A vehicle trajectory closed-loop drift control method, device and storage medium

Abstract

The present application belongs to the field of vehicle dynamics control and trajectory tracking control technology, and particularly relates to a vehicle trajectory closed-loop drift control method, equipment and storage medium; the method constructs ideal yaw angular velocity and rear axle wheel longitudinal force reference value of drift trajectory tracking, takes ideal yaw angular velocity, longitudinal vehicle speed reference value, lateral error reference value and heading angle deviation reference value as state quantity reference vector, takes front wheel steering angle reference value and rear axle wheel longitudinal force reference value as control quantity reference vector, combines NMPC controller to take yaw angular velocity, longitudinal vehicle speed, lateral error and heading angle deviation as state quantity, takes rear axle longitudinal force and front wheel steering angle as control quantity to define cost function, solves, realizes closed-loop control of vehicle drift trajectory tracking; the present application can realize trajectory stable tracking under drift state under different adhesion and working condition change conditions, improves applicability, real-time performance and reproducibility of the closed-loop drift control method.

Description

Technical Field

[0001] This invention belongs to the field of vehicle dynamics control and trajectory tracking control technology, and particularly relates to a vehicle trajectory closed-loop drift control method, device and storage medium. Background Technology

[0002] Vehicle drift is a state of extreme motion that occurs when the rear wheels are deeply saturated and the vehicle is severely oversteer. It requires the driver to precisely adjust the pedal opening and steering wheel angle to keep the vehicle in this critical stable state. With the development of intelligent driving and active safety technologies, vehicle trajectory tracking and drift control under extreme conditions has gradually become an important research topic in the field of vehicle dynamics control.

[0003] However, in existing drift trajectory tracking control methods, the state variables (such as the center of gravity sideslip angle and yaw rate) and control variables (such as the front wheel steering angle and the rear axle longitudinal driving force) of the vehicle reference drift equilibrium point usually depend on the vehicle dynamics model, tire model, and road surface adhesion conditions, and are obtained through offline table lookup, numerical solution, or online real-time optimization.

[0004] However, the aforementioned control methods relying on drift equilibrium points have the following shortcomings: Firstly, the process of solving for drift equilibrium points typically depends on relatively accurate vehicle parameters, tire models, and road surface adhesion information. When vehicle speed, road surface adhesion, trajectory curvature, and tire operating conditions change, the equilibrium point position is prone to shift, leading to reference mismatch. Secondly, the process of solving for drift equilibrium points is computationally complex and applicable to limited operating conditions, making it difficult to simultaneously meet real-time performance, robustness, and engineering application requirements. Especially in trajectory closed-loop drift control scenarios, if the vehicle's reference state and reference control quantities rely excessively on the pre-solved drift equilibrium points, the controller becomes highly sensitive to changes in operating conditions and model mismatch, easily affecting trajectory tracking accuracy and control stability during drift.

[0005] Therefore, it is necessary to propose a vehicle trajectory closed-loop drift control method that does not rely on drift equilibrium point information, so as to realize the trajectory closed-loop drift control of the vehicle under different adhesion and operating conditions without having to solve the drift equilibrium state in advance. Summary of the Invention

[0006] In view of this, the present invention aims to provide a vehicle trajectory closed-loop drift control method, device and storage medium, which can realize the trajectory closed-loop drift control of the vehicle under different adhesion and operating conditions without the need to solve the drift equilibrium state in advance.

[0007] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0008] A closed-loop drift control method for vehicle trajectory includes the following:

[0009] Step 1: Construct the ideal yaw rate for drift trajectory tracking and input it into the NMPC controller;

[0010] The ideal yaw rate is defined as the sum of the yaw rate component rotating around the vehicle's own center of mass, the yaw rate component rotating around the vehicle's instantaneous center of rotation, and the additional yaw rate component required to compensate for trajectory tracking errors.

[0011] Step 2: Generate a reference value for the longitudinal force of the rear axle based on the target slip ratio error and input it into the NMPC controller;

[0012] The reference value of the longitudinal force of the rear axle is obtained based on the average slip ratio of the rear axle wheels and the ideal longitudinal slip ratio of the rear axle wheels under drift conditions.

[0013] Step 3: Generate trajectory tracking state reference vectors and control reference vectors, and input them to the NMPC controller;

[0014] The state variable reference vector includes the ideal yaw rate, longitudinal vehicle speed reference value, lateral error reference value, and heading angle deviation reference value; the control variable reference vector includes the front wheel steering angle reference value and the rear axle longitudinal force reference value.

[0015] Step 4: Custom-penalized NMPC closed-loop drift control method;

[0016] The NMPC controller uses yaw rate Longitudinal speed lateral error and heading angle deviation As a state variable, the longitudinal force of the rear axis and front wheel steering angle The cost function is defined as the control quantity, and a slip ratio penalty term is added to the cost function. The optimal control quantity vector sequence is obtained by minimizing the cost function. The first sequence of the control quantity vector sequence is applied to the vehicle system to realize closed-loop control for vehicle drift trajectory tracking.

[0017] In step 1, the formula for the ideal yaw rate is as follows:

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] in For the ideal yaw rate, Let yaw rate be the component of the vehicle's rotation about its instantaneous center of rotation. Let yaw rate be the component of the vehicle's yaw rate as it rotates around its center of mass. The additional yaw rate component required to compensate for trajectory tracking errors Indicates the centroid sideslip angle. This represents the rate of change of the centroid sideslip angle. For reference road curvature, For longitudinal vehicle speed, For lateral error, The rate of change of the lateral error. The differential coefficients are... This is the proportionality coefficient.

[0023] In step 2, the slip ratios of the left and right rear axle wheels are defined as follows:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] In the formula: and These represent the slip ratios of the left and right wheels on the rear axle, respectively. This indicates the average slip ratio of the rear axle wheels. and These represent the longitudinal speeds of the left and right wheels on the rear axle, respectively. Indicates the rolling radius of the wheel. and These represent the angular velocities of the left and right wheels on the rear axle, respectively. Indicates yaw rate. Indicates the rear track width of the vehicle;

[0030] The expression for the reference value of the rear axle longitudinal force is as follows:

[0031] ;

[0032] ;

[0033] In the formula: For the ideal longitudinal slip ratio of the rear axle wheels, This is a reference value for the longitudinal force on the rear axle. This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For slip ratio deviation, The rate of change of slip ratio deviation This is the integral of the slip ratio deviation over time.

[0034] The state reference vector and control reference vector information in step 3 are as follows:

[0035] ;

[0036] ;

[0037] In the formula For state variable reference vectors, For the control quantity reference vector, This is a reference value for lateral error. This is a reference value for the heading angle deviation. The sideslip angle is the vehicle's center of gravity. This is a longitudinal speed reference value. This is a reference value for the front wheel steering angle of the vehicle. The front wheel steering angle at the previous moment.

[0038] The slip ratio penalty term added to the cost function in step 4 is: .

[0039] In step 4, the NMPC controller combines the vehicle state, reference trajectory, and Fiala tire model to define a custom cost function, which is specifically as follows:

[0040] ;

[0041] ;

[0042] in Indicates in Predicting the future The state vector at time t. Indicates in Predicting the future Control vector information at any given time. Indicates in Predicting the future Control vector information at time -1 express Control vector information at any given time. for Control vector information at time -1 for Time prediction The change in the control vector at time 1 relative to the previous time 2. The average slip ratio of the rear axle wheels. For the ideal longitudinal slip ratio of the rear axle wheels, Indicates in Predicting the future The state vector at time t. The weight matrix is ​​the state vector. For the control vector weight matrix, The weight matrix is ​​used to control the change of the quantity vector. For the terminal cost matrix, This represents the weighting coefficient for the slip ratio penalty.

[0043] In step 4: Define the state vector. Upper and lower limits, control vector The maximum and minimum values, and the change vector of the control quantity vector in each cycle. The upper and lower limits are used together as constraints; the corresponding expressions are shown below:

[0044] ;

[0045] ;

[0046] ;

[0047] in for The state vector at time t. for The control vector at time t, for The change in the control vector at any given time; and Tables The upper and lower bounds of the state vector at any given time. and They represent The upper and lower limits of the control vector are constantly being set. and They represent The upper and lower limits of the change in the control vector within a given time period.

[0048] In step 4, the NMPC controller uses the vehicle's lateral error... Heading angle deviation yaw rate and longitudinal speed The state variables are used to establish the state-space expression to predict the state vector at future time steps; the state-space expression is as follows:

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] In the formula The rate of change of yaw angular velocity, The rate of change of longitudinal vehicle speed, The rate of change of the lateral error. The rate of change of the heading angle deviation. and These are the vehicle's curb weight and related information. Moment of inertia of the shaft and These are the distances from the center of gravity to the front and rear axles of the vehicle, respectively. Let be the target radius of curvature of the vehicle. The lateral force on the front axle wheels. This refers to the longitudinal force on the rear axle wheels.

[0054] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described vehicle trajectory closed-loop drift control method.

[0055] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described vehicle trajectory closed-loop drift control method.

[0056] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0057] This invention eliminates the need to pre-calculate drift equilibrium point information and enables stable trajectory tracking under different adhesion and operating conditions, thereby improving the applicability, real-time performance, and reproducibility of the closed-loop drift control method. Attached Figure Description

[0058] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0059] Figure 1This refers to the centroid sideslip angle during trajectory tracking of the vehicle in this invention under ideal vehicle speed of 8 m / s and reference radius of curvature of -14 m. yaw rate speed Graph showing changes over time.

[0060] Figure 2 This is a graph showing the changes in the front wheel angle and rear wheel longitudinal force over time during the trajectory tracking process of the vehicle in this invention under the conditions of an ideal vehicle speed of 8m / s and a reference radius of curvature of -14m.

[0061] Figure 3 This is a graph showing the change of trajectory tracking error over time for the vehicle in this invention under the conditions of an ideal vehicle speed of 8 m / s and a reference radius of curvature of -14 m.

[0062] Figure 4 This is a comparison diagram of the ideal and actual slip ratios during the trajectory tracking process of the vehicle in this invention under the conditions of an ideal vehicle speed of 8m / s and a reference radius of curvature of -14m.

[0063] Figure 5 This is a schematic diagram of the CarSim and Simulink joint simulation results of the vehicle in this invention under the conditions of an ideal vehicle speed of 8m / s and a reference radius of curvature of -14m.

[0064] Figure 6 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof. It should be noted that, without conflict, the embodiments and features in the embodiments of this invention can be combined to form various implementations. Furthermore, the steps or actions described in the method description can be rearranged or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0066] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0067] Please see Figure 6 A closed-loop drift control method for vehicle trajectory includes the following:

[0068] Step 1: Construct the ideal yaw rate for drift trajectory tracking and input the constructed ideal yaw rate into the NMPC controller;

[0069] The ideal yaw rate is defined as consisting of three parts: the yaw rate component rotating around the vehicle's center of mass, the yaw rate component rotating around the vehicle's instantaneous center of rotation, and the additional yaw rate component required to compensate for trajectory tracking errors. The specific formula for the ideal yaw rate is as follows:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] in For the ideal yaw rate, Let yaw rate be the component of the vehicle's rotation about its instantaneous center of rotation. Let yaw rate be the component of the vehicle's yaw rate as it rotates around its center of mass. The additional yaw rate component required to compensate for trajectory tracking errors (obtained by proportional-differential calculation of the vehicle's trajectory tracking lateral error). Indicates the centroid sideslip angle. This represents the rate of change of the centroid sideslip angle. For reference road curvature, For longitudinal vehicle speed, For lateral error, The rate of change of the lateral error. The differential coefficients are... Both are proportional coefficients, and are PD parameters used by PD control to correct the ideal yaw rate based on trajectory error;

[0075] Step 2: Generate a rear axle longitudinal force reference value based on the target slip ratio error, and input the generated rear axle longitudinal force reference value into the NMPC controller;

[0076] The final reference value of the rear axle longitudinal force under the drift condition is obtained by PID calculation based on the average slip ratio of the rear axle wheels (i.e., the average slip ratio of the left rear wheel and the right rear wheel) and the ideal longitudinal slip ratio of the rear axle wheels under the drift condition calibrated under the required road surface adhesion conditions.

[0077] The ideal longitudinal slip ratio of the rear axle wheels under the drift condition is determined based on the balance condition of lateral force, longitudinal force, and yaw moment under the road adhesion coefficient. During the calibration process, under each determined road adhesion coefficient, the driver drives the vehicle into a stable and controllable drift state. Based on the balance conditions of the vehicle's longitudinal force, lateral force, and yaw moment under the steady-state drift condition, the longitudinal slip ratio of the vehicle's rear axle wheels is matched to obtain the optimal target longitudinal slip ratio of the rear axle wheels that can maintain the drift steady state under that road adhesion coefficient.

[0078] In step 2, the slip ratios of the left and right rear axle wheels are defined as follows:

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] In the formula: and These represent the slip ratios of the left and right wheels on the rear axle, respectively. This indicates the average slip ratio of the rear axle wheels. and These represent the longitudinal speeds of the left and right wheels on the rear axle, respectively. This represents the rolling radius of the wheel (assuming that all wheels have the same rolling radius). and These represent the angular velocities of the left and right wheels on the rear axle, respectively. This represents the yaw rate measured by the sensor. This indicates the rear track width of the vehicle.

[0085] The expression for the reference value of the longitudinal force of the rear axle wheel under the drift condition in step 2 is as follows:

[0086] ;

[0087] ;

[0088] In the formula: To obtain the ideal longitudinal slip ratio of the rear axle wheels under the required road surface adhesion conditions, This is a reference value for the longitudinal force of the rear axle under drifting conditions. This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... This is the integral of the slip ratio deviation over time. This refers to the slip ratio deviation, which is the difference between the ideal longitudinal slip ratio of the rear axle wheels and the current average slip ratio of the rear axle wheels of the vehicle. The rate of change of slip ratio deviation (i.e. (the derivative of) For time.

[0089] Step 3: Generate trajectory tracking state reference vector and control reference vector, that is, further construct other reference vectors required for trajectory tracking, and input the generated state reference vector and control reference vector to the NMPC controller;

[0090] Having obtained the ideal yaw rate and the reference value of the rear axle longitudinal force under drift conditions in steps 1 and 2, it is necessary to define the reference value of the longitudinal vehicle speed under drift conditions in order to complete the trajectory closed-loop drift control. The vehicle's lateral error reference value and heading angle deviation reference value are defined as follows: the lateral error reference value is defined as 0. Due to severe sideslip and a large center-of-gravity sideslip angle under extreme drift conditions, the heading angle deviation reference value is defined as a negative center-of-gravity sideslip angle to align the vehicle's center-of-gravity velocity direction with the tangential direction of the reference trajectory. For the control variable reference values, it is not necessary to pre-calculate the front wheel angle and rear axle longitudinal force corresponding to the drift equilibrium point; instead, the rear axle longitudinal force reference value under drift conditions and the front wheel angle at the previous moment are used. The final state variable reference vector and control variable reference vector information are shown below:

[0091] ;

[0092] ;

[0093] In the formula For state variable reference vectors, For the control quantity reference vector, To track the vehicle's lateral error reference value (relative to the reference trajectory), To track the reference value of the vehicle's heading angle deviation (relative to the reference trajectory), The sideslip angle is the vehicle's center of gravity. This is the longitudinal speed reference value, or the ideal longitudinal speed, which is the longitudinal speed to be achieved during drifting. This is a reference value for the front wheel steering angle of the vehicle. The turning angle of the front wheel based on the decision made at the previous moment.

[0094] Step 4: Custom-penalized NMPC closed-loop drift control method;

[0095] The NMPC controller uses yaw rate Longitudinal speed lateral error and heading angle deviation As state variables (using their current values ​​as the initial values ​​for predictive control), the longitudinal force of the rear axis and front wheel steering angle As a control variable, a custom cost function is defined by combining vehicle state, reference trajectory, and Fiala tire model, and a slip ratio penalty term is added to the cost function. The optimal control vector sequence is obtained by minimizing the cost function, and the first sequence of the control vector sequence is applied to the vehicle system to achieve closed-loop control for vehicle drift trajectory tracking.

[0096] The NMPC method described above does not require offline solution of the drift equilibrium point and its corresponding equivalent values ​​of the centroid sideslip angle, yaw rate, front wheel steering angle, and rear axle longitudinal force, etc., to achieve drift condition triggering and drift trajectory closed-loop tracking.

[0097] In step 4: A penalty term for slip ratio error is added to the NMPC optimization objective to maintain the longitudinal slip ratio of the vehicle's rear axle wheels near the preset ideal longitudinal slip ratio of the rear axle wheels. This ensures stable drift attitude while maintaining trajectory tracking performance. Therefore, a slip ratio penalty term is added to the cost function defined by the NMPC controller. This invention will address the slip ratio deviation. that is Incorporate optimization objectives and participate in integrated optimization solutions _min; the final cost function is shown below:

[0098] ;

[0099] ;

[0100] in Indicates in Predicting the future The state vector at time t. Indicates in Predicting the future The control vector at time t, Indicates in Predicting the future The control vector at time -1 express The control vector at time t, For the previous moment The control vector at time -1 for Time prediction The time relative to the previous time is The change in the control vector at time -1 The average slip ratio of the rear axle wheels. For the ideal longitudinal slip ratio of the rear axle wheels, Indicates in Predicting the future The state vector at time t. The weight matrix is ​​the state vector. For the control vector weight matrix, The weight matrix is ​​used to control the change of the quantity vector. The terminal cost matrix has the following coefficients. This represents the weighting coefficient for the slip ratio penalty. The cost function value defined for the corresponding NMPC controller. =0… -1.

[0101] In step 4: Define the state vector. Upper and lower limits, control vector The maximum and minimum values, and the change vector of the control quantity vector in each cycle. The upper and lower limits are used together as constraints; the corresponding expressions are shown below:

[0102] ;

[0103] ;

[0104] ;

[0105] in for The state vector corresponding to time step for The control vector corresponding to each time step. for The change value of the control vector at time (i.e.) The control quantity at a given time relative to the previous time is... (Change value of control vector at time -1). and They represent the corresponding times, i.e. The upper and lower bounds of the state vector at any given time. and They represent the corresponding times, i.e. The upper and lower limits of the control vector are constantly being set. and They represent The upper and lower limits of the change in the control vector within a given time period.

[0106] In step 4: the NMPC controller uses the vehicle's lateral error Heading angle deviation yaw rate and longitudinal speed State variables are used to establish a state-space expression to predict state vector information at future times; where the state-space expression is: ,in For state vectors, For the control vector, the corresponding expression is as follows:

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] In the formula The rate of change of yaw rate with respect to time. The rate of change of longitudinal vehicle speed with respect to time. This represents the rate of change of the lateral error with respect to time. The rate of change of the heading angle deviation with respect to time. and These are the vehicle's curb weight and related information. Moment of inertia of the shaft and These are the distances from the center of gravity to the front and rear axles of the vehicle, respectively. Let be the target radius of curvature of the vehicle. The lateral force on the front axle wheels. This refers to the longitudinal force on the rear axle wheels.

[0112] The NMPC controller, based on the current time... , , and As the initial state, the rate of change of the state quantity corresponding to each future time moment is calculated iteratively in the prediction time domain to predict the state quantity vector of the future time moment.

[0113] The centroid side slip angle The real-time state quantities are obtained by the vehicle state estimator online. As an estimate, it enters the prediction equation, not as a state variable in the state-space equation. It is included in each rolling optimization cycle, i.e., at each time step. It is treated as a constant in the prediction time domain and updated according to the latest estimate in the next control cycle. .

[0114] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described vehicle trajectory closed-loop drift control method.

[0115] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described vehicle trajectory closed-loop drift control method.

[0116] This invention conducted joint simulations using CarSim and Simulink under ideal longitudinal vehicle speeds of 8 m / s and a reference radius of curvature of -14 m, where the centroid sideslip angle during trajectory tracking was measured. yaw rate speed The graph shows the changes over time. Figure 1 As shown; a schematic diagram of the front wheel steering angle and rear wheel longitudinal force during trajectory tracking under this condition (a graph showing changes over time) is shown below. Figure 2 As shown; a schematic diagram of the trajectory tracking error (varying over time) during the trajectory tracking process under this condition is shown below. Figure 3 As shown in the figure; the comparison between the ideal and actual slip rates during trajectory tracking under this condition is illustrated in the figure below. Figure 4 As shown; a schematic diagram of the vehicle visualization results from the joint simulation of CarSim and Simulink is shown below. Figure 5 As shown.

[0117] Depend on Figure 5 It is evident that the vehicle is in a drifting state during the simulation using this method. Figure 1 , Figure 2 and Figure 4 The data also shows that the vehicle remained in a drifting state. Figure 3 It can be seen that the lateral error of the tracking error is very small. Therefore, this method does not require pre-solving the drift equilibrium point information. It can maintain a very small trajectory tracking error while maintaining the vehicle's drift attitude, and achieve stable trajectory tracking in the drift state under different adhesion and operating conditions.

[0118] In summary, the above description is merely a preferred embodiment of this specification and is not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

[0119] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A closed-loop drift control method for vehicle trajectory, characterized in that, Includes the following: Step 1: Construct the ideal yaw rate for drift trajectory tracking and input it into the NMPC controller; The ideal yaw rate is defined as the sum of the yaw rate component rotating around the vehicle's own center of mass, the yaw rate component rotating around the vehicle's instantaneous center of rotation, and the additional yaw rate component required to compensate for trajectory tracking errors. Step 2: Generate the rear axle longitudinal force reference value and input it into the NMPC controller; The reference value of the longitudinal force of the rear axle is obtained based on the average slip ratio of the rear axle wheels and the ideal longitudinal slip ratio of the rear axle wheels. Step 3: Generate trajectory tracking state reference vectors and control reference vectors, and input them to the NMPC controller; The state variable reference vector includes the ideal yaw rate, longitudinal vehicle speed reference value, lateral error reference value, and heading angle deviation reference value; the control variable reference vector includes the front wheel steering angle reference value and the rear axle longitudinal force reference value. Step 4: Custom-penalized NMPC closed-loop drift control method; The NMPC controller uses yaw rate Longitudinal speed lateral error and heading angle deviation As a state variable, the longitudinal force of the rear axis and front wheel steering angle The cost function is defined as the control quantity, and a slip ratio penalty term is added to the cost function. The optimal control quantity vector sequence is obtained by minimizing the cost function. The first sequence of the control quantity vector sequence is applied to the vehicle system to realize closed-loop control for vehicle drift trajectory tracking.

2. The vehicle trajectory closed-loop drift control method according to claim 1, characterized in that, In step 1, the formula for the ideal yaw rate is as follows: ； ； ； ； in For the ideal yaw rate, Let yaw rate be the component of the vehicle's rotation about its instantaneous center of rotation. Let yaw rate be the component of the vehicle's yaw rate as it rotates around its center of mass. The additional yaw rate component required to compensate for trajectory tracking errors Indicates the centroid sideslip angle. This represents the rate of change of the centroid sideslip angle. For reference road curvature, For longitudinal vehicle speed, For lateral error, The rate of change of the lateral error. The differential coefficients are... This is the proportionality coefficient.

3. The vehicle trajectory closed-loop drift control method according to claim 2, characterized in that, In step 2, the slip ratios of the left and right rear axle wheels are defined as follows: ； ； ； ； ； In the formula: and These represent the slip ratios of the left and right wheels on the rear axle, respectively. This indicates the average slip ratio of the rear axle wheels. and These represent the longitudinal speeds of the left and right wheels on the rear axle, respectively. Indicates the rolling radius of the wheel. and These represent the angular velocities of the left and right wheels on the rear axle, respectively. Indicates yaw rate. Indicates the rear track width of the vehicle; The expression for the reference value of the longitudinal force on the rear axle wheel is as follows: ； ； In the formula: For the ideal longitudinal slip ratio of the rear axle wheels, This is a reference value for the longitudinal force on the rear axle. This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... For slip ratio deviation, The rate of change of slip ratio deviation This is the integral of the slip ratio deviation over time.

4. The vehicle trajectory closed-loop drift control method according to claim 3, characterized in that, The state reference vector and control reference vector information in step 3 are as follows: ； ； In the formula For state variable reference vectors, For the control quantity reference vector, This is a reference value for lateral error. This is a reference value for the heading angle deviation. The sideslip angle is the angle of the centroid. This is a longitudinal speed reference value. This is a reference value for the front wheel steering angle. The front wheel steering angle at the previous moment.

5. The vehicle trajectory closed-loop drift control method according to claim 4, characterized in that, The slip ratio penalty term added to the cost function in step 4 is: .

6. The vehicle trajectory closed-loop drift control method according to claim 1, characterized in that, In step 4, the NMPC controller combines the vehicle state, reference trajectory, and Fiala tire model to define a custom cost function, which is specifically as follows: ； ； in Indicates in Predicting the future The state vector at time t. Indicates in Predicting the future The control vector at time t, Indicates in Predicting the future The control vector at time -1 express The control vector at time t, for The control vector at time -1 for Time prediction The change in the control vector at time 1 relative to the previous time 2. The average slip ratio of the rear axle wheels. For the ideal longitudinal slip ratio of the rear axle wheels, Indicates in Predicting the future The state vector at time t. The weight matrix is ​​the state vector. For the control vector weight matrix, The weight matrix is ​​used to control the change of the quantity vector. For the terminal cost matrix, This represents the weighting coefficient for the slip ratio penalty.

7. The vehicle trajectory closed-loop drift control method according to claim 6, characterized in that, In step 4: Define the state vector. Upper and lower limits, control vector The maximum and minimum values, and the change vector of the control quantity vector in each cycle. The upper and lower limits are used together as constraints; the corresponding expressions are shown below: ； ； ； in for The state vector at time t. for The control vector at time t, for The change in the control vector at any given time; and Tables The upper and lower bounds of the state vector at any given time. and They represent The upper and lower limits of the control vector are constantly being set. and They represent The upper and lower limits of the change in the control vector within a given time period.

8. The vehicle trajectory closed-loop drift control method according to claim 2, characterized in that, In step 4, the NMPC controller uses the vehicle's lateral error... Heading angle deviation yaw rate and longitudinal speed The state variables are used to establish the state-space expression to predict the state vector at future time steps; the state-space expression is as follows: ； ； ； ； In the formula The rate of change of yaw angular velocity, The rate of change of longitudinal vehicle speed, The rate of change of the lateral error. The rate of change of the heading angle deviation. and These are the vehicle's curb weight and related information. Moment of inertia of the shaft and These are the distances from the center of gravity to the front and rear axles of the vehicle, respectively. Let be the target radius of curvature of the vehicle. The lateral force on the front axle wheels. This refers to the longitudinal force on the rear axle wheels.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the vehicle trajectory closed-loop drift control method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the vehicle trajectory closed-loop drift control method as described in any one of claims 1-8.

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