Vehicle path tracking control method and device, medium and product

Abstract

The invention discloses a vehicle path tracking control method and device, a medium and a product, and relates to the field of path tracking, and the method comprises the steps: building an integrated prediction model according to a vehicle dynamics model through employing nonlinear tire force and actuation system characteristics; determining an optimization objective function based on vehicle path tracking performance and actuation optimization according to a control input variable and an output variable in the integrated prediction model; determining a real-time dynamic stability domain boundary of the vehicle according to a control input variable, a vehicle state and an elliptical parameter function in the integrated prediction model; solving the integrated prediction model by using a rolling horizon optimization method based on the optimization objective function and the constraint condition, and determining the front wheel turning angle of the vehicle and the torque of each wheel; the constraint condition at least comprises a real-time dynamic stability domain boundary of the vehicle. According to the invention, the vehicle can be dynamically controlled in real time, and the vehicle path tracking precision is improved.

Description

Technical Field

[0001] The present application relates to the field of path tracking, and particularly to a vehicle path tracking control method, device, medium and product. Background Art

[0002] Intelligent vehicle path tracking control is the key and core to ensure vehicle driving safety and driving stability. Time-varying vehicle speed and road surface conditions affect the vehicle state. Existing path tracking and stability control can be divided into decoupled and integrated types. The decoupled type is the combined action of a path tracking controller and a stability controller. The path tracking controller calculates the front wheel steering angle of the vehicle, and the stability controller estimates the additional yaw moment of the vehicle through an observer, and distributes the wheel braking torque in combination with the vehicle steering characteristics. However, in the case where the tire force tends to be saturated, the decoupled control method will cause the path tracking and stability control objectives to affect and restrict each other, deteriorating the path tracking performance. The integrated type is based on the rolling horizon optimization method, combining the vehicle's non-linear tire force, vehicle stability constraints, and actuator characteristics, to achieve the rolling optimization of the front wheel steering angle and wheel torque within the constraint conditions, ensuring path tracking performance and driving stability. For the integrated path tracking and stability control method, linear stability constraint conditions are usually used to achieve stability control. However, the relatively conservative stability constraints increase the control trigger frequency, and the change amplitude and change rate of the actuator input are limited, restricting the path tracking performance of the vehicle on low-adhesion road surfaces. Existing path tracking control methods combine relatively conservative stability conditions to trigger braking to maintain the lateral stability of the vehicle during the path tracking process. In extreme conditions, existing path tracking control methods will act frequently to keep the vehicle state stable rather than tracking the desired path, deteriorating the path tracking performance. Therefore, there is an urgent need for a method that can dynamically and real-time control the vehicle and improve the vehicle path tracking accuracy. Summary of the Invention

[0003] The purpose of the present application is to provide a vehicle path tracking control method, device, medium and product, which can dynamically and real-time control the vehicle and improve the vehicle path tracking accuracy.

[0004] To achieve the above purpose, the present application provides the following solutions.

[0005] In the first aspect, the present application provides a vehicle path tracking control method, including: constructing a vehicle dynamics model.

[0006] Constructing an integrated prediction model according to the vehicle dynamics model by using non-linear tire force and actuator system characteristics; the actuator system characteristics include an actuator system time constant and an actuator system gain.

[0007] Determining an optimization objective function based on the control input variables and output variables in the integrated prediction model according to vehicle path tracking performance and actuator optimization.

[0008] Determine the dynamic stability domain boundary of the vehicle in real time according to the control input variables, vehicle state, and elliptical parameter function in the integrated prediction model.

[0009] Solve the integrated prediction model by using the rolling horizon optimization method based on the optimization objective function and constraint conditions to determine the front wheel steering angle and the torques of each wheel; the constraint conditions at least include the dynamic stability domain boundary of the vehicle in real time.

[0010] Optionally, the formula of the vehicle dynamics model is.

[0011]

[0012] where v x is the longitudinal vehicle speed, v y is the lateral speed of the vehicle center of mass, r is the yaw rate of the vehicle, ψ e is the yaw angle, is the derivative of the longitudinal position in the global coordinate system, is the derivative of the lateral coordinate in the global coordinate system, is the derivative of the yaw angle.

[0013] Optionally, the state space equation of the integrated prediction model is:

[0014] The state variable x is:

[0015] The control input variable u is:

[0016] The disturbance variable v is:

[0017] The output variable y is:

[0018] where, is the corresponding matrix of, is the differential form of the state variable, is the system matrix, is the control matrix, is the disturbance matrix, is the output matrix, ij = fl, fr, rl, rr respectively represent the left front, right front, left rear, and right rear positions of the vehicle, v y is the lateral speed of the vehicle center of mass, r is the yaw rate of the vehicle, δ f is the front wheel steering angle, s i is the side slip angle of the tire, is the translational speed of the wheel center, X e , Ye , ψ e are the longitudinal position, lateral position, and yaw angle of the vehicle in the global coordinate system, a e is the acceleration of the vehicle's center of mass, is the desired front wheel steering angle, T ij is the torque of the wheel, and are both relaxation factors, is the perturbation variable of the lateral velocity v y , ζ r is the perturbation variable of the yaw rate r, represents the longitudinal forces of each tire, and are the longitudinal position X e , lateral position Y e in the global coordinate system, the perturbation term, ζ res represents the perturbation term of the driving resistance, T represents matrix transpose.

[0019] Optionally, before the step of determining the optimization objective function based on the control input variables and output variables in the integrated prediction model according to the vehicle path tracking performance and actuation optimization, it further includes: discretizing the integrated prediction model using a zero-order hold.

[0020] Optionally, the formula of the optimization objective function is.

[0021]

[0022] Wherein, are the output variable, control input variable, and the control variable u at the previous moment, respectively p in the prediction horizon N p , the weight coefficient diagonal matrix, J is the optimization objective function, is the control input matrix formed by the control input variable in the control horizon N c , is the matrix transpose, is the matrix formed by the output variable in N p , is the matrix formed by the control variable at the previous moment in N c .

[0023] Optionally, the real-time dynamic stability domain boundary of the vehicle is.

[0024]

[0025] Wherein, is the reference value of the lateral velocity v of the vehicle's center of mass y at the reference point, represents the reference value of the yaw rate r of the vehicle at the reference point, g(v y , r) is the vehicle real-time dynamic stability domain, is the vehicle real-time dynamic stability domain after approximate reflection transformation.

[0026] Optionally, the integrated prediction model is solved by using the rolling horizon optimization method based on the optimization objective function and the constraint conditions to determine the front wheel steering angle and the torques of each wheel. The specific formula is as follows.

[0027] u * = argmin J

[0028]

[0029] u l ≤ u(k) ≤ u u ; x l ≤ x(k) ≤ x u

[0030]

[0031] where u * is the optimal control sequence of the front wheel steering angle and the wheel torques, J is the optimization objective function, k is the prediction step length, is the real number field, f a is the major axis function, f b is the minor axis function, is the rotation angle function, u(k) is the control input variable at each prediction step length, x(k) is the state variable at each prediction step length, x(1) is the state variable at the 1st prediction step length, x(t) is the initial value of the state variable. The lower and upper boundaries of the state variable and the control input variable are respectively expressed as u l , u u , x l , x u , and the upper boundary of the wheel torque is expressed as: where is the maximum wheel driving torque that the actuator can provide, is the vertical force of the vehicle tire, μ is the road surface adhesion coefficient, R e is the effective radius of the tire, and are the admissible sets of the state variable and the control input variable under the constrained optimization conditions, represents that the state terminal constraint is the equilibrium point

[0032] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the vehicle path tracking control method described above.

[0033] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the vehicle path tracking control method described above.

[0034] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the vehicle path tracking control method described above.

[0035] According to the specific embodiments provided by the present application, the present application has the following technical effects.

[0036] The present application provides a vehicle path tracking control method, device, medium, and product. By integrating the control input variables and geometric boundaries in the prediction model to determine the real-time dynamic stability domain boundary of the vehicle, the existing relatively conservative stability domain is expanded. The stability domain characterized by geometric parameters is integrated into the constraint conditions of vehicle path tracking control using geometric boundaries. On the premise of ensuring driving stability, the vehicle state fluctuations caused by braking intervention under high-speed and low-adhesion conditions are reduced, so as to realize dynamic real-time control of the vehicle and improve the vehicle path tracking accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is an application environment diagram of a vehicle path tracking control method in an embodiment of the present application.

[0039] Figure 2 It is a schematic flow chart of a vehicle path tracking control method provided in an embodiment of the present application.

[0040] Figure 3 It is an overall flow chart of a vehicle path tracking control method provided in an embodiment of the present application.

[0041] Figure 4 It is a schematic structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0043] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0044] Intelligent vehicle: Without driver intervention, according to the results of environmental perception, it autonomously generates control signals such as steering and braking to achieve vehicle control.

[0045] Stability: The vehicle can follow a given path, and when encountering external disturbances, the vehicle can resist the disturbances and maintain stable driving.

[0046] The vehicle path tracking control method provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the vehicle information to be processed to the server 104. After receiving the vehicle information to be processed, for the vehicle information to be processed, the server 104 constructs a vehicle dynamics model; constructs an integrated prediction model according to the vehicle dynamics model by using the nonlinear tire force and the characteristics of the actuation system; the characteristics of the actuation system include the actuation system time constant and the actuation system gain; determines an optimization objective function based on the vehicle path tracking performance and actuation optimization according to the control input variables and output variables in the integrated prediction model; determines the real-time dynamic stability domain boundary of the vehicle according to the control input variables and geometric boundaries in the integrated prediction model; uses the rolling horizon optimization method to solve the integrated prediction model based on the optimization objective function and constraint conditions to determine the front wheel steering angle and the torque of each wheel; the constraint condition is the real-time dynamic stability domain boundary of the vehicle. The server 104 can feedback the obtained front wheel steering angle and the torque of each wheel to the terminal 102. In addition, in some embodiments, the vehicle path tracking control method can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly perform tracking control on the vehicle to be processed, or the server 104 can obtain the vehicle to be processed from the data storage system and construct a vehicle dynamics model for tracking control.

[0047] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0048] In an exemplary embodiment, as Figure 2 and Figure 3 shown, a vehicle path tracking control method is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or can be jointly executed by a terminal and a server. In the embodiment of the present application, taking this method applied to Figure 1 the server 104 therein as an example for illustration, it includes the following steps 201 to 205. Among them:

[0049] Step 201: Construct a vehicle dynamics model.

[0050] Step 202: Construct an integrated prediction model according to the vehicle dynamics model by using non-linear tire forces and actuator system characteristics; the actuator system characteristics include the actuator system time constant and the actuator system gain.

[0051] Step 203: Determine an optimization objective function based on the vehicle path tracking performance and actuator optimization according to the control input variables and output variables in the integrated prediction model.

[0052] Step 204: Determine the boundary of the vehicle's real-time dynamic stability domain according to the control input variables, vehicle state, and elliptical parameter function in the integrated prediction model.

[0053] Step 205: Solve the integrated prediction model by using the rolling horizon optimization method based on the optimization objective function and constraint conditions to determine the front wheel steering angle and the torque of each wheel of the vehicle; the constraint conditions at least include the boundary of the vehicle's real-time dynamic stability domain. In practical applications, the constraint conditions also include the upper and lower limits of the state, the upper and lower limits of the actuator, etc.

[0054] Implementing the above steps 201 to 205 can dynamically and real-time control the vehicle and improve the vehicle path tracking accuracy.

[0055] In an exemplary embodiment of the present application, before the step of determining the optimization objective function based on the vehicle path tracking performance and actuator optimization according to the control input variables and output variables in the integrated prediction model, it further includes: discretizing the integrated prediction model by using a zero-order hold.

[0056] In an exemplary embodiment of the present application, to further illustrate the solution of the present application, a proof process in practical applications is provided. As Figure 2 shown, the details are as follows.

[0057] Part 1: Integrated prediction model.

[0058] The LuGre combined slip tire model is used to represent the lateral tire force The relative tire longitudinal speed is used in the LuGre tire force and the lateral speed to represent the combined slip characteristics of the tire which is expressed as: where ω ij 、R e and s i respectively represent the translational speed of the wheel center, the rotational speed, the effective radius of the tire, and the sideslip angle of the front and rear wheels of the tires located at ij = fl, fr, rl, rr (left front fl, right front fr, left rear rl, right rear rr). i represents the front and rear wheels, and j represents the left and right wheels. Since the relative speed is included, the wheel torque is expressed as.

[0059]

[0060] where J w 、T ij 、 respectively represent the difference in translational acceleration of the wheel center, the moment of inertia of the wheel, the wheel torque, and the longitudinal tire force of the wheel. is the difference in translational acceleration at the wheel center, R e is the effective radius of the tire, and the acceleration a of the vehicle center of mass e is expressed as.

[0061]

[0062] where m, f, C d ,ρ e ,A r ,and v x respectively represent the mass of the vehicle, the rolling resistance coefficient, the air resistance coefficient, the air density, the frontal area, and the longitudinal vehicle speed. g is the acceleration due to gravity. A first-order inertia link is used to represent the characteristics of the vehicle acceleration system.

[0063]

[0064] where T a is the drive system time constant, and K a is the drive system gain. is the derivative of the actual vehicle acceleration, is the actual vehicle acceleration. Among them, Figure 2 the v in e is the vehicle speed, and the lateral-longitudinal dynamics of the vehicle is expressed as

[0065]

[0066] Among them, v y , r, and δ f respectively represent the lateral vehicle speed, yaw rate, and front wheel steering angle. J z , l f , l r and W respectively represent the vehicle moment of inertia, the longitudinal distances from the front axle and the rear axle to the vehicle center of mass position, and the wheel width. is the lateral acceleration, is the total lateral tire force of the front axle, is the total lateral tire force of the rear axle, is the total longitudinal tire force of the front axle, is the yaw angular acceleration, is the difference between the left front lateral tire force and the right front lateral tire force, is the difference between the left rear longitudinal tire force and the right rear longitudinal tire force, is the right front longitudinal tire force. In addition, is the left front longitudinal tire force, is the total longitudinal tire force of the rear axle, is the left rear longitudinal tire force, is the right rear longitudinal tire force, is the left front lateral tire force, is the right front lateral tire force, is the left rear lateral tire force, is the right rear lateral tire force, and the steering system characteristics are also expressed by a first-order inertia link as

[0067]

[0068] is the differential of the front wheel angle, K δ , T δ , and respectively represent the steering system time constant, the steering system gain, and the desired front wheel angle. The tire force is written in the form of a first-order Taylor expansion of the tire force at the equilibrium point expanded as:

[0069] is the equilibrium point of the stability domain, Is the equilibrium point of the tire force. Is the lateral tire force of the wheel, Is the tire force at the equilibrium point, Is the partial derivative of the tire force with respect to the wheel speed difference At that point, Is the partial derivative of the tire force at s, where s is the slip angle of the tire, Is the lateral tire force of the wheel, and the vehicle tire slip angle is denoted as.

[0070]

[0071] Can be expressed as: Is the rate of change of the tire slip angle, Is γ i The partial derivative at r, Is γ i At v y The partial derivative at that point. s f Is the front axle tire slip angle, and the kinematic equation of the vehicle in the global coordinate system, i.e., the dynamic model of the vehicle, is expressed as: X e , Y e And ψ e Respectively represent the longitudinal position, lateral position, and yaw angle of the vehicle in the global coordinate system. Is the derivative of the longitudinal position in the global coordinate system, i.e., the longitudinal speed of the vehicle, Is the derivative of the lateral coordinate system in the global coordinate system, i.e., the lateral speed, Is the derivative of the yaw angle. Combining (1)-(6), an integrated prediction model is constructed and represented using the state space equation The state variable x is expressed as.

[0072]

[0073] Among them, v y Is the lateral speed of the vehicle's center of mass, r is the yaw angular velocity of the vehicle, δ f Is the front wheel steering angle, s i Is the slip angle of the tire, Is the translational speed of the wheel center, X e , Y e , ψ e Are the longitudinal position, lateral position, and yaw angle of the vehicle in the global coordinate system, a e Is the acceleration of the vehicle's center of mass.

[0074] The control input variable u is expressed as.

[0075]

[0076] wherein, is the desired front wheel angle, and T ij is the torque of the wheel, and are relaxation factors. The perturbation variable v is defined as.

[0077]

[0078] wherein, ζ r represents the perturbation variable of the lateral velocity v y and the yaw rate r, represents the longitudinal force of each tire, and represent the longitudinal position X e and the lateral position Y e in the local coordinate system, and ζ res represents the perturbation term of the driving resistance.

[0079] The output variable y is defined as.

[0080]

[0081] wherein, v y is the lateral velocity of the vehicle's center of mass, r is the yaw rate of the vehicle, and δ f is the front wheel angle, X e , Y e , ψ e are the longitudinal position, lateral position, and yaw angle of the vehicle in the global coordinate system. is the corresponding matrix, is the differential form of the state variable, is the system matrix, is the control matrix, is the perturbation matrix, is the output matrix, and ij = fl, fr, rl, rr respectively represent the left front, right front, left rear, and right rear positions of the vehicle.

[0082] Using the zero-order hold discrete prediction model, it is expressed as.

[0083]

[0084] In the formula: where t p is the discrete time step. x k+1 is the state variable at the k+1 step.

[0085] Part Two: Establishment of Constraints and Optimization Objectives.

[0086] Combined with the system output variables and control variables, considering the path tracking performance and actuation optimization of the vehicle, the optimization objective function formula is as follows.

[0087]

[0088] In the formula: are the output variable, the control input variable, and the control variable u at the previous moment respectively p The weight coefficient diagonal matrix within the prediction horizon N p , J is the optimization objective function, The control input matrix formed by the control input variable within the control horizon N c , y is the prediction matrix formed by the output matrix within N p , is the matrix transpose, is the matrix formed by the output variable within N p , is the matrix formed by the control variable at the previous moment within N c .

[0089] Based on the major axis a, minor axis b, and rotation angle ψ of the ellipse parameters es The geometric boundary constructed And the mapping relationship between the vehicle speed and road surface adhesion coefficient after ellipse parameter identification and the ellipse parameters x e is the abscissa of the ellipse boundary, y e is the ordinate of the ellipse boundary, f q (v e , μ) is a function of the ellipse parameters, v e is the vehicle speed, w ij is the identified coefficient is the i1-th power of the vehicle speed, μ j1 is the j1-th power of the road surface adhesion coefficient, q is the subscript of the function, and is the set of ellipse parameters, including a, b, ψ es , the boundary of the vehicle's real-time dynamic stability domain is expressed as.

[0090]

[0091] Among them, f a is the major axis function, f b is the minor axis function, is the rotation angle function.

[0092] To ensure convex optimization solution, combined with the affine transformation of the non-linear function, the boundary of the vehicle's real-time dynamic stability domain is expressed as.

[0093]

[0094] Among them, The lateral velocity v of the vehicle's center of mass y The reference value at the reference point, Indicates the reference value of the vehicle's yaw rate r at the reference point, g(v y , r) is the vehicle's real-time dynamic stability domain, Is the vehicle's real-time dynamic stability domain after approximate reflection transformation.

[0095] The optimal control sequence u of the vehicle's front wheel angle and wheel torque * Can be calculated using FORCESPRO.

[0096] u * = argminJ

[0097]

[0098] u l ≤ u(k) ≤ u u ; x l ≤ x(k) ≤ x u

[0099]

[0100] In the formula: u * Is the optimal control sequence of the vehicle's front wheel angle and wheel torque, J is the optimization objective function, k is the prediction step size, Is the real number field, f a Is the major axis function, f b Is the minor axis function, Is the rotation angle function, u(k) is the control input variable for each prediction step, x(k) is the state variable for each prediction step, x(1) is the state variable for the 1st prediction step, x(t) is the initial value of the state variable, and the lower and upper bounds of the state variable and control input variable are represented as u l , u u , x l , x u , and the upper bound of the wheel torque Is expressed as: Where Is the maximum wheel driving torque that the actuator can provide, Is the vertical force of the vehicle tire, μ is the road adhesion coefficient, R e Is the effective radius of the tire, And Represent the admissible sets of the state and control input under the constrained optimization conditions, Indicates that the state terminal constraint is the equilibrium point Ensures the asymptotic stability of the controller.

[0101] This application constructs an integrated prediction model. The LuGre tire model is constructed and a double-rail vehicle dynamics model is adopted. A prediction model is constructed by combining the non-linear tire force and the characteristics of the actuation system. Among them, the non-linear tire force refers to the lateral tire force, and the actuation system includes the steering system and the drive system, and the establishment of constraints and optimization objectives. Combining the system output variables and control variables, considering the path tracking performance and actuation optimization of the vehicle, an optimization objective function is established. Combining the boundary of the real-time dynamic stability domain of the vehicle, the constraint conditions of the controller are designed, and an intelligent vehicle path tracking controller considering the dynamic stability boundary of the vehicle is constructed. Finally, based on the constructed integrated prediction model, the real-time dynamic stability boundary is integrated into the constraints, and the front wheel angle and the torque of each wheel of the vehicle are calculated in real time by using the rolling horizon optimization method.

[0102] This application expands the existing relatively conservative stability domain, and uses a geometric model to explicitly characterize the expanded vehicle system stability domain under the influence of multiple driving factors, so as to realize the real-time change of the stability domain boundary with driving conditions; the stability domain characterized by geometric parameters is integrated into the constraint conditions of the path tracking controller, and on the premise of ensuring driving stability, the vehicle state fluctuations caused by braking intervention under high-speed and low-adhesion conditions are reduced, and the path tracking accuracy of the vehicle is improved. It avoids the low stability margin caused by traditional linear stability constraints and the poor path tracking performance caused by frequent actuator intervention, so while ensuring the active safety of the vehicle, it realizes better path tracking performance, improves the stability and ride comfort of the vehicle, and provides a systematic design method and design theory for improving the path tracking accuracy of intelligent vehicles under high-speed and low-adhesion conditions. Integrating the dynamic real-time stability domain into the constraint conditions enables the vehicle to optimize the vehicle actuation input within the expanded real-time dynamic stability boundary, reduces the vehicle state fluctuations caused by braking intervention under high-speed and low-adhesion conditions, and improves the path tracking accuracy of the vehicle in extreme scenarios, providing ideas and methods for the integrated control of intelligent vehicles in extreme scenarios. The path tracking control method integrating the real-time dynamic stability domain can be used for emergency collision avoidance of intelligent vehicles, overtaking, lane change, merging, etc. of vehicles, and can also be used to integrate the stability domain of vehicle systems characterized by other geometric model parameters or the stability domain boundaries obtained by other processing methods such as machine learning, and can also be used to integrate the boundary constraints of other vehicle motion states.

[0103] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store vehicle tracking data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a vehicle path tracking control method.

[0104] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method embodiments are implemented.

[0105] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0106] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0107] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0108] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0109] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0110] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0111] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A vehicle path tracking control method, characterized in that, The vehicle path tracking control method includes: Constructing a vehicle dynamics model; Constructing an integrated prediction model based on the vehicle dynamics model by using non-linear tire forces and actuator system characteristics; the actuator system characteristics include an actuator system time constant and an actuator system gain; Determining an optimization objective function based on vehicle path tracking performance and actuator optimization according to the control input variables and output variables in the integrated prediction model; Determining the boundary of the vehicle's real-time dynamic stability domain according to the control input variables, vehicle state and elliptical parameter function in the integrated prediction model; Solving the integrated prediction model by using a receding horizon optimization method based on the optimization objective function and constraint conditions to determine the front wheel steering angle and the torques of each wheel; the constraint conditions at least include the boundary of the vehicle's real-time dynamic stability domain.

2. The vehicle path tracking control method according to claim 1, characterized in that The formula of the vehicle dynamics model is: where, v x is the longitudinal vehicle speed, v y is the lateral speed of the vehicle's center of mass, r is the yaw rate of the vehicle, ψ e is the yaw angle, is the derivative of the longitudinal position in the global coordinate system, is the derivative of the lateral coordinate system in the global coordinate system, is the derivative of the yaw angle.

3. The vehicle path tracking control method according to claim 1, characterized in that, The state space equation of the integrated prediction model is: The state variable x is: The control input variable u is: The disturbance variable v is: The output variable y is: wherein, is 's corresponding matrix, is the differential form of the state variable, is the system matrix, is the control matrix, is the perturbation matrix, is the output matrix, where ij = fl, fr, rl, rr respectively represent the left front, right front, left rear and right rear positions of the vehicle, v y is the lateral velocity of the vehicle's center of mass, r is the yaw rate of the vehicle, δ f is the front wheel steering angle, s i is the side slip angle of the tire, is the translational velocity of the wheel center, X e , Y e , ψ e are the longitudinal position, lateral position and yaw angle of the vehicle in the global coordinate system, a e is the acceleration of the vehicle's center of mass, is the desired front wheel steering angle, T ij is the torque of the wheel, and are both relaxation factors, is the perturbation variable of the lateral velocity v y , ζ r is the perturbation variable of the yaw rate r, represents the longitudinal force of each tire, and are the perturbation terms of the longitudinal position X e , lateral position Y e in the global coordinate system, ζ res represents the perturbation term of the driving resistance, and T represents matrix transpose.

4. The vehicle path tracking control method according to claim 1, wherein Before the step of determining the optimization objective function based on vehicle path tracking performance and actuator optimization according to the control input variables and output variables in the integrated prediction model, it further includes: Discretizing the integrated prediction model by using a zero-order hold.

5. The vehicle path tracking control method according to claim 1, characterized in that, The formula of the optimization objective function is: Among them, are the output variable, the control input variable, and the control variable u at the previous moment respectively p is the diagonal matrix of weight coefficients within the prediction horizon N p , J is the optimization objective function is the control input matrix formed by the control input variable within the control horizon N c is the matrix transpose is the matrix formed by the output variable within N p is the matrix formed by the control variable at the previous moment within N c ​​​ 6. The vehicle path tracking control method according to claim 1, wherein The boundary of the vehicle's real-time dynamic stability domain is: Among them, is the lateral velocity v of the vehicle's center of mass y is the reference value at the reference point, represents the reference value of the vehicle's yaw rate r at the reference point, g(v y , r) is the vehicle's real-time dynamic stability domain, is the vehicle's real-time dynamic stability domain after approximate reflection transformation.

7. The vehicle path tracking control method according to claim 1, wherein Solving the integrated prediction model by using a receding horizon optimization method based on the optimization objective function and constraint conditions to determine the front wheel steering angle and the torques of each wheel, and the specific formula is: where, u * is the optimal control sequence of the front wheel angle and wheel torque of the vehicle, J is the optimization objective function, k is the prediction step length, is the real number field, f a is the major axis function, f b is the minor axis function, is the rotation angle function, u(k) is the control input variable at each prediction step, x(k) is the state variable at each prediction step, x(1) is the state variable at the 1st prediction step, x(t) is the initial value of the state variable, and the lower and upper bounds of the state variable and control input variable are denoted as u l , u u , x l , x u , and the upper bound of the wheel torque is expressed as: where is the maximum wheel driving torque that the actuator can provide, is the vertical force of the vehicle tire, μ is the road surface adhesion coefficient, R e is the effective radius of the tire, and are the admissible sets of the state variable and control input variable under the constrained optimization condition, indicates that the state terminal constraint is the equilibrium point 8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle path tracking control method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vehicle path tracking control method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle path tracking control method according to any one of claims 1-7.

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