Articulated vehicle trajectory tracking control method and system and vehicle

Abstract

The invention discloses an articulated vehicle trajectory tracking control method and system and a vehicle, and belongs to the technical field of articulated vehicle intelligent driving control, and the method comprises the steps: obtaining basic parameters and real-time state parameter data of an articulated vehicle, and setting a linear tire cornering stiffness initial value; constructing a linear three-degree-of-freedom dynamical model of the articulated vehicle, and constructing a trajectory tracking controller which comprises a tire cornering stiffness estimator based on a forgetting factor recursive least square method and a linear time-varying model prediction controller fused with trailer state influence; inputting the real-time acquired data and the linear tire cornering stiffness initial value into a trajectory tracking controller, and estimating and updating the cornering stiffness value at the current moment through a tire cornering stiffness estimator; and according to the updated cornering stiffness value and the real-time acquired data, the linear time-varying model prediction controller solves and outputs the optimal front wheel steering angle at the current moment according to the reference trajectory and considering the influence of the trailer state. Accurate control of trajectory tracking of the articulated vehicle can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent driving control of articulated vehicles, and in particular relates to a trajectory tracking control method, system and vehicle for an articulated vehicle. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] An articulated vehicle refers to a vehicle combination consisting of a tractor and a trailer connected by an articulated device. It has high transportation efficiency. Semi-trailer trains are common in the commercial vehicle field, and trailer RVs are also used in the civilian vehicle field. However, due to the long body of the articulated vehicle, the complex coupling relationship between the tractor and the trailer, and the fixed value of the tire lateral stiffness, it is difficult to describe the real-time dynamics of the vehicle. Compared with a single vehicle, the articulated vehicle will be affected by the trailer state when performing trajectory tracking control, resulting in low trajectory tracking accuracy or trajectory tracking failure. The current trajectory tracking control technology for articulated vehicles has the problems of low trajectory tracking accuracy and poor performance. The main problems are as follows:

[0004] (1) The dynamic model can accurately reflect the dynamic response of the vehicle at higher speeds, but the tire cornering stiffness cannot be measured directly. Most existing methods usually use fixed values ​​or offline update methods to obtain the tire cornering stiffness, which cannot well describe the real-time dynamic response of the vehicle;

[0005] (2) There is a complex nonlinear coupling relationship between the tractor and trailer of an articulated vehicle. The influence of the trailer state must be considered when performing trajectory tracking control. Existing methods often ignore the influence of the trailer state, which will lead to final tracking failure. Summary of the invention

[0006] To address the deficiencies of the above-mentioned prior art, the present invention provides a trajectory tracking control method, system and vehicle for an articulated vehicle. When the articulated vehicle is performing trajectory tracking control, a trajectory tracking control model in which the tire lateral stiffness can be updated online is constructed based on the same dynamic model, and the influence of the trailer state is considered in the model to achieve precise control of the trajectory tracking of the articulated vehicle. By integrating the method of online updating of the tire lateral stiffness and the influence of the trailer state, the trajectory tracking performance of the articulated vehicle during driving is improved, and the problems of poor trajectory tracking accuracy of articulated vehicles under complex working conditions, and trajectory tracking failure caused by the inability to update the tire lateral stiffness in real time during large curvature curves or emergency lane changes, which affects the driving experience of intelligent driving or endangers driving safety are solved.

[0007] In a first aspect, the present invention provides a trajectory tracking control method for an articulated vehicle.

[0008] A trajectory tracking control method for an articulated vehicle, comprising:

[0009] Obtain basic parameter data and real-time status parameter data of the articulated vehicle, and set the initial value of the linear tire cornering stiffness;

[0010] Based on the state parameters and basic parameters of the articulated vehicle, a linear three-degree-of-freedom dynamic model of the articulated vehicle is constructed;

[0011] Based on the dynamic model, a trajectory tracking controller is constructed; the trajectory tracking controller includes a tire cornering stiffness estimator based on a forgetting factor recursive least squares method, and a linear time-varying model predictive controller integrating the effects of trailer states;

[0012] The real-time acquired data and the initial value of the linear tire cornering stiffness are input into the trajectory tracking controller, and the cornering stiffness value of the articulated vehicle at the current moment is estimated and updated by the tire cornering stiffness estimator; according to the cornering stiffness value at the current moment and the real-time acquired data, the linear time-varying model predictive controller solves and outputs the optimal front wheel steering angle at the current moment based on the reference trajectory and taking into account the influence of the trailer state.

[0013] In a second aspect, the present invention provides an articulated vehicle trajectory tracking control system.

[0014] An articulated vehicle trajectory tracking control system, comprising:

[0015] A data acquisition module is used to acquire basic parameter data and real-time status parameter data of the articulated vehicle and set the initial value of the linear tire cornering stiffness;

[0016] A trajectory tracking controller building module is used to build a linear three-degree-of-freedom dynamic model of the articulated vehicle based on the state parameters and basic parameters of the articulated vehicle; based on the dynamic model, a trajectory tracking controller is built; the trajectory tracking controller includes a tire cornering stiffness estimator based on a forgetting factor recursive least squares method, and a linear time-varying model predictive controller that incorporates the effects of trailer states;

[0017] The trajectory tracking control module is used to input the real-time acquired data and the initial value of the linear tire cornering stiffness into the trajectory tracking controller, and estimate and update the cornering stiffness value of the articulated vehicle at the current moment through the tire cornering stiffness estimator; according to the cornering stiffness value at the current moment and the real-time acquired data, the linear time-varying model predictive controller solves and outputs the optimal front wheel steering angle at the current moment based on the reference trajectory and taking into account the influence of the trailer state.

[0018] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned articulated vehicle trajectory tracking control method when executing the executable instructions stored in the memory.

[0019] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned articulated vehicle trajectory tracking control method.

[0020] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when a processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned articulated vehicle trajectory tracking control method is implemented.

[0021] In a sixth aspect, the present invention further provides a vehicle, which adopts the above-mentioned articulated vehicle trajectory tracking control method.

[0022] One or more of the above technical solutions have the following beneficial effects:

[0023] 1. In order to solve the problem of poor trajectory tracking accuracy caused by the inability to update the tire cornering stiffness in real time when an articulated vehicle is on a curve with large curvature or during an emergency lane change, the present invention provides a trajectory tracking control method, system and vehicle for an articulated vehicle. When the articulated vehicle is performing trajectory tracking control, a trajectory tracking control model in which the tire cornering stiffness can be updated online is constructed based on the same dynamic model, and the influence of the trailer state is considered in the model to achieve precise control of the trajectory tracking of the articulated vehicle. By integrating the method of online updating of the tire cornering stiffness and the influence of the trailer state, the trajectory tracking performance of the articulated vehicle during driving is improved, and the trajectory tracking accuracy of the articulated vehicle under complex working conditions is effectively improved.

[0024] 2. The present invention designs a tire cornering stiffness estimator and a trajectory tracking controller based on the same dynamic model: first, the basic parameters of the vehicle and the real-time dynamic parameters are obtained by measurement and input, and a set of initial values ​​of the tire cornering stiffness are given; secondly, a three-degree-of-freedom dynamic model of an articulated vehicle is established, and a tire cornering stiffness estimator is designed based on the recursive least squares method with a forgetting factor, and the tire cornering stiffness at each moment is estimated according to the real-time state of the vehicle; finally, based on the linear time-varying model predictive control algorithm, the trailer state variable is introduced into the prediction model, and the real-time value of the tire cornering stiffness is assigned to the trajectory tracking controller, and iterative optimization and feedback correction are performed considering the position of the tractor and the real-time state of the trailer, and the optimal front wheel steering angle is output to improve the trajectory tracking accuracy.

[0025] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 It is an overall flow chart of the trajectory tracking control method of an articulated vehicle according to an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of a linear three-degree-of-freedom dynamics model of an articulated vehicle in an embodiment of the present invention;

[0029] Figure 3 It is a schematic diagram of the local coordinate system of the tractor and trailer of the articulated vehicle in an embodiment of the present invention;

[0030] Figure 4 4 is a system block diagram of the trajectory tracking control of an articulated vehicle in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are exemplary only, are intended to describe specific embodiments, are intended to provide further explanation of the present invention, and are not intended to limit exemplary embodiments according to the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0032] The invention proposes a trajectory tracking control method, system and vehicle for an articulated vehicle, which integrates the online update of tire cornering stiffness and the influence of trailer state to perform more accurate trajectory tracking control of the articulated vehicle, and belongs to the technical field of intelligent driving control of articulated vehicles. In the method, firstly, basic parameters of the vehicle and real-time dynamic parameters obtained by measurement are obtained and input, and a set of initial values ​​of tire cornering stiffness are given; then, a three-degree-of-freedom dynamic model of the articulated vehicle is established, and a tire cornering stiffness estimator is designed based on the recursive least squares method with a forgetting factor. The tire cornering stiffness estimator can estimate the tire cornering stiffness at each moment according to the real-time state of the vehicle, so as to obtain tire dynamic parameters with time-varying characteristics; finally, based on a linear time-varying model predictive control algorithm, a corresponding trajectory tracking controller is constructed, and by introducing a trailer state variable in the prediction model, the real-time value of the tire cornering stiffness is assigned to the trajectory tracking controller, and combined with a reference trajectory determined by a vehicle trajectory planner, and considering the position of the tractor and the real-time state of the trailer, iterative optimization and feedback correction are performed, and the optimal front wheel steering angle is solved and output, so that the trajectory tracking accuracy of the articulated vehicle under complex working conditions can be effectively improved.

[0033] Embodiment 1

[0034] This embodiment provides a trajectory tracking control method for an articulated vehicle. Figure 1 As shown, the specific steps include:

[0035] Step S1, obtaining basic parameter data and real-time status parameter data of an articulated vehicle, and setting an initial value of the linear tire cornering stiffness.

[0036] Specifically, an articulated vehicle is a vehicle combination formed by an articulated tractor and a trailer. Sensors are used for measurement to obtain basic parameter data of the tractor and trailer and real-time measured state parameter data as the basis for the dynamic model and subsequent trajectory planning.

[0037] In this embodiment, the basic parameters of the articulated vehicle are divided into basic parameters of the tractor and basic parameters of the trailer. The basic parameters of the tractor should include: the moment of inertia of the tractor, the mass of the tractor, the initial values ​​of the lateral stiffness of the front and rear axles of the tractor, the distance from the center of mass of the tractor to the front axle, the distance from the center of mass of the tractor to the rear axle, the distance from the center of mass of the tractor to the hinge point, etc.; the basic parameters of the trailer should include: the moment of inertia of the trailer, the mass of the trailer, the initial value of the lateral stiffness of the trailer axle (the trailer only has the rear axle), the distance from the center of mass of the trailer to the hinge point, the distance from the center of mass of the trailer to the rear axle, etc.

[0038] In addition, the real-time status parameters of the articulated vehicle include the real-time status parameters of the tractor and the real-time status parameters of the trailer. The real-time status parameters of the tractor shall include: the real-time lateral speed of the tractor, the real-time longitudinal speed of the tractor, the real-time sideslip angle of the center of mass of the tractor, the real-time sideslip angular velocity of the center of mass of the tractor, the real-time yaw angle of the tractor, the real-time yaw angular velocity of the tractor, the real-time lateral force of the front and rear axles of the tractor, the real-time global position of the tractor, etc.; the real-time status parameters of the trailer shall include: the real-time lateral speed of the trailer, the real-time sideslip angle of the center of mass of the trailer, the real-time sideslip angular velocity of the center of mass of the trailer, the real-time yaw angle of the trailer, the real-time yaw angular velocity of the trailer, the real-time lateral force of the trailer axle, the real-time articulation angle, etc.

[0039] Step S2: constructing a linear three-degree-of-freedom dynamic model of the articulated vehicle based on the state parameters and basic parameters of the articulated vehicle.

[0040] In this embodiment, the articulated vehicle model is simplified to a linear three-degree-of-freedom vehicle dynamics model, where the three degrees of freedom are: lateral motion of the tractor and trailer, yaw motion of the tractor, and yaw motion of the trailer, and it is assumed that the tractor and trailer have the same travel speed. Figure 2 As shown in FIG. 1 , the construction of the dynamic model of the articulated vehicle specifically includes:

[0041] First, based on the state parameters and basic parameters of the articulated vehicle and the balance of force and torque, the dynamic equation of the articulated vehicle is constructed, that is, the dynamic equations of the tractor and trailer and the articulation constraint relationship equation between the tractor and trailer are constructed respectively, which are:

[0042] For tractor:

[0043]

[0044] For trailers:

[0045]

[0046] There is a complex articulation constraint relationship between the tractor and the trailer, which can be expressed as:

[0047]

[0048] Secondly, for further mechanical analysis, the tractor x1o1y1 coordinate system and the trailer x2o2y2 coordinate system are established respectively, as follows: Figure 3 As shown, according to the properties of the linear three-degree-of-freedom monorail vehicle, we can get:

[0049]

[0050]

[0051] Based on the assumption that the tractor and trailer travel at the same speed, we have v x1 =v x2 =u, when the tire is running in the linear region and the tire slip angle is small, the integration yields:

[0052]

[0053] Select the state quantity as X = [v y1 ω1 ω2 θ] T , the control quantity is U=d f .

[0054] The linear three-degree-of-freedom dynamic model of the articulated vehicle can be further organized as follows:

[0055]

[0056] Where A = M -1 N; B = M -1 P;

[0057]

[0058] P = [-k1-k1(c+a1)-k1a20] T ;

[0059]

[0060] In the above formula, m1 and m2 are the total mass of the tractor and trailer respectively; a1 and a2 are the distances m from the center of mass of the tractor and trailer to the front axle respectively; b1 and b2 are the distances from the center of mass of the tractor and trailer to the rear axle respectively; ω1 and ω2 are the yaw angular velocities of the tractor and trailer respectively; are the yaw accelerations of the tractor and trailer, respectively; β1 and β2 are the sideslip angles of the center of mass of the tractor and trailer, respectively; I is the sideslip angular velocity of the tractor's center of mass; z1 ,I z2 are the moments of inertia of the tractor and trailer respectively; k1 and k2 are the lateral stiffness of the front and rear axles of the tractor respectively; k3 is the lateral stiffness of the rear axle of the trailer; θ is the articulation angle; is the articulation angular velocity; is the articulation angular acceleration; c is the distance from the center of mass of the tractor to the articulation point; δ f is the front wheel turning angle of the tractor; u is the longitudinal speed; a y1 、a y2 are the lateral accelerations of the tractor and trailer respectively; ΣF Y1 ,ΣF Y2 are the lateral combined external forces on the tractor and trailer respectively; ΣM Z1 ,∑M Z2 are the combined rotational moments of the tractor and trailer respectively; F yf 、F yr are the lateral forces on the front and rear axles of the tractor respectively; F ys is the lateral force on the trailer axle; F yh is the external force on the hinge point.

[0061] Step S3, constructing a trajectory tracking controller based on the dynamic model; the trajectory tracking controller includes a tire cornering stiffness estimator based on a forgetting factor recursive least squares method, and a linear time-varying model predictive controller that incorporates the effects of trailer states.

[0062] Specifically, the tire cornering stiffness estimator of the articulated vehicle is used to estimate the real-time cornering stiffness estimation value of the vehicle under different vehicle speed states, and the cornering stiffness includes the cornering stiffness of the front axle of the tractor, the cornering stiffness of the rear axle of the tractor, and the cornering stiffness of the trailer axle; the construction of the tire cornering stiffness estimator adopts a recursive least squares estimation algorithm with a forgetting factor, wherein the recursive model is obtained by the linear three-degree-of-freedom dynamic discretization transformation of the articulated vehicle established in step S2, and the specific process of constructing the tire cornering stiffness estimator includes:

[0063] First, the linear three-degree-of-freedom dynamic model equation of the articulated vehicle is discretized to obtain the tire cornering stiffness equation, which can be expressed as:

[0064]

[0065] In the above formula, Y m (k) is the system output matrix at time k, j T (k) is the regression equivalent at time k, Φ(k) is the parameter to be estimated at time k, and e(k) is the residual at time k.

[0066] in:

[0067]

[0068] In the above formula, T is the sampling time, and

[0069]

[0070] Φ(k)=[k1(k)k2(k)k3(k)] T (17)

[0071] Secondly, based on the above tire cornering stiffness equation, in order to minimize the square difference between the actual value and the estimated value, the objective function introducing the forgetting factor is constructed as follows:

[0072]

[0073] Among them, J is the system objective function, λ m is the forgetting factor, Y m (k) is the estimated output at time k, Represents the estimated value of the model parameters at time k, and N is the length of the historical data window.

[0074] Finally, according to the above objective function, the mathematical model of the recursive least square method with forgetting factor can be obtained as follows:

[0075]

[0076] In the above formula, K(k) is the gain matrix, P(k) is the covariance matrix, To update the parameter estimation value. The above model is the tire cornering stiffness estimator, from which the real-time cornering stiffness estimation value of the vehicle at different vehicle speeds can be obtained.

[0077] At the same time, according to the linear three-degree-of-freedom dynamics model of the articulated vehicle, a linear time-varying model predictive controller is designed. The trajectory tracking controller of the articulated vehicle is actually implemented by a linear time-varying model predictive controller, which is implemented using a linear time-varying model predictive control algorithm, specifically including:

[0078] First, based on the linear three-degree-of-freedom dynamics model of the articulated vehicle, a linear time-varying prediction model that introduces the vehicle state is constructed. The above formula (13) is rewritten as shown in formula (22):

[0079]

[0080] In the above formula, v y1 ,ω1,Y1,X1 can characterize the state of the tractor, and ω2,θ can characterize the state of the trailer. When the vehicles are driving, the state information of the tractor and trailer will be fed back to the prediction model, thereby performing continuous rolling optimization within a limited prediction time domain.

[0081] Furthermore, the state quantity is selected as: The control quantity is: u = d f , the output matrix is: Extending to any sampling time t and discretizing the model, it can be expressed as:

[0082]

[0083] In the above formula: A k,t =I+TA k,0 ,B k,t =TB k,0 , T is the sampling time, A k,t With B k,t The specific expression is:

[0084] in:

[0085]

[0086]

[0087] In order to improve the real-time performance of MPC (Model Predictive Control), further simplification is made, assuming that:

[0088]

[0089] In formula (24), N p For the prediction time domain.

[0090] The control increment is used to replace the control quantity, and the updated linear time-varying prediction model is obtained by discretization. Its state equation is:

[0091]

[0092] In formula (25),

[0093] Secondly, based on the above model, constraints are designed and added, including: front wheel angle and front wheel angle increment constraints, output constraints, articulation angle constraints, etc. It should be noted that the constraint parameters designed here include but are not limited to the following forms, which can be used as a reference for subsequent implementation of this embodiment.

[0094] In an emergency, a large turning angle can easily cause the vehicle to become unstable, so the following constraints are imposed on the front wheel turning angle and the front wheel turning angle increment:

[0095]

[0096] In addition, hard and soft constraints on output volume need to be added, which can be expressed as:

[0097]

[0098] Y c is a hard constraint, limiting the range of output; Y sc It is a soft constraint and can be adjusted dynamically through the relaxation factor; Column vector.

[0099] In addition, the articulation angle reflects the state of the tractor and trailer. Under extreme working conditions, a large articulation angle indicates that the vehicle is in an unstable state. Therefore, the following constraints are imposed on the articulation angle:

[0100] [-0.1744rad]≤θ d (t)≤[0.1744rad] (28)

[0101] Based on the above constraints, in each control cycle, a series of control increments can be obtained by solving the constrained objective function, which is:

[0102] ΔU(t)=[Δu(t),Δu(t+1),…,Δu(t+N c -1)] (29)

[0103] The first term is used as the actual control increment to act on the system, that is:

[0104] u(t)=u(t-1)+Δu(t) (30)

[0105] On the basis of the above, by solving the objective function, a series of optimal control quantities of the system in the future can be obtained, and the first optimal control quantity can be applied to the system. In order to avoid the failure of solution, a relaxation factor is added to the objective function, and the objective function with the relaxation factor is constructed as follows:

[0106]

[0107] Where N p is the prediction time domain; Nc To control the time domain; h ref is the reference output; Q and R are weight matrices, S and r are weight coefficients; e is the relaxation factor.

[0108] Considering that directly solving the above objective function will increase the operating burden of the computer, in order to facilitate the solution, it is converted into a quadratic programming (QP) form, that is, the above formula is converted into a quadratic programming form to satisfy the QP solution, and we can get:

[0109]

[0110] In formula (32):

[0111]

[0112] Step S4, input the real-time acquired data and the initial value of the linear tire cornering stiffness into the trajectory tracking controller, and estimate and update the cornering stiffness value of the articulated vehicle at the current moment through the tire cornering stiffness estimator; according to the cornering stiffness value at the current moment and the real-time acquired data, the linear time-varying model predictive controller solves and outputs the optimal front wheel steering angle at the current moment based on the reference trajectory and taking into account the influence of the trailer state.

[0113] like Figure 4 As shown in the figure, a trajectory tracking controller is constructed based on the dynamic model with basic parameters and measured state parameters as input. The controller module includes a tire cornering stiffness estimator and a linear time-varying model predictive controller. The tire cornering stiffness estimator estimates and updates the cornering stiffness value of the articulated vehicle at the current moment according to the input data, assigns the cornering stiffness value to the linear time-varying model predictive controller, and returns to the dynamic model to replace the initial value of the cornering stiffness. The linear time-varying model predictive controller is based on the reference trajectory decided by the upper trajectory planner of the articulated vehicle, while considering the positions of the tractor and trailer in the reference trajectory and the influence of the trailer state, and obtains the optimal control quantity, that is, the optimal front wheel steering angle, by rolling to solve the constrained optimization problem (that is, the above-mentioned objective function with constraints and the introduction of relaxation factors), so as to achieve more accurate trajectory tracking control.

[0114] Embodiment 2

[0115] This embodiment provides an articulated vehicle trajectory tracking control system, which specifically includes:

[0116] A data acquisition module is used to acquire basic parameter data and real-time state parameter data of an articulated vehicle, and set an initial value of the linear tire cornering stiffness; wherein the data acquisition module specifically includes a parameter input module of the articulated vehicle, which is used to input the corresponding basic parameter data, and a perception fusion module, which is used to measure and acquire the state parameter data in real time;

[0117] A trajectory tracking controller building module is used to build a linear three-degree-of-freedom dynamic model of the articulated vehicle based on the state parameters and basic parameters of the articulated vehicle; based on the dynamic model, a trajectory tracking controller is built; the trajectory tracking controller includes a tire cornering stiffness estimator based on a forgetting factor recursive least squares method, and a linear time-varying model predictive controller that incorporates the effects of trailer states;

[0118] The trajectory tracking control module is used to input the real-time acquired data and the initial value of the linear tire cornering stiffness into the trajectory tracking controller, and estimate and update the cornering stiffness value of the articulated vehicle at the current moment through the tire cornering stiffness estimator; according to the cornering stiffness value at the current moment and the real-time acquired data, the linear time-varying model predictive controller solves and outputs the optimal front wheel steering angle at the current moment based on the reference trajectory and taking into account the influence of the trailer state.

[0119] Preferably, the articulated vehicle trajectory tracking control system further comprises a trajectory planning module, which is a trajectory planner on the upper layer of the articulated vehicle and is used for making decisions to generate a reference trajectory.

[0120] Embodiment 3

[0121] This embodiment provides an electronic device, including: a memory, used to store executable instructions; and a processor, used to implement the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0122] Embodiment 4

[0123] This embodiment also provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided by this embodiment.

[0124] Embodiment 5

[0125] This embodiment provides a computer program product, which includes an executable instruction, which is a computer instruction; the executable instruction is stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instruction from the computer-readable storage medium and the processor executes the executable instruction, the electronic device executes the above method provided in this embodiment.

[0126] Embodiment 6

[0127] This embodiment provides a vehicle, and the vehicle adopts the above method provided by this embodiment.

[0128] The steps involved in the above embodiments 2 to 6 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0129] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0130] The above description is only a preferred embodiment of the present invention. Although the specific implementation mode of the present invention is described in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A trajectory tracking control method for an articulated vehicle, characterized in that: include: Obtain basic parameter data and real-time status parameter data of the articulated vehicle, and set the initial value of the linear tire cornering stiffness; Based on the state parameters and basic parameters of the articulated vehicle, a linear three-degree-of-freedom dynamic model of the articulated vehicle is constructed; Based on the dynamic model, a trajectory tracking controller is constructed; the trajectory tracking controller includes a tire cornering stiffness estimator based on a forgetting factor recursive least squares method, and a linear time-varying model predictive controller integrating the effects of trailer states; The real-time acquired data and the initial value of the linear tire cornering stiffness are input into the trajectory tracking controller, and the cornering stiffness value of the articulated vehicle at the current moment is estimated and updated by the tire cornering stiffness estimator; according to the cornering stiffness value at the current moment and the real-time acquired data, the linear time-varying model predictive controller solves and outputs the optimal front wheel steering angle at the current moment based on the reference trajectory and taking into account the influence of the trailer state.

2. The articulated vehicle trajectory tracking control method according to claim 1, characterized in that: The articulated vehicle is a vehicle combination formed by articulating a tractor and a trailer; the basic parameters of the articulated vehicle are divided into basic parameters of the tractor and basic parameters of the trailer; wherein the basic parameters of the tractor include: the moment of inertia of the tractor, the mass of the tractor, the initial value of the lateral stiffness of the front and rear axles of the tractor, the distance from the center of mass of the tractor to the front axle, the distance from the center of mass of the tractor to the rear axle, and the distance from the center of mass of the tractor to the articulation point; the basic parameters of the trailer include: the moment of inertia of the trailer, the mass of the trailer, the initial value of the lateral stiffness of the trailer axle, the distance from the center of mass of the trailer to the articulation point, and the distance from the center of mass of the trailer to the rear axle; The real-time status parameters of the articulated vehicle include real-time status parameters of the tractor and real-time status parameters of the trailer; wherein, the real-time status parameters of the tractor include: real-time lateral speed of the tractor, real-time longitudinal speed of the tractor, real-time sideslip angle of the center of mass of the tractor, real-time sideslip angular velocity of the center of mass of the tractor, real-time yaw angle of the tractor, real-time yaw angular velocity of the tractor, real-time lateral force of front and rear axles of the tractor, and real-time global position of the tractor; the real-time status parameters of the trailer include: real-time lateral speed of the trailer, real-time sideslip angle of the center of mass of the trailer, real-time sideslip angular velocity of the center of mass of the trailer, real-time yaw angle of the trailer, real-time yaw angular velocity of the trailer, real-time lateral force of the trailer axle, and real-time articulation angle.

3. The articulated vehicle trajectory tracking control method according to claim 1, characterized in that: The construction of the linear three-degree-of-freedom dynamic model of the articulated vehicle includes: Based on the state parameters and basic parameters of the articulated vehicle and the balance of forces and moments, the dynamic equations of the tractor and trailer and the articulation constraint relationship between the tractor and trailer are constructed respectively; Based on the dynamic equation and articulation constraint relationship, the tractor coordinate system and trailer coordinate system are established respectively. At the same time, it is assumed that the tractor and trailer have the same travel speed. According to the properties of the linear three-degree-of-freedom monorail vehicle, the linear three-degree-of-freedom dynamic model of the articulated vehicle is integrated. The three degrees of freedom refer to: the lateral motion of the tractor and trailer, the yaw motion of the tractor and the yaw motion of the trailer.

4. The articulated vehicle trajectory tracking control method according to claim 1, characterized in that: The tire cornering stiffness estimator of the articulated vehicle is used to estimate the real-time cornering stiffness estimation value of the vehicle at different vehicle speeds, the cornering stiffness including the cornering stiffness of the front axle of the tractor, the cornering stiffness of the rear axle of the tractor and the cornering stiffness of the trailer axle; The construction of the tire cornering stiffness estimator comprises: Discretize the linear three-degree-of-freedom dynamic model equation of the articulated vehicle to obtain the tire cornering stiffness equation; Based on the tire cornering stiffness equation, an objective function based on minimizing the square difference between the actual value and the estimated value is constructed; According to the objective function, the recursive least squares estimation algorithm with forgetting factor is adopted to obtain the tire cornering stiffness estimator.

5. The articulated vehicle trajectory tracking control method according to claim 1, characterized in that: The construction of the linear time-varying model predictive controller includes: Based on the linear three-degree-of-freedom dynamics model of the articulated vehicle, a linear time-varying prediction model that introduces the vehicle state is constructed, and the model is discretized to obtain an updated linear time-varying prediction model; Based on the linear time-varying prediction model, constraints are designed and an objective function with a relaxation factor is constructed; the constraints include: front wheel steering angle and front wheel steering angle increment constraints, output constraints, and articulation angle constraints; The objective function is transformed into a quadratic programming form, and a trajectory tracking controller based on linear time-varying model predictive control, namely, a linear time-varying model predictive controller, is obtained.

6. An articulated vehicle trajectory tracking control system, characterized in that: include: A data acquisition module is used to acquire basic parameter data and real-time status parameter data of the articulated vehicle and set the initial value of the linear tire cornering stiffness; A trajectory tracking controller building module is used to build a linear three-degree-of-freedom dynamic model of the articulated vehicle based on the state parameters and basic parameters of the articulated vehicle; based on the dynamic model, a trajectory tracking controller is built; the trajectory tracking controller includes a tire cornering stiffness estimator based on a forgetting factor recursive least squares method, and a linear time-varying model predictive controller that incorporates the effects of trailer states; The trajectory tracking control module is used to input the real-time acquired data and the initial value of the linear tire cornering stiffness into the trajectory tracking controller, and estimate and update the cornering stiffness value of the articulated vehicle at the current moment through the tire cornering stiffness estimator; according to the cornering stiffness value at the current moment and the real-time acquired data, the linear time-varying model predictive controller solves and outputs the optimal front wheel steering angle at the current moment based on the reference trajectory and taking into account the influence of the trailer state.

7. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the articulated vehicle trajectory tracking control method as described in any one of claims 1 to 5 when executing the executable instructions stored in the memory.

8. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the articulated vehicle trajectory tracking control method as described in any one of claims 1-5.

9. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the articulated vehicle trajectory tracking control method as described in any one of claims 1 to 5 is implemented.

10. A vehicle, characterized in that: The vehicle adopts the articulated vehicle trajectory tracking control method as described in any one of claims 1-5.

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