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

Abstract

The invention discloses a vehicle control method and device based on automatic driving, a medium, equipment and a vehicle. The method comprises the steps that a kinematic equation with state variables including transverse position deviation and yaw angle deviation, parameter variables including vehicle speed and curvature and control variables including expected yaw velocity is established; determining a kinematics outer loop control law by using a first nominal model determined by the kinematics equation and a backstepping method, and calculating an expected yaw velocity based on the kinematics outer loop control law; establishing a kinetic equation of which state quantities comprise lateral speed and actual yaw velocity, parameter quantities comprise longitudinal speed, front and rear axle cornering stiffness, distances from a mass center point to front and rear axles of the vehicle, vehicle body mass and yaw moment of inertia, and control quantities comprise a front wheel steering angle; and a second nominal model determined based on the kinetic equation and the expected yaw velocity, a small gain theorem and a Laplace transformation final value theorem determine a state feedback control law and a feed-forward control law respectively, and vehicle steering is controlled based on front wheel steering angles calculated based on the two control laws.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to a vehicle control method and device based on automatic driving, a medium, equipment and a vehicle. BACKGROUND

[0002] An automatic driving vehicle realizes real-time closed loop of the vehicle and external traffic environment through environment perception, interactive decision and control module, and the path tracking control with fast convergence, high precision and strong robustness is the basis for the automatic driving vehicle to accurately respond to the changes of external traffic environment. The path tracking control of the automatic driving vehicle involves vehicle kinematics and dynamics, and the inherent coupling and nonlinear characteristics of the vehicle kinematics and dynamics make the control nominal model have high dimension and strong nonlinearity, resulting in high complexity and poor stability of the automatic driving vehicle path tracking control system designed based on the high-dimensional and strongly nonlinear control nominal model. The dynamics of the automatic driving vehicle involves real-time changes of the front axle and rear axle side stiffness of the vehicle with the changes of road adhesion, vehicle load and driving conditions, and the control performance and robustness of the automatic driving vehicle path tracking control system designed based on the high-dimensional and strongly nonlinear control nominal model are low. SUMMARY

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

[0004] The specific technical solutions are as follows:

[0005] In a first aspect, the embodiments of the present application provide a vehicle control method based on automatic driving, which comprises:

[0006] The established state quantity includes lateral position deviation and yaw angle deviation between a reference point on a target planning path and a vehicle mass center point, the parameter quantity includes vehicle speed and curvature at the reference point on the target planning path, and the control quantity includes path tracking error kinematics equation of vehicle expected yaw angular velocity, wherein the reference point is the point on the target planning path closest to the vehicle mass center point;

[0007] A first nominal model is determined based on the path tracking error kinematics equation, a kinematics outer loop control law is determined by using the first nominal model and backstepping method, and the vehicle expected yaw angular velocity is calculated based on the kinematics outer loop control law;

[0008] The established state variables include a vehicle lateral velocity, a vehicle actual yaw rate, the parameter variables include a vehicle longitudinal velocity, a vehicle front axle cornering stiffness, a vehicle rear axle cornering stiffness, a distance from a vehicle mass center point to a front axle of the vehicle, a distance from the vehicle mass center point to a rear axle of the vehicle, a vehicle body mass and a vehicle yaw moment of inertia, and the control variable includes a vehicle front wheel steering angle.

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

[0010] In a first possible implementation manner of the first aspect, the first nominal model is determined based on the path tracking error kinematic equation, and includes:

[0011] The first nominal model is determined according to the path tracking error kinematic equation, a system bounded disturbance, first system state variables, the kinematic outer loop control law, virtual state variables and a virtual control law, wherein the system bounded disturbance is equivalent to the vehicle lateral velocity, 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 vehicle desired yaw rate, the virtual state variables include virtual state variables 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.

[0012] In a second possible implementation manner of the first aspect, the path tracking error kinematic equation includes:

[0013]

[0014] wherein the e represents the lateral position deviation, the represents the yaw angle deviation, the v x , v y respectively represent a vehicle longitudinal velocity and a vehicle lateral velocity in the vehicle velocity, the p represents a curvature at the reference point on the target planning path, and the * represents the vehicle desired yaw rate.

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

[0016] When the system has a bounded disturbance d=v y , the first system state quantity The kinematic outer loop control law u γ =γ * , a first formula is obtained according to the path tracking error kinematic equation;

[0017] The second formula is substituted into the first formula to obtain 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 represents a component of the first system state quantity x γ equivalent to e, the x γ2 represents a component of the first system state quantity x γ equivalent to , the z γ1 represents a virtual control quantity, the z γ2 represents a virtual control quantity, and the tau represents a virtual control law.

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

[0026]

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

[0028] In a fifth possible implementation of the first aspect, a second nominal model is determined based on the linear two-degree-of-freedom dynamics equation and the vehicle desired yaw rate, including:

[0029] The second nominal model is determined according to the linear two-degree-of-freedom dynamics equation, a second system state quantity, and a 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 a yaw rate difference value, the yaw rate difference value being a difference between the actual yaw rate of the vehicle and the desired yaw rate of the vehicle.

[0030] In a sixth possible implementation form of the first aspect, the linear two-DOF dynamics equation comprises:

[0031]

[0032] wherein the v x , v y represent the vehicle longitudinal velocity and the vehicle lateral velocity, respectively, the y represents the vehicle actual yaw rate, the C f represents the vehicle front axle cornering stiffness, the C r represents the vehicle rear axle cornering stiffness, the l f represents the distance from the vehicle center of mass to the front axle, the l r represents the distance from the vehicle center of mass to the rear axle, the m represents the vehicle body mass, the I z represents the vehicle yaw moment of inertia, and the d f represents the vehicle front wheel steering angle.

[0033] In a seventh possible implementation form of the first aspect, determining a second nominal model based on the linear two-DOF dynamics equation comprises:

[0034] when a second system state quantity x δ = [x δ1 x δ2 ] T = [v y y - y * ] T , a system control input u δ = d f , the second nominal model is obtained according to the linear two-DOF dynamics equation;

[0035] The second nominal model comprises:

[0036]

[0037] wherein the x δ1 represents a component of the second system state quantity x δ equivalent to the vehicle lateral velocity, and the x δ2 represents a difference between the vehicle actual yaw rate y and the vehicle desired yaw rate y * .

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

[0039] u δ2 = W * (x* ) -1 x δ ;

[0040] wherein, the u δ2 denotes the state feedback control law, the W * , X * denote matrix variables respectively.

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

[0042]

[0043] wherein, the u δ1 denotes the feedforward control law, the ψ des denotes a desired heading angle, and the f3 denotes a component of a preset matrix.

[0044] In a tenth possible implementation of the first aspect, the vehicle front wheel steering angle is calculated based on the state feedback control law and the feedforward control law, comprising:

[0045] determining a sum of the state feedback control law and the feedforward control law as the vehicle front wheel steering angle.

[0046] In a second aspect, the embodiments of the present application provide a vehicle control device based on automatic driving, the device comprising:

[0047] a first establishing unit configured to establish a state quantity comprising a lateral position deviation and a yaw angle deviation between a reference point on a target planning path and a vehicle mass center point, a parameter quantity comprising a vehicle speed and a curvature at the reference point on the target planning path, and a control quantity comprising a path tracking error kinematics equation of a vehicle desired yaw angular velocity, wherein the reference point is a point on the target planning path closest to the vehicle mass center point;

[0048] a first determining unit configured to determine a first nominal model based on the path tracking error kinematics equation;

[0049] a second determining unit configured to determine a kinematics outer loop control law by using the first nominal model and a backstepping method;

[0050] a first calculating unit configured to calculate the vehicle desired yaw angular velocity based on the kinematics outer loop control law;

[0051] a second establishing unit, configured to establish a linear two-degree-of-freedom dynamics equation of a state quantity including a vehicle lateral velocity and a vehicle actual yaw rate, a parameter quantity including a vehicle longitudinal velocity, a vehicle front axle cornering stiffness, a vehicle rear axle cornering stiffness, a distance from a vehicle mass center point to a front axle of the vehicle, a distance from the vehicle mass center point to a rear axle of the vehicle, a vehicle body mass, and a vehicle yaw moment of inertia, and a control quantity including a vehicle front wheel steering angle;

[0052] a third determining unit, configured to determine a second nominal model based on the linear two-degree-of-freedom dynamics equation and the vehicle desired yaw rate;

[0053] a fourth determining unit, configured to determine a state feedback control law based on the second nominal model and a small gain theorem, and determine a feedforward control law based on the second nominal model and a Lyapunov terminal theorem;

[0054] a second calculating unit, configured to calculate the vehicle front wheel steering angle based on the state feedback control law and the feedforward control law;

[0055] a control unit, configured to control the vehicle to steer according to the vehicle front wheel steering angle.

[0056] In a first possible implementation manner of the second aspect, the first determining unit is configured to determine the first nominal model according to the path tracking error kinematics equation, a system bounded disturbance, a first system state quantity, the kinematics outer loop control law, a virtual state quantity, and a virtual control law, where 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 kinematics outer loop control law is equivalent to the vehicle 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.

[0057] In a second possible implementation manner of the second aspect, the path tracking error kinematics equation includes:

[0058]

[0059] where e represents the lateral position deviation, and represents the yaw angle deviation, v x , v y represent a vehicle longitudinal velocity and a vehicle lateral velocity in the vehicle velocity respectively, p represents a curvature at the reference point on the target planning path, and * represents the vehicle desired yaw rate.

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

[0061] When the system has a bounded disturbance d=v y , the first system state variable The kinematic outer loop control law u γ =γ * , a first formula is obtained according to the path tracking error kinematic equation;

[0062] The second formula is substituted into the first formula to obtain 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 represents a component of the first system state variable x γ equivalent to e, the x γ2 represents a component of the first system state variable x γ equivalent to The z γ1 represents a virtual state variable, the z γ2 represents a virtual state variable, and the tau represents a virtual control law.

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

[0071]

[0072] Wherein, the kappa1, kappa2, kappa3 respectively represent kinematic outer loop control law adjustable parameters.

[0073] In a fifth possible implementation manner of the second aspect, the third determining unit is configured to determine the second nominal model according to the linear two-degree-of-freedom dynamics equation, a second system state quantity, and a system control input, wherein the second system state quantity comprises a first component of the second system state quantity equivalent to the lateral velocity of the vehicle and a second component of the second system state quantity equivalent to a yaw rate difference value, the yaw rate difference value being a difference between the actual yaw rate of the vehicle and the expected yaw rate of the vehicle.

[0074] In a sixth possible implementation manner of the second aspect, the linear two-degree-of-freedom dynamics equation comprises:

[0075]

[0076] wherein the v x , the v y , the γ represents the actual yaw rate of the vehicle, the C f represents the front axle cornering stiffness of the vehicle, the C r represents the rear axle cornering stiffness of the vehicle, the l f represents a distance from the center of mass of the vehicle to the front axle of the vehicle, the l r represents a distance from the center of mass of the vehicle to the rear axle of the vehicle, the m represents the mass of the vehicle body, and the I z represents the yaw moment of inertia of the vehicle, and the δ f represents the front wheel steering angle of the vehicle.

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

[0078] when a second system state quantity x δ = [x δ1 x δ2 ] T = [v y γ-γ * ] T , and a system control input u δ = δ f , the second nominal model is obtained according to the linear two-degree-of-freedom dynamics equation.

[0079] The second nominal model comprises:

[0080]

[0081] wherein the x δ1 represents a component of the second system state quantity x δ equivalent to the lateral velocity of the vehicle, and the x δ2represents a difference between the actual yaw rate γ of the vehicle and the expected yaw rate γ of the vehicle. *

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

[0083] u δ2 =W * (X * ) -1 x δ ;

[0084] wherein the u δ2 represents the state feedback control law, the W * , X * respectively represent matrix variables.

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

[0086]

[0087] wherein the u δ1 represents the feedforward control law, the ψ des represents an expected heading angle, and the f3 represents a component of a preset matrix.

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

[0089] In a third aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the method according to any possible implementation manner of the first aspect.

[0090] In a fourth aspect, an embodiment of the present application provides an electronic device, which comprises:

[0091] one or more processors;

[0092] the processor is coupled with a storage device, and the storage device is configured to store one or more programs;

[0093] when the one or more programs are executed by the one or more processors, the electronic device implements the method according to any possible implementation manner of the first aspect.

[0094] In a fifth aspect, an embodiment of the present application provides a vehicle, which comprises the apparatus according to any possible implementation manner of the second aspect, or comprises the electronic device according to the fourth aspect.

[0095] ​In a sixth aspect, an embodiment of the present application provides a computer program product, which comprises instructions. When the instructions are executed on a computer or a processor, the computer or the processor executes the method in any possible implementation manner of the first aspect.

[0096] The vehicle control method, device, medium, equipment and vehicle based on automatic driving provided by the embodiments of the present application decouple the construction of a kinematic control nominal model (i.e., a first nominal model) and a dynamic control nominal model (i.e., a second nominal model), the outer loop control based on the first nominal model calculates an expected vehicle yaw rate according to the deviation of the current driving path of the vehicle from the target planning path, the inner loop control based on the second nominal model controls the front wheel steering angle of the vehicle in real time, so that the vehicle stably, quickly and accurately tracks the expected vehicle yaw rate and minimizes the lateral speed of the vehicle, thereby making the path tracking control of the automatic driving vehicle present a high-performance performance of "fast convergence, high precision and strong robustness".

[0097] The double-closed-loop decoupling design simplifies the control nominal model, thereby solving the problems of high complexity and poor stability of the automatic driving vehicle path tracking control system designed based on a high-dimensional and strong nonlinear control nominal model, and the state feedback control law and the feedforward control law determined based on the small gain theorem and the final value theorem of the Laplace transform take into account the control performance and robustness requirements, so that the automatic driving vehicle path tracking control system can better cope with real-time change scenarios of factors such as road adhesion, vehicle load and driving conditions.

[0098] Based on the path tracking error kinematic equation, the backstepping method is used to design the kinematic outer loop control law, based on the linear two-degree-of-freedom dynamic equation containing the perturbation of the front axle and rear axle cornering stiffness parameters 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 in the absence of disturbance terms, and the feedforward control law is determined by using the final value theorem of the Laplace transform, so as to suppress the influence of the disturbance term caused by the expected vehicle yaw rate on the control performance of the closed-loop system. BRIEF DESCRIPTION OF DRAWINGS

[0099] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application. Those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0100] Figure 1 A flowchart of a vehicle control method based on automatic driving provided by an embodiment of the present application is shown in the figure.

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

[0102] Figure 3 This is an example diagram of a linear two-degree-of-freedom dynamic model provided in an embodiment of the present application;

[0103] Figure 4 A block diagram of a vehicle control device based on autonomous driving provided in an embodiment of the present application;

[0104] Figure 5 A schematic diagram of the structure of an electronic device or computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0105] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

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

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

[0108] S110: Establishing state quantities including the lateral position deviation and yaw angle deviation between the reference point on the target planning path and the vehicle center of mass, parameter quantities including the vehicle speed and the curvature at the reference point on the target planning path, and control quantities including the path tracking error kinematic equation of the vehicle's desired yaw angular velocity.

[0109] The reference point is the point on the target planned path that is the smallest distance from the vehicle's center of mass. The target planned path is the path automatically planned by the navigation system after the user enters the starting point (which can be the default current location) and the end point in the navigation system interface. The yaw angle deviation is the yaw angle deviation between the reference point on the target planned path and the vehicle's center of mass.

[0110] The path tracking error kinematics equation can be an equation finally established in the Serret-Frenet coordinate system according to the relationship among the inertial coordinate system, the Serret-Frenet coordinate system and the vehicle coordinate system.

[0111] The path tracking error kinematics equation comprises:

[0112]

[0113] wherein e represents a lateral position deviation, represents a yaw angle deviation, v x , v y respectively represent a vehicle longitudinal speed and a vehicle lateral speed in the vehicle speed, p represents a curvature at a reference point on the target planning path, and g * represents a vehicle expected yaw angular speed,

[0114] The establishment process of the path tracking error kinematics equation is described in detail as follows:

[0115] As shown in a vehicle path tracking error schematic diagram, Figure 2 {N}, {F} and {B} are respectively an inertial coordinate system, a Serret-Frenet coordinate system with a point on a target planning path (i.e. a target path in the figure) closest to a vehicle mass center point as an origin and a vehicle coordinate system with the vehicle mass center point as an origin. It is known that a vector radius of the vehicle mass center point relative to the origin of the inertial coordinate system {N} is r B / N , a vector radius of the origin of the Serret-Frenet coordinate system {F} relative to the origin of the inertial coordinate system {N} is r F / N , and a vector radius of the vehicle mass center point relative to the origin of the Serret-Frenet coordinate system {F} is r B / F , so that

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

[0117] In the Serret-Frenet coordinate system {F}, the absolute derivative of the vector radius r B / N in the Serret-Frenet coordinate system {F} is obtained by differentiating formula (1), and the array is

[0118]

[0119] In the formula, and are respectively the vector radius r F / Nabsolute derivative of in the Serret-Frenet coordinate system {F}, the column matrix of the absolute derivative of in the Serret-Frenet coordinate system {F}, the column matrix of the absolute derivative of in the Serret-Frenet coordinate system {F}, and the vector magnitude r B / F relative derivative of in the Serret-Frenet coordinate system {F}, the angular velocity vector of the Serret-Frenet coordinate system {F} in the Serret-Frenet coordinate system {F}, the column matrix of the angular velocity vector of the Serret-Frenet coordinate system {F} in the Serret-Frenet coordinate system {F}, and the vector magnitude r B / F the column matrix in the Serret-Frenet coordinate system {F}, which can be respectively expressed as

[0120]

[0121]

[0122]

[0123]

[0124] where s is the arc length of the target planning path curve; 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 mass center point and the origin of the Serret-Frenet coordinate system. If the origin of the Serret-Frenet coordinate system is selected as the reference point, e also represents the lateral position deviation of the vehicle mass center point from the reference point on the target planning path.

[0125] If the vector magnitude r B / N the column matrix of the absolute derivative of in the vehicle coordinate system {B} is then and satisfy the following coordinate transformation relationship

[0126]

[0127] where is the yaw angle deviation of the vehicle mass center point from the reference point on the target planning path, which can be expressed as

[0128]

[0129] where is the yaw angle of the vehicle.

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

[0131]

[0132] According to the Frenet formula, we have

[0133]

[0134] where p is the curvature at the reference point on the target planning path.

[0135] Substituting formula (10) into formula (9), the above path tracking error kinematics equation can be obtained.

[0136] S120: determining a first nominal model based on the path tracking error kinematics equation, determining a kinematics outer loop control law by using the first nominal model and backstepping method, and calculating a vehicle expected yaw rate based on the kinematics outer loop control law.

[0137] Nominal Model is simply a model used for designing a controller or performing system analysis, because the True Model is generally difficult to obtain in reality, and in practical applications, the parameterized model (such as transfer function or state space equation) or non-parameterized model (such as frequency response) of the controlled object is obtained through system identification and other methods, and these models are only an approximation of the True Model in a strict sense, and therefore are called Nominal Model.

[0138] After obtaining the path tracking error kinematics equation, a first nominal model is determined according to the path tracking error kinematics equation, system bounded disturbance, first system state quantity, kinematics outer loop control law, virtual state quantity and 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 kinematics outer loop control law is equivalent to the vehicle expected yaw rate, 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.

[0139] When the vehicle longitudinal velocity remains unchanged and the vehicle lateral velocity satisfies |v y |≤η1v x , the system bounded disturbance d=v y , the first system state quantity The kinematics outer loop control law u γ =γ * . Wherein η1 represents an adjustable parameter, x γ1 represents a component of the first system state quantity x γ equivalent to e, and x γ2 represents a component of the first system state quantity x γ equivalent to .

[0140] When the system bounded disturbance d=v y , the first system state quantity The kinematics outer loop control law uγ = γ * wherein the first formula is obtained according to a path tracking error kinematics equation;

[0141] substituting the second formula into the first formula, a first nominal model is obtained;

[0142] wherein the first formula comprises:

[0143]

[0144] In the process of tracking the target planning path by the vehicle, it is generally required that the vehicle head direction always points to the target planning path, and the yaw angle deviation change trend of the vehicle mass center point and the reference point on the target planning path depends on the lateral position deviation change trend of the two, so that the vehicle smoothly approaches the reference point on the target planning path. Therefore, the second formula comprises:

[0145]

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

[0147]

[0148] wherein z γ1 represents a virtual state variable, z γ2 represents a virtual state variable, τ represents a virtual control law, and ε represents an adjustable parameter, and ε > 0.

[0149] In an embodiment, the kinematics outer loop control law u γ comprises:

[0150]

[0151] wherein κ1, κ2, κ3 respectively represent adjustable parameters of the kinematics outer loop control law.

[0152] The specific implementation process of determining the kinematics outer loop control law by using the first nominal model and the backstepping method is described as follows:

[0153] First, a Lyapunov candidate function is constructed as

[0154]

[0155] In the formula, κ1 > 0.

[0156] Deriving the formula (11), the following formula is obtained:

[0157]

[0158] By and |v y |≤η1v x , we have

[0159]

[0160] Augmented Lyapunov candidate function V1, we have

[0161]

[0162] where κ2>0.

[0163] Taking the derivative of equation (14) and combining with inequality (13), we have

[0164]

[0165] Define the kinematic outer-loop control law as

[0166]

[0167] where κ3>0.

[0168] Substitute equation (16) into inequality (15), we have

[0169]

[0170] where 0<θ<1, θ represents an adjustable parameter.

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

[0172]

[0173] where η2represents an adjustable parameter.

[0174] From inequality (17) and inequality (18), we have

[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 state trajectory of the closed-loop system constituted by the kinematic outer-loop control law defined by equation (16) is uniformly ultimately bounded. Meanwhile, by applying the trigonometric inequality relation to equation (14), it can be obtained that

[0179]

[0180] According to the definition of the vector infinite norm, it can be obtained that

[0181]

[0182]

[0183] Substituting inequalities (22) and (23) into inequality (21), it can be obtained that

[0184] α1(||z γ || ∞ )≤V2≤α2(||z γ || ∞ ) (24)

[0185] In the formula, α1(||z γ || ∞ ) and α2(||z γ || ∞ ) are K-type functions, which can be respectively represented as

[0186]

[0187]

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

[0189]

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

[0191] Through equations (11)-(15) and (17)-(27), not only the kinematic outer-loop control law is obtained, but also its rationality is verified.

[0192] S130: A linear two-degree-of-freedom dynamics equation including state variables such as lateral velocity of the vehicle, actual yaw rate of the vehicle, and parameters such as longitudinal velocity of the vehicle, front axle side stiffness of the vehicle, rear axle side stiffness of the vehicle, distance from the mass center point of the vehicle to the front axle of the vehicle, distance from the mass center point of the vehicle to the rear axle of the vehicle, mass of the vehicle body, and yaw moment of inertia of the vehicle, and control variables such as linear two-degree-of-freedom dynamics equation of the front wheel steering angle of the vehicle is established.

[0193] The linear two-degree-of-freedom dynamics equation includes:

[0194]

[0195] where v x , v y represent the vehicle longitudinal speed and the vehicle lateral speed, respectively, γ represents the vehicle actual yaw rate, C f represents the vehicle front axle cornering stiffness, C r represents the vehicle rear axle cornering stiffness, l f represents the distance from the vehicle mass center point to the front axle of the vehicle, l r represents the distance from the vehicle mass center point to the rear axle of the vehicle, m represents the vehicle body mass, I z represents the vehicle yaw moment of inertia, δ f represents the vehicle front wheel steering angle.

[0196] The establishment process of the linear two-degree-of-freedom dynamics equation is described below:

[0197] The dynamics relationship between the vehicle yaw rate dynamics and the vehicle lateral speed dynamics and the vehicle front wheel steering angle will be established below based on the linear two-degree-of-freedom dynamics model shown in FIG. 1. Figure 3

[0198] The following equation is established:

[0199]

[0200] In the formula, F yf and F yr are the lateral forces of the front axle and the rear axle of the vehicle, respectively, and can be represented as

[0201]

[0202] Substituting formula (29) into formula (28), the linear two-degree-of-freedom dynamics equation can be obtained.

[0203] S140: determining a second nominal model based on the linear two-degree-of-freedom dynamics equation and the expected vehicle yaw rate, determining a state feedback control law based on the second nominal model and the small gain theorem, and determining a feedforward control law based on the second nominal model and the final value theorem of Laplace transform, calculating the vehicle front wheel steering angle based on the state feedback control law and the feedforward control law, and controlling the vehicle to steer according to the vehicle front wheel steering angle.

[0204] The target of the inner loop control of vehicle dynamics is to make the vehicle track the expected vehicle yaw rate stably, quickly and accurately, and minimize the vehicle lateral speed, so as to reduce the steady-state tracking error of the outer loop control of vehicle kinematics, by controlling the vehicle front wheel steering angle in real time.

[0205] ​After obtaining the linear two-degree-of-freedom dynamics equation, a second nominal model is determined according to the linear two-degree-of-freedom dynamics equation, second system state quantity and system control input, wherein the second system state quantity comprises a first component of the second system state quantity equivalent to the lateral velocity of the vehicle and a second component of the second system state quantity equivalent to the difference between the actual yaw rate of the vehicle and the expected yaw rate of the vehicle.

[0206] To this end, the second system state quantity x δ is defined as δ1 x δ2 ] T = [v y γ-γ * ] T , the system control input u δ = δ f , and the second nominal model is obtained according to the linear two-degree-of-freedom dynamics equation based on this;

[0207] The second nominal model comprises:

[0208]

[0209] wherein x δ1 represents the component of the second system state quantity x δ equivalent to the lateral velocity of the vehicle, and x δ2 represents the difference between the actual yaw rate γ of the vehicle and the expected yaw rate γ * of the vehicle.

[0210] In an embodiment, the state feedback control law comprises:

[0211] u δ2 = W * (X * ) -1 x δ ;

[0212] wherein u δ2 represents the state feedback control law, W * and X * represent matrix variables respectively.

[0213] In an embodiment, the feedforward control law comprises:

[0214]

[0215] wherein u δ1 represents the feedforward control law, ψ des represents the expected heading angle, and f3 represents a component of a preset matrix. The preset matrix comprises a matrix KC, which is the product of a matrix K and a matrix C as follows.

[0216] The following describes the specific implementation process 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 Lyapunov transform final value theorem:

[0217] Considering the problem of parameter perturbation of the front axle and rear axle cornering stiffness of the vehicle caused by the strong coupling and nonlinear characteristics of the vehicle during driving, the front axle and rear axle cornering stiffness of the vehicle are corrected as

[0218]

[0219] In the formula, C f0 and C r0 are the nominal values of the front axle and rear axle cornering stiffness of the vehicle; C fe and C re are the maximum perturbation values of the front axle and rear axle cornering stiffness of the vehicle.

[0220] Substituting formula (30) into the formula of the second nominal model, the following formula can be obtained:

[0221]

[0222] In the formula, A, B and C are nominal matrices of the system, and ΔA, ΔB and ΔC are uncertainty matrices reflecting the perturbation of the front axle and rear axle cornering stiffness parameters of the vehicle, which can be respectively represented as

[0223]

[0224]

[0225]

[0226] [ΔA ΔB ΔC]=DF[E1 E2 E3] (35)

[0227] In the formula, F T is the perturbation matrix satisfying F D, E1, E2 and E3 are constant matrices representing the perturbation structure of the system, which can be respectively represented as

[0228]

[0229]

[0230]

[0231]

[0232] In the formula, the expected vehicle yaw rate in formula (31) is considered as a disturbance term, and the feedforward control law u δ1The influence of the disturbance term on the control performance is inhibited. Meanwhile, in view of the model uncertainty caused by the perturbation of the front and rear axle cornering stiffness parameters of the vehicle included in equation (31), the embodiment of the application independently designs a state feedback control law u δ2 so that the state trajectory of the closed-loop system is asymptotically stable in the absence of the disturbance term. Therefore, in the design of the state feedback control law, equation (31) can be simplified as

[0233]

[0234] Separating the nominal part and the uncertainty part of equation (40), we have

[0235]

[0236] The state feedback control law u δ2 = Kx δ , and the closed-loop system is

[0237]

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

[0239]

[0240] By left and right multiplying the matrix on the left side of the nonlinear matrix inequality (43) with the diagonal matrix diag{P -1 , I, I} respectively, we have

[0241]

[0242] Define X = P -1 and W = KP -1 , and the nonlinear matrix inequality (44) can be converted into an equivalent linear matrix inequality about the matrix variables X and W:

[0243]

[0244] If the feasible solution of 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] Substitute formula (46) into the second nominal model, and use the Laplace transform final value theorem to obtain the state feedback control law and the feedforward control law that make the closed-loop system state steady-state value x δ1_ss = 0 and The feedforward control law is

[0247]

[0248] In an embodiment, 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 vehicle front wheel steering angle.

[0249] The vehicle control method based on automatic driving provided by the embodiments of the present application decouples the kinematic control nominal model (i.e., the first nominal model) and the dynamic control nominal model (i.e., the second nominal model), calculates the expected vehicle yaw rate based on the deviation between the current driving path and the target planning path of the vehicle according to the outer loop control of the first nominal model, controls the vehicle front wheel steering angle in real time based on the inner loop control of the second nominal model, so that the vehicle stably, quickly and accurately tracks the expected vehicle yaw rate and minimizes the vehicle lateral speed, thereby making the path tracking control of the automatic driving vehicle present a high-performance performance of "fast convergence, high precision and strong robustness".

[0250] The double closed-loop decoupling design simplifies the control nominal model, thereby solving the problems of high complexity and poor stability of the automatic driving vehicle path tracking control system based on the design of a high-dimensional and strong nonlinear control nominal model, and the state feedback control law and the feedforward control law determined based on the small gain theorem and the Laplace transform final value theorem take into account the control performance and robustness requirements, so that the automatic driving vehicle path tracking control system can better cope with real-time change scenarios of factors such as road adhesion, vehicle load and driving conditions.

[0251] Based on the path tracking error kinematic equation, the kinematic outer loop control law is designed by using the backstepping method, based on the linear two-degree-of-freedom dynamic equation containing the perturbation of the front axle and rear axle side stiffness parameters 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 in the absence of disturbance terms, and the feedforward control law is determined by using the Laplace transform final value theorem, so as to suppress the influence of the disturbance term caused by the expected vehicle yaw rate on the control performance of the closed-loop system.

[0252] It should be noted that the embodiments of the present application can not only be applied to automatic driving scenarios such as high-speed piloting driving, urban piloting driving and autonomous guest parking of passenger cars, but also be applied to automatic driving scenarios such as high-speed piloting driving and high-speed formation driving of commercial vehicles. Therefore, the passenger car automatic driving system and the commercial vehicle automatic driving system using the method both belong to the scope of protection of the embodiments of the present application.

[0253] Based on the above method embodiments, another embodiment of the present application provides a framework of a vehicle control system based on automatic driving, which comprises a kinematics outer loop control module and a dynamics inner loop control module;

[0254] The kinematics outer loop control module is configured to establish a state quantity comprising a lateral position deviation and a yaw angle deviation between a reference point on a target planning path and a vehicle center point, a parameter quantity comprising a vehicle speed and a curvature at the reference point on the target planning path, and a control quantity comprising a path tracking error kinematics equation of a vehicle expected yaw angular velocity, wherein the reference point is a point on the target planning path closest to the vehicle center point; determine a first nominal model based on the path tracking error kinematics equation, determine a kinematics outer loop control law by using the first nominal model and a backstepping method, and calculate the vehicle expected yaw angular velocity based on the kinematics outer loop control law.

[0255] The dynamics inner loop control module is configured to establish a state quantity comprising a vehicle lateral speed and a vehicle actual yaw angular velocity, a parameter quantity comprising a vehicle longitudinal speed, a vehicle front axle cornering stiffness, a vehicle rear axle cornering stiffness, a distance from a vehicle center point to a front axle of the vehicle, a distance from the vehicle center point to a rear axle of the vehicle, a vehicle body mass, and a vehicle yaw moment of inertia, and a control quantity comprising a linear two-degree-of-freedom dynamics equation of a vehicle front wheel steering angle; determine a second nominal model based on the linear two-degree-of-freedom dynamics equation and the vehicle expected yaw angular velocity, determine a state feedback control law based on the second nominal model and a small gain theorem and determine a feedforward control law based on the second nominal model and a Lyapunov transform final value theorem, calculate the vehicle 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 front wheel steering angle.

[0256] In the framework of the vehicle control system based on automatic driving provided by the embodiments of the present application, the kinematics outer loop control module calculates an output expected vehicle yaw angular velocity according to a deviation between a current driving path of the vehicle and a target planning path, and the dynamics inner loop control module controls a vehicle front wheel steering angle in real time to enable the vehicle to stably, quickly and accurately track the expected vehicle yaw angular velocity and minimize a vehicle lateral speed.

[0257] Corresponding to the above method embodiments, another embodiment of the present application provides a vehicle control device based on automatic driving, as shown in Figure 4 The device comprises:

[0258] A first establishment unit 210 is configured to establish a state quantity comprising a lateral position deviation and a yaw angle deviation between a reference point on a target planning path and a vehicle center point, a parameter quantity comprising a vehicle speed and a curvature at the reference point on the target planning path, and a control quantity comprising a path tracking error kinematics equation of a vehicle expected yaw angular velocity, wherein the reference point is a point on the target planning path closest to the vehicle center point.

[0259] The first determining unit 220 is configured to determine a first nominal model based on the path tracking error kinematics equation.

[0260] The second determining unit 230 is configured to determine a kinematics outer loop control law by using the first nominal model and a backstepping method.

[0261] The first calculating unit 240 is configured to calculate the vehicle desired yaw rate based on the kinematics outer loop control law.

[0262] The second establishing unit 250 is configured to establish a linear two-degree-of-freedom dynamics equation of a state variable including a vehicle lateral velocity, a vehicle actual yaw rate, a parameter variable including a vehicle longitudinal velocity, a vehicle front axle cornering stiffness, a vehicle rear axle cornering stiffness, a distance from a vehicle mass center point to a front axle of the vehicle, a distance from the vehicle mass center point to a rear axle of the vehicle, a vehicle body mass and a vehicle yaw moment of inertia, and a control variable including a vehicle front wheel steering angle.

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

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

[0265] The second calculating unit 280 is configured to calculate the vehicle front wheel steering angle based on the state feedback control law and the feedforward control law.

[0266] The control unit 290 is configured to control the vehicle to steer according to the vehicle front wheel steering angle.

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

[0268] In a possible implementation, the path tracking error kinematics equation comprises:

[0269]

[0270] wherein the e represents the lateral position deviation, the represents the yaw angle deviation, the v x , v y represent a vehicle longitudinal speed and a vehicle lateral speed in the vehicle speed respectively, the p represents a curvature at the reference point on the target planning path, the g * represents a desired yaw angular speed of the vehicle.

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

[0272] When the system has a bounded disturbance d = v y , a first system state quantity , the kinematic outer loop control law u γ = g * , a first formula is obtained according to the path tracking error kinematic equation;

[0273] The second formula is substituted into the first formula to obtain the first nominal model;

[0274] wherein the first formula comprises:

[0275]

[0276] The second formula comprises:

[0277]

[0278] The first nominal model comprises:

[0279]

[0280] wherein the x γ1 represents a component of the first system state quantity x γ equivalent to e, the x γ2 represents a component of the first system state quantity x γ equivalent to , the z γ1 represents a virtual state quantity, the z γ2 represents a virtual state quantity, and the t represents a virtual control law.

[0281] In a possible implementation, the kinematic outer loop control law comprises:

[0282]

[0283] wherein, the κ1, κ2, κ3 respectively represent a kinematics outer loop control law adjustable parameter.

[0284] In a possible implementation, the third determining unit 260 is configured to determine the second nominal model according to the linear two-degree-of-freedom dynamics equation, a second system state quantity, and a system control input, where 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 a yaw rate difference value, the yaw rate difference value being a difference between the actual yaw rate of the vehicle and the expected yaw rate of the vehicle.

[0285] In a possible implementation, the linear two-degree-of-freedom dynamics equation includes:

[0286]

[0287] wherein, the v x , v y respectively represent the vehicle longitudinal velocity and the vehicle lateral velocity, the γ represents the actual yaw rate of the vehicle, the C f represents the vehicle front axle cornering stiffness, the C r represents the vehicle rear axle cornering stiffness, the l f represents a distance from the vehicle mass center point to the front axle of the vehicle, the l r represents a distance from the vehicle mass center point to the rear axle of the vehicle, the m represents the vehicle body mass, the I z represents the vehicle yaw moment of inertia, and the δ f represents the front wheel steering angle of the vehicle.

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

[0289] when the second system state quantity x δ = [x δ1 x δ2 ] T = [v y γ-γ * ] T , the system control input u δ = δ f , the second nominal model is obtained according to the linear two-degree-of-freedom dynamics equation.

[0290] The second nominal model includes:

[0291]

[0292] wherein, the x δ1a second system state quantity x equivalent to a lateral velocity of the vehicle δ a component of x δ2 a difference between an actual yaw rate γ of the vehicle and an expected yaw rate γ * of the vehicle.

[0293] In a possible implementation, the state feedback control law comprises:

[0294] u δ2 = W * (X * ) -1 x δ ;

[0295] wherein u δ2 represents the state feedback control law, W * and X * respectively represent matrix variables.

[0296] In a possible implementation, the feedforward control law comprises:

[0297]

[0298] wherein u δ1 represents the feedforward control law, ψ des represents an expected heading angle, and f3 represents a component of a preset matrix.

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

[0300] The vehicle control device based on automatic driving provided by the embodiments of the present application decouples a kinematic control nominal model (i.e., a first nominal model) and a dynamic control nominal model (i.e., a second nominal model), calculates an expected vehicle yaw rate based on a deviation between a current driving path of the vehicle and a target planning path according to an outer loop control of the first nominal model, controls a front wheel steering angle of the vehicle in real time based on an inner loop control of the second nominal model, so that the vehicle stably, quickly and accurately tracks the expected vehicle yaw rate and minimizes a lateral velocity of the vehicle, thereby making the path tracking control of the automatic driving vehicle present a high performance performance of "fast convergence, high precision and strong robustness".

[0301] The control nominal model is simplified through double closed-loop decoupling design, so as to solve the problems of high complexity and poor stability of the path tracking control system of the autonomous vehicle designed based on a high-dimensional and strong nonlinear control nominal model, and the state feedback control law and the feedforward control law determined based on the small gain theorem and the final value theorem of Laplace transform take into account the control performance and robustness requirements, so that the path tracking control system of the autonomous vehicle can better cope with real-time change scenarios of road adhesion, vehicle load and driving conditions and the like.

[0302] Based on the kinematic equation of the path tracking error, a kinematic outer loop control law is designed by using the backstepping method, based on the linear two-degree-of-freedom dynamic equation containing the perturbation of the front axle and rear axle side stiffness parameters of the vehicle, a 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 in the absence of disturbance terms, and a feedforward control law is determined by using the final value theorem of Laplace transform, so as to suppress the influence of the disturbance term caused by the expected vehicle yaw rate on the control performance of the closed-loop system.

[0303] Based on the above method embodiment, another embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method according to any one of the above embodiments.

[0304] Based on the above method embodiment, another embodiment of the present application provides an electronic device or a computer device, as shown in the following Figure 5 The electronic device or the computer device comprises:

[0305] one or more processors 310;

[0306] The processor 310 is coupled with a storage device 320, and the storage device 320 is configured 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 the computer device implements the method according to any one of the above embodiments.

[0308] Based on the above method embodiment, another embodiment of the present application provides a vehicle comprising the device according to any one of the above embodiments, or comprising the electronic device as described above.

[0309] The vehicle includes a CPU (Central Processing Unit), a T-Box (Telematics Box), and various sensors, such as a speed sensor, an angular velocity sensor, a position sensor, and the like. Different sensors are used to measure different information, such as a speed sensor measuring a lateral speed of the vehicle and a longitudinal speed of the vehicle. After the CPU obtains the information measured by the sensors, the vehicle can be controlled to travel by executing the vehicle control method based on automatic driving provided in any of the above embodiments. The CPU can also upload the information measured by the sensors to a server through the T-Box, and obtain the front wheel steering angle of the vehicle by executing the vehicle control method based on automatic driving provided in any of the above embodiments, and issue the front wheel steering angle of the vehicle to the T-Box, so that the T-Box transmits the front wheel steering angle of the vehicle to a relevant controller to control the steering of the vehicle.

[0310] Based on the above embodiments, another embodiment of the present application provides a computer program product containing instructions, when the instructions are executed on a computer or a processor, the computer or the processor executes the method according to any of the above embodiments.

[0311] The device embodiments correspond to the method embodiments and have the same technical effects. For details, refer to the method embodiments. The device embodiments are based on the method embodiments. For details, refer to the method embodiments, which will not be described here. Those skilled in the art can understand that the drawings are only schematic diagrams of an embodiment, and the modules or processes in the drawings are not necessarily required to implement the present application.

[0312] Those skilled in the art can understand that the modules in the device in the embodiments can be distributed in the device in the embodiments as described in the embodiments, or can be changed and located in one or more devices different from the embodiments. The modules in the above embodiments can be combined into one module, or can be further split into multiple sub-modules.

[0313] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle control method based on autonomous driving, characterized by, The method comprises: The state quantity comprises lateral position deviation and yaw angle deviation between a reference point on a target planning path and a vehicle mass center point, the parameter quantity comprises vehicle speed and curvature at the reference point on the target planning path, and the control quantity comprises a path tracking error kinematics equation of vehicle expected yaw angular velocity, wherein the reference point is a point on the target planning path closest to the vehicle mass center point; A first nominal model is determined based on the path tracking error kinematics equation, a kinematics outer loop control law is determined by using the first nominal model and a backstepping method, and the vehicle expected yaw angular velocity is calculated based on the kinematics outer loop control law; The state quantity comprises vehicle lateral speed and vehicle actual yaw angular velocity, the parameter quantity comprises vehicle longitudinal speed, vehicle front axle side stiffness, vehicle rear axle side stiffness, distance from the vehicle mass center point to the front axle of the vehicle, distance from the vehicle mass center point to the rear axle of the vehicle, vehicle body mass and vehicle yaw rotational inertia, and the control quantity comprises a linear two-degree-of-freedom dynamics equation of vehicle front wheel steering angle; A second nominal model is determined based on the linear two-degree-of-freedom dynamics equation and the vehicle expected yaw angular velocity, a state feedback control law is determined based on the second nominal model and a small gain theorem, a feedforward control law is determined based on the second nominal model and a Lyapunov transform final value theorem, the vehicle front wheel steering angle is calculated based on the state feedback control law and the feedforward control law, and the vehicle is controlled to steer according to the vehicle front wheel steering angle.

2. The method of claim 1, wherein, The first nominal model is determined based on the path tracking error kinematics equation, comprising: The first nominal model is determined based on the path tracking error kinematics equation, system bounded disturbance, first system state quantity, the kinematics outer loop control law, virtual state quantity and virtual control law, wherein the system bounded disturbance is equivalent to the vehicle lateral speed, the first system state quantity comprises 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 kinematics outer loop control law is equivalent to the vehicle expected yaw angular velocity, the virtual state quantity comprises 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 of claim 1, wherein, The path tracking error kinematics equation comprises: Wherein, the e represents the lateral position deviation, the represents the yaw angle deviation, the v x 、v y denote the longitudinal velocity and lateral velocity of the vehicle in the vehicle velocity respectively, ρ denotes the curvature at the reference point on the target planning path, and γ * represents the desired yaw rate of the vehicle.

4. The method of claim 3, wherein, The first nominal model is determined based on the path tracking error kinematics equation, comprising: When the system has a bounded disturbance d = v y , the first system state quantity The kinematic outer loop control law u γ = γ * , a first formula is obtained according to the path tracking error kinematic equation; The second formula is substituted into the first formula to obtain the first nominal model; The first formula comprises: The second formula comprises: The first nominal model comprises: wherein said x γ1 represents a first component of a first system state quantity x γ equivalent to e, said x γ2 represents a second component of a first system state quantity x equivalent to e, said z γ represents a second component of a first system state quantity x γ1 represents a virtual state quantity, said z γ2 represents a virtual state quantity, said τ represents a virtual control law.

5. The method of claim 4, wherein, The kinematics outer loop control law comprises: Wherein, κ1, κ2, κ3 respectively represent adjustable parameters of the kinematics outer loop control law.

6. The method according to any one of claims 1-5, characterized in that, The second nominal model is determined based on the linear two-degree-of-freedom dynamics equation and the vehicle expected yaw angular velocity, comprising: The second nominal model is determined according to the linear two-degree-of-freedom dynamics equation, second system state variables, and system control input, wherein the second system state variables include a first component of the second system state variables equivalent to the vehicle lateral velocity and a second component of the second system state variables equivalent to a yaw rate difference value, the yaw rate difference value being a difference between the actual yaw rate of the vehicle and the desired yaw rate of the vehicle.

7. An automatic driving-based vehicle control device characterized by comprising: The device comprises: A first establishing unit configured to establish state variables including lateral position deviation and yaw angle deviation between a reference point on a target planning path and a vehicle center of mass point, parameter variables including vehicle speed and curvature at the reference point on the target planning path, and control variables including a path tracking error kinematics equation of a desired yaw rate of the vehicle, wherein the reference point is a point on the target planning path closest to the vehicle center of mass point; A first determining unit configured to determine a first nominal model based on the path tracking error kinematics equation; A second determining unit configured to determine a kinematics outer loop control law by using the first nominal model and a backstepping method; A first calculating unit configured to calculate the desired yaw rate of the vehicle based on the kinematics outer loop control law; A second establishing unit configured to establish state variables including vehicle lateral velocity and actual yaw rate of the vehicle, parameter variables including vehicle longitudinal velocity, vehicle front axle cornering stiffness, vehicle rear axle cornering stiffness, distance from the vehicle center of mass point to a front axle of the vehicle, distance from the vehicle center of mass point to a rear axle of the vehicle, vehicle body mass, and vehicle yaw moment of inertia, and control variables including a linear two-degree-of-freedom dynamics equation of a vehicle front wheel steering angle; A third determining unit configured to determine a second nominal model based on the linear two-degree-of-freedom dynamics equation and the desired yaw rate of the vehicle; A fourth determining unit configured to determine a state feedback control law based on the second nominal model and a small gain theorem and to determine a feedforward control law based on the second nominal model and a Laplace transform final value theorem; A second calculating unit configured to calculate the vehicle front wheel steering angle based on the state feedback control law and the feedforward control law; A control unit configured to control the vehicle to steer according to the vehicle front wheel steering angle.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the method of any one of claims 1-6.

9. An electronic device, characterized in that: The electronic device comprises: One or more processors; The processor is coupled with a storage device, and the storage device is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the electronic device implements the method of any one of claims 1-6.

10. A vehicle characterized by comprising: The vehicle comprises the device of claim 7, or comprises the electronic device of claim 9. The program, when executed by a processor, implements the method of any one of claims 1-6. The electronic device comprises: One or more processors; The processor is coupled with a storage device, and the storage device is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the electronic device implements the method of any one of claims 1-6. The vehicle comprises the device of claim 7, or comprises the electronic device of claim 9.

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