the state feedback control law includes:

[0294] u δ2 =W * (X * ) -1 x δ ;

[0295] Wherein, the u δ2 This represents the state feedback control law, where W... * X * These represent matrix variables.

[0296] In one possible implementation, the feedforward control law includes:

[0297]

[0298] Wherein, the u δ1 Denotes the feedforward control law, where ψ des f3 represents the desired heading angle, and f3 represents the components of the preset matrix.

[0299] In one possible implementation, the second calculation unit 280 is used to determine the sum of the state feedback control law and the feedforward control law as the vehicle's front wheel steering angle.

[0300] The vehicle control device based on autonomous driving provided in this application decouples and constructs a kinematic control nominal model (i.e., the first nominal model) and a dynamic control nominal model (i.e., the second nominal model). The outer loop control based on the first nominal model calculates and outputs the desired vehicle yaw rate based on the deviation between the current driving path and the target planned path. The inner loop control based on the second nominal model controls the steering angle of the vehicle's front wheels in real time, enabling the vehicle to stably, quickly, and accurately track the desired vehicle yaw rate and minimize the vehicle's lateral speed. As a result, the path tracking control of the autonomous vehicle exhibits high performance with "fast convergence, high accuracy, and strong robustness".

[0301] The dual-loop decoupling design simplifies the nominal control model, thus solving the problems of high complexity and poor stability in the path tracking control system for autonomous vehicles designed based on high-dimensional and strongly nonlinear nominal control models. Furthermore, the state feedback control law and feedforward control law determined based on the small gain theorem and the Laplace transform final value theorem take into account both control performance and robustness requirements, enabling the path tracking control system for autonomous vehicles to better cope with real-time changes in factors such as road surface adhesion, vehicle load, and driving conditions.

[0302] Based on the kinematic equations of path tracking error, the kinematic outer loop control law is designed using the backstepping method. Based on the linear two-degree-of-freedom dynamic equations containing perturbations of the lateral stiffness parameters of the front and rear axles of the vehicle, the state feedback control law is independently determined based on the small gain theorem, so that the state trajectory of the closed-loop system is asymptotically stable when there are no disturbance terms. The feedforward control law is determined using the Laplace transform final value theorem to suppress the influence of disturbance terms caused by the desired vehicle yaw rate on the control performance of the closed-loop system.

[0303] Based on the above method embodiments, another embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the above embodiments.

[0304] Based on the above method embodiments, another embodiment of this application provides an electronic device or computer device, such as... Figure 5 As shown, it includes:

[0305] One or more processors 310;

[0306] The processor 310 is coupled to a storage device 320, the storage device 320 being used to store one or more programs;

[0307] When the one or more programs are executed by the one or more processors 310, the electronic device or computer device performs the method as described in any of the above embodiments.

[0308] Based on the above method embodiments, another embodiment of this application provides a vehicle that includes the apparatus as described in any of the above embodiments, or includes electronic devices as described above.

[0309] The vehicle includes a CPU (Central Processing Unit), a T-Box (Telematics Box), and various sensors, such as speed sensors, angular velocity sensors, and position sensors. Different sensors measure different information; for example, the speed sensor measures the vehicle's lateral and longitudinal speeds. After acquiring the information measured by the sensors, the CPU can control the vehicle's movement by executing the vehicle control method based on autonomous driving provided in any of the above embodiments. The CPU can also upload this sensor-measured information to a server via the T-Box. The server then executes the vehicle control method based on autonomous driving provided in any of the above embodiments to obtain the vehicle's front wheel steering angle and sends it back to the T-Box, so that the T-Box can transmit the vehicle's front wheel steering angle to the relevant controller to control the vehicle's steering.

[0310] Based on the above embodiments, another embodiment of this application provides a computer program product, which includes instructions that, when executed on a computer or processor, cause the computer or processor to perform the method described in any of the above embodiments.

[0311] The above-described apparatus embodiments correspond to the method embodiments and have the same technical effects. For detailed descriptions, please refer to the method embodiments. The apparatus embodiments are derived from the method embodiments; detailed descriptions can be found in the method embodiments section, and will not be repeated here. Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.

[0312] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0313] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A vehicle control method based on autonomous driving, characterized in that, The method includes: The state variables include the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of gravity. The parameter variables include the vehicle speed and the curvature at the reference point on the target planning path. The control variables include the kinematic equation of the path tracking error for the vehicle's desired yaw rate. The reference point is the point on the target planning path that is the closest to the vehicle's center of gravity. A first nominal model is determined based on the kinematic equation of the path tracking error. The outer loop control law of the kinematics is determined using the first nominal model and the backstepping method. The desired yaw rate of the vehicle is calculated based on the outer loop control law of the kinematics. The established state variables include the vehicle's lateral velocity and actual yaw rate; the parameter variables include the vehicle's longitudinal velocity, front axle lateral stiffness, rear axle lateral stiffness, distance from the vehicle's center of gravity to its own front axle, distance from the vehicle's center of gravity to its own rear axle, vehicle mass, and vehicle yaw moment of inertia; and the control variables include the linear two-degree-of-freedom dynamic equation for the vehicle's front wheel steering angle. The second nominal model is determined based on the linear two-degree-of-freedom dynamic equations and the desired yaw rate of the vehicle. The state feedback control law is determined based on the second nominal model and the small gain theorem, and the feedforward control law is determined based on the second nominal model and the Laplace transform final value theorem. The front wheel steering angle of the vehicle is calculated based on the state feedback control law and the feedforward control law, and the vehicle is controlled to steer according to the front wheel steering angle. The second nominal model is determined based on the linear two-degree-of-freedom dynamic equations and the desired yaw rate of the vehicle, including: The second nominal model is determined based on the linear two-degree-of-freedom dynamic equation, the second system state variables, and the system control input. The second system state variables include a first component of the second system state variables that is equivalent to the lateral velocity of the vehicle, and a second component of the second system state variables that is equivalent to the yaw rate difference. The yaw rate difference is the difference between the actual yaw rate of the vehicle and the desired yaw rate of the vehicle.

2. The method according to claim 1, characterized in that, The first nominal model is determined based on the kinematic equations of the path tracking error, including: The first nominal model is determined based on the path tracking error kinematic equation, the system bounded disturbance, the first system state quantity, the kinematic outer loop control law, the virtual state quantity, and the virtual control law. The system bounded disturbance is equivalent to the vehicle's lateral velocity. The first system state quantity includes a first component equivalent to the lateral position deviation and a second component equivalent to the yaw angle deviation. The kinematic outer loop control law is equivalent to the vehicle's desired yaw rate. The virtual state quantity includes a virtual state quantity equivalent to the first component of the first system state quantity and a virtual state component determined based on the second component of the first system state quantity and the virtual control law.

3. The method according to claim 1, characterized in that, The kinematic equations for the path tracking error include: ； Wherein, 'e' represents the lateral position deviation, and the Indicates the yaw angle deviation, the , These represent the vehicle's longitudinal velocity and lateral velocity, respectively. The curvature at the reference point on the target planning path represents the curvature of the path. This indicates the desired yaw rate of the vehicle.

4. The method according to claim 3, characterized in that, The first nominal model is determined based on the kinematic equations of the path tracking error, including: When the system is bounded by a disturbance The first system state variable The kinematic outer loop control law At that time, the first formula is obtained based on the kinematic equation of the path tracking error; Substituting the second formula into the first formula yields the first nominal model; The first formula includes: The second formula includes: ； The first nominal model includes: ； Among them, the This represents the first system state quantity equivalent to e. The first component, the Indicates and Equivalent first system state quantity The second component, the Represents a virtual state quantity, the Represents a virtual state quantity, the This represents a virtual control law.

5. The method according to claim 4, characterized in that, The kinematic outer loop control law includes: ； Among them, the , , These represent the adjustable parameters of the kinematic outer loop control law.

6. A vehicle control device based on autonomous driving, characterized in that, The device includes: The first establishment unit is used to establish state variables including the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle's center of gravity, parameter variables including the vehicle speed and the curvature at the reference point on the target planning path, and control variables including the path tracking error kinematic equation of the vehicle's desired yaw rate, wherein the reference point is the point on the target planning path that is the closest to the vehicle's center of gravity. The first determining unit is used to determine a first nominal model based on the kinematic equation of the path tracking error; The second determining unit is used to determine the kinematic outer loop control law using the first nominal model and the backstepping method; The first calculation unit is used to calculate the vehicle's desired yaw rate based on the kinematic outer loop control law. The second establishment unit is used to establish state variables including vehicle lateral velocity and actual vehicle yaw rate, parameter variables including vehicle longitudinal velocity, vehicle front axle lateral stiffness, vehicle rear axle lateral stiffness, distance from the vehicle's center of gravity to its own front axle, distance from the vehicle's center of gravity to its own rear axle, vehicle mass and vehicle yaw moment of inertia, and control variables including the linear two-degree-of-freedom dynamic equation of the vehicle's front wheel steering angle. The third determining unit is used to determine the second nominal model based on the linear two-degree-of-freedom dynamic equation and the vehicle's desired yaw rate. The fourth determining unit is used to determine the state feedback control law based on the second nominal model and the small gain theorem, and to determine the feedforward control law based on the second nominal model and the Laplace transform final value theorem. The second calculation unit is used to calculate the steering angle of the vehicle's front wheels based on the state feedback control law and the feedforward control law; A control unit is used to control the vehicle to steer according to the steering angle of the vehicle's front wheels; The third determining unit is used to determine the second nominal model based on the linear two-degree-of-freedom dynamic equation, the second system state quantity, and the system control input. The second system state quantity includes a first component of the second system state quantity that is equivalent to the lateral velocity of the vehicle, and a second component of the second system state quantity that is equivalent to the yaw rate difference. The yaw rate difference is the difference between the actual yaw rate of the vehicle and the desired yaw rate of the vehicle.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; The processor is coupled to a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the electronic device performs the method as described in any one of claims 1-5.

9. A vehicle, characterized in that, The vehicle includes the device as described in claim 6, or the electronic device as described in claim 8.

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