Vehicle model online identification method and device for path tracking tasks

Abstract

The present invention discloses a method and device for online vehicle model identification for path tracking tasks. The method includes: constructing multiple basic models of the vehicle during motion; constructing a vehicle trajectory tracking model based on the multiple basic models, input matrices, and system matrices; calculating an intermediate matrix based on vehicle operation data to obtain a first calculation result, and solving the input matrix to be identified based on a comparison result of the first calculation result and a preset accuracy threshold to obtain a second calculation result; calculating a system matrix to be identified based on a third calculation result obtained by calculating a temporary matrix based on the second calculation result, and completing the online identification of vehicle parameters of the vehicle trajectory tracking model based on the fourth calculation result. The present invention realizes online identification of vehicle parameters through an adaptive dynamic programming method. It can also achieve rapid, rigorous and accurate vehicle parameter identification.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle chassis control, and in particular to a vehicle model online identification method and device for path tracking tasks. Background Art

[0002] Autonomous driving shows great potential for improving social traffic efficiency and further freeing human drivers from the arduous task of driving. Path tracking, as an essential functional module, controls the vehicle with the help of a steer-by-wire system to follow the desired trajectory given by the upper planning layer as accurately as possible. Therefore, as a fundamental control module for autonomous vehicles, numerous vehicle models and related control methods have been proposed to achieve specific performance requirements from various perspectives. However, obtaining the detailed vehicle parameters required for path tracking control is a difficult task, currently requiring extensive offline testing and even requiring information from the manufacturer. Summary of the Invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the present invention proposes an online vehicle model identification method for path tracking tasks, which can realize the rapid identification of vehicle parameters in trajectory tracking tasks.

[0005] Another object of the present invention is to provide an online vehicle model identification device for path tracking tasks.

[0006] To achieve the above objectives, the present invention provides, on one hand, a method for online identification of a vehicle model for a path tracking task, comprising:

[0007] S1, construct multiple basic models of vehicle motion;

[0008] S2, constructing a vehicle trajectory tracking model based on the multiple basic models, the input matrix, and the system matrix;

[0009] S3, calculating an intermediate matrix based on the vehicle operation data to obtain a first calculation result, and solving the input matrix to be identified to obtain a second calculation result based on the comparison result of the first calculation result and a preset accuracy threshold; and calculating the system matrix to be identified based on a third calculation result obtained by calculating a temporary matrix based on the second calculation result, so as to complete the online identification of the vehicle parameters of the vehicle trajectory tracking model according to the calculated fourth calculation result.

[0010] To achieve the above objectives, the present invention further provides an online vehicle model identification device for path tracking tasks, comprising:

[0011] Vehicle basic model building module, used to build various basic models of vehicle motion;

[0012] A trajectory tracking model building module, configured to build a vehicle trajectory tracking model based on the multiple basic models, the input matrix, and the system matrix;

[0013] The vehicle online identification module is configured to calculate an intermediate matrix based on vehicle operation data to obtain a first calculation result, and solve the input matrix to be identified to obtain a second calculation result based on a comparison result of the first calculation result and a preset accuracy threshold; and calculate the system matrix to be identified based on a third calculation result obtained by calculating a temporary matrix based on the second calculation result, so as to complete the online identification of vehicle parameters of the vehicle trajectory tracking model based on the calculated fourth calculation result.

[0014] The vehicle model online identification method and device for path tracking tasks of the embodiments of the present invention can strictly and accurately identify the vehicle model required in the vehicle trajectory tracking process. The identification process is separated from the control process and adopts an online method of real-time identification of the vehicle in real time.

[0015] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0017] Figure 1 is a flow chart of a vehicle model online identification method for a path tracking task according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a vehicle dynamics model according to an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of vehicle trajectory tracking according to an embodiment of the present invention;

[0020] Figure 4 is a flowchart of online model identification according to an embodiment of the present invention;

[0021] Figure 5 4 is a schematic structural diagram of a vehicle model online identification device for path tracking tasks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 efforts should fall within the scope of protection of the present invention.

[0024] The following describes a method and apparatus for online identification of a vehicle model for a path tracking task proposed according to an embodiment of the present invention with reference to the accompanying drawings.

[0025] Figure 1 4 is a flow chart of a vehicle model online identification method for a path tracking task according to an embodiment of the present invention.

[0026] like Figure 1 As shown, the method includes but is not limited to the following steps:

[0027] S1, construct multiple basic models of vehicle motion;

[0028] S2, constructs a vehicle trajectory tracking model based on multiple basic models, input matrices, and system matrices;

[0029] S3, calculating an intermediate matrix based on the vehicle operation data to obtain a first calculation result, and solving the input matrix to be identified to obtain a second calculation result based on the comparison result of the first calculation result and a preset accuracy threshold; and calculating the system matrix to be identified based on the third calculation result obtained by calculating the temporary matrix based on the second calculation result, so as to complete the online identification of the vehicle parameters of the vehicle trajectory tracking model according to the calculated fourth calculation result.

[0030] The vehicle model online identification method for a path tracking task according to an embodiment of the present invention can realize rapid identification of vehicle parameters in a trajectory tracking task.

[0031] The following describes in detail the vehicle model online identification method for path tracking tasks according to an embodiment of the present invention with reference to the accompanying drawings.

[0032] like Figure 2 Shown and Figure 3 , which are respectively a schematic diagram of a vehicle dynamics model and a schematic diagram of vehicle trajectory tracking according to an embodiment of the present invention:

[0033] In one embodiment of the present invention, Figure 2 For the vehicle shown in the figure, the trajectory tracking model of the vehicle is constructed as follows:

[0034] The following dynamic model is established for the vehicle during motion:

[0035]

[0036] where ω r (rad / s) represents the yaw rate of the vehicle, represents the vehicle's yaw angular acceleration; v y (m / s) represents the lateral speed of the vehicle, represents the lateral acceleration of the vehicle; v x (m / s) represents the longitudinal running speed of the vehicle; F yf and F yr Respectively represent the lateral forces on the front and rear wheels of the vehicle, both in N; l f and l r Respectively represent the horizontal distances between the center of mass of the vehicle and the front and rear axles, both in meters; m (kg) represents the overall mass of the vehicle; I z (kgm 2 ) represents the vehicle's yaw moment of inertia about the axis perpendicular to the horizontal ground plane and about the center of mass. The above symbols and the parts in parentheses are units.

[0037] The following model is established for the lateral force during vehicle operation:

[0038]

[0039] where α f and α r Respectively represent the sideslip angle of the front wheel and the sideslip angle of the rear wheel of the vehicle, both in rad; C f and C r Respectively represent the lateral stiffness of the front and rear wheels of the vehicle, both in N / rad.

[0040] The following model is established for the sideslip angles of the front and rear wheels during vehicle operation:

[0041]

[0042] where δ f Represents the front wheel steering angle in rad.

[0043] From formula (0.1)-(0.3) we can get

[0044]

[0045] In view of the difference between the actual trajectory and the reference trajectory during vehicle operation, the following model is established:

[0046]

[0047] Among them, Y,Y d and e YRespectively represent the actual lateral displacement of the vehicle, the reference lateral displacement and the lateral displacement error, all in m; φ, φ d and e φ Respectively represent the actual heading angle of the vehicle, the reference heading angle and the heading angle error, all in rad; and Represents e y ,e φ and φ d , the units are m / s, rad / s and rad / s respectively.

[0048] The trajectory tracking during vehicle operation is established as follows:

[0049] Based on the basic model established above, this embodiment adopts the state space model to describe the vehicle trajectory tracking process. First, the state vector of the vehicle is selected as Select virtual control input in Represents the second derivative of the heading angle with respect to time, in rad / s 2 Here x and u are commonly used expressions in control theory. Therefore, the vehicle trajectory tracking model can be constructed as follows:

[0050]

[0051] in, Represents the derivative of x with respect to time. The system matrix A and input matrix B are as follows:

[0052]

[0053] At this point, the vehicle trajectory tracking model has been constructed. The above models (0.6)-(0.7) fully describe the system changes during the vehicle trajectory tracking process. The purpose of the method proposed in this invention is to identify the above two matrices.

[0054] As an embodiment of the present invention, the data collection method of the present invention is as follows:

[0055] First, the following method is used to collect vehicle operation data online:

[0056]

[0057] in Represents the mathematical operation Kronecker product; t i ,i=1,2,…,s represents the data sampling time, no unit; s represents the number of data samples, no unit; D xx ,I xx and I xuThey represent three different types of data collected, are data sets, and have no units.

[0058] Then continue to use the above data collection method and calculate the following rank ([I xx I xu ]) until the following conditions are met:

[0059] rank([I xx I xu ])=22(0.9)

[0060] Where rank([I xx I xu ]) represents the data I xx and I xu The rank of the constructed data matrix, where rank is a general mathematical operation.

[0061] As an embodiment of the present invention, Figure 4 This is the online model identification flow chart of the present invention, as shown in Figure 4 As shown in Figure 2, data-based online model identification includes the following steps:

[0062] Step 1: Select any positive definite matrices Q and R, where the dimensions of matrix Q are 4×4 and the dimensions of matrix R are 3×3; apply any steering input δ to the vehicle to control the vehicle tracking trajectory f (t) and maintain, δ f (t) can be generated by simple control methods such as proportional-integral-differential control. It should be noted that δ f (t) is a time variable, and it only needs to ensure that the vehicle tries its best to track the predetermined trajectory and maintain the stability of the vehicle. There are no other requirements. Set the initial feedback gain K0 = [0 0 0] T , set the initial iteration knowledge parameter k = 0, and set the model identification accuracy parameter ∈ according to actual needs. It has no unit and the recommended value is between 0.001-0.0001.

[0063] Step 2: Collect data online until the collection stop condition is met.

[0064] Step 3: Calculate the intermediate matrix shown below using the collected data and the selected rank

[0065]

[0066] Solve the following update equation to get a new set of P k ,K k+1

[0067]

[0068] Step 4: Determine whether the following accuracy conditions are met

[0069] |P k -P k+1 |<∈(0.12)

[0070] If the condition is not met, increase the iteration indicator parameter by one and return to step 3:

[0071] k=k+1(0.13)

[0072] If the conditions are met, proceed to the next step

[0073] Step 5: Use the following formula to solve the input matrix B to be identified

[0074]

[0075] Step 6: Calculate the system matrix A to be identified

[0076] Construct the following temporary matrix Q l , calculated as follows:

[0077] Q l =P k BR -1 B T P k -Q(0.15)

[0078] Construct the following temporary matrix A lvec ,P lvec and Q lvec

[0079]

[0080] where a i,j , i=3,4,j=2,3,4 represent the elements in the corresponding i-th row and j-th column of the system matrix A, and the system matrix A actually has 6 unknown elements; p i,j ,i=1,2,3,4,j=1,2,3,4 represents the matrix P k The element corresponding to row i and column j in q i,j ,i=1,2,3,4,j=1,2,3,4 represents the matrix Q l The element corresponding to row i and column j in .

[0081] Calculate A using the following formula vec

[0082]

[0083] Thus, the unknown coefficients in the matrix A of the vehicle trajectory tracking system are all obtained. At this point, the data-based online trajectory tracking model identification method is completed.

[0084] The online vehicle model identification method for path tracking tasks according to an embodiment of the present invention utilizes an adaptive dynamic programming approach to achieve online identification of vehicle parameters. This method enables rapid, rigorous, and accurate vehicle parameter identification. It not only separates conventional vehicle control from vehicle model parameter identification, but also allows the identified vehicle model to track any reference trajectory.

[0085] In order to implement the above embodiment, Figure 5 As shown, this embodiment also provides a vehicle model online identification device 10 for path tracking tasks. The device 10 includes a vehicle basic model construction module 100, a trajectory tracking model construction module 200 and a vehicle online identification module 300.

[0086] The vehicle basic model building module 100 is used to build multiple basic models of the vehicle during movement;

[0087] A trajectory tracking model building module 200 is used to build a vehicle trajectory tracking model based on the multiple basic models, the input matrix and the system matrix;

[0088] The vehicle online identification module 300 is used to calculate an intermediate matrix based on the vehicle operation data to obtain a first calculation result, and solve the input matrix to be identified to obtain a second calculation result based on the comparison result of the first calculation result and a preset accuracy threshold; and calculate the system matrix to be identified based on a third calculation result obtained by calculating the temporary matrix based on the second calculation result, so as to complete the online identification of vehicle parameters of the vehicle trajectory tracking model based on the calculated fourth calculation result.

[0089] Furthermore, the vehicle basic model building module 100 is further configured to:

[0090] The first basic model is constructed based on the dynamics of the vehicle during motion:

[0091]

[0092]

[0093] Among them, ω r (rad / s) is the yaw rate of the vehicle, is the vehicle's yaw angular acceleration; v y (m / s) is the lateral speed of the vehicle, is the lateral acceleration of the vehicle; v x (m / s) is the longitudinal running speed of the vehicle; F yf and F yr are the lateral forces acting on the front and rear wheels of the vehicle, respectively, l f and l rare the horizontal distances from the center of mass of the vehicle to the front and rear axles respectively; m (kg) is the overall mass of the vehicle; I z (kgm 2 ) is the yaw moment of inertia of the vehicle about the axis perpendicular to the horizontal ground about the centre of mass;

[0094] The second basic model is constructed based on the lateral force during vehicle operation:

[0095] F yf =-C f α f

[0096] F yr =-C r α r

[0097] Among them, α f and α r are the sideslip angles of the front and rear wheels of the vehicle respectively; C f and C r are the cornering stiffness of the front and rear wheels of the vehicle respectively;

[0098] The third basic model is constructed based on the side slip angles of the front and rear wheels during vehicle operation:

[0099]

[0100]

[0101] where δ f is the front wheel turning angle;

[0102] From formula (0.1)-(0.3), we can get:

[0103]

[0104]

[0105] The fourth basic model is constructed based on the difference data between the actual trajectory and the reference trajectory during vehicle operation:

[0106] e Y =YY d

[0107] e φ =φ-φ d

[0108]

[0109]

[0110] Among them, Y,Y d and eY are the actual lateral displacement of the vehicle, the reference lateral displacement and the lateral displacement error; φ, φ d and e φ are the actual heading angle of the vehicle, the reference heading angle, and the heading angle error; and e y ,e φ and φ d .

[0111] Furthermore, the trajectory tracking model building module 200 is further configured to:

[0112] The state vector of the selected vehicle is Select virtual control input in is the second derivative of the heading angle with respect to time, and the vehicle trajectory tracking model is constructed as follows:

[0113]

[0114] in, Represents the derivative of x with respect to time. The system matrix A and input matrix B are as follows:

[0115]

[0116] Furthermore, obtaining the vehicle operation data includes:

[0117]

[0118]

[0119]

[0120] in, Kronecker product is a mathematical operation; t i ,i=1,2,…,s is the data sampling time; s is the number of data samples; D xx ,I xx and I xu There are three different types of data collected;

[0121] Calculate rank([I xx I xu ]) until the collection stop condition is met:

[0122] rank([I xx I xu ])=22

[0123] Among them, rank([I xx Ixu ]) represents the data I xx and I xu The rank of the constructed data matrix.

[0124] Furthermore, the vehicle online identification module 300 includes:

[0125] The initial parameter setting subunit is used to select arbitrary positive definite matrices Q and R, where the dimension of matrix Q is 4×4 and the dimension of matrix R is 3×3; apply arbitrary steering input δ to the vehicle to control the vehicle tracking trajectory f (t) and maintain, set the initial feedback gain K0 = [0 0 0] T , set the initial iteration knowledge parameter k = 0, set the model identification accuracy parameter ∈;

[0126] a data collection subunit, configured to adopt a method for acquiring the vehicle operation data until a collection stop condition is met;

[0127] The intermediate matrix calculation subunit is used to calculate the intermediate matrix using the collected data and the selected rank:

[0128]

[0129] Ξ i =-I xx vec(Q)

[0130] Solve the following update equation to get a new set of P k ,K k+1 :

[0131]

[0132] The accuracy judgment subunit is used to judge whether the following accuracy conditions are met:

[0133] |P k -P k+1 |<∈

[0134] If the condition is not met, the iteration indicator parameter is increased by one and the process returns to the intermediate matrix calculation subunit:

[0135] k=k+1

[0136] If the conditions are met, proceed to the next subunit:

[0137] The input matrix solving subunit is used to solve the input matrix B to be identified using the following formula:

[0138]

[0139] The system matrix solving subunit is used to calculate the system matrix A to be identified:

[0140] Construct the following temporary matrix Q l , calculated as:

[0141] Q l =P k BR -1 B T P k -Q

[0142] Construct the following temporary matrix A lvec ,P lvec and Q lvec :

[0143] A lvec =[a 32 a 33 a 34 a 42 a 43 a 44 ] T

[0144]

[0145]

[0146] where a i,j ,i=3,4,j=2,3,4 represent the elements in the corresponding i-th row and j-th column of the system matrix A; p i,j ,i=1,2,3,4,j=1,2,3,4 represents the matrix P k The element corresponding to row i and column j in q i,j ,i=1,2,3,4,j=1,2,3,4 represents the matrix Q l The element corresponding to row i and column j in ;

[0147] Calculate A using the following formula vec :

[0148]

[0149] The unknown coefficients in the system matrix A of the vehicle trajectory tracking system are solved to complete the online identification of vehicle parameters of the vehicle trajectory tracking model.

[0150] The vehicle model online identification device for path tracking tasks according to an embodiment of the present invention implements online identification of vehicle parameters through an adaptive dynamic programming method. This allows for rapid, rigorous, and accurate vehicle parameter identification. This not only separates conventional vehicle control from vehicle model parameter identification, but also allows the identified vehicle model to track any reference trajectory.

[0151] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0152] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Claims

1. A vehicle model online identification method for path tracking tasks, characterized in that: The following steps are involved: S1, construct multiple basic models of vehicle motion; S2, constructing a vehicle trajectory tracking model based on the multiple basic models, the input matrix, and the system matrix; S3, calculating an intermediate matrix based on the vehicle operation data to obtain a first calculation result, and solving the input matrix to be identified to obtain a second calculation result based on a comparison result of the first calculation result and a preset accuracy threshold; and calculating the system matrix to be identified based on a third calculation result obtained by calculating the temporary matrix based on the second calculation result, so as to complete the online identification of the vehicle parameters of the vehicle trajectory tracking model according to the fourth calculation result; The multiple basic models of the vehicle motion process are constructed, including: The first basic model is constructed based on the dynamics of the vehicle during motion: in, is the vehicle's yaw rate, is the vehicle's yaw angular acceleration; is the lateral velocity of the vehicle, is the lateral acceleration of the vehicle; is the longitudinal running speed of the vehicle; and are the lateral forces acting on the front and rear wheels of the vehicle, and are the horizontal distances from the vehicle's center of mass to the front and rear axles, respectively; is the overall mass of the vehicle; is the vehicle's yaw moment of inertia about the axis perpendicular to the horizontal ground and about the center of mass; The second basic model is constructed based on the lateral force during vehicle operation: in, and are the sideslip angles of the front and rear wheels of the vehicle respectively; and are the cornering stiffness of the front and rear wheels of the vehicle respectively; The third basic model is constructed based on the side slip angles of the front and rear wheels during vehicle operation: in is the front wheel turning angle; From the first basic model, the second basic model and the third basic model, we can get: in is the sideslip angle of the center of mass; The fourth basic model is constructed based on the difference data between the actual trajectory and the reference trajectory during vehicle operation: in and are the actual lateral displacement of the vehicle, the reference lateral displacement, and the lateral displacement error; , and are the actual heading angle of the vehicle, the reference heading angle, and the heading angle error; and They are and First derivative with respect to time; The vehicle trajectory tracking model is constructed according to the multiple basic models, the input matrix and the system matrix, including: The state vector of the selected vehicle is , select virtual control input ,in is the second derivative of the heading angle with respect to time, and the vehicle trajectory tracking model is constructed as follows: in, Representatives Taking the derivative with respect to time, the system matrix and the input matrix They are in the following forms: Acquiring the vehicle operation data, including: in, For mathematical operations Kronecker product; is the data sampling time; is the number of data samples; and There are three different types of data collected; calculate Until the collection stop condition is met: in, Represented by data and The rank of the constructed data matrix; Said S3 comprises: Step S3.1, select any positive definite matrix and , where the matrix The dimension is ,matrix The dimension is Apply any steering input to the vehicle to control the vehicle's tracking trajectory and hold, set the initial feedback gain , set the initial iteration knowledge parameters , set the model identification accuracy parameters ; Step S3.2, using a method for acquiring the vehicle operation data until a collection stop condition is met; Step S3.3, calculate the intermediate matrix using the selected rank of the collected data: Solve the following update equation to get a new set of : Step S3.4: Determine whether the following accuracy conditions are met: If the condition is not met, the iteration indicator parameter is increased by one and the process returns to step S3.3: If the conditions are met, proceed to the next step: Step S3.5, use the following formula to solve the input matrix to be identified : Step S3.6, calculate the system matrix to be identified : Construct the following temporary matrix , calculated as: Construct the following temporary matrix and : in Represents the corresponding first Row, No. Elements of the column; Representative Matrix The corresponding Row, No. Elements of the column; Representative Matrix The corresponding Row, No. Elements of the column; Calculate using the following formula : Solve the system matrix of the vehicle trajectory tracking system The unknown coefficients in are used to complete the online identification of vehicle parameters of the vehicle trajectory tracking model.

2. A vehicle model online identification device for path tracking tasks, characterized in that: include: Vehicle basic model building module, used to build various basic models of vehicle motion; A trajectory tracking model building module, configured to build a vehicle trajectory tracking model based on the multiple basic models, the input matrix, and the system matrix; A vehicle online identification module, configured to calculate an intermediate matrix based on vehicle operation data to obtain a first calculation result, and solve the input matrix to be identified to obtain a second calculation result based on a comparison result of the first calculation result and a preset accuracy threshold; and calculating the system matrix to be identified based on a third calculation result obtained by calculating the temporary matrix based on the second calculation result, so as to complete the online identification of the vehicle parameters of the vehicle trajectory tracking model according to the fourth calculation result; The vehicle basic model building module is further used to: The first basic model is constructed based on the dynamics of the vehicle during motion: in, is the vehicle's yaw rate, is the vehicle's yaw angular acceleration; is the lateral velocity of the vehicle, is the lateral acceleration of the vehicle; is the longitudinal running speed of the vehicle; and are the lateral forces acting on the front and rear wheels of the vehicle, and are the horizontal distances from the vehicle's center of mass to the front and rear axles, respectively; is the overall mass of the vehicle; is the vehicle's yaw moment of inertia about the axis perpendicular to the horizontal ground and about the center of mass; The second basic model is constructed based on the lateral force during vehicle operation: in, and are the sideslip angles of the front and rear wheels of the vehicle respectively; and are the cornering stiffness of the front and rear wheels of the vehicle respectively; The third basic model is constructed based on the side slip angles of the front and rear wheels during vehicle operation: in is the front wheel turning angle; From the first basic model, the second basic model and the third basic model, we can get: in is the sideslip angle of the center of mass; The fourth basic model is constructed based on the difference data between the actual trajectory and the reference trajectory during vehicle operation: in and are the actual lateral displacement of the vehicle, the reference lateral displacement, and the lateral displacement error; , and are the actual heading angle of the vehicle, the reference heading angle, and the heading angle error; and They are and First derivative with respect to time; The trajectory tracking model building module is further used to: The state vector of the selected vehicle is , select virtual control input ,in is the second derivative of the heading angle with respect to time, and the vehicle trajectory tracking model is constructed as follows: in, Representatives Taking the derivative with respect to time, the system matrix and the input matrix They are in the following forms: Acquiring the vehicle operation data, including: in, For mathematical operations Kronecker product; is the data sampling time; is the number of data samples; and There are three different types of data collected; calculate Until the collection stop condition is met: in, Represented by data and The rank of the constructed data matrix; The vehicle online identification module includes: Initial parameter setting subunit, used to select any positive definite matrix and , where the matrix The dimension is ,matrix The dimension is Apply any steering input to the vehicle to control the vehicle's tracking trajectory and hold, set the initial feedback gain , set the initial iteration knowledge parameters , set the model identification accuracy parameters ; a data collection subunit, configured to adopt a method for acquiring the vehicle operation data until a collection stop condition is met; The intermediate matrix calculation subunit is used to calculate the intermediate matrix using the collected data and the selected rank: Solve the following update equation to get a new set of : The accuracy judgment subunit is used to judge whether the following accuracy conditions are met: If the condition is not met, the iteration indicator parameter is increased by one and the process returns to the intermediate matrix calculation subunit: If the conditions are met, proceed to the next subunit: The input matrix solving subunit is used to solve the input matrix to be identified using the following formula : System matrix solving subunit, used to calculate the system matrix to be identified : Construct the following temporary matrix , calculated as: Construct the following temporary matrix and : in Represents the corresponding first Row, No. Elements of the column; Representative Matrix The corresponding Row, No. Elements of the column; Representative Matrix The corresponding Row, No. Elements of the column; Calculate using the following formula : Solve the system matrix of the vehicle trajectory tracking system The unknown coefficients in are used to complete the online identification of vehicle parameters of the vehicle trajectory tracking model.

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