Automatic parking path tracking control method and apparatus, and device and medium

Abstract

The present application relates to the technical field of vehicle control. Provided are an automatic parking path tracking control method and apparatus, and a device and a medium. The method comprises: establishing a kinematic relationship between the yaw velocity and front-wheel steering angle of a tractor, so as to determine a proportionality coefficient; establish a function relationship between a first velocity of the tractor and a second velocity of a semi-trailer by using a articulation-point velocity decomposition method, so as to obtain an automatic parking path tracking control model; on the basis of the model, determining an automatic parking path tracking control equation; solving the control equation by using an active set method, so as to obtain a system nominal state variable and a system nominal control variable; and on the basis of the system nominal state variable, determining an error tube that includes system uncertainty, so as to determine a system true state variable, and outputting a front-wheel steering angle increment control command for the tractor, so as to control the tractor. By means of the technical solution, an articulated vehicle is prevented from jackknifing and instability during the process of reversing into a parking space, thereby improving the success rate of the articulated vehicle reversing into a parking space.

Description

Automatic parking path tracking control methods, devices, equipment and media

[0001] This application claims priority to Chinese Patent Application No. 202410492569.8, filed on April 23, 2024, entitled “Automatic Parking Path Tracking Control Method, Apparatus, Device and Medium”, the entire contents of which are incorporated herein by reference. [Technical Field]

[0002] This application relates to the field of vehicle control technology, and in particular to an automatic parking path tracking control method, device, equipment and medium for articulated vehicles. [Background Technology]

[0003] Fully automated parking systems have become the most effective means of solving the problem of parking difficulties in tight spaces. Currently, domestic and international OEMs and mainstream component suppliers are exploring the extension of fully automated parking systems to the application scenarios of reversing articulated vehicles, attempting to solve the problem of reversing articulated vehicles into parking spaces using fully automated parking systems. However, because articulated vehicles are prone to folding and instability during reversing, and the kinematic models describing articulated vehicles have highly nonlinear characteristics, this poses a significant challenge to using fully automated parking systems to solve the problem of reversing articulated vehicles into parking spaces.

[0004] [Application Content]

[0005] This application provides an automatic parking path tracking control method, device, equipment, and medium for articulated vehicles, aiming to solve the technical problems in related technologies, such as the tendency of articulated vehicles to fold and become unstable during the reversing and parking process.

[0006] In a first aspect, embodiments of this application provide an automatic parking path tracking control method for an articulated vehicle, the articulated vehicle including a tractor and a semi-trailer, the method comprising:

[0007] Using the steady-state circular motion of the tractor and the vehicle parameter information of the articulated vehicle, the kinematic relationship between the yaw rate of the tractor and the front wheel rotation angle is established to determine the proportionality coefficient between the yaw rate of the tractor and the front wheel rotation angle.

[0008] The articulation point velocity decomposition method is used to establish the functional relationship between the first speed of the tractor and the second speed of the semi-trailer, so as to obtain an automatic parking path tracking control model including the proportional coefficient, the yaw angle of the semi-trailer, the angle between the tractor and the semi-trailer, and the front wheel rotation angle of the tractor.

[0009] Based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle driving in the stable state neighborhood during the reversing process are used to determine the automatic parking path tracking control equation.

[0010] The effective set method is used to solve the automatic parking path tracking control equations to obtain the system nominal state variables and system nominal control variables;

[0011] An error pipeline containing system uncertainties is determined based on the nominal state variables of the system, and the true state variables of the system are determined based on the error pipeline.

[0012] Based on the actual state of the system, the system outputs an incremental control command for the front wheel angle of the tractor, thereby controlling the tractor through the incremental control command for the front wheel angle.

[0013] In one embodiment, optionally, the kinematic relationship between the yaw rate of the tractor and the front wheel steering angle includes:

[0014] Where, δ f L1 represents the front wheel steering angle of the tractor, R represents the distance from the center of the front axle to the center of the rear axle of the tractor, and K represents the steady-state circumference radius. v Indicates the parameter to be identified, v x This indicates the speed of the center of gravity of the tractor unit. The yaw rate of the tractor in steady-state circular motion;

[0015] The proportionality coefficient between the yaw rate of the tractor and the front wheel steering angle includes:

[0016] The quasi-Newton method is used to identify c1 and c2 in order to obtain the proportionality coefficient.

[0017] In one embodiment, optionally, the automatic parking path tracking control equation takes the minimization of the weighted sum of the first deviation and the second deviation as the optimization objective, wherein the first deviation is the deviation between the actual system state quantity and the corresponding system reference state quantity, and the second deviation is the deviation between the actual system control quantity and the corresponding system reference control quantity, wherein the system reference state quantity and the system reference control quantity are calculated based on the reference trajectory.

[0018] In one embodiment, optionally, based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle traveling in the stable state neighborhood during the reversing into parking space are used to determine the automatic parking path tracking control equations, including:

[0019] The automatic parking path tracking control model is discretized using the fourth-order Runge-Ku integral method to obtain the initial nonlinear control equations;

[0020] Based on the first deviation and the second deviation, the initial nonlinear equation is linearized to obtain a linearized control equation, wherein the linearized control equation includes system uncertainties;

[0021] Based on the linearized control equations, the preset constraints and reference trajectory of the articulated vehicle during the reversing into parking space within the stable state neighborhood are used to determine the automatic parking path tracking control equations. The preset constraints include equality constraints and inequality constraints.

[0022] In one embodiment, optionally, the automatic parking path tracking control equations are solved using the effective set method to obtain the system nominal state variables and system nominal control variables, including:

[0023] Without considering the system uncertainties, the automatic parking path tracking control equations are iteratively solved using the effective set method to obtain the system nominal state variables and system nominal control variables.

[0024] In one embodiment, optionally, the process of iteratively solving the automatic parking path tracking control equations using the effective set method includes:

[0025] In each iterative solution process, a subset containing all equality constraints and some inequality constraints is selected from all preset constraints as the working set.

[0026] Modify some of the inequality constraints in the work set into equality constraints;

[0027] The feasible solution is obtained by using the Lagrange multiplier method, and the KKT conditions are used to determine whether the feasible solution is the optimal solution.

[0028] If the feasible solution is the optimal solution, the iteration terminates.

[0029] If the feasible solution is not the optimal solution, find a new working set and iteration point that effectively reduces the optimization objective by solving the sub-optimization problem, and start a new round of iterative calculation.

[0030] In one embodiment, optionally, determining an error pipeline containing system uncertainties based on the nominal system state quantities, and determining the true system state quantities based on the error pipeline, includes:

[0031] Taking into account the system uncertainty, an error pipeline incorporating the system uncertainty is constructed centered on the system's nominal state variables.

[0032] Based on the error pipeline, a secondary control model is determined so that the actual state variables of the system converge to the steady-state neighborhood of the nominal state variables of the system.

[0033] The secondary control model includes:

[0034] Where, δu k This represents the actual state quantity of the system. K represents the nominal state quantity of the system. k Represents the feedback matrix. This represents the error between the actual state quantity of the system and the nominal state quantity of the system;

[0035] The actual state variables of the system are determined based on the nominal state variables of the system and the secondary control model.

[0036] In the above embodiments, the kinematic relationship between the yaw rate and the front wheel rotation angle of the tractor is established using the steady-state circular motion of the tractor, and unknown parameters are identified. The velocity decomposition method at the articulation point is used to establish the functional relationship between the tractor speed and the semi-trailer speed, thereby obtaining a parking path tracking control model for the articulated vehicle, including the coordinates of the midpoint of the semi-trailer's rear axle, the yaw angle, the angle between the tractor and the semi-trailer, and the rotation angle of the tractor's front wheels. Subsequently, the parking path tracking control equation is constructed by locally linearizing the automatic parking path tracking control model in the steady-state neighborhood. The nominal state variables and nominal control variables of the system are obtained by solving the parking path tracking control equation using the effective set method. An error pipeline containing system uncertainties is constructed centered on the nominal state variables, and a secondary controller is designed to make the actual system state variables within the pipeline converge to the nominal state variables. This ensures that the articulated vehicle always operates within the steady-state domain during the reversing process, preventing the articulated vehicle from folding and becoming unstable during reversing and improving the success rate of reversing into parking spaces.

[0037] Secondly, embodiments of this application provide an automatic parking path tracking control device for an articulated vehicle, comprising:

[0038] The first determining module is used to establish the kinematic relationship between the yaw rate of the tractor and the front wheel rotation angle by using the steady-state circular motion of the tractor and the vehicle parameter information of the articulated vehicle, so as to determine the proportional coefficient between the yaw rate of the tractor and the front wheel rotation angle.

[0039] The model building module is used to establish a functional relationship between the first speed of the tractor and the second speed of the semi-trailer using the articulation point velocity decomposition method, so as to obtain an automatic parking path tracking control model including the proportional coefficient, the yaw angle of the semi-trailer, the angle between the tractor and the semi-trailer, and the front wheel turning angle of the tractor.

[0040] The second determining module is used to determine the automatic parking path tracking control equation based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle driving in the stable state neighborhood during the reversing process;

[0041] The solution module is used to solve the automatic parking path tracking control equations using the effective set method to obtain the system nominal state variables and system nominal control variables;

[0042] The third determining module is used to determine an error pipeline containing system uncertainties based on the nominal state quantity of the system, and to determine the true state quantity of the system based on the error pipeline.

[0043] The output module is used to output the front wheel angle incremental control command of the tractor vehicle according to the actual state of the system, so as to control the tractor vehicle through the front wheel angle incremental control command.

[0044] Thirdly, a computer device is provided, comprising: at least one processor; the processor being coupled to a memory; wherein the memory stores instructions executed by the at least one processor, the instructions being configured to perform steps of the above-described automatic parking path tracking control method for articulated vehicles.

[0045] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described automatic parking path tracking control method for articulated vehicles.

[0046] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the above-described automatic parking path tracking control method for articulated vehicles. [Attached Image Description]

[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 shows a schematic diagram of the architecture of an automatic parking system for an articulated vehicle according to an embodiment of this application.

[0049] Figure 2 shows a schematic flowchart of an automatic parking path tracking control method for an articulated vehicle according to an embodiment of this application.

[0050] Figure 3 shows a schematic diagram of the kinematic relationship between the tractor and semi-trailer of an articulated vehicle according to an embodiment of this application.

[0051] Figure 4 shows a schematic diagram of the velocity breakdown at the articulation point of the tractor and semi-trailer of an articulated vehicle according to an embodiment of this application.

[0052] Figure 5 shows a block diagram of an automatic parking path tracking control device for an articulated vehicle according to an embodiment of this application.

[0053] Figure 6 shows a schematic diagram of a computer device according to an embodiment of this application.

[0054] Figure 7 shows another structural schematic diagram of a computer device according to an embodiment of this application.

Detailed Implementation Methods

[0055] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0056] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0057] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0058] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0059] As shown in Figure 1, the automatic parking method for articulated vehicles includes: perception fusion, local map rasterization, reversing path planning, and reversing path tracking control. Perception fusion outputs environmental information about the articulated vehicle's surroundings detected by multiple sensors, including ultrasonic waves, lidar, and cameras. A raster scanning algorithm is used to convert this environmental information into a local raster map, which is then used to complete reversing path planning and tracking control. Addressing unresolved issues in the field of reversing path tracking control for articulated vehicles, this application proposes an automatic parking path tracking control method. A robust model predictive control law for parking paths is designed within the stable state neighborhood of the articulated vehicle, ensuring that the vehicle always operates within its stable state neighborhood during reversing, preventing folding instability and improving the success rate of reversing into parking spaces.

[0060] Please refer to Figure 2, which shows a schematic flowchart of an automatic parking path tracking control method for an articulated vehicle according to an embodiment of this application. This automatic parking path tracking control method for articulated vehicles addresses technical problems in related technologies, such as the tendency for articulated vehicles to fold and become unstable during the reversing and parking process.

[0061] As shown in Figure 2, according to an embodiment of this application, an automatic parking path tracking control method for an articulated vehicle includes the following steps:

[0062] Step S201: Using the steady-state circular motion of the tractor and the vehicle parameter information of the articulated vehicle, establish the kinematic relationship between the yaw rate of the tractor and the front wheel rotation angle, so as to determine the proportional coefficient between the yaw rate of the tractor and the front wheel rotation angle.

[0063] As shown in Figure 3, the coordinates of the midpoint of the rear axle of the tractor in the geodetic coordinate system OXY are (x1, y1), the yaw angle of the tractor is θ1, and the rotation angle of the front wheels of the tractor is δ. f The midpoint of the semi-trailer's rear axle has coordinates (x2, y2) in the geodetic coordinate system OXY. The semi-trailer's yaw angle is θ2, the angle between the tractor and the semi-trailer is β1, the distance from the center of the tractor's front axle to the center of its rear axle is L1, the distance from the articulation point to the midpoint of the semi-trailer's rear axle is L2, and the distance from the midpoint of the tractor's rear axle to the articulation point is M1. Therefore, the kinematic equations of the tractor-semi-trailer can be described as follows:

[0064] In the formula, k represents the proportionality coefficient between the yaw rate of the tractor and the front wheel steering angle, derived from steady-state circular motion. Specifically, the front wheel steering angle δ of the tractor... f The relationship between the steady-state circumference radius R and the steady-state circumference radius R is as follows:

[0065] In the formula, C fand C r These represent the equivalent lateral stiffness of the front and rear axles of the tractor unit, respectively; f and l r These represent the distances from the center point of the front axle of the tractor to its center of gravity, and the distances from the center point of the rear axle of the tractor to its center of gravity, respectively; m and v x These represent the mass of the tractor and the velocity of the tractor's center of gravity, respectively.

[0066] The yaw rate of a tractor in steady-state circular motion can be expressed as: From this we can obtain

[0067] Further simplification of the above formula yields:

[0068] Further simplification of the above formula yields:

[0069] From equation (2), we can see that Substituting it into equation (5), we can obtain the expression for the proportionality coefficient between the yaw rate and the front wheel steering angle as follows:

[0070] In the formula, the unknown parameter and The quasi-Newton method can be used for identification.

[0071] Step S202: The articulation point velocity decomposition method is used to establish the functional relationship between the first speed of the tractor and the second speed of the semi-trailer, so as to obtain an automatic parking path tracking control model including the proportional coefficient, the yaw angle of the semi-trailer, the angle between the tractor and the semi-trailer, and the front wheel turning angle of the tractor.

[0072] As shown in Figure 4, applying the velocity decomposition method to the hinge point P1, we can obtain...

[0073] Further rearranging the above equation, we get v2=v1cosβ1(1+kM1δ) f tanβ1) (8)

[0074] Substituting equation (8) into equation (1), and using a first-order inertial element to describe the inertial delay characteristics of the tractor steering system, we can obtain:

[0075] In the formula, τ is the parameter of the first-order inertial element; δ c This is a command to control the steering angle of the front wheels of the tractor.

[0076] Let the x-coordinate y2 of the midpoint of the semi-trailer's rear axle in the geodetic coordinate system OXY, the yaw angle θ2 of the semi-trailer, the angle β1 between the tractor and the semi-trailer, and the front wheel rotation angle δ of the tractor be the coordinates of the semi-trailer's rear axle midpoint in the geodetic coordinate system OXY. fBased on this, the system state vector is defined as x=[y2 θ2 β1 δ f ] T The control command δ for the front wheel steering angle of the tractor c The basic control vector is defined as u = [δ] c Then equation (9) can be simplified to

[0077] Step S203: Based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle driving in the stable state neighborhood during the reversing process, determine the automatic parking path tracking control equation.

[0078] Discretizing equation (10) using the fourth-order Runge-Kutta integral method, we obtain the system equation as follows:

[0079] In the formula, the calculation step size h and coefficients K1, K2, K3 and K4 can be expressed as follows:

[0080] In the formula, t f N and N represent the prediction duration and the number of discrete points for the pipeline model predictive control, respectively.

[0081] Based on state variable x k Its reference value x k,ref deviation δx k =x k -x k,ref Control quantity u k Its reference value u k,ref deviation δu k =u k -u k,ref For g(x) in equation (11) k ,u k ) in (x k,ref ,x N,ref ,u k,ref Performing a first-order Taylor expansion (i.e., linearization) within the domain yields:

[0082] Further rearranging equation (13), we can obtain

[0083] In the formula, w k This represents the uncertainty introduced by the first-order Taylor expansion, i.e., the system uncertainty, satisfying |w k |≤b w Among them, b w This represents the upper bound of uncertainty.

[0084] Therefore, the deviation δx between the state quantity and its reference quantity is used as a reference. kThe deviation δu between the control quantity and its reference quantity k The weighted sum minimization is used as the optimization objective to establish the tractor-semi-trailer path tracking control problem (i.e., automatic parking path tracking control equations).

[0085] In the formula, x k,max and x k,min u represents the upper and lower boundaries of the system state variables. k,max and u k,min These are the upper and lower boundaries of the system control variables.

[0086] use and Let the nominal state variables and nominal control variables represent the system's nominal state variables and nominal control variables, then the nominal system equation can be expressed as:

[0087] Define the error between the actual state variables and the nominal state variables of the system as:

[0088] Based on the real system equation in equation (14) and the nominal system equation described by equation (16), we can obtain

[0089] To ensure that the actual state variables within the pipeline converge to the neighborhood of the nominal state variables, the following secondary controller is designed.

[0090] In the formula, K k This is the feedback matrix.

[0091] Substituting the secondary controller described by equation (19) into equation (18), we can obtain

[0092] From equation (20) and |w k |≤b w It can be deduced Combining equations (15) and (20), we can obtain

[0093] Furthermore, combining the secondary controller described by equation (19), we can obtain

[0094] Therefore, as long as the system nominal state quantity and nominal control quantity If equations (21) and (22) are satisfied, then the true state quantity δx of the system is... k and the actual control quantity δu k The constraints must be satisfied. Therefore, ignoring system uncertainties w k Under the premise that equation (15) can be transformed into

[0095] In the formula, A, C, and d x and d u They can be represented as follows:

[0096] Step S204: Solve the automatic parking path tracking control equations using the effective set method to obtain the system nominal state variables and system nominal control variables;

[0097] For the constrained optimization problem described by equation (23), the effective set method is used to solve it iteratively. In each iteration, a subset containing all equality constraints and some inequality constraints is selected from all constraints as the working set. Some inequality constraints in the working set are modified into equality constraints, transforming the original constrained optimization problem into an optimization problem containing only equality constraints. Then, the feasible solution is solved using the Lagrange multiplier method, and the KKT conditions are used to determine whether the feasible solution is the optimal solution. If the feasible solution is the optimal solution, the iteration terminates; otherwise, a new working set and iteration point that effectively reduces the optimization objective are found by solving the sub-optimal problem, and a new round of iteration calculation begins. Thus, equation (23) is rewritten as

[0098] The specific calculation steps of the effective set method are as follows:

[0099] Step 1: Initialize the iteration starting point and working set as σ0 and W0, such that i∈W0, we have Make have

[0100] Step 2: Assume the working set for the m-th iteration is W. m Solving the following equality-constrained optimization problem yields s. m ;

[0101] Further simplification of equation (25) yields:

[0102] In the formula, Y m =Z k σ m +g k .

[0103] The optimality condition for optimization problem (26) is:

[0104] In the formula, λ i ,i∈W m Let L(s,λ) be a Lagrange multiplier and L(s,λ) be a Lagrange function, which can be expressed as...

[0105] Equation (28) can be further simplified to obtain

[0106] Solving equation (29) will yield s. m .

[0107] Step 3: If s m =0, calculate the Lagrange multiplier λ i ,i∈W m If λ i ≥0, i∈W m ∩I, output the optimal solution σ m Stop iteration if necessary; otherwise, update working set W. m+1 =W m / {iarg(λ i <0, i∈W m ∩I)},σ m+1 =σ m Proceed to Step 2;

[0108] Step 4 If s m ≠0, calculate step size Calculate σ m+1 =σ m +β m s m If it exists Update the working set Wm+1 = Wm∪{i}; otherwise, W k+1 =W k Set m = m + 1; jump to step 2.

[0109] Step S205: Determine the error pipeline containing system uncertainty based on the nominal state quantity of the system, and determine the true state quantity of the system based on the error pipeline;

[0110] In one embodiment, optionally, determining an error pipeline containing system uncertainties based on the nominal system state quantities, and determining the true system state quantities based on the error pipeline, includes:

[0111] Taking into account the system uncertainty, an error pipeline incorporating the system uncertainty is constructed centered on the system's nominal state variables.

[0112] Based on the error pipeline, a secondary control model is determined so that the actual state variables of the system converge to the steady-state neighborhood of the nominal state variables of the system.

[0113] The secondary control model includes:

[0114] Where, δu k This represents the actual state quantity of the system. K represents the nominal state quantity of the system. k Represents the feedback matrix. This represents the error between the actual state quantity of the system and the nominal state quantity of the system;

[0115] The actual state variables of the system are determined based on the nominal state variables of the system and the secondary control model.

[0116] Step S206: Based on the actual system state, output the front wheel angle incremental control command of the tractor vehicle to control the tractor vehicle through the front wheel angle incremental control command.

[0117] In summary, the constraint optimization problem described by equation (23) is solved using the effective set method to obtain the nominal control quantity. Combined with the secondary controller described by equation (19), the incremental control command for the front wheel steering angle of the tractor is obtained.

[0118] In the above embodiments, the kinematic relationship between the yaw rate and the front wheel rotation angle of the tractor is established using the steady-state circular motion of the tractor, and unknown parameters are identified. The velocity decomposition method at the articulation point is used to establish the functional relationship between the tractor speed and the semi-trailer speed, thereby obtaining a parking path tracking control model for the articulated vehicle, including the coordinates of the midpoint of the semi-trailer's rear axle, the yaw angle, the angle between the tractor and the semi-trailer, and the rotation angle of the tractor's front wheels. Subsequently, the parking path tracking control equation is constructed by locally linearizing the automatic parking path tracking control model in the steady-state neighborhood. The nominal state variables and nominal control variables of the system are obtained by solving the parking path tracking control equation using the effective set method. An error pipeline containing system uncertainties is constructed centered on the nominal state variables, and a secondary controller is designed to make the actual system state variables within the pipeline converge to the nominal state variables. This ensures that the articulated vehicle always operates within the steady-state domain during the reversing process, preventing the articulated vehicle from folding and becoming unstable during reversing and improving the success rate of reversing into parking spaces.

[0119] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] Figure 5 shows a block diagram of an automatic parking path tracking control device for an articulated vehicle according to an embodiment of this application.

[0121] As shown in Figure 5, in a second aspect, embodiments of this application provide an automatic parking path tracking control device 50 for articulated vehicles, comprising:

[0122] The first determining module 51 is used to establish the kinematic relationship between the yaw rate of the tractor and the front wheel rotation angle by using the steady-state circular motion of the tractor and the vehicle parameter information of the articulated vehicle, so as to determine the proportional coefficient between the yaw rate of the tractor and the front wheel rotation angle.

[0123] Model building module 52 is used to establish a functional relationship between the first speed of the tractor and the second speed of the semi-trailer using the articulation point velocity decomposition method, so as to obtain an automatic parking path tracking control model including the proportional coefficient, the yaw angle of the semi-trailer, the angle between the tractor and the semi-trailer, and the front wheel turning angle of the tractor.

[0124] The second determining module 53 is used to determine the automatic parking path tracking control equation based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle driving in the stable state neighborhood during the reversing process;

[0125] Solver module 54 is used to solve the automatic parking path tracking control equations using the effective set method to obtain the system nominal state variables and system nominal control variables;

[0126] The third determining module 55 is used to determine an error pipeline containing system uncertainty based on the nominal state quantity of the system, and to determine the true state quantity of the system based on the error pipeline.

[0127] The output module 56 is used to output the front wheel angle incremental control command of the tractor according to the actual state of the system, so as to control the tractor through the front wheel angle incremental control command.

[0128] In one embodiment, optionally, the kinematic relationship between the yaw rate of the tractor and the front wheel steering angle includes:

[0129] Where, δ f L1 represents the front wheel steering angle of the tractor, R represents the distance from the center of the front axle to the center of the rear axle of the tractor, and K represents the steady-state circumference radius. v Indicates the parameter to be identified, v x This indicates the speed of the center of gravity of the tractor unit. The yaw rate of the tractor in steady-state circular motion;

[0130] The proportionality coefficient between the yaw rate of the tractor and the front wheel steering angle includes:

[0131] The quasi-Newton method is used to identify c1 and c2 in order to obtain the proportionality coefficient.

[0132] In one embodiment, optionally, the automatic parking path tracking control equation takes the minimization of the weighted sum of the first deviation and the second deviation as the optimization objective, wherein the first deviation is the deviation between the actual system state quantity and the corresponding system reference state quantity, and the second deviation is the deviation between the actual system control quantity and the corresponding system reference control quantity, wherein the system reference state quantity and the system reference control quantity are calculated based on the reference trajectory.

[0133] In one embodiment, optionally, the second determining module includes:

[0134] Discrete unit, used to discretize the automatic parking path tracking control model using the fourth-order Runge-Ku integral method to obtain the initial nonlinear control equations;

[0135] The processing unit is configured to linearize the initial nonlinear equation based on the first deviation and the second deviation to obtain a linearized control equation, wherein the linearized control equation includes system uncertainties.

[0136] The determining unit is used to determine the automatic parking path tracking control equation based on the linearized control equation, the preset constraints and reference trajectory of the articulated vehicle traveling in the steady state neighborhood during the reversing into parking process, wherein the preset constraints include equality constraints and inequality constraints.

[0137] In one embodiment, optionally, the solver module is used for:

[0138] Without considering the system uncertainties, the automatic parking path tracking control equations are iteratively solved using the effective set method to obtain the system nominal state variables and system nominal control variables.

[0139] In one embodiment, optionally, the process of iteratively solving the automatic parking path tracking control equations using the effective set method includes:

[0140] In each iterative solution process, a subset containing all equality constraints and some inequality constraints is selected from all preset constraints as the working set.

[0141] Modify some of the inequality constraints in the work set into equality constraints;

[0142] The feasible solution is obtained by using the Lagrange multiplier method, and the KKT conditions are used to determine whether the feasible solution is the optimal solution.

[0143] If the feasible solution is the optimal solution, the iteration terminates.

[0144] If the feasible solution is not the optimal solution, find a new working set and iteration point that effectively reduces the optimization objective by solving the sub-optimization problem, and start a new round of iterative calculation.

[0145] In one embodiment, optionally, the third determining module includes:

[0146] A construction unit is used to construct an error pipeline containing system uncertainties, centered on the nominal state variables of the system, taking into account the system uncertainties.

[0147] The model determination unit is used to determine the secondary control model based on the error pipeline, so that the actual state variables of the system converge to the steady state neighborhood of the nominal state variables of the system.

[0148] The secondary control model includes:

[0149] Where, δu k This represents the actual state quantity of the system. K represents the nominal state quantity of the system. k Represents the feedback matrix. This represents the error between the actual state quantity of the system and the nominal state quantity of the system;

[0150] The actual state variables of the system are determined based on the nominal state variables of the system and the secondary control model.

[0151] Specific limitations regarding the automatic parking path tracking control device for articulated vehicles can be found in the above description of the automatic parking path tracking control method for articulated vehicles, and will not be repeated here. Each module in the aforementioned automatic parking path tracking control device for articulated vehicles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0152] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 6. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a server-side automatic parking path tracking control method for articulated vehicles.

[0153] In one embodiment, a computer device is provided, which can be a client, and its internal structure diagram is shown in Figure 7. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a client-side automatic parking path tracking control method for an articulated vehicle.

[0154] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0155] The computer device in this application embodiment exists in various forms, including but not limited to:

[0156] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0157] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0158] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes audio and video players (such as iPods), handheld game consoles, e-book readers, as well as smart toys and portable car navigation devices.

[0159] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0160] (5) Other electronic devices with data interaction functions.

[0161] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described automatic parking path tracking control method for articulated vehicles.

[0162] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or electronic device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0163] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0164] It should be understood that although the terms "first," "second," etc., may be used to describe the setting units in the embodiments of this application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of this application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0165] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0166] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0167] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0169] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An automatic parking path tracking control method for an articulated vehicle, wherein, The articulated vehicle includes a tractor and a semi-trailer, and the method includes: Using the steady-state circular motion of the tractor and the vehicle parameter information of the articulated vehicle, the kinematic relationship between the yaw rate of the tractor and the front wheel rotation angle is established to determine the proportionality coefficient between the yaw rate of the tractor and the front wheel rotation angle. The articulation point velocity decomposition method is used to establish the functional relationship between the first speed of the tractor and the second speed of the semi-trailer, so as to obtain an automatic parking path tracking control model including the proportional coefficient, the yaw angle of the semi-trailer, the angle between the tractor and the semi-trailer, and the front wheel rotation angle of the tractor. Based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle driving in the stable state neighborhood during the reversing process are used to determine the automatic parking path tracking control equation. The effective set method is used to solve the automatic parking path tracking control equations to obtain the system nominal state variables and system nominal control variables; An error pipeline containing system uncertainties is determined based on the nominal state variables of the system, and the true state variables of the system are determined based on the error pipeline. Based on the actual state of the system, the system outputs an incremental control command for the front wheel angle of the tractor, thereby controlling the tractor through the incremental control command for the front wheel angle.

2. The method according to claim 1, wherein, The kinematic relationship between the yaw rate and the front wheel steering angle of the tractor unit includes: Where, δ f L1 represents the front wheel steering angle of the tractor, R represents the distance from the center of the front axle to the center of the rear axle of the tractor, and K represents the steady-state circumference radius. v Indicates the parameter to be identified, v x This indicates the speed of the center of gravity of the tractor unit. The yaw rate of the tractor in steady-state circular motion; The proportionality coefficient between the yaw rate of the tractor and the front wheel steering angle includes: The quasi-Newton method is used to identify c1 and c2 in order to obtain the proportionality coefficient.

3. The method according to claim 1, wherein, The automatic parking path tracking control equation aims to minimize the weighted sum of the first deviation and the second deviation. The first deviation is the deviation between the actual system state quantity and the corresponding system reference state quantity, and the second deviation is the deviation between the actual system control quantity and the corresponding system reference control quantity. The system reference state quantity and the system reference control quantity are calculated based on the reference trajectory.

4. The method according to claim 3, wherein, Based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle during its reversing into a parking space within its stable neighborhood are used to determine the automatic parking path tracking control equations, including: The automatic parking path tracking control model is discretized using the fourth-order Runge-Ku integral method to obtain the initial nonlinear control equations; Based on the first deviation and the second deviation, the initial nonlinear equation is linearized to obtain a linearized control equation, wherein the linearized control equation includes system uncertainties; Based on the linearized control equations, the preset constraints and reference trajectory of the articulated vehicle during the reversing into parking space within the stable state neighborhood are used to determine the automatic parking path tracking control equations. The preset constraints include equality constraints and inequality constraints.

5. The method according to claim 3, wherein, The effective set method is used to solve the automatic parking path tracking control equations to obtain the system nominal state variables and system nominal control variables, including: Without considering the system uncertainties, the automatic parking path tracking control equations are iteratively solved using the effective set method to obtain the system nominal state variables and system nominal control variables.

6. The method according to claim 5, wherein, The process of iteratively solving the automatic parking path tracking control equations using the effective set method includes: In each iterative solution process, a subset containing all equality constraints and some inequality constraints is selected from all preset constraints as the working set. Modify some of the inequality constraints in the work set into equality constraints; The feasible solution is obtained by using the Lagrange multiplier method, and the KKT conditions are used to determine whether the feasible solution is the optimal solution. If the feasible solution is the optimal solution, the iteration terminates. If the feasible solution is not the optimal solution, find a new working set and iteration point that effectively reduces the optimization objective by solving the sub-optimization problem, and start a new round of iterative calculation.

7. The method according to claim 5, wherein, Based on the nominal state variables of the system, an error pipeline containing system uncertainties is determined, and based on the error pipeline, the true state variables of the system are determined, including: Taking into account the system uncertainty, an error pipeline incorporating the system uncertainty is constructed centered on the system's nominal state variables. Based on the error pipeline, a secondary control model is determined so that the actual state variables of the system converge to the steady-state neighborhood of the nominal state variables of the system. The secondary control model includes: Where, δu k This represents the actual state quantity of the system. K represents the nominal state quantity of the system. k Represents the feedback matrix. This represents the error between the actual state quantity of the system and the nominal state quantity of the system; The actual state variables of the system are determined based on the nominal state variables of the system and the secondary control model.

8. An automatic parking path tracking control device for an articulated vehicle, wherein, The articulated vehicle includes a tractor unit and a semi-trailer, and the device includes: The first determining module is used to establish the kinematic relationship between the yaw rate of the tractor and the front wheel rotation angle by using the steady-state circular motion of the tractor and the vehicle parameter information of the articulated vehicle, so as to determine the proportional coefficient between the yaw rate of the tractor and the front wheel rotation angle. The model building module is used to establish a functional relationship between the first speed of the tractor and the second speed of the semi-trailer using the articulation point velocity decomposition method, so as to obtain an automatic parking path tracking control model including the proportional coefficient, the yaw angle of the semi-trailer, the angle between the tractor and the semi-trailer, and the front wheel turning angle of the tractor. The second determining module is used to determine the automatic parking path tracking control equation based on the automatic parking path tracking control model, the preset constraints and reference trajectory of the articulated vehicle driving in the stable state neighborhood during the reversing process; The solution module is used to solve the automatic parking path tracking control equations using the effective set method to obtain the system nominal state variables and system nominal control variables; The third determining module is used to determine an error pipeline containing system uncertainties based on the nominal state quantity of the system, and to determine the true state quantity of the system based on the error pipeline. The output module is used to output the front wheel angle incremental control command of the tractor vehicle according to the actual state of the system, so as to control the tractor vehicle through the front wheel angle incremental control command.

9. A computer device, wherein, include: At least one processor; The processor is coupled to the memory; The memory stores instructions executed by the at least one processor, the instructions being configured to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 7.

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