Vehicle path tracking control method and device capable of adaptively adjusting step length

Abstract

The invention provides a vehicle path tracking control method and device capable of adaptively adjusting discrete step length. The method comprises the following steps: establishing a state-space equation based on a kinetic model of a vehicle, performing Taylor expansion on a state quantity and ignoring a high-order term to obtain a linearized state quantity error; a discretized linear state quantity error is obtained based on a forward Euler method, and the discretization step length is adaptively adjusted along with the size of a front wheel steering angle; the increment of the steering angle of the front wheel is used as the control quantity of path tracking, and path tracking control of the vehicle is achieved. According to the method, the discretization step length is adaptively adjusted along with the size of the front wheel steering angle, the front wheel steering angle increment is adopted to replace the front wheel steering angle to serve as the control quantity of path tracking, and the path tracking precision can be obviously improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of path tracking control, and in particular relates to a vehicle path tracking control method and device for adaptively adjusting step length. Background Art

[0002] Path tracking control is one of the key areas of intelligent vehicle research, and its purpose is to reduce the error between the intelligent driving vehicle's own position and the reference path.

[0003] The path tracking control method based on the linear time-varying model predictive control algorithm has good results. First, a simplified two-degree-of-freedom vehicle dynamics model is established for path tracking control. On this basis, a linear error model is obtained by linearization, discretization and difference. Then the information of the reference trajectory point at the current moment is obtained, and then the system state quantity at the corresponding moment in the sequence is predicted in combination with the current state quantity. Taking the minimization of the deviation between the reference trajectory and the model prediction output as part of the optimization goal, the control quantity or control increment change requirements, state constraints and control variable constraints are taken into account, and then the optimal control input sequence in the sequence is obtained by online optimization. Then the first element in the control sequence acts on the controlled object to track the reference trajectory point. Repeat the above process at each sampling time, and solve the problem by rolling optimization, so that the output trajectory of the prediction model can be close to the reference trajectory.

[0004] The path tracking control method based on the linear time-varying model predictive control algorithm has the following problems: when the vehicle speed is high, the fixed discrete step length is difficult to meet the needs under different working conditions. Within a certain range, a larger discrete step length can improve stability, but will reduce control accuracy; a smaller discrete step length may cause trajectory jitter. In order to improve the control accuracy at high speed, the present invention proposes a vehicle path tracking control method and device with adaptive adjustment of discrete step length. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a vehicle path tracking control method and device with adaptive adjustment of discrete steps, so as to solve the problems of low accuracy or trajectory jitter caused by improper selection of discrete steps in the path tracking method based on traditional model predictive control, and ensure that the vehicle can reliably track the reference path at a high speed.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions.

[0007] In a first aspect, the present invention provides a vehicle path tracking control method for adaptively adjusting discrete step lengths, comprising the following steps:

[0008] The state space equation is established based on the vehicle's dynamic model, and the state quantity is Taylor expanded and high-order terms are ignored to obtain the linearized state quantity error.

[0009] The discretized linear state error is obtained based on the forward Euler method, and the discretization step size is adaptively adjusted with the size of the front wheel steering angle.

[0010] The front wheel steering angle increment is used as the control variable for path tracking to achieve path tracking control of the vehicle.

[0011] Furthermore, the state space equation is:

[0012]

[0013] Where m is the vehicle mass, δ f is the front wheel steering angle, s f 、s r are the front and rear wheel slip rates, Ψ and Ψ′ are the vehicle yaw angle and yaw velocity, respectively. lf , C lr are the longitudinal cornering stiffness coefficients of the front and rear wheels, I z is the yaw moment of inertia of the vehicle, l f , l r are the distances from the front and rear axles to the center of gravity of the vehicle, respectively; X and Y are the plane coordinates of the vehicle in the inertial coordinate system; X and Y are the two-dimensional coordinates of the vehicle in the geodetic coordinate system; the positive direction of the X axis is the positive direction of the initial travel of the vehicle; x and y are the two-dimensional coordinates in the vehicle's own coordinate system; the positive direction of the x axis is the positive direction of the current travel of the vehicle; the state quantity is ξ=[y′,x′,ψ,ψ′,Y,X] T .

[0014] Furthermore, the discretized linear state error is:

[0015] ξ′(t)=(ξ(t+T)-ξ(t)) / T (2)

[0016] Where ξ′(t) is the discretized linear state error at time t, and T is the discretization step size.

[0017] Furthermore, the expression of the discretization step size T is:

[0018] T = round[2 + exp(-180 | δ f | / π)] / 100 (3)

[0019] Where, the discretization step size T is in seconds, δ f It is the front wheel steering angle in radians. round() indicates rounding operation.

[0020] Furthermore, the optimization objective function of path tracking control is:

[0021]

[0022] Where F is the value of the optimization objective function, η(k+i|k), η ref (k+i|k) are the predicted value and reference value at the i-th moment after the current moment k, respectively. N p is the number of steps of the predicted value, Δu(k+i|k) is the control amount at the i-th moment after the current moment k, that is, the increment of the front wheel steering angle, N c is the number of steps of the control quantity, Q and R are weights, Q is a 2*2 order diagonal matrix, and R is a constant;

[0023] By solving the optimization function, the optimal control increment sequence Δ*u(k), Δ*u(k+1), Δ*u(k+2), ..., Δ*u(k+N) of the current control cycle K is obtained. c -1); add Δ*u(k) to the front wheel steering angle u(K-1) of the previous control cycle K-1 to obtain the front wheel steering angle u(K) of the current control cycle K:

[0024] u(K)=Δ*u(k)+u(K-1) (5)

[0025] Wherein, Δ*u(k) is the front wheel steering angle increment of the current control cycle obtained by calculation.

[0026] In a second aspect, the present invention provides a vehicle path tracking control device capable of adaptively adjusting discrete step lengths, comprising:

[0027] The model building module is used to establish the state space equation based on the vehicle's dynamic model, perform Taylor expansion on the state quantity and ignore the high-order terms to obtain the linearized state quantity error;

[0028] The linear discretization module is used to obtain the discretized linear state error based on the forward Euler method. The discretization step size is adaptively adjusted according to the size of the front wheel steering angle.

[0029] The tracking control module is used to use the front wheel steering angle increment as the control amount of path tracking to achieve path tracking control of the vehicle.

[0030] Furthermore, the state space equation is:

[0031]

[0032] Where m is the vehicle mass, δ f is the front wheel steering angle, s f 、s r are the front and rear wheel slip rates, Ψ and Ψ′ are the vehicle yaw angle and yaw velocity, respectively. lf , C lr are the longitudinal cornering stiffness coefficients of the front and rear wheels, I zis the yaw moment of inertia of the vehicle, l f , l r are the distances from the front and rear axles to the center of gravity of the vehicle, respectively; X and Y are the plane coordinates of the vehicle in the inertial coordinate system; X and Y are the two-dimensional coordinates of the vehicle in the geodetic coordinate system; the positive direction of the X axis is the positive direction of the initial travel of the vehicle; x and y are the two-dimensional coordinates in the vehicle's own coordinate system; the positive direction of the x axis is the positive direction of the current travel of the vehicle; the state quantity is ξ=[y′,x′,ψ,ψ′,Y,X] T .

[0033] Furthermore, the discretized linear state error is:

[0034] ξ′(t)=(ξ(t+T)-ξ(t)) / T (2)

[0035] Where ξ′(t) is the discretized linear state error at time t, and T is the discretization step size.

[0036] Furthermore, the expression of the discretization step size T is:

[0037] T = round[2 + exp(-180 | δ f | / π)] / 100 (3)

[0038] Where, the discretization step size T is in seconds, δ f It is the front wheel steering angle in radians. round() indicates rounding operation.

[0039] Furthermore, the optimization objective function of path tracking control is:

[0040]

[0041] Where F is the value of the optimization objective function, η(k+i|k), η ref (k+i|k) are the predicted value and reference value at the i-th moment after the current moment k, respectively. N p is the number of steps of the predicted value, Δu(k+i|k) is the control amount at the i-th moment after the current moment k, that is, the increment of the front wheel steering angle, N c is the number of steps of the control quantity, Q and R are weights, Q is a 2*2 order diagonal matrix, and R is a constant;

[0042] By solving the optimization function, the optimal control increment sequence Δ*u(k), Δ*u(k+1), Δ*u(k+2), ..., Δ*u(k+N) of the current control cycle K is obtained. c -1); add Δ*u(k) to the front wheel steering angle u(K-1) of the previous control cycle K-1 to obtain the front wheel steering angle u(K) of the current control cycle K:

[0043] u(K)=Δ*u(k)+u(K-1) (5)

[0044] Wherein, Δ*u(k) is the front wheel steering angle increment of the current control cycle K obtained by calculation.

[0045] Compared with the prior art, the present invention has the following beneficial effects.

[0046] The present invention establishes a state space equation based on the dynamic model of the vehicle, performs Taylor expansion on the state quantity to obtain a linearized state quantity error, obtains a discretized linear state quantity error based on the forward Euler method, and the discretization step size is adaptively adjusted with the size of the front wheel steering angle, and the front wheel steering angle increment is used as the control quantity of path tracking to achieve path tracking control of the vehicle. The present invention can significantly improve the path tracking accuracy by using the discretization step size adaptively adjusted with the size of the front wheel steering angle, and using the front wheel steering angle increment to replace the front wheel steering angle as the control quantity of path tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flowchart of a vehicle path tracking control method for adaptively adjusting discrete step lengths according to an embodiment of the present invention.

[0048] Figure 2 Schematic diagram of the vehicle's dynamic model.

[0049] FIG3(a) is a double lane-changing path tracking curve when the vehicle speed is 80 km / h, and FIG3(b) is a serpentine line path tracking curve when the vehicle speed is 80 km / h.

[0050] Figure 4 The present invention is a block diagram of a vehicle path tracking control device that is capable of adaptively adjusting discrete step lengths according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer and more understandable, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] Figure 1 This is a flow chart of a vehicle path tracking control method for adaptively adjusting discrete step lengths according to an embodiment of the present invention, comprising the following steps:

[0053] Step 101, establishing a state space equation based on a dynamic model of the vehicle, performing Taylor expansion on the state quantity and ignoring high-order terms, to obtain a linearized state quantity error;

[0054] Step 102, obtaining a discretized linear state error based on the forward Euler method, wherein the discretization step length is adaptively adjusted according to the size of the front wheel steering angle;

[0055] Step 103, using the front wheel steering angle increment as the control amount of path tracking to implement path tracking control of the vehicle.

[0056] In this embodiment, step 101 is mainly used to establish the state space equation. First, a two-degree-of-freedom dynamic model of the vehicle is established, and then the state space equation is established based on the dynamic model. The state space equation is the differential equation satisfied by the state quantity. The state quantity includes the position coordinates, speed, vehicle yaw angle and yaw angular velocity of the vehicle, etc., and the system control quantity is the front wheel steering angle. Since the actual system is a nonlinear system, the state space equation is a nonlinear equation. In order to simplify the calculation, the state quantity is Taylor expanded and the high-order terms are ignored to obtain the linearized state space equation and state quantity error.

[0057] In this embodiment, step 102 is mainly used to obtain the discretized linear state error and discretization step size by discretization processing. The state space equation obtained in step 101 is an equation based on continuous time change. In order to facilitate computer control, discretization processing is generally performed (assuming that the values ​​of each variable remain unchanged within a discretization step size), and the continuous equation is transformed into a discrete equation. This embodiment performs discretization processing based on the forward Euler method to obtain the discretized linear state error. The discretization step size of the prior art is generally fixed. This method generally has the advantages of simplicity and feasibility. However, when the vehicle speed is high, a single discrete step size is difficult to meet the needs under different working conditions. Within a certain range, a larger discrete step size can improve stability but reduce control accuracy; a smaller discrete step size may cause trajectory jitter. For this reason, this embodiment improves the fixed discretization step size to adaptively adjust with the size of the front wheel steering angle. The larger the front wheel steering angle, the smaller the discrete step size. A smaller discrete step size is conducive to improving the accuracy of path tracking; the smaller the front wheel steering angle, the larger the discrete step size. A larger discrete step size is conducive to reducing the possibility of trajectory jitter.

[0058] In this embodiment, step 103 is mainly used to control the path tracking of the vehicle by using the front wheel steering angle increment as the control amount of path tracking. The prior art generally uses the front wheel steering angle as the control amount of path tracking, and this control scheme is not conducive to reducing the trajectory jitter. For this reason, in this embodiment, the front wheel steering angle increment (the difference between the front wheel steering angle of the current control cycle and the front wheel steering angle of the previous control cycle) is used as the control amount of path tracking. Compared with directly controlling the front wheel steering angle, controlling the front wheel steering angle increment can avoid the situation where a feasible solution does not exist, and it is also convenient to constrain the front wheel steering angle increment, thereby reducing the trajectory jitter and improving the stability of the vehicle during the path tracking process.

[0059] As an optional embodiment, the state space equation is:

[0060]

[0061] Where m is the vehicle mass, δ f is the front wheel steering angle, s f 、s r are the front and rear wheel slip rates, Ψ and Ψ′ are the vehicle yaw angle and yaw velocity, respectively. lf , C lr are the longitudinal cornering stiffness coefficients of the front and rear wheels, I z is the yaw moment of inertia of the vehicle, l f , l r are the distances from the front and rear axles to the center of gravity of the vehicle, respectively; X and Y are the plane coordinates of the vehicle in the inertial coordinate system; X and Y are the two-dimensional coordinates of the vehicle in the geodetic coordinate system; the positive direction of the X axis is the positive direction of the initial travel of the vehicle; x and y are the two-dimensional coordinates in the vehicle's own coordinate system; the positive direction of the x axis is the positive direction of the current travel of the vehicle; the state matrix is ​​ξ=[y′,x′,ψ,ψ′,Y,X] T .

[0062] This embodiment provides the state space equation when the vehicle is in motion. This embodiment establishes the state space equation on the basis of establishing the vehicle dynamics model. In order to simplify the model, the following approximate processing is performed when establishing the vehicle dynamics model: ignoring the vertical movement of the suspension system and the vehicle; ignoring the left and right axle load transfer, simplifying the dynamic model to a single track model; ignoring the front and rear axle load transfer, so that the longitudinal vehicle speed changes slowly enough; ignoring the coupling relationship between the lateral tire force and the longitudinal tire force.

[0063] The schematic diagram of the kinetic model is shown in Figure 2 As shown, the vehicle dynamics model is established based on Newton's second law and the torque balance equation:

[0064]

[0065] Where m is the vehicle mass, Ψ′ is the vehicle's inherent parameter, δ is the vehicle's yaw rate, f is the front wheel steering angle, which can be obtained through the sensor; F xf 、F yf are the longitudinal and lateral forces of the front tire; F xr 、F yr are the longitudinal and lateral forces of the rear tire; I z is the yaw moment of inertia of the vehicle, which is an inherent parameter of the vehicle; l f , l r It is the distance from the front axle and rear axle to the center of gravity of the vehicle and is an inherent parameter of the vehicle.

[0066] The coordinate transformation relationship between the earth coordinate system and the vehicle coordinate system is:

[0067]

[0068] Due to the small angle assumption, a simplified linear tire model is used, and the longitudinal and lateral tire forces of the front and rear wheels are expressed as:

[0069]

[0070] In the formula, s f 、s r are the slip rates of the front and rear wheels, α f , α r Represent the sideslip angles of the front and rear wheels respectively; Ψ and Ψ′ are the vehicle yaw angle and yaw velocity respectively, C cf , C cr Represent the longitudinal cornering stiffness coefficients of the front and rear wheels respectively.

[0071] The linear tire model assumption is adopted, that is, the tire is considered to work mainly in the linear region, and the front and rear wheel slip angles are:

[0072]

[0073] Substituting each parameter into the previous dynamic model, the state space equation is obtained after sorting, as shown in formula (1). Obviously, the state space equation shown in formula (1) is a nonlinear equation.

[0074] As an optional embodiment, the discretized linear state error is:

[0075] ξ″(t)=(ξ(t+T)-ξ(t)) / T (2)

[0076] Where ξ′(t) is the discretized linear state error at time t, and T is the discretization step size.

[0077] This embodiment provides an expression for the discretized linear state quantity error. This embodiment performs linear approximation processing and discretization processing on the nonlinear state space equation to obtain the discretized linear state quantity error.

[0078] The system state space expression is abbreviated as: ξ′(t)=f(ξ(t),u(t)), y=Cξ(t). The system state quantity is ξ=[y′,x′,ψ,ψ′,Y,X] T , the system control quantity is u(k)=[δ f ],y=[ψ,y],

[0079] Assume that all reference path information has been acquired so that the predicted state quantity can be obtained by applying a constant control input in each control cycle. At all reference points outside the prediction range, the dynamic state of the vehicle remains unchanged. The reference state quantity can be expressed as: ξ′=f(ξ r ,u r ), using the first-order Taylor expansion and ignoring the higher-order terms, we can get:

[0080]

[0081] Then we can get the linearized state error, namely:

[0082] ξ′=A(t)ξ+B(t)(uu r )

[0083] Where ξ′=ξ-ξ r , the expressions of A(t) and B(t) are:

[0084]

[0085] in,

[0086] The forward Euler method is used for discretization, and the first-order difference quotient is used to approximate the first-order derivative, that is:

[0087]

[0088] When the discrete step length T is small enough, the first-order difference quotient can be approximately considered to be equal to the first-order derivative, as shown in formula (2).

[0089] As an optional embodiment, the expression of the discretization step length T is:

[0090] T = round[2 + exp(-180 | δ f | / π)] / 100 (3)

[0091] Where, the discretization step size T is in seconds, δ f It is the front wheel steering angle in radians. round() indicates rounding operation.

[0092] This embodiment provides a specific expression for the discretization step length T. The expression for the discrete step length T is shown in equation (3). Obviously, the front wheel steering angle δ f The larger the value, the smaller the discrete step length T; the front wheel steering angle δ f The smaller it is, the larger the discrete step length T is. f When it is small, exp(-180δ f | / π)]≈1-180δf | / π, T varies with δ f Approximately linear decrease.

[0093] As an optional embodiment, the optimization objective function of the path tracking control is:

[0094]

[0095] Where F is the value of the optimization objective function, η(k+i|k), η ref (k+i|k) are the predicted value and reference value at the i-th moment after the current moment k, respectively. N p is the number of steps of the predicted value, Δu(k+i|k) is the control amount at the i-th moment after the current moment k, that is, the increment of the front wheel steering angle, N c is the number of steps of the control quantity, Q and R are weights, Q is a 2*2 order diagonal matrix, and R is a constant;

[0096] By solving the optimization function, the optimal control increment sequence Δ*u(k), Δ*u(k+1), Δ*u(k+2), ..., Δ*u(k+N) of the current control cycle K is obtained. c -1); add Δ*u(k) to the front wheel steering angle u(K-1) of the previous control cycle K-1 to obtain the front wheel steering angle u(K) of the current control cycle K:

[0097] u(K)=Δ*u(k)+u(K-1) (5)

[0098] Wherein, Δ*u(k) is the front wheel steering angle increment of the current control cycle K obtained by calculation.

[0099] This embodiment provides an optimization objective function for path tracking control. The expression of the optimization objective function is shown in formula (4), which consists of two parts: the first part is used to reduce the deviation between the actual path and the reference path; and the second part is used to minimize the control amount, that is, the change of the front wheel angle within the control cycle to ensure the stability of the vehicle. The weights Q and R are set to balance the proportion of the two parts. In order to ensure the existence of a feasible solution to the optimization objective function, the actual optimization objective function generally also includes a relaxation term.

[0100] By solving the optimization function in each control cycle, the control increment of each control cycle can be obtained, and then the front wheel steering angle can be obtained.

[0101] In order to verify the feasibility of the technical solution of the present invention, the method proposed by the present invention and the traditional fixed-step model predictive control method are used to perform path tracking control simulation tests under different paths, and the simulation results are shown in Figure 3. As can be seen from the figure, the method proposed by the present invention can obtain higher accuracy than the traditional model predictive control method at a speed of 80km / h.

[0102] Figure 4 This is a schematic diagram of the composition of a vehicle path tracking control device for adaptively adjusting discrete steps according to an embodiment of the present invention, the device comprising:

[0103] The model building module 11 is used to build a state space equation based on the dynamic model of the vehicle, perform Taylor expansion on the state quantity and ignore high-order terms to obtain a linearized state quantity error;

[0104] A linear discretization module 12, used for obtaining a discretized linear state error based on the forward Euler method, wherein the discretization step length is adaptively adjusted according to the size of the front wheel steering angle;

[0105] The tracking control module 13 is used to use the front wheel steering angle increment as the control amount of path tracking to achieve path tracking control of the vehicle.

[0106] The device of this embodiment can be used to perform Figure 1 The technical solution of the method embodiment shown has similar implementation principles and technical effects, which will not be described in detail here. The same is true for the following embodiments, which will not be described in detail.

[0107] As an optional embodiment, the state space equation is:

[0108]

[0109] Where m is the vehicle mass, δ f is the front wheel steering angle, s f 、s r are the front and rear wheel slip rates, Ψ and Ψ′ are the vehicle yaw angle and yaw velocity, respectively. lf , C lr are the longitudinal cornering stiffness coefficients of the front and rear wheels, I z is the yaw moment of inertia of the vehicle, l f , l r are the distances from the front and rear axles to the center of gravity of the vehicle, respectively; X and Y are the plane coordinates of the vehicle in the inertial coordinate system; X and Y are the two-dimensional coordinates of the vehicle in the geodetic coordinate system; the positive direction of the X axis is the positive direction of the initial travel of the vehicle; x and y are the two-dimensional coordinates in the vehicle's own coordinate system; the positive direction of the x axis is the positive direction of the current travel of the vehicle; the state quantity is ξ=[y′,x′,ψ,ψ′,Y,X] T .

[0110] As an optional embodiment, the discretized linear state error is:

[0111] ξ′(t)=(ξ(t+T)-ξ(t)) / T (2)

[0112] Where ξ′(t) is the discretized linear state error at time t, and T is the discretization step size.

[0113] As an optional embodiment, the expression of the discretization step length T is:

[0114] T = round[2 + exp(-180 | δ f | / π)] / 100 (3)

[0115] Where, the discretization step size T is in seconds, δ f It is the front wheel steering angle in radians. round() indicates rounding operation.

[0116] As an optional embodiment, the optimization objective function of the path tracking control is:

[0117]

[0118] Where F is the value of the optimization objective function, η(k+i|k), η ref (k+i|k) are the predicted value and reference value at the i-th moment after the current moment k, respectively. N p is the number of steps of the predicted value, Δu(k+i|k) is the control amount at the i-th moment after the current moment k, that is, the increment of the front wheel steering angle, N c is the number of steps of the control quantity, Q and R are weights, Q is a 2*2 order diagonal matrix, and R is a constant;

[0119] By solving the optimization function, the optimal control increment sequence Δ*u(k), Δ*u(k+1), Δ*u(k+2), ..., Δ*u(k+N) of the current control cycle K is obtained. c -1); add Δ*u(k) to the front wheel steering angle u(K-1) of the previous control cycle K-1 to obtain the front wheel steering angle u(K) of the current control cycle K:

[0120] u(K)=Δ*u(k)+u(K-1) (5)

[0121] Wherein, Δ*u(k) is the front wheel steering angle increment of the current control cycle obtained by calculation.

[0122] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A vehicle path tracking control method for adaptively adjusting discrete step lengths, characterized in that: The following steps are involved: The state space equation is established based on the vehicle's dynamic model, and the state quantity is Taylor expanded and high-order terms are ignored to obtain the linearized state quantity error. The discretized linear state error is obtained based on the forward Euler method, and the discretization step size is adaptively adjusted with the size of the front wheel steering angle. The front wheel steering angle increment is used as the control variable for path tracking to realize the path tracking control of the vehicle.

2. The vehicle path tracking control method with adaptive adjustment of discrete steps according to claim 1, characterized in that: The state space equation is: Where m is the vehicle mass, δ f is the front wheel steering angle, s f 、s r are the front and rear wheel slip rates, Ψ and Ψ′ are the vehicle yaw angle and yaw velocity, respectively. lf , C lr are the longitudinal cornering stiffness coefficients of the front and rear wheels, I z is the yaw moment of inertia of the vehicle, l f , l r are the distances from the front and rear axles to the center of gravity of the vehicle, respectively; X and Y are the plane coordinates of the vehicle in the inertial coordinate system; X and Y are the two-dimensional coordinates of the vehicle in the geodetic coordinate system; the positive direction of the X axis is the positive direction of the initial travel of the vehicle; x and y are the two-dimensional coordinates in the vehicle's own coordinate system; the positive direction of the x axis is the positive direction of the current travel of the vehicle; the state quantity is ξ=[y′,x′,ψ,ψ′,Y,X] T .

3. The vehicle path tracking control method for adaptively adjusting discrete steps according to claim 2, characterized in that: The discretized linear state error is: ξ′(t)=(ξ(t+T)-ξ(t)) / T (2) Where ξ′(t) is the discretized linear state error at time t, and T is the discretization step size.

4. The vehicle path tracking control method for adaptively adjusting discrete steps according to claim 3, characterized in that: The expression of discretization step size T is: T=round[2+exp(-180|δ f | / π)] / 100 (3) Where, the discretization step size T is in seconds, δ f It is the front wheel steering angle in radians. round() indicates rounding operation.

5. The vehicle path tracking control method for adaptively adjusting discrete steps according to claim 4, characterized in that: The optimization objective function of path tracking control is: Where F is the value of the optimization objective function, η(k+i|k), η ref (k+i|k) are the predicted value and reference value at the i-th moment after the current moment k, respectively. N p is the number of steps of the predicted value, Δu(k+i|k) is the control amount at the i-th moment after the current moment k, that is, the increment of the front wheel steering angle, N c is the number of steps of the control quantity, Q and R are weights, Q is a 2*2 order diagonal matrix, and R is a constant; By solving the optimization function, the optimal control increment sequence Δ*u(k), Δ*u(k+1), Δ*u(k+2), ..., Δ*u(k+N) of the current control cycle K is obtained. c -1); add Δ*u(k) to the front wheel steering angle u(K-1) of the previous control cycle K-1 to obtain the front wheel steering angle u(K) of the current control cycle K: u(K)=Δ*u(k)+u(K-1) (5) Wherein, Δ*u(k) is the front wheel steering angle increment of the current control cycle K obtained by calculation.

6. A vehicle path tracking control device capable of adaptively adjusting discrete step lengths, characterized in that: include: The model building module is used to establish the state space equation based on the vehicle's dynamic model, perform Taylor expansion on the state quantity and ignore the high-order terms to obtain the linearized state quantity error; The linear discretization module is used to obtain the discretized linear state error based on the forward Euler method. The discretization step size is adaptively adjusted according to the size of the front wheel steering angle. The tracking control module is used to use the front wheel steering angle increment as the control amount of path tracking to achieve path tracking control of the vehicle.

7. The vehicle path tracking control device for adaptively adjusting discrete step lengths according to claim 6, characterized in that: The state space equation is: Where m is the vehicle mass, δ f is the front wheel steering angle, s f 、s r are the front and rear wheel slip rates, Ψ and Ψ′ are the vehicle yaw angle and yaw velocity, respectively. lf , C lr are the longitudinal cornering stiffness coefficients of the front and rear wheels, I z is the yaw moment of inertia of the vehicle, l f , l r are the distances from the front and rear axles to the center of gravity of the vehicle, respectively; X and Y are the plane coordinates of the vehicle in the inertial coordinate system; X and Y are the two-dimensional coordinates of the vehicle in the geodetic coordinate system; the positive direction of the X axis is the positive direction of the initial travel of the vehicle; x and y are the two-dimensional coordinates in the vehicle's own coordinate system; the positive direction of the x axis is the positive direction of the current travel of the vehicle; the state quantity is ξ=[y′,x′,ψ,ψ′,Y,X] T .

8. The vehicle path tracking control device for adaptively adjusting discrete step lengths according to claim 7, characterized in that: The discretized linear state error is: ξ′(t)=(ξ(t+T)-ξ(t)) / T (2) Where ξ′(t) is the discretized linear state error at time t, and T is the discretization step size.

9. The vehicle path tracking control device for adaptively adjusting discrete step lengths according to claim 8, characterized in that: The expression of discretization step size T is: T=round[2+exp(-180|δ f | / π)] / 100 (3) Where, the discretization step size T is in seconds, δ f It is the front wheel steering angle in radians. round() indicates rounding operation.

10. The vehicle path tracking control device for adaptively adjusting discrete step lengths according to claim 9, characterized in that: The optimization objective function of path tracking control is: Where F is the value of the optimization objective function, η(k+i|k), η ref (k+i|k) are the predicted value and reference value at the i-th moment after the current moment k, respectively. N p is the number of steps of the predicted value, Δu(k+i|k) is the control amount at the i-th moment after the current moment k, that is, the increment of the front wheel steering angle, N c is the number of steps of the control quantity, Q and R are weights, Q is a 2*2 order diagonal matrix, and R is a constant; By solving the optimization function, the optimal control increment sequence Δ*u(k), Δ*u(k+1), Δ*u(k+2), ..., Δ*u(k+N) of the current control cycle K is obtained. c -1); add Δ*u(k) to the front wheel steering angle u(K-1) of the previous control cycle K-1 to obtain the front wheel steering angle u(K) of the current control cycle K: u(K)=Δ*u(k)+u(K-1) (5) Wherein, Δ*u(k) is the front wheel steering angle increment of the current control cycle K obtained by calculation.

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