A vehicle trajectory tracking control method and device based on a fuzzy adaptive model predictive control

Abstract

This application discloses a vehicle trajectory tracking control method and device based on fuzzy adaptive model predictive control, relating to the field of autonomous driving. The method includes: calculating path tracking error state data; constructing and discretizing the vehicle lateral error dynamic equation based on a vehicle dynamics model, using the path tracking error state data as state variables; constructing a model predictive control system through the discretized state equation and objective function; adaptively adjusting the lateral error and control increment weight matrix using fuzzy control to obtain a real-time weight matrix; predicting the optimal front wheel steering angle using the model predictive control system and controlling the front wheels of the target vehicle to steer to the optimal front wheel steering angle; and continuously tracking until a termination condition is met. This application overcomes the limitation of insufficient adaptability of fixed weights under different operating conditions, improves the accuracy of vehicle tracking of reference trajectories under different vehicle speeds and complex road conditions, and improves the smoothness and stability of vehicle driving.

Description

Technical Field

[0001] This application relates to the field of autonomous driving, and in particular to a vehicle trajectory tracking control method and device based on fuzzy adaptive model predictive control. Background Technology

[0002] Trajectory tracking control is a crucial control module in autonomous driving. Its primary objective is to control the vehicle to follow a reference trajectory while ensuring smooth and stable driving. Model Predictive Control (MPC), a commonly used optimization control method, is widely applied in fields such as robotics control and autonomous driving. In autonomous driving, MPC can calculate the front wheel steering angle based on the vehicle's real-time operating state and the weight matrix in the objective function, thereby controlling the vehicle's steering system. The weight matrix in the objective function is a critical parameter determining the controller's performance. In existing technologies, the weight matrix is ​​typically tuned based on human experience and uses fixed weight parameters. Inappropriate weight parameter settings can easily lead to a decline in controller performance, affecting the accuracy of trajectory tracking and the smoothness and stability of driving.

[0003] To address the insufficient adaptability of fixed-weight MPC under different operating conditions, existing research has proposed various adaptive optimization schemes, such as adaptive strategies with variable prediction time domains, offline parameter optimization methods based on intelligent algorithms like particle swarm optimization, and online parameter tuning methods based on Lyapunov stability theory. However, these methods still have certain limitations: offline parameter optimization methods based on intelligent algorithms can only cover a limited number of operating conditions and are difficult to achieve online adaptation across all operating conditions; parameter tuning methods based on stability theory have high computational complexity and are highly dependent on the accuracy of the vehicle dynamics model, making it difficult to meet the real-time computing requirements of onboard controllers.

[0004] In summary, existing technologies suffer from problems such as poor adaptability of fixed-weight model predictive control under different operating conditions, leading to inaccurate vehicle trajectory tracking. Summary of the Invention

[0005] The purpose of this application is to provide a vehicle trajectory tracking control method and device based on fuzzy adaptive model predictive control, which can overcome the limitation of insufficient adaptability of fixed weights under different working conditions, improve the accuracy of vehicle tracking reference trajectory under different vehicle speeds and complex road conditions, and improve the smoothness and stability of vehicle driving.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a vehicle trajectory tracking control method based on fuzzy adaptive model predictive control. This method is applied to an MPC controller. The method includes: acquiring real-time operating state parameters of the target vehicle under a reference trajectory, and calculating path tracking error state data of the target vehicle based on the operating state parameters; the path tracking error state data includes: lateral position error, lateral error rate of change, heading angle error, and heading angle error rate of change; using a two-degree-of-freedom vehicle lateral dynamics model as the basic model and the path tracking error state data as state variables, constructing a vehicle lateral error dynamic equation, and discretizing the vehicle lateral error dynamic equation to obtain a discretized state equation; using the discretized state equation, with the front wheel steering angle as the output, and the lateral error weight matrix, control increment weight matrix, slack variables, and terminal state weight matrix as the objective function. A model predictive control system is constructed using the objective function derived from quadratic programming. Lateral position error and heading angle error are used as fuzzy inputs, and the lateral position error weight adjustment coefficient, heading angle error weight adjustment coefficient, and control increment weight adjustment coefficient are used as fuzzy outputs. The TS fuzzy control method is used to adaptively adjust the lateral error weight matrix and the control increment weight matrix to obtain a real-time weight matrix. The real-time operating state parameters and the real-time weight matrix are input to the model predictive control system to predict the optimal front wheel steering angle, and the front wheels of the target vehicle are controlled to steer to the optimal angle. The system then retrieves the real-time operating state parameters of the target vehicle under the reference trajectory and calculates the path tracking error state data based on these parameters. Trajectory tracking and front wheel steering of the target vehicle are continuously performed according to a preset sampling period until a termination condition is met. The termination condition is that the reference trajectory has been completed or a trajectory tracking control termination command has been received.

[0007] Secondly, this application provides a computer device, 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 vehicle trajectory tracking control method based on fuzzy adaptive model predictive control described above.

[0008] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application calculates path tracking error state data; based on a vehicle dynamics model, using the path tracking error state data as state variables, it constructs and discretizes the vehicle lateral error dynamic equation; it constructs a model predictive control system using the discretized state equation and objective function; it adaptively adjusts the lateral error and control increment weight matrix using fuzzy control to obtain a real-time weight matrix; it uses the model predictive control system to predict the optimal front wheel steering angle and controls the target vehicle's front wheels to steer to the optimal angle; and it continues tracking until the termination condition is met. This application overcomes the limitation of insufficient adaptability of fixed weights under different operating conditions. By using a model predictive control system for vehicle control, it improves the accuracy of vehicle tracking of reference trajectories under different speeds and complex road conditions, and also improves the smoothness and stability of vehicle driving, enhancing the robustness and adaptability of vehicle trajectory tracking control. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, 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.

[0010] Figure 1 A flowchart illustrating the vehicle trajectory tracking control method based on fuzzy adaptive model predictive control provided in this application embodiment. Figure 1 .

[0011] Figure 2 A flowchart illustrating the vehicle trajectory tracking control method based on fuzzy adaptive model predictive control provided in this application embodiment. Figure 2 .

[0012] Figure 3 This is a schematic diagram of a two-degree-of-freedom vehicle dynamics model provided in an embodiment of this application.

[0013] Figure 4 This is a block diagram illustrating the principle of fuzzy adaptive MPC closed-loop control provided in an embodiment of this application.

[0014] Figure 5 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Example 1, such as Figures 1-2 As shown, this embodiment provides a vehicle trajectory tracking control method based on fuzzy adaptive model predictive control. This method is applied to an MPC controller and includes: S1. Obtain the real-time operating status parameters of the target vehicle under the reference trajectory, and calculate the path tracking error status data of the target vehicle based on the operating status parameters; the path tracking error status data includes: lateral position error, lateral error change rate, heading angle error and heading angle error change rate.

[0018] In practical applications, path tracking error status data is obtained in real time through the target vehicle's onboard positioning system, inertial measurement unit, and body sensors.

[0019] The reference trajectory information is provided by the trajectory planning module. To describe the vehicle's tracking error relative to the reference trajectory, a Frenet coordinate system based on the reference trajectory is introduced. In this coordinate system, the lateral position error, lateral error rate of change, heading angle error, and heading angle error rate of change are defined as follows: .

[0020] in, This refers to the actual lateral position of the vehicle. The horizontal position of the reference trajectory.

[0021] .

[0022] in, The actual lateral speed of the vehicle. This represents the lateral velocity at the point corresponding to the reference trajectory.

[0023] .

[0024] in, This is the vehicle's actual heading angle. The heading angle is the reference trajectory.

[0025] .

[0026] in, This is the vehicle's actual angular velocity. This represents the heading angular velocity at the point corresponding to the reference trajectory.

[0027] S2. For example Figure 3 As shown, a two-degree-of-freedom vehicle lateral dynamics model is used as the basic model, and path tracking error state data is used as the state variables to construct the vehicle lateral error dynamics equation. The vehicle lateral error dynamics equation is then discretized to obtain the discretized state equation.

[0028] Step S2 specifically includes: S21. A two-degree-of-freedom vehicle lateral dynamics model is established using the mechanism modeling method.

[0029] Optionally, the core dynamic equations of the two-degree-of-freedom vehicle lateral dynamics model are as follows: .

[0030] .

[0031] in, For the total mass of the vehicle. For the longitudinal speed of the vehicle, The lateral acceleration of the vehicle's center of gravity. The vehicle's angular velocity. The vehicle's heading angle acceleration, and These are the lateral forces on a single wheel of the front and rear axles, respectively. Let be the moment of inertia of the vehicle about the z-axis. and This is the distance from the vehicle's center of gravity to the front and rear axles.

[0032] In practical applications, when establishing a two-degree-of-freedom vehicle lateral dynamics model, the longitudinal dynamics changes of the vehicle, suspension motion, and tire load transfer are ignored, and only the lateral translation and yaw motion around the z-axis of the vehicle are considered.

[0033] S22. Based on the two-degree-of-freedom vehicle lateral dynamics model, the vehicle lateral error dynamics equation is constructed using the path tracking error state data as the state variable.

[0034] Optionally, the expression for the state quantity is: .

[0035] S23. Discretize the vehicle lateral error dynamic equation using the forward Euler method to obtain the discretized state equation.

[0036] In practical applications, a linear approximation of the tire lateral force can be used to obtain a linear relationship between the front and rear wheel lateral forces and the slip angle: .

[0037] .

[0038] in, , These are the tire lateral stiffness of the front and rear axles of the vehicle, respectively. , These are the slip angles of the front and rear wheels of the vehicle, respectively.

[0039] Based on the aforementioned vehicle dynamics relationships, the relationship between the front and rear wheel slip angles and the vehicle state variables and front wheel steering angle can be derived: .

[0040] .

[0041] in, This refers to the steering angle of the vehicle's front wheels. For the vehicle's lateral speed, This represents the vehicle's longitudinal speed.

[0042] Substituting the expressions for the front and rear wheel slip angles into the two-degree-of-freedom vehicle dynamics model, we can obtain the vehicle's lateral error dynamic state equation: .

[0043] in, Let be the system error state vector. Let S be the front wheel steering angle, and S and G be the state matrix and input matrix of the lateral error dynamics, respectively.

[0044] The forward Euler method is used to discretize the above transverse error dynamic state equation, with a discretization sampling period of 0.05s, resulting in a discretized state equation suitable for MPC controller design: .

[0045] Where k is the current sampling time, A is the discretized state matrix, and B is the discretized input matrix.

[0046] S3. By discretizing the state equations, taking the front wheel steering angle as the output, and using the lateral error weight matrix, control increment weight matrix, slack variables, and terminal state weight matrix as the objective function, a model predictive control system is constructed using the objective function after quadratic programming.

[0047] Step S3 specifically includes: S31. Based on the prediction time domain Np and the control time domain Nc, the prediction model is obtained by recursively calculating the discretized state equations.

[0048] S32. Construct an objective function based on the prediction model; the expression of the objective function is as follows: .

[0049] In the formula, This is the horizontal error weight matrix; The incremental weight matrix is ​​used to control the weights; F is the terminal weight matrix. These are slack variables; Weights for slack variables; for Always Predicted output at time step; for Always Predictive control increment at any given time; For prediction in the time domain; To control the time domain.

[0050] Optionally, the first term (lateral error weight matrix) is a tracking error penalty term in the prediction time domain, used to constrain the tracking deviation between the vehicle and the reference trajectory; the second term (control increment weight matrix) is a control increment penalty term in the control time domain, used to constrain the fluctuation of the steering angle and ensure the smoothness of steering control; the third term (terminal weight matrix) is a terminal state penalty term, used to ensure the system stability under the finite prediction time domain; and the fourth term (slack variable) is a slack variable term, used to ensure the feasibility of the quadratic programming problem.

[0051] S33. Perform quadratic programming on the objective function to obtain the objective function after quadratic programming.

[0052] Furthermore, the expression for the objective function after the quadratic programming is as follows: .

[0053] .

[0054] in, The objective function after quadratic programming; To optimize variables; The symmetric positive definite Hessian matrix for a quadratic programming problem; A vector of linear terms; These are slack variables.

[0055] S34. Using the front wheel steering angle as the output, a model predictive control system is constructed using the predictive model and the objective function after quadratic programming.

[0056] In practical applications, to improve the smoothness of steering control, an incremental front wheel steering angle control is introduced: .

[0057] in, The front wheel steering angle at the current sampling moment. This represents the front wheel steering angle at the previous sampling time.

[0058] To facilitate the description of system dynamics, the system error state vector is augmented with the control input at the previous sampling time to construct the system augmented state vector: .

[0059] Based on the discretized state equations, the augmented discrete state equations and the system output equations can be further derived: .

[0060] .

[0061] Where C is the system output matrix, and in this embodiment, C = [I 0] is taken as the augmented system matrix. , .

[0062] Set the prediction time domain and control time domain And satisfy .

[0063] Based on the above augmented discrete state equations, recursive calculations are performed in the control time domain to obtain the system output at the next Np sampling times, thus yielding the predicted output sequence of the model predictive control system: .

[0064] in, The predicted output sequence for the next Np time steps. To control the control increment sequence in the time domain, W and Z are constructed from the augmented system matrices M and N and the output matrix.

[0065] To ensure both trajectory tracking accuracy and steering control smoothness and system stability, and to improve the feasibility of optimization solutions, a quadratic objective function containing multi-objective penalty terms is constructed.

[0066] Lateral error weight matrix , As the weight for lateral position error, The control increment weight matrix is ​​used to control the heading angle error weight. , This is the weighting of the front wheel steering angle increment.

[0067] S4. For example Figure 4 As shown, the lateral position error and heading angle error are used as fuzzy inputs, and the lateral position error weight adjustment coefficient, heading angle error weight adjustment coefficient, and control increment weight adjustment coefficient are used as fuzzy outputs. The TS fuzzy control method is used to adaptively adjust the lateral error weight matrix and the control increment weight matrix to obtain the real-time weight matrix.

[0068] Furthermore, step S4 specifically includes: S41. Transverse position error and heading angle error As fuzzy input quantities, the universe of discourse corresponding to the fuzzy input quantities is defined, and multiple fuzzy subsets are divided respectively to establish corresponding membership functions.

[0069] Lateral position error and heading angle error The corresponding domains are respectively and .

[0070] Optionally, the two fuzzy inputs can be divided into five fuzzy subsets: very low (VL), low (L), medium (M), high (H), and very high (VH), covering the entire universe of discourse.

[0071] To simplify calculations and improve inference efficiency, while also meeting the real-time operational requirements of the vehicle controller, both fuzzy inputs use triangular membership functions.

[0072] S42. Use the lateral position error weight adjustment coefficient, heading angle error weight adjustment coefficient and control increment weight adjustment coefficient as fuzzy output quantities, and set the universe of discourse corresponding to the fuzzy output quantities.

[0073] Furthermore, the universe of discourse corresponding to the fuzzy output is as follows: The lateral position error weighting adjustment coefficient The corresponding domain is .

[0074] The heading angle error weighting adjustment coefficient The corresponding domain is [1, 8].

[0075] The control increment weight adjustment coefficient The corresponding domain is [0.5, 2].

[0076] In practical applications, the universe of discourse of the three fuzzy output quantities is designed based on their different roles in trajectory tracking, among which... Its domain of discussion is the widest, which is used to adapt to the adjustment requirements of lateral tracking accuracy under different working conditions; The domain of discussion is secondary, used to ensure the directional stability of the vehicle; The domain of discussion is the narrowest, used to limit drastic fluctuations in the steering angle and ensure smooth steering control.

[0077] S43. Establish a TS fuzzy rule base, perform fuzzy inference using the TS fuzzy control method based on the TS fuzzy rule base to obtain weight adjustment parameters, and adaptively adjust the lateral error weight matrix and control increment weight matrix in combination with the preset initial value of the weight matrix to obtain the real-time weight matrix.

[0078] In practical applications, this embodiment constructs 25 fuzzy rules based on the combination of fuzzy subsets of two input variables. The fuzzy rules adopt the if-then form, and the general expression of a single fuzzy rule is as follows: .

[0079] in, This represents the i-th fuzzy rule; These are the corresponding output variables; Represents a fuzzy subset in the i-th rule; The constant parameter corresponding to the i-th fuzzy rule is used to determine the specific value of the output variable under that rule.

[0080] Based on lateral position error and heading angle error Based on the fuzzy rule base, fuzzy inference calculations are performed to obtain the precise real-time values ​​of the three weight adjustment coefficients.

[0081] Initial base value of lateral position error weight Set to 5, initial base value for heading angle error weight. Set to 2, initial base value for steering angle increment weight. Set it to 900.

[0082] To achieve online updating of the weight matrix in the model's predictive control objective function, the weight update is calculated using the following formula based on the obtained real-time weight adjustment coefficients: .

[0083] .

[0084] .

[0085] S5. Input the real-time operating status parameters and real-time weight matrix into the model predictive control system to predict the optimal front wheel steering angle, and control the front wheels of the target vehicle to steer to the optimal front wheel steering angle.

[0086] Furthermore, step S5 specifically includes: S51. Input the real-time operating status parameters and real-time weight matrix into the model predictive control system.

[0087] S52. Convert the front wheel steering angle constraint and the front wheel steering angle increment constraint into a quadratic programming problem; the expressions for the front wheel steering angle constraint and the front wheel steering angle increment constraint are: .

[0088] .

[0089] In the formula, This is the minimum value of the front wheel steering angle; This represents the maximum value of the front wheel steering angle; This is the minimum value of the front wheel steering angle increment; This represents the maximum value of the front wheel steering angle increment.

[0090] In practical applications, to ensure that the vehicle steering actuator matches its physical characteristics, constraints are set on the amplitude of the front wheel steering angle and the rate of change of the front wheel steering angle increment.

[0091] S53. Solve the quadratic programming problem to obtain the optimal control increment sequence. .

[0092] S54. Select the optimal control increment sequence according to the rolling time-domain optimization strategy. The first element is used as the optimal front wheel steering angle increment at the current moment. And update the current front wheel steering angle.

[0093] Furthermore, the expression for the front wheel steering angle is as follows: .

[0094] In the formula, for The optimal front wheel steering angle at any given time; for The optimal front wheel steering angle at any given time; for The optimal front wheel steering angle increment at any given time.

[0095] S55. Output the current front wheel steering angle as the optimal front wheel steering angle.

[0096] In practical applications, the interior-point method is used to solve the constrained standard quadratic programming problem to obtain the optimal control increment sequence in the control time domain. .

[0097] The rolling time-domain optimization strategy based on model predictive control uses only the first element of the obtained optimal control increment sequence as the optimal front wheel steering angle increment at the current sampling time. The optimization solution is then re-executed in the next sampling period to ensure the robustness of the control.

[0098] Combined with the optimal front wheel steering angle at the previous sampling time The optimal front wheel steering angle at the current sampling moment is calculated using the incremental update formula. The incremental update formula is: .

[0099] The target vehicle will calculate the optimal front wheel steering angle at the current moment. The control commands are converted into control commands and sent to the steering actuator, which then drives the vehicle's front wheels to adjust to the target steering angle, thus completing one trajectory tracking control operation.

[0100] S6. Return to step S1, continue to track the trajectory and control the front wheel steering of the target vehicle according to the preset sampling period until the termination condition is met; the termination condition is that the reference trajectory has been completed or the trajectory tracking control termination command has been received.

[0101] Furthermore, the preset sampling period is 0.05s (i.e., an interval of 50ms).

[0102] The technical effects of this application are as follows: This application overcomes the limitation of insufficient adaptability of fixed weights under different operating conditions. It uses a model predictive control system for vehicle control, which improves the accuracy of the vehicle in tracking the reference trajectory under different vehicle speeds and complex road conditions, and also improves the smoothness and stability of vehicle driving, and enhances the robustness and adaptability of vehicle trajectory tracking control.

[0103] Example 2: This example provides a computer device, which can be a server or a terminal, and its internal structure diagram can be as follows. Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned methods.

[0104] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0105] Those skilled in the art will understand that all or part of the processes in 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. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0106] The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited thereto.

[0107] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0108] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A vehicle trajectory tracking control method based on fuzzy adaptive model predictive control, wherein the vehicle trajectory tracking control method based on fuzzy adaptive model predictive control is applied to an MPC controller, characterized in that, The method includes: The system acquires the real-time operating status parameters of the target vehicle under the reference trajectory and calculates the path tracking error status data of the target vehicle based on the operating status parameters. The path tracking error status data includes: lateral position error, lateral error rate of change, heading angle error, and heading angle error rate of change. Using a two-degree-of-freedom vehicle lateral dynamics model as the basic model and path tracking error state data as the state variables, the vehicle lateral error dynamics equation is constructed, and the vehicle lateral error dynamics equation is discretized to obtain the discretized state equation. By discretizing the state equations, taking the front wheel steering angle as the output, and using the lateral error weight matrix, control increment weight matrix, slack variables and terminal state weight matrix as the objective function, a model predictive control system is constructed using the objective function after quadratic programming. Using lateral position error and heading angle error as fuzzy inputs, and lateral position error weight adjustment coefficient, heading angle error weight adjustment coefficient and control increment weight adjustment coefficient as fuzzy outputs, the TS fuzzy control method is used to adaptively adjust the lateral error weight matrix and control increment weight matrix to obtain the real-time weight matrix. The real-time operating status parameters and real-time weight matrix are input into the model predictive control system to predict the optimal front wheel steering angle, and the front wheels of the target vehicle are controlled to steer to the optimal front wheel steering angle. The system retrieves the real-time operating status parameters of the target vehicle under the reference trajectory, calculates the path tracking error status data of the target vehicle based on the operating status parameters, continuously performs trajectory tracking and controls the front wheel steering of the target vehicle according to a preset sampling period, until the termination condition is met; the termination condition is that the reference trajectory has been completed or a trajectory tracking control termination command has been received.

2. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 1, characterized in that, Using a two-degree-of-freedom vehicle lateral dynamics model as the basic model and path tracking error state data as state variables, a vehicle lateral error dynamics equation is constructed. This equation is then discretized to obtain the discretized state equation, which specifically includes: A two-degree-of-freedom vehicle lateral dynamics model was established using the mechanistic modeling method. Based on a two-degree-of-freedom vehicle lateral dynamics model, the path tracking error state data is used as the state variable to construct the vehicle lateral error dynamics equation. The forward Euler method is used to discretize the vehicle lateral error dynamic equation to obtain the discretized state equation.

3. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 1, characterized in that, By discretizing the state equations, taking the front wheel steering angle as the output, and using the lateral error weight matrix, control increment weight matrix, slack variables, and terminal state weight matrix as the objective function, a model predictive control system is constructed using the objective function derived from quadratic programming. Specifically, this includes: Based on the prediction time domain and the control time domain, the prediction model is obtained by recursively calculating the discretized state equations; A target function is constructed based on the prediction model; the expression of the target function is as follows: ； In the formula, This is the horizontal error weight matrix; The incremental weight matrix is ​​used to control the weights; F is the terminal weight matrix. These are slack variables; Weights for slack variables; for Always Predicted output at time step; for Always Predictive control increment at any given time; For prediction in the time domain; To control the time domain; The objective function is calculated using quadratic programming to obtain the quadratic objective function. Using the front wheel steering angle as the output, a model predictive control system is constructed using a predictive model and a quadratic programming objective function.

4. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 3, characterized in that, The expression for the objective function after quadratic programming is as follows: ； ； in, The objective function after quadratic programming; To optimize variables; The symmetric positive definite Hessian matrix for a quadratic programming problem; It is a vector of linear terms; These are slack variables.

5. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 1, characterized in that, Using lateral position error and heading angle error as fuzzy inputs, and lateral position error weight adjustment coefficient, heading angle error weight adjustment coefficient, and control increment weight adjustment coefficient as fuzzy outputs, the TS fuzzy control method is used to adaptively adjust the lateral error weight matrix and control increment weight matrix to obtain the real-time weight matrix, which specifically includes: Using lateral position error and heading angle error as fuzzy inputs, we set the universe of discourse corresponding to the fuzzy inputs, and divided them into multiple fuzzy subsets to establish corresponding membership functions. The lateral position error weight adjustment coefficient, the heading angle error weight adjustment coefficient, and the control increment weight adjustment coefficient are used as fuzzy output quantities, and the universe of discourse corresponding to the fuzzy output quantities is set. A TS fuzzy rule base is established. Fuzzy inference is performed using the TS fuzzy control method based on the TS fuzzy rule base to obtain the weight adjustment parameters. Combined with the preset initial value of the weight matrix, the horizontal error weight matrix and the control increment weight matrix are adaptively adjusted to obtain the real-time weight matrix.

6. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 5, characterized in that, The universe of discourse corresponding to the fuzzy output is as follows: The universe of discourse corresponding to the lateral position error weighting adjustment coefficient is ; The universe of discourse corresponding to the heading angle error weighting adjustment coefficient is [1, 8]; The universe of discourse corresponding to the control increment weight adjustment coefficient is [0.5, 2].

7. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 1, characterized in that, The real-time operating status parameters and real-time weight matrix are input into the model predictive control system to predict the optimal front wheel steering angle, and the target vehicle's front wheels are controlled to steer to the optimal front wheel steering angle. Specifically, this includes: The real-time operating status parameters and real-time weight matrix are input into the model prediction and control system. The front wheel steering angle constraint and the front wheel steering angle increment constraint are transformed into a quadratic programming problem; the expressions for the front wheel steering angle constraint and the front wheel steering angle increment constraint are: ； ； In the formula, This is the minimum value of the front wheel steering angle; This represents the maximum value of the front wheel steering angle; This is the minimum value of the front wheel steering angle increment; This represents the maximum value of the front wheel steering angle increment; Solving the quadratic programming problem yields the optimal control increment sequence; Based on the rolling time-domain optimization strategy, the first element of the optimal control increment sequence is selected as the optimal front wheel steering angle increment at the current moment, and the front wheel steering angle at the current moment is updated. The current front wheel steering angle is output as the optimal front wheel steering angle.

8. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 7, characterized in that, The expression for the front wheel steering angle is as follows: ； In the formula, for The optimal front wheel steering angle at any given time; for The optimal front wheel steering angle at any given time; for The optimal front wheel steering angle increment at any given time.

9. The vehicle trajectory tracking control method based on fuzzy adaptive model predictive control according to claim 1, characterized in that, The preset sampling period is 0.05s.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the vehicle trajectory tracking control method based on fuzzy adaptive model predictive control as described in any one of claims 1-9.

Images