Vehicle control model parameter calibration method, device, electronic equipment and storage medium

Abstract

The present application relates to a parameter calibration method, device, electronic device and storage medium of a vehicle control model, the method also includes: obtaining a pre-configured closed-loop pole, and determining a first characteristic equation according to the pre-configured closed-loop pole; obtaining a closed-loop system matrix of the vehicle control model, and determining a second characteristic equation according to the closed-loop system matrix; determining the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop pole according to the first characteristic equation and the second characteristic equation; determining the feedback gain matrix based on the target relationship, and obtaining the parameter calibration result of the vehicle control model based on the feedback gain matrix. In this way, the feedback gain matrix can be quickly determined according to the target relationship, thereby quickly realizing the parameter calibration of the vehicle control model, thereby improving the parameter calibration efficiency of the vehicle control model.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a parameter calibration method, device, electronic device and storage medium for a vehicle control model. Background Art

[0002] In the field of vehicle control, parameter calibration is particularly important because it directly affects the performance and safety of the vehicle. However, in the existing technology, linear quadratic regulator (LQR) and model predictive control (MPC) are usually used for parameter calibration. However, this method has a large number of calibrations and the target solution is not unique, making the parameter calibration process more complicated. Therefore, how to improve the efficiency of parameter calibration of vehicle control models has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a method, device, electronic device and storage medium for parameter calibration of a vehicle control model to solve the problem that the prior art generally uses linear quadratic regulators, model predictive control and other methods for parameter calibration, and the parameter calibration process is relatively complicated.

[0004] In a first aspect, the present application provides a method for calibrating parameters of a vehicle control model, the method comprising:

[0005] Obtaining pre-configured closed-loop poles, and determining a first characteristic equation according to the pre-configured closed-loop poles, wherein the real part of the pre-configured closed-loop poles is a negative number;

[0006] Acquire a closed-loop system matrix of the vehicle control model, and determine a second characteristic equation according to the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix, and a feedback gain matrix in the vehicle control model;

[0007] Determining, according to the first characteristic equation and the second characteristic equation, target relationships between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix, and the pre-configured closed-loop poles;

[0008] Based on the target relationship, the feedback gain matrix is ​​determined, and based on the feedback gain matrix, a parameter calibration result of the vehicle control model is obtained.

[0009] Optionally, the acquiring a closed-loop system matrix of the vehicle control model includes:

[0010] Determining, according to the type of the vehicle control model, a target system parameter that matches the type of the vehicle control model, wherein different types of vehicle control models correspond to different system parameters;

[0011] Calibrate the target system parameters;

[0012] Based on the calibrated target system parameters, determining the initial system matrix and the control matrix corresponding to the vehicle control model;

[0013] The closed-loop system matrix is ​​calculated based on the initial system matrix and the control matrix.

[0014] Optionally, the calculation formula of the closed-loop system matrix is ​​as follows:

[0015] ;

[0016] in, represents the closed-loop system matrix, represents the initial system matrix, represents the control matrix, Denotes the feedback gain matrix.

[0017] Optionally, when the type of the vehicle control model is a two-degree-of-freedom model, the target system parameters include vehicle mass, distance from the center of the front wheel to the center of mass, distance from the center of the rear wheel to the center of mass, vehicle moment of inertia, rear wheel cornering stiffness, and front wheel cornering stiffness;

[0018] The calibrating the target system parameters includes:

[0019] Obtain the weighing mass at each wheel of the vehicle and the vehicle wheelbase;

[0020] According to the weighed mass at each wheel of the vehicle and the wheelbase of the vehicle, respectively calculate the calibration values ​​of the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, and calibrate the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia based on the calibration values;

[0021] The cornering stiffnesses in the preset cornering stiffness set are traversed in sequence, and a target cornering stiffness is determined from the cornering stiffness set according to the traversed cornering stiffnesses, as well as the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, wherein the target cornering stiffness corresponds to an error between an actual response curve and a theoretical response curve that is less than a preset error;

[0022] The rear wheel cornering stiffness and the front wheel cornering stiffness are calibrated based on the target cornering stiffness.

[0023] Optionally, the cornering stiffnesses in a preset cornering stiffness set are traversed in sequence, and a target cornering stiffness is determined from the cornering stiffness set according to the traversed cornering stiffnesses, as well as the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, including:

[0024] Traversing the cornering stiffnesses in the cornering stiffness set in turn;

[0025] Based on the currently traversed cornering stiffness and the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, the initial system matrix and the control matrix corresponding to the currently traversed cornering stiffness are calculated;

[0026] The feedback gain matrix corresponding to the currently traversed cornering stiffness is calculated based on the pre-configured closed-loop poles, the initial system matrix corresponding to the currently traversed cornering stiffness, and the control matrix;

[0027] Based on the initial system matrix and the control matrix corresponding to the currently traversed cornering stiffness, and the feedback gain matrix corresponding to the currently traversed cornering stiffness, a theoretical response curve corresponding to the currently traversed cornering stiffness is calculated;

[0028] Based on the feedback gain matrix corresponding to the currently traversed cornering stiffness, actual vehicle control is performed to measure the actual response curve corresponding to the currently traversed cornering stiffness;

[0029] Determining whether an error between the actual response curve and the theoretical response curve is less than a preset error;

[0030] In a case where it is determined that the error between the actual response curve and the theoretical response curve is less than the preset error, determining the cornering stiffness currently traversed as the target cornering stiffness;

[0031] When it is determined that the error between the actual response curve and the theoretical response curve is greater than or equal to the preset error, the cornering stiffnesses in the cornering stiffness set continue to be traversed until all the cornering stiffnesses in the cornering stiffness set have been traversed.

[0032] Optionally, determining, according to the first characteristic equation and the second characteristic equation, a target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix, and the pre-configured closed-loop poles, comprises:

[0033] According to the second characteristic equation, a first intermediate equation is determined, wherein the parameters involved in the first intermediate equation include each element in the feedback gain matrix, each element in the initial system matrix and each element in the control matrix, and the first intermediate equation is a univariate Nth-order equation, where N is an integer greater than 1;

[0034] Determine a second intermediate equation according to the first characteristic equation, wherein the parameters involved in the second intermediate equation include the pre-configured closed-loop poles, the second intermediate equation is a univariate M-order equation, the value of M is determined based on the number of the closed-loop poles, and M is an integer greater than 1;

[0035] According to the relationship between the first intermediate equation and the second intermediate equation, the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles is determined.

[0036] Optionally, determining the feedback gain matrix based on the target relationship includes:

[0037] Acquire a preset vehicle speed set, wherein the vehicle speed set includes a plurality of vehicle speed sampling points, and the intervals between any two adjacent vehicle speed sampling points in the plurality of vehicle speed sampling points are equal;

[0038] The vehicle speed sampling points in the vehicle speed set are traversed in sequence, and based on the vehicle speed sampling points traversed each time and the target relationship, the feedback gain matrix corresponding to each vehicle speed sampling point is calculated.

[0039] In a second aspect, the present application also provides a parameter calibration device for a vehicle control model, the device comprising:

[0040] A first determination module, configured to obtain a pre-configured closed-loop pole, and determine a first characteristic equation according to the pre-configured closed-loop pole, wherein the real part of the pre-configured closed-loop pole is a negative number;

[0041] A second determination module is used to obtain a closed-loop system matrix of the vehicle control model and determine a second characteristic equation according to the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix, and a feedback gain matrix in the vehicle control model;

[0042] A third determination module is used to determine the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop pole according to the first characteristic equation and the second characteristic equation;

[0043] The fourth determination module is used to determine the feedback gain matrix based on the target relationship, and obtain the parameter calibration result of the vehicle control model based on the feedback gain matrix.

[0044] In a third aspect, the present application further provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0045] Memory, used to store computer programs;

[0046] The processor is used to implement the parameter calibration method of the vehicle control model described in the first aspect when executing the program stored in the memory.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the parameter calibration method of the vehicle control model described in the first aspect is implemented.

[0048] The above-mentioned technical solution provided by the present application has the following advantages over the prior art: the method provided by the present application obtains pre-configured closed-loop poles, and determines a first characteristic equation based on the pre-configured closed-loop poles, wherein the real part of the pre-configured closed-loop poles is a negative number; obtains a closed-loop system matrix of the vehicle control model, and determines a second characteristic equation based on the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix and a feedback gain matrix in the vehicle control model; determines a target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles, respectively, based on the first characteristic equation and the second characteristic equation; determines the feedback gain matrix based on the target relationship, and obtains a parameter calibration result of the vehicle control model based on the feedback gain matrix. Through the above method, the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles can be directly determined according to the relationship between the first characteristic equation and the second characteristic equation, and then the feedback gain matrix can be quickly determined according to the target relationship, so as to quickly realize the parameter calibration of the vehicle control model, thereby improving the parameter calibration efficiency of the vehicle control model. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0051] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0052] Figure 1 A schematic diagram of a flow chart of a parameter calibration method for a vehicle control model provided in an embodiment of the present application;

[0053] Figure 2 A system block diagram provided for an embodiment of the present application;

[0054] Figure 3 Another system block diagram provided for an embodiment of the present application;

[0055] Figure 4 A schematic diagram of an equivalent system provided in an embodiment of the present application;

[0056] Figure 5 A schematic diagram of a pole configuration of an equivalent system provided in an embodiment of the present application;

[0057] Figure 6 A detailed flow chart of system parameter calibration provided in an embodiment of the present application;

[0058] Figure 7 A detailed flow chart of a control parameter calibration provided in an embodiment of the present application;

[0059] Figure 8 A modified structural block diagram provided for an embodiment of the present application;

[0060] Fig. 9 A schematic flow chart of another method for calibrating parameters of a vehicle control model provided in an embodiment of the present application;

[0061] Fig.10 A schematic structural diagram of a field of view adjustment device for an electronic rearview mirror provided in an embodiment of the present application;

[0062] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0064] The disclosure below provides many different embodiments or examples to implement different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0065] In the prior art, linear quadratic regulators, model predictive control and other methods are usually used for parameter calibration, which has the problem that the parameter calibration process is relatively complicated. The present application provides a parameter calibration method, device, electronic device and storage medium for a vehicle control model, which can improve the parameter calibration efficiency of the vehicle control model.

[0066] See also Figure 1 , Figure 1 A flow chart of a method for calibrating parameters of a vehicle control model provided in an embodiment of the present application. Figure 1 As shown, the parameter calibration method of the vehicle control model may include the following steps:

[0067] Step 101: Obtain pre-configured closed-loop poles, and determine a first characteristic equation according to the pre-configured closed-loop poles, wherein the real part of the pre-configured closed-loop poles is a negative number.

[0068] It should be noted that the pre-configured closed-loop poles can obtain equivalent poles from different closed-loop feedback methods (for example, obtained at one time from the vehicle LQR debugging results), or can be directly set according to empirical values, and the embodiments of the present application do not make specific limitations. The number of pre-configured closed-loop poles is multiple, and the specific needs need to be determined according to the closed-loop system selected by the vehicle control model. Since the dynamic performance of the closed-loop system is determined by the eigenvalues ​​of the system, when the real part of the eigenvalues ​​of the system is less than zero, the closed-loop system is in a stable state. Therefore, in order to ensure the stability of the closed-loop system, the closed-loop poles can be pre-configured as the eigenvalues ​​of the system, so that the real part of each closed-loop pole is a negative number. The above-mentioned first characteristic equation refers to the characteristic equation determined according to the pre-configured closed-loop poles. For example, assuming that the pre-configured closed-loop poles are , then the first characteristic equation is: , where s represents the eigenvalue of the system.

[0069] Step 102: Obtain a closed-loop system matrix of the vehicle control model, and determine a second characteristic equation based on the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix, and a feedback gain matrix in the vehicle control model.

[0070] Specifically, the vehicle control model can be a two-degree-of-freedom model, a seven-degree-of-freedom model, an eleven-degree-of-freedom model, or other control models. The closed-loop system matrix of the vehicle control model refers to the system matrix of the closed-loop system calculated according to the state equation of the negative feedback system. It should be noted that, according to modern control theory, the state equation of a common control system can be expressed as:

[0071] ; (1)

[0072] in, is the system state quantity, A is the initial system matrix, B is the control matrix, and u is the control quantity. The system block diagram is as follows Figure 2 As shown. Among them, the eigenvalue of the system can be solved by solving the characteristic equation:

[0073] ; (2)

[0074] Among them, s is the eigenvalue of the system, I is the identity matrix, and A is the initial system matrix.

[0075] In such Figure 3 In the negative feedback system shown, the control quantity is: , where r is the reference input, i.e. the control target, is the feedback gain matrix.

[0076] The state equation of the negative feedback system can be expressed as:

[0077] (3)

[0078] in, , is the closed-loop system matrix.

[0079] At this time, the second characteristic equation can be obtained according to the above formulas (2) to (3): .

[0080] Step 103: According to the first characteristic equation and the second characteristic equation, determine the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles.

[0081] It should be noted that in order to find the conditions that make the closed-loop system stable, there are many control methods such as the commonly used LQR, MPC, pole configuration and other methods, so that the eigenvalue of the closed-loop system has a negative real part.

[0082] Different control methods can be used to generate different closed-loop systems S1 and S2 for the open-loop system S. When the closed-loop systems S1 and S2 have the same characteristic value, the closed-loop response characteristics of the two are the same. In this case, the two closed-loop systems can be considered to be equivalent systems. Figure 4 As shown. It can be seen that the eigenvalue of the system can be used as a bridge to connect different closed-loop systems, making the control effects obtained under different control methods equivalent. Due to the concept of equivalent system, the eigenvalue of the system can be used as a medium to obtain the pole configuration of the equivalent system from the perspective of different closed-loop feedback methods. Figure 5 As shown, the process includes the following steps:

[0083] Step 501: Select an existing closed-loop system, such as LQR, MPC, etc.

[0084] Step 502: Calculate the negative feedback gain K based on an existing closed-loop system.

[0085] Step 503: Calculate the closed-loop system matrix A1 based on the negative feedback gain K.

[0086] Step 504: Calculate the eigenvalue of the system based on the closed-loop system matrix A1.

[0087] Step 505: Use the calculated eigenvalues ​​of the system as closed-loop poles, and configure the closed-loop poles.

[0088] Step 506: Determine the configured negative feedback gain K according to the configured closed-loop poles.

[0089] Step 507 : Obtain a configured closed-loop system according to the configured negative feedback gain K. At this time, the response effect of the configured closed-loop system is equivalent to that of the closed-loop system in step 501 .

[0090] In this way, the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles can be determined according to the first characteristic equation and the second characteristic equation. That is, the feedback gain matrix is ​​determined according to the following formula: Each element in is respectively related to the initial system matrix Each element in the control matrix The target relationship between each element in and the pre-configured closed-loop poles:

[0091] . (4)

[0092] Step 104: Based on the target relationship, a feedback gain matrix is ​​determined, and based on the feedback gain matrix, a parameter calibration result of the vehicle control model is obtained.

[0093] After determining the feedback gain matrix Each element in is respectively related to the initial system matrix Each element in the control matrix After determining the target relationship between each element in and the pre-configured closed-loop poles, the feedback gain matrix can be determined based on the target relationship, and the parameter calibration result of the vehicle control model can be obtained based on the feedback gain matrix.

[0094] In the prior art, when LQR is used to calculate the feedback gain matrix K, a linear steady-state system is considered, whose state space is described as: . Its linear quadratic optimal index is:

[0095] ; (5)

[0096] in, is the optimal linear quadratic index, and are positive definite symmetric matrices, used to describe the state and control input weights, respectively, is the system state quantity, u is the control quantity, u= -K , at this time r = 0. According to the optimal control theory, the feedback gain matrix K that meets the above indicators is:

[0097] ; (6)

[0098] in, represents the solution of the Riccati equation, Since the solution of the Riccati equation requires many iterations, its solution efficiency is low. It takes a long time to calibrate the parameters in this way.

[0099] It can be seen that compared with the prior art, the present embodiment can directly determine the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles according to the relationship between the first characteristic equation and the second characteristic equation, and then can quickly determine the feedback gain matrix according to the target relationship, thereby quickly realizing the parameter calibration of the vehicle control model, thereby improving the parameter calibration efficiency of the vehicle control model.

[0100] Furthermore, the above step 102, obtaining a closed-loop system matrix of the vehicle control model, includes:

[0101] According to the type of the vehicle control model, a target system parameter matching the type of the vehicle control model is determined, wherein different types of vehicle control models correspond to different system parameters;

[0102] Calibrate the target system parameters;

[0103] Based on the calibrated target system parameters, the initial system matrix and control matrix corresponding to the vehicle control model are determined;

[0104] According to the initial system matrix and the control matrix, the closed-loop system matrix is ​​calculated.

[0105] It should be noted that different types of vehicle control models correspond to different system parameters. Therefore, when obtaining the closed-loop system matrix of the vehicle control model, it is necessary to first determine the target system parameters that match the type of vehicle control model according to the type of vehicle control model, and then calibrate the target system parameters. Then, based on the calibrated target system parameters, determine the initial system matrix and control matrix corresponding to the vehicle control model. Finally, based on the initial system matrix and control matrix, calculate the closed-loop system matrix.

[0106] For example, for a two-degree-of-freedom model, the system parameters that need to be calibrated may include the vehicle mass m, the distance from the center of the front wheel to the center of mass , the distance from the rear wheel center to the center of mass , vehicle moment of inertia , rear wheel cornering stiffness , front wheel cornering stiffness And other parameters, and then according to the calibrated vehicle mass m, the distance from the center of the front wheel to the center of mass , the distance from the rear wheel center to the center of mass , vehicle moment of inertia , rear wheel cornering stiffness , front wheel cornering stiffness The initial system matrix A and the control matrix B can be obtained by using the same parameters. In this way, the closed-loop system matrix can be determined based on the initial system matrix A and the control matrix B.

[0107] Through the above method, the closed-loop system matrix can be quickly determined, and then the feedback gain matrix can be quickly determined to achieve rapid calibration of vehicle control model parameters, thereby improving the parameter calibration efficiency of the vehicle control model.

[0108] Furthermore, the closed-loop system matrix is ​​calculated as follows:

[0109] ; (7)

[0110] in, represents the closed-loop system matrix, represents the initial system matrix, represents the control matrix, represents the feedback gain matrix.

[0111] In one embodiment, the closed-loop system matrix can be quickly determined according to the above formula, and then the feedback gain matrix can be quickly determined to achieve rapid calibration of vehicle control model parameters, thereby improving the parameter calibration efficiency of the vehicle control model.

[0112] Further, when the type of the vehicle control model is a two-degree-of-freedom model, the target system parameters include vehicle mass, distance from the center of the front wheel to the center of mass, distance from the center of the rear wheel to the center of mass, vehicle moment of inertia, rear wheel cornering stiffness, and front wheel cornering stiffness;

[0113] The above steps, calibrating the target system parameters, include:

[0114] Obtain the weighing mass at each wheel of the vehicle and the vehicle wheelbase;

[0115] According to the weighing mass at each wheel of the vehicle and the wheelbase of the vehicle, the calibration values ​​of the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia are calculated respectively, and based on the calibration values, the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia are calibrated;

[0116] The cornering stiffnesses in the preset cornering stiffness set are traversed in sequence, and a target cornering stiffness is determined from the cornering stiffness set according to the traversed cornering stiffnesses, as well as the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, wherein the target cornering stiffness corresponds to an error between the actual response curve and the theoretical response curve that is less than a preset error;

[0117] Based on the target cornering stiffness, the rear wheel cornering stiffness and the front wheel cornering stiffness are calibrated.

[0118] In one embodiment, when the type of vehicle control model is a two-degree-of-freedom model, the above-mentioned target system parameters may include the mass of the whole vehicle, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, the vehicle moment of inertia, the rear wheel cornering stiffness and the front wheel cornering stiffness. Among them, the mass of the whole vehicle, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia are constants, and their values ​​are determined by the vehicle hardware and can be directly calibrated by measurement and calculation. The rear wheel cornering stiffness and the front wheel cornering stiffness are variables and are more difficult to calibrate. Therefore, when calibrating the rear wheel cornering stiffness and the front wheel cornering stiffness, it can be assumed that the rear wheel cornering stiffness is and front wheel cornering stiffness Consistent, that is At this time, the initial system matrix A and control matrix B are related to vehicle speed v and lateral stiffness The function is:

[0119] ; (8)

[0120] ; (9)

[0121] Under the given vehicle speed v, Among the possible intervals, select multiple possible Value, for each The value is determined by the matrices A and B. On this basis, the closed-loop response poles are configured as p1, p2, p3, and p4, and the corresponding feedback gain matrix K can be obtained. At this time, the theoretical response curve of the closed-loop system can be expressed by the following formula:

[0122] ; (10)

[0123] in, represents the theoretical response at different times t, represents the initial system matrix, represents the control matrix, represents the feedback gain matrix, Represents the initial state of the closed-loop system.

[0124] At this time, the control quantity u is u=-Kx+r, r=0;

[0125] The control quantity is controlled in the actual vehicle under the same initial conditions to obtain the actual response curve. It is assumed that the actual response curve can be expressed by the following formula:

[0126] ; (11)

[0127] in, represents the actual response at different times t, Represents the initial state of the closed-loop system.

[0128] Loop through all The theoretical and actual response are solved respectively, and finally the curve that makes the theoretical approximation equal to the actual is obtained, that is:

[0129] ; (12)

[0130] At this time The value is the cornering stiffness closest to the true value.

[0131] Through the above method, the calibration of each system parameter in the two-degree-of-freedom model can be quickly achieved.

[0132] Furthermore, the above steps of sequentially traversing the cornering stiffnesses in the preset cornering stiffness set, and determining the target cornering stiffness from the cornering stiffness set according to the traversed cornering stiffnesses, as well as the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, include:

[0133] The cornering stiffnesses in the cornering stiffness set are traversed in turn;

[0134] Based on the currently traversed cornering stiffness and the calibrated vehicle mass, the distance from the front wheel center to the center of mass, the distance from the rear wheel center to the center of mass, and the vehicle moment of inertia, the initial system matrix and control matrix corresponding to the currently traversed cornering stiffness are calculated;

[0135] The feedback gain matrix corresponding to the currently traversed cornering stiffness is calculated based on the pre-configured closed-loop poles, the initial system matrix corresponding to the currently traversed cornering stiffness, and the control matrix;

[0136] Based on the initial system matrix and the control matrix corresponding to the currently traversed cornering stiffness, and the feedback gain matrix corresponding to the currently traversed cornering stiffness, a theoretical response curve corresponding to the currently traversed cornering stiffness is calculated;

[0137] Based on the feedback gain matrix corresponding to the currently traversed cornering stiffness, actual vehicle control is performed to measure the actual response curve corresponding to the currently traversed cornering stiffness;

[0138] Determine whether the error between the actual response curve and the theoretical response curve is less than a preset error;

[0139] When it is determined that the error between the actual response curve and the theoretical response curve is less than a preset error, the currently traversed cornering stiffness is determined as the target cornering stiffness;

[0140] When it is determined that the error between the actual response curve and the theoretical response curve is greater than or equal to the preset error, the cornering stiffnesses in the cornering stiffness set continue to be traversed until all the cornering stiffnesses in the cornering stiffness set have been traversed.

[0141] In one embodiment, when the target cornering stiffness is determined, the cornering stiffnesses in the cornering stiffness set may be traversed in sequence, and then based on the cornering stiffness currently traversed and the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, the initial system matrix and the control matrix corresponding to the cornering stiffness currently traversed are calculated, and then based on the pre-configured closed-loop poles, the initial system matrix and the control matrix corresponding to the cornering stiffness currently traversed, the feedback gain matrix corresponding to the cornering stiffness currently traversed is calculated, and then based on the initial system matrix and the control matrix corresponding to the cornering stiffness currently traversed, the theoretical response curve corresponding to the cornering stiffness currently traversed is calculated, and based on the feedback gain matrix corresponding to the cornering stiffness currently traversed, the actual vehicle control is performed to measure the actual response curve corresponding to the cornering stiffness currently traversed. Then, it is determined whether the error between the actual response curve and the theoretical response curve is less than the preset error. If the error between the actual response curve and the theoretical response curve is less than the preset error, the currently traversed cornering stiffness is determined as the target cornering stiffness. If the error between the actual response curve and the theoretical response curve is greater than or equal to the preset error, the cornering stiffnesses in the cornering stiffness set are continued to be traversed until all the cornering stiffnesses in the cornering stiffness set have been traversed.

[0142] Specifically, the process of calibrating the system parameters of the two-degree-of-freedom model is as follows: Figure 6 As shown, the following steps may be specifically included:

[0143] Step 601: Obtain the weighed mass at each wheel of the vehicle, represented by m1, m2, m3 and m4 respectively.

[0144] Step 602: Measure and obtain the vehicle wheelbase L value.

[0145] Step 603: Calculate the vehicle mass m and the distance from the center of the front wheel to the center of mass using the weighed mass of each wheel and the wheelbase of the vehicle. , the distance from the rear wheel center to the center of mass , vehicle moment of inertia Specifically, the calculation process is as follows:

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] .

[0152] Step 604: Given the vehicle speed v, set the cornering stiffness value.

[0153] At a given vehicle speed v, the cornering stiffness From the possible range of (i.e., the set of cornering stiffnesses), select one cornering stiffness at a time. value.

[0154] Step 605: Solve the initial system matrix A and control matrix B corresponding to the currently traversed cornering stiffness.

[0155] Step 606: Set the closed-loop poles p1, p2, p3 and p4 (which can be obtained from the vehicle LQR debugging results at one time).

[0156] Step 607: Closed-loop pole configuration and solving the feedback gain matrix K.

[0157] Step 608: Solve the theoretical response curve .

[0158] Step 609: Set the same initial conditions, control the actual vehicle on the straight target lane according to the control amount u, and obtain the actual response curve .

[0159] Step 610: Compare theoretical response curves And the actual response curve .

[0160] Step 611: Determine the theoretical response curve And the actual response curve Whether the consistency meets the preset conditions.

[0161] When the theoretical response curve And the actual response curve When the consistency of As the best cornering stiffness , end the process; when the theoretical response curve And the actual response curve When the consistency does not meet the preset condition, execute step 612.

[0162] Step 612: Cache the cornering stiffness value with better consistency.

[0163] Step 613: Determine whether the cornering stiffness set has been traversed.

[0164] If the cornering stiffness set has not been completely traversed, the process returns to step 604 ; if the cornering stiffness set has been completely traversed, the process ends.

[0165] Through the above method, the cornering stiffness can be accurately calibrated to solve the problem that the cornering stiffness is difficult to calibrate.

[0166] Furthermore, the above step 103, based on the first characteristic equation and the second characteristic equation, determines the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles, including:

[0167] According to the second characteristic equation, a first intermediate equation is determined, wherein the parameters involved in the first intermediate equation include each element in the feedback gain matrix, each element in the initial system matrix and each element in the control matrix, and the first intermediate equation is a univariate Nth-order equation, where N is an integer greater than 1;

[0168] According to the first characteristic equation, a second intermediate equation is determined, wherein the parameters involved in the second intermediate equation include pre-configured closed-loop poles, the second intermediate equation is a univariate M-order equation, the value of M is determined based on the number of closed-loop poles, and M is an integer greater than 1;

[0169] According to the relationship between the first intermediate equation and the second intermediate equation, the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles is determined.

[0170] It should be noted that for the two-degree-of-freedom model, the corresponding error state dynamic model is as follows:

[0171] ;

[0172] in, represents the lateral error, represents the lateral error change rate, represents the heading error, represents the rate of change of heading error, m represents the vehicle mass, v represents the vehicle speed, represents the distance from the center of the front wheel to the center of mass, Represents the distance from the rear wheel center to the center of mass, represents the vehicle's moment of inertia, is the rear wheel cornering stiffness, represents the front wheel cornering stiffness, represents the front wheel swing angle, It represents the expected turning angle rate, and its value is related to the vehicle speed and the curvature of the tracking curve. As can be seen from the above, when the target trajectory is a straight line, the curvature addition term is zero, and the system degenerates into a linear system.

[0173] In this way, the initial system matrix can be expressed as:

[0174] ; (13)

[0175] in, represents the element in the i-th row and j-th column of the initial system matrix A, where i and j are both positive integers less than or equal to 4. The value of is the element of the i-th row and j-th column in the first matrix of the error state dynamics model corresponding to the two-degree-of-freedom model. For example, The value of , The value of , The value of , The value of , The value of , The value of .

[0176] And the control matrix can be expressed as:

[0177] ; (14)

[0178] in, represents the element in the mth row and nth column of the control matrix B, where m is a positive integer less than or equal to 4, and n is 1. The value of is the element in the mth row and nth column of the second matrix in the error state dynamics model corresponding to the two-degree-of-freedom model. For example, The value of , The value of .

[0179] According to the above initial system matrix and control matrix, the closed-loop system matrix is:

[0180] ; (15)

[0181] Since the second characteristic equation is , thus according to the second characteristic equation, the first intermediate equation can be determined, and the first intermediate equation can be the following univariate multi-time equation:

[0182] ; (16)

[0183] in, represents the element in the i-th row and j-th column of the initial system matrix A, where i and j are both positive integers less than or equal to 4. represents the element in the mth row and nth column of the control matrix B, where m is a positive integer less than or equal to 4, and n is 1. represents the qth element in the feedback gain matrix K, where q is a positive integer less than or equal to 4.

[0184] Similarly, assuming that the pre-configured closed-loop poles are 4 poles, the 4 closed-loop poles are: .

[0185] Since the first characteristic equation is expressed as

[0186] , thus according to the first characteristic equation, the second intermediate equation can be determined, and the second intermediate equation can be the following univariate multi-time equation:

[0187] ; (17)

[0188] According to the above formula (16) and formula (17), the following four equations can be obtained:

[0189] ; (18)

[0190] ; (19)

[0191] ; (20)

[0192] ;(twenty one)

[0193] In this way, by rearranging the above formulas (18) to (21), we can obtain the following equation:

[0194] ;(twenty two)

[0195] By solving the above formula (22), we can get the elements of the feedback gain K: , , and The value of each element in the feedback gain K , , and The value of can be expressed by the following formula:

[0196] ;(twenty three)

[0197] ;

[0198] ;

[0199] ;

[0200] ;

[0201] ;

[0202] ;

[0203] ;

[0204] ;

[0205] in, represents the element in the i-th row and j-th column of the initial system matrix A, where i and j are both positive integers less than or equal to 4. represents the element in the mth row and nth column of the control matrix B, where m is a positive integer less than or equal to 4, and n is 1. represents the qth element in the feedback gain matrix K, where q is a positive integer less than or equal to 4. , , and Represents the preconfigured closed-loop poles.

[0206] Through the above method, according to the first characteristic equation and the second characteristic equation, the relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles can be quickly and accurately determined, so as to quickly and accurately solve the feedback gain matrix.

[0207] Furthermore, the above step 104, based on the target relationship, determines a feedback gain matrix, including:

[0208] Acquire a preset vehicle speed set, wherein the vehicle speed set includes a plurality of vehicle speed sampling points, and the intervals between any two adjacent vehicle speed sampling points in the plurality of vehicle speed sampling points are equal;

[0209] The vehicle speed sampling points in the vehicle speed set are traversed in sequence, and based on the relationship between the vehicle speed sampling points traversed each time and the target, the feedback gain matrix corresponding to each vehicle speed sampling point is calculated.

[0210] In one embodiment, after the cornering stiffness calibration is completed, the initial system matrix A and the control matrix B are related functions of the vehicle speed v, that is:

[0211] ;(twenty four)

[0212] ; (25)

[0213] From the speed range, sampling is performed every 10 kph, and the speed set is obtained as follows:

[0214] ;

[0215] Configure the closed-loop poles p1, p2, p3 and p4, obtain the initial system matrix A and control matrix B corresponding to each speed sampling point for each speed sampling point, configure the closed-loop poles, and then obtain the feedback gain matrix K value corresponding to each speed sampling point according to the initial system matrix A and control matrix B corresponding to each speed sampling point, and save it, and the parameter calibration of the vehicle control model can be completed. This process can be completed offline. Specifically, the process of calibrating the control parameters of the two-degree-of-freedom model is as follows: Figure 7 As shown, the following steps may be included:

[0216] Step 701: Set an offline vehicle speed set v.

[0217] Step 702: Select a vehicle speed v.

[0218] Step 703: Solve the initial system matrix and control matrix .

[0219] Step 704: Set closed-loop poles.

[0220] The closed-loop pole is calculated according to the LQR algorithm or obtained through artificial experience summary.

[0221] Step 705: configure closed-loop poles and solve the feedback gain matrix K.

[0222] Step 706: Record the feedback gain matrix K at all vehicle speeds.

[0223] Step 707: Complete parameter calibration.

[0224] Through the above method, the feedback gain matrix K corresponding to each vehicle speed can be obtained, which is convenient for subsequent control of the vehicle using the obtained feedback gain matrix K corresponding to each vehicle speed.

[0225] It should be noted that in the process of modeling the two-degree-of-freedom model, some non-modeling factors (such as plane assumptions, etc.) are inevitably present, which are easily ignored in the modeling process. Therefore, the system uncertainty caused by these non-modeling factors will inevitably affect the system control effect. Although the impact of these uncertainties is small enough, they can be further corrected under certain conditions. The condition for correcting non-modeling uncertainty is: the uncertainty of non-modeling factors is greater than the system measurement uncertainty.

[0226] Under the above conditions, the uncertainty of non-modeling factors can be effectively perceived. At this time, non-modeling control algorithms such as Proportional-Integral-Derivative (PID) can be used for further correction. Among them, the PID algorithm can be automatically calibrated using methods such as Ziegler-Nichols, which will not be expanded here. The correction structure block diagram is as follows Figure 8 As shown, a non-modeling controller is added between the reference input and the closed-loop system, and the non-modeling controller is used to perform PID calculation on the reference input and the output of the closed-loop system. The calculation result is then used as the input of the closed-loop system and input into the closed-loop system together with the reference input.

[0227] The parameter calibration method of the vehicle control model provided in the embodiment of the present application is as follows Fig. 9 As shown, it includes the following steps:

[0228] Step 901: Calibrate system parameters of the dynamics model.

[0229] The system parameters of the dynamic model can include basic parameters (such as vehicle mass, distance from the front wheel center to the center of mass, distance from the rear wheel center to the center of mass, and vehicle moment of inertia) and cornering stiffness, etc. The basic parameters can be directly calculated and measured, and the cornering stiffness needs to be calibrated in combination with the pole configuration and actual response.

[0230] Step 902: Calibrate the control parameters of the dynamics model.

[0231] Among them, the control parameters of the dynamic model include feedback gain matrix, etc., and the calibration method is automatic calibration using the pole configuration method.

[0232] Step 903: Calibration of the non-modeling controller of the dynamic model.

[0233] The non-modeling controller is used to perform PID calculation to reduce the system uncertainty caused by non-modeling factors.

[0234] In this way, through calibration in three dimensions, an ideal vehicle control model can be obtained, which has the following characteristics: (1) it is related to the dynamic model; (2) the modeling parameters are accurate enough; and (3) the control parameter calibration is simple enough.

[0235] It can be seen that the parameter calibration method of the vehicle control model provided in the embodiment of the present application has the following beneficial effects:

[0236] (1) The parameter debugging process can be standardized and obtained by pole configuration, without the need for indirect acquisition through LQR parameters, thus simplifying the parameter debugging process;

[0237] (2) The control parameters can be calibrated offline. The feedback gain matrix K can be calibrated directly offline to improve the computational efficiency.

[0238] (3) The control parameters can also be calibrated online, avoiding the iterative solution of the Riccati equation and effectively improving the solution efficiency;

[0239] (4) During system parameter calibration, only the vehicle needs to be weighed, avoiding the use of complex equipment;

[0240] (5) During the system parameter calibration, the cornering stiffness is calibrated according to the standard response, ensuring the accuracy of the calibration results;

[0241] (6) During the system parameter calibration, the cornering stiffness is calibrated according to the standard response, which can produce a certain degree of compensation characteristics for non-modeling factors, so that the system has a certain degree of adaptive target model characteristics;

[0242] (7) Further eliminate model inaccuracies by further correcting non-modeling factors;

[0243] (8) The parameter calibration process can be made fast and efficient, which can effectively save manpower.

[0244] See also Fig.10 , Fig.10 This is a schematic diagram of the structure of a parameter calibration device for a vehicle control model provided in an embodiment of the present application. Fig.10 As shown, the parameter calibration device 1000 of the vehicle control model includes:

[0245] A first determination module 1001 is used to obtain a pre-configured closed-loop pole and determine a first characteristic equation according to the pre-configured closed-loop pole, wherein the real part of the pre-configured closed-loop pole is a negative number;

[0246] The second determination module 1002 is used to obtain a closed-loop system matrix of the vehicle control model and determine a second characteristic equation according to the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix, and a feedback gain matrix in the vehicle control model;

[0247] The third determination module 1003 is used to determine the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles according to the first characteristic equation and the second characteristic equation;

[0248] The fourth determination module 1004 is used to determine a feedback gain matrix based on the target relationship, and obtain a parameter calibration result of the vehicle control model based on the feedback gain matrix.

[0249] Further, the second determining module 1002 includes:

[0250] A first determination submodule is used to determine, according to the type of the vehicle control model, a target system parameter that matches the type of the vehicle control model, wherein different types of vehicle control models correspond to different system parameters;

[0251] A calibration submodule, used to calibrate the target system parameters;

[0252] A second determination submodule is used to determine an initial system matrix and a control matrix corresponding to the vehicle control model based on the calibrated target system parameters;

[0253] The first calculation submodule is used to calculate the closed-loop system matrix according to the initial system matrix and the control matrix.

[0254] Furthermore, the closed-loop system matrix is ​​calculated as follows:

[0255] ;

[0256] in, represents the closed-loop system matrix, represents the initial system matrix, represents the control matrix, represents the feedback gain matrix.

[0257] Furthermore, when the type of the vehicle control model is a two-degree-of-freedom model, the target system parameters include vehicle mass, distance from the center of the front wheel to the center of mass, distance from the center of the rear wheel to the center of mass, vehicle moment of inertia, rear wheel cornering stiffness, and front wheel cornering stiffness; the calibration submodule includes:

[0258] An acquisition unit, used for acquiring the weighing mass at each wheel of the vehicle and the wheelbase of the vehicle;

[0259] A calculation unit, for calculating the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the calibration values ​​of the vehicle moment of inertia according to the weighed mass at each wheel of the vehicle and the wheelbase of the vehicle, and calibrating the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia based on the calibration values;

[0260] A traversal unit is used to traverse the cornering stiffnesses in a preset cornering stiffness set in turn, and determine a target cornering stiffness from the cornering stiffness set according to the traversed cornering stiffnesses, as well as a calibrated vehicle mass, a distance from a front wheel center to a mass center, a distance from a rear wheel center to a mass center, and a vehicle moment of inertia, wherein the target cornering stiffness corresponds to an error between an actual response curve and a theoretical response curve that is less than a preset error;

[0261] The calibration unit is used for calibrating the rear wheel cornering stiffness and the front wheel cornering stiffness based on the target cornering stiffness.

[0262] Furthermore, the traversal unit is specifically used for:

[0263] The cornering stiffnesses in the cornering stiffness set are traversed in turn;

[0264] Based on the currently traversed cornering stiffness and the calibrated vehicle mass, the distance from the front wheel center to the center of mass, the distance from the rear wheel center to the center of mass, and the vehicle moment of inertia, the initial system matrix and control matrix corresponding to the currently traversed cornering stiffness are calculated;

[0265] The feedback gain matrix corresponding to the currently traversed cornering stiffness is calculated based on the pre-configured closed-loop poles, the initial system matrix corresponding to the currently traversed cornering stiffness, and the control matrix;

[0266] Based on the initial system matrix and the control matrix corresponding to the currently traversed cornering stiffness, and the feedback gain matrix corresponding to the currently traversed cornering stiffness, a theoretical response curve corresponding to the currently traversed cornering stiffness is calculated;

[0267] Based on the feedback gain matrix corresponding to the currently traversed cornering stiffness, actual vehicle control is performed to measure the actual response curve corresponding to the currently traversed cornering stiffness;

[0268] Determine whether the error between the actual response curve and the theoretical response curve is less than a preset error;

[0269] When it is determined that the error between the actual response curve and the theoretical response curve is less than a preset error, the currently traversed cornering stiffness is determined as the target cornering stiffness;

[0270] When it is determined that the error between the actual response curve and the theoretical response curve is greater than or equal to the preset error, the cornering stiffnesses in the cornering stiffness set continue to be traversed until all the cornering stiffnesses in the cornering stiffness set have been traversed.

[0271] Further, the third determining module 1003 includes:

[0272] A third determination submodule is used to determine a first intermediate equation according to the second characteristic equation, wherein the parameters involved in the first intermediate equation include each element in the feedback gain matrix, each element in the initial system matrix and each element in the control matrix, and the first intermediate equation is a univariate Nth-order equation, where N is an integer greater than 1;

[0273] a fourth determination submodule, configured to determine a second intermediate equation according to the first characteristic equation, wherein the parameters involved in the second intermediate equation include pre-configured closed-loop poles, the second intermediate equation is a univariate M-order equation, the value of M is determined based on the number of closed-loop poles, and M is an integer greater than 1;

[0274] The fifth determination submodule is used to determine the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles according to the relationship between the first intermediate equation and the second intermediate equation.

[0275] Further, the fourth determining module 1004 includes:

[0276] An acquisition submodule, used for acquiring a preset vehicle speed set, wherein the vehicle speed set includes a plurality of vehicle speed sampling points, and the intervals between any two adjacent vehicle speed sampling points in the plurality of vehicle speed sampling points are equal;

[0277] The second calculation submodule is used to sequentially traverse the vehicle speed sampling points in the vehicle speed set, and calculate the feedback gain matrix corresponding to each vehicle speed sampling point based on the relationship between the vehicle speed sampling points traversed each time and the target.

[0278] It should be noted that the parameter calibration device 1000 of the vehicle control model can implement the parameter calibration method of the vehicle control model provided by any of the aforementioned method embodiments, and can achieve the same technical effects, which will not be described one by one here.

[0279] like Fig.11 As shown, the embodiment of the present application further provides an electronic device, including a processor 1111, a communication interface 1112, a memory 1113 and a communication bus 1114, wherein the processor 1111, the communication interface 1112, and the memory 1113 communicate with each other through the communication bus 1114.

[0280] Memory 1113, used for storing computer programs;

[0281] In one embodiment of the present application, the processor 1111 is used to implement the parameter calibration method of the vehicle control model provided by any one of the aforementioned method embodiments when executing the program stored in the memory 1113.

[0282] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the parameter calibration method of the vehicle control model provided by any of the aforementioned method embodiments is implemented.

[0283] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0284] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0285] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0286] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for calibrating parameters of a vehicle control model, characterized in that: The method comprises: Obtaining pre-configured closed-loop poles, and determining a first characteristic equation according to the pre-configured closed-loop poles, wherein the real part of the pre-configured closed-loop poles is a negative number; Acquire a closed-loop system matrix of the vehicle control model, and determine a second characteristic equation according to the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix, and a feedback gain matrix in the vehicle control model; Determining, according to the first characteristic equation and the second characteristic equation, target relationships between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix, and the pre-configured closed-loop poles; Based on the target relationship, determining the feedback gain matrix, and based on the feedback gain matrix, obtaining a parameter calibration result of the vehicle control model; The first characteristic equation is determined by the following formula: ； The second characteristic equation is determined using the following formula: ； The target relationship is determined using the following formula: ； in, represents the eigenvalue of the system, represents the identity matrix, represents the initial system matrix, represents the control matrix, represents the feedback gain matrix, represents the pre-configured closed-loop poles, represents the closed-loop system matrix, .

2. The method for calibrating parameters of a vehicle control model according to claim 1, characterized in that: The step of obtaining a closed-loop system matrix of the vehicle control model includes: Determining, according to the type of the vehicle control model, a target system parameter that matches the type of the vehicle control model, wherein different types of vehicle control models correspond to different system parameters; Calibrate the target system parameters; Based on the calibrated target system parameters, determining the initial system matrix and the control matrix corresponding to the vehicle control model; The closed-loop system matrix is ​​calculated based on the initial system matrix and the control matrix.

3. The method for calibrating parameters of a vehicle control model according to claim 2, characterized in that: The calculation formula of the closed-loop system matrix is ​​as follows: ； in, represents the closed-loop system matrix, represents the initial system matrix, represents the control matrix, Denotes the feedback gain matrix.

4. The method for calibrating parameters of a vehicle control model according to claim 2, characterized in that: In the case where the type of the vehicle control model is a two-degree-of-freedom model, the target system parameters include vehicle mass, distance from the center of the front wheel to the center of mass, distance from the center of the rear wheel to the center of mass, vehicle moment of inertia, rear wheel cornering stiffness, and front wheel cornering stiffness; The calibrating the target system parameters includes: Obtain the weighing mass at each wheel of the vehicle and the vehicle wheelbase; According to the weighed mass at each wheel of the vehicle and the wheelbase of the vehicle, respectively calculate the calibration values ​​of the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, and calibrate the vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia based on the calibration values; The cornering stiffnesses in the preset cornering stiffness set are traversed in sequence, and a target cornering stiffness is determined from the cornering stiffness set according to the traversed cornering stiffnesses, as well as the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, wherein the target cornering stiffness corresponds to an error between an actual response curve and a theoretical response curve that is less than a preset error; The rear wheel cornering stiffness and the front wheel cornering stiffness are calibrated based on the target cornering stiffness.

5. The method for calibrating parameters of a vehicle control model according to claim 4, characterized in that: The cornering stiffnesses in the preset cornering stiffness set are sequentially traversed, and a target cornering stiffness is determined from the cornering stiffness set according to the traversed cornering stiffnesses, as well as the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, including: Traversing the cornering stiffnesses in the cornering stiffness set in turn; Based on the currently traversed cornering stiffness and the calibrated vehicle mass, the distance from the center of the front wheel to the center of mass, the distance from the center of the rear wheel to the center of mass, and the vehicle moment of inertia, the initial system matrix and the control matrix corresponding to the currently traversed cornering stiffness are calculated; The feedback gain matrix corresponding to the currently traversed cornering stiffness is calculated based on the pre-configured closed-loop poles, the initial system matrix corresponding to the currently traversed cornering stiffness, and the control matrix; Based on the initial system matrix and the control matrix corresponding to the currently traversed cornering stiffness, and the feedback gain matrix corresponding to the currently traversed cornering stiffness, a theoretical response curve corresponding to the currently traversed cornering stiffness is calculated; Based on the feedback gain matrix corresponding to the currently traversed cornering stiffness, actual vehicle control is performed to measure the actual response curve corresponding to the currently traversed cornering stiffness; Determining whether an error between the actual response curve and the theoretical response curve is less than a preset error; In a case where it is determined that the error between the actual response curve and the theoretical response curve is less than the preset error, determining the cornering stiffness currently traversed as the target cornering stiffness; When it is determined that the error between the actual response curve and the theoretical response curve is greater than or equal to the preset error, the cornering stiffnesses in the cornering stiffness set continue to be traversed until all the cornering stiffnesses in the cornering stiffness set have been traversed.

6. The method for calibrating parameters of a vehicle control model according to claim 1, characterized in that: Determining the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles according to the first characteristic equation and the second characteristic equation includes: According to the second characteristic equation, a first intermediate equation is determined, wherein the parameters involved in the first intermediate equation include each element in the feedback gain matrix, each element in the initial system matrix and each element in the control matrix, and the first intermediate equation is a univariate Nth-order equation, where N is an integer greater than 1; Determine a second intermediate equation according to the first characteristic equation, wherein the parameters involved in the second intermediate equation include the pre-configured closed-loop poles, the second intermediate equation is a univariate M-order equation, the value of M is determined based on the number of the closed-loop poles, and M is an integer greater than 1; According to the relationship between the first intermediate equation and the second intermediate equation, the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop poles is determined.

7. The method for calibrating parameters of a vehicle control model according to claim 1, characterized in that: The step of determining the feedback gain matrix based on the target relationship includes: Acquire a preset vehicle speed set, wherein the vehicle speed set includes a plurality of vehicle speed sampling points, and the intervals between any two adjacent vehicle speed sampling points in the plurality of vehicle speed sampling points are equal; The vehicle speed sampling points in the vehicle speed set are traversed in sequence, and based on the vehicle speed sampling points traversed each time and the target relationship, the feedback gain matrix corresponding to each vehicle speed sampling point is calculated.

8. A parameter calibration device for a vehicle control model, characterized in that: The device comprises: A first determination module, configured to obtain a pre-configured closed-loop pole, and determine a first characteristic equation according to the pre-configured closed-loop pole, wherein the real part of the pre-configured closed-loop pole is a negative number; A second determination module is used to obtain a closed-loop system matrix of the vehicle control model and determine a second characteristic equation according to the closed-loop system matrix, wherein the closed-loop system matrix is ​​associated with an initial system matrix, a control matrix, and a feedback gain matrix in the vehicle control model; A third determination module is used to determine the target relationship between each element in the feedback gain matrix and each element in the initial system matrix, each element in the control matrix and the pre-configured closed-loop pole according to the first characteristic equation and the second characteristic equation; A fourth determination module, configured to determine the feedback gain matrix based on the target relationship, and obtain a parameter calibration result of the vehicle control model based on the feedback gain matrix; The first characteristic equation is determined by the following formula: ； The second characteristic equation is determined using the following formula: ； The target relationship is determined using the following formula: ； in, represents the eigenvalue of the system, represents the identity matrix, represents the initial system matrix, represents the control matrix, represents the feedback gain matrix, represents the pre-configured closed-loop poles, represents the closed-loop system matrix, .

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the parameter calibration method of the vehicle control model described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the parameter calibration method of the vehicle control model described in any one of claims 1-7 is implemented.

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