Unmanned vehicle trajectory tracking control method, system and unmanned vehicle

Abstract

The present invention belongs to the field of autonomous driving technology, and specifically relates to an unmanned vehicle trajectory tracking control method, system and unmanned vehicle. The method obtains the current state information and preset reference trajectory of the unmanned vehicle, calculates the attitude error and introduces an integral error compensation mechanism, and designs a multi-error compensation mechanism in combination with the Lyapunov function to generate primary control instructions. The rear wheel motion state is further adjusted through the rear wheel feedback control mechanism to generate a final control instruction to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking. The present invention significantly improves the trajectory tracking accuracy and robustness, especially in complex environments, it can quickly and adaptively adjust the control strategy, solves the problems of insufficient error compensation and sensitivity to initial conditions in traditional methods, and achieves high-precision, low-jitter stable tracking performance, which is suitable for the deployment of actual autonomous driving systems.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving, and specifically relates to an unmanned vehicle trajectory tracking control method, system and unmanned vehicle. Background Art

[0002] In the modern automobile industry, autonomous driving technology is gradually becoming an important driving force for industry innovation. Trajectory tracking control, as one of the key technologies, directly affects the stability, safety and driving efficiency of autonomous vehicles. Traditional trajectory tracking methods often have difficulty maintaining high accuracy and strong robustness in complex road environments, especially when vehicle dynamic characteristics, environmental disturbances and initial states are variable; more specifically, the existing technology still has the following problems:

[0003] 1. Limitations of traditional control methods: Existing trajectory tracking methods mainly rely on sliding mode control, neural network reconstruction and traditional backstepping control. However, sliding mode control is easily affected by chattering, neural network methods rely on a large amount of training data, and traditional backstepping control requires repeated differentiation when dealing with nonlinear systems, resulting in high computational complexity.

[0004] 2. Insufficient environmental adaptability: In complex traffic scenarios, vehicles need to adapt to external disturbances and unknown dynamic changes. For example, factors such as road friction coefficient, wind resistance, and vehicle load changes may affect trajectory tracking. However, most existing control methods fail to fully consider environmental uncertainties and it is difficult to ensure the stability of the vehicle under various road conditions.

[0005] 3. Insufficient error compensation: Traditional trajectory tracking methods usually control based on the current position and velocity errors, but fail to make full use of the integral error for compensation. Especially under high-precision tracking requirements, ignoring the integral error may cause system response lag or oscillation, thus affecting the tracking effect. Summary of the invention

[0006] The purpose of the present invention is to provide an unmanned vehicle trajectory tracking control method, system and unmanned vehicle to solve the problems raised in the background technology.

[0007] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0008] In a first aspect, the present invention proposes a trajectory tracking control method for an unmanned vehicle, comprising the following steps:

[0009] S1. Input the current state information of the unmanned vehicle, including position, speed, yaw angle, and preset reference trajectory information;

[0010] S2. Based on the kinematic model of the unmanned vehicle, calculate the attitude error between the current state and the reference trajectory, including speed error, yaw angle error, longitudinal error and lateral error;

[0011] S3. Introducing the integral term of the attitude error into the design of the virtual control amount, introducing an auxiliary virtual input to compensate for the speed error and yaw angle error, and generating a compensation control amount;

[0012] S4. Based on the compensation control amount, a multi-error compensation mechanism is designed in combination with the Lyapunov function to generate a primary control instruction;

[0013] S5. Based on the preliminary control instruction, the motion state of the rear wheel is adjusted through the rear wheel feedback control mechanism to generate a final control instruction;

[0014] S6. Output the final control instruction to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking.

[0015] Optionally, step S2 includes:

[0016] The kinematic model of the vehicle is determined as follows:

[0017] .

[0018] in, and Indicates the horizontal and vertical coordinates of the vehicle’s current position, Indicates the current yaw angle, Indicates the current speed of the vehicle. represents the desired vehicle speed, Indicates the length of the vehicle, Indicates the steering angle of the front wheels;

[0019] The reference posture As input to the vehicle controller, the length of the vehicle Steering angle of the front wheels As the output of the controller, , where and are the horizontal and vertical coordinates of the reference trajectory, is the yaw angle of the reference trajectory;

[0020] Calculate the attitude error between the current state and the reference trajectory as follows:

[0021] .

[0022] in, represents the speed error, represents the yaw angle error, represents the longitudinal error, Indicates lateral error.

[0023] Optionally, step S3 includes:

[0024] Calculate the integral term of the yaw angle error to obtain the original rate of change of the reference trajectory steering angle , as follows:

[0025] .

[0026] in, Indicates the steering angle error in the time interval The integral value within , are two undetermined constants, Represents the integral gain of the yaw angle error;

[0027] Integral error As the last term, we get the improvement rate of yaw angle error , the improvement rate is used to adjust the convergence speed of the yaw angle error, as shown in the following formula:

[0028] .

[0029] Optionally, step S4 includes designing a multi-error compensation mechanism in combination with the Lyapunov function to generate a primary control instruction for compensating the current speed of the vehicle, including:

[0030] Determine the current vehicle speed based on a superimposable dynamic model ;

[0031] .

[0032] in, are two undetermined normal constants that can be adjusted in the above dynamic model;

[0033] The Lyapunov candidate function is selected as:

[0034] .

[0035] .

[0036] in, is a positive constant, in order to satisfy , then the current speed of the vehicle after compensation is As follows:

[0037] .

[0038] Optionally, step S4 includes designing a multi-error compensation mechanism in combination with the Lyapunov function to generate a primary control instruction for compensating the current steering angle of the vehicle, including:

[0039] Considering the integral error term and the integral term , select the Lyapunov candidate function as:

[0040] .

[0041] According to the Lyapunov candidate function, a compensated steering angle is generated, and the compensated steering angle is used to adjust the steering accuracy of the vehicle, and obtain The expression is:

[0042] .

[0043] in, , , is a positive constant to be determined, the steering angle after compensation The expression is:

[0044] .

[0045] Optionally, the method further includes:

[0046] The auxiliary virtual inputs corresponding to the lateral and longitudinal displacements are incorporated into the backstepping control system, and the state equation of the improved control system is obtained:

[0047] .

[0048] in, All of them are positive numbers, and the Lyapunov candidate function is selected as:

[0049] .

[0050] Select virtual inputs related to lateral and longitudinal displacements and for:

[0051] .

[0052] in, , is a normal number, replace , replace , the current speed of the vehicle can be reconstructed as follows:

[0053] .

[0054] Reconstruct the current speed of the vehicle based on the virtual input :

[0055] .

[0056] Optionally, step S5 includes:

[0057] Obtain the theoretical and actual steering angle errors of the unmanned vehicle , to compensate for the original steering angle , as follows:

[0058] .

[0059] in, is the yaw rate, which is intended to be compensated by the rear wheel feedback algorithm in the standard controller;

[0060] The vehicle normal vector error is calculated using the F coordinate system, and the compensation control input is designed to obtain the control law of the steering angle. , as follows:

[0061] .

[0062] In a second aspect, the present invention proposes an unmanned vehicle trajectory tracking control system, which is applied to execute any of the above methods, and the system includes an input module, a backstepping controller and an output module:

[0063] The input module is used to obtain the current state information of the unmanned vehicle, including position, speed, yaw angle and preset reference trajectory information;

[0064] The backstepping controller also includes:

[0065] The error calculation module is used to calculate the attitude error between the current state and the reference trajectory, including longitudinal error, lateral error, velocity error and yaw angle error;

[0066] A compensation control module is used to design a virtual control amount based on the error calculation result and to compensate for the error by introducing an auxiliary virtual input;

[0067] A control command generation module is used to generate primary control commands based on the compensation control amount and in combination with the Lyapunov function, and to generate final control commands through a rear wheel feedback control mechanism;

[0068] The output module is used to output the final control instruction to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking.

[0069] In a third aspect, the present invention proposes an unmanned vehicle, comprising the unmanned vehicle trajectory tracking control system as described above, and performing automatic driving under the control of the control system.

[0070] The beneficial effects of the present invention are:

[0071] 1. The present invention proposes a trajectory tracking control method for an unmanned vehicle based on an adaptive error integral backstepping method, which significantly improves the trajectory tracking accuracy and robustness of autonomous driving vehicles in complex environments. By introducing an integral error compensation mechanism, the present invention can quickly adjust the control strategy under external disturbances and dynamic changes, effectively avoiding the system response lag or oscillation problems caused by insufficient error compensation in traditional methods. In addition, combined with the rear-wheel feedback control strategy, the vehicle can quickly converge to the target trajectory under different initial position deviations, solving the problem that traditional methods are sensitive to initial conditions and have a long convergence distance. Experiments show that the overshoot of the present invention is only one-third of that of the comparison method, and in the steering saturation scenario, the deviation can still be quickly corrected through robust adjustment, achieving high-precision, low-jitter stable tracking performance.

[0072] 2. The present invention constructs an adaptive control scheme suitable for complex road conditions, which significantly reduces the computational complexity and is suitable for the deployment of actual autonomous driving systems. Compared with traditional methods, the present invention not only improves the real-time and adaptability of trajectory tracking, but also adaptively adjusts the control quantity through dynamic constraints to ensure smooth changes in speed and steering angle inputs, and always within the hardware allowable range, thereby enhancing the safety and feasibility of the system. Simulation and experimental verification show that the present invention can achieve high-precision trajectory tracking under a variety of working conditions, especially in complex traffic scenarios, showing strong anti-interference ability and stability, providing reliable technical support for the practical application of autonomous driving technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flow chart of the unmanned vehicle trajectory tracking control method in the present invention;

[0074] Figure 2 It is the overall framework diagram of the backstepping controller and the input and output modules in the present invention;

[0075] Figure 3 It is a schematic diagram of a two-wheeled bicycle model in the present invention;

[0076] Figure 4 It is a simplified diagram of the coordinate system in the present invention;

[0077] Figure 5-Figure 8 It is a simulation of trajectory tracking effect in the simulation case of the present invention and a comparison chart between different controllers;

[0078] Fig. 9It is a schematic diagram of the experimental device of the Quanser platform autonomous driving car (QCar) in the simulation case of the present invention;

[0079] Figure 10-13 It is a diagram of the QCar experiment results in the simulation case of the present invention. DETAILED DESCRIPTION

[0080] The following description provides specific application scenarios and requirements of this specification, with the purpose of enabling those skilled in the art to make and use the contents of this specification. Various local modifications to the disclosed embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of this specification. Therefore, this specification is not limited to the embodiments shown, but to the widest scope consistent with the claims.

[0081] The terms used herein are only used for the purpose of describing specific example embodiments and are not restrictive. For example, unless the context clearly indicates otherwise, as used herein, the singular forms "a", "an" and "the" may also include plural forms. When used in this specification, the terms "include", "comprise" and / or "contain" mean that the associated integers, steps, operations, elements and / or components exist, but do not exclude the existence of one or more other features, integers, steps, operations, elements, components and / or groups or that other features, integers, steps, operations, elements, components and / or groups may be added in the system / method.

[0082] In view of the following description, these and other features of the present specification, as well as the operation and function of the related elements of the structure, and the economy of the combination and manufacture of the parts can be significantly improved. Reference is made to the accompanying drawings, all of which form a part of this specification. However, it should be clearly understood that the drawings are for illustration and description purposes only and are not intended to limit the scope of this specification. It should also be understood that the drawings are not drawn to scale.

[0083] The flowcharts used in this specification illustrate the operations implemented by the system according to some embodiments in this specification. It should be clearly understood that the operations of the flowcharts may not be implemented in sequence. On the contrary, the operations may be implemented in reverse order or simultaneously. In addition, one or more other operations may be added to the flowchart. One or more operations may be removed from the flowchart.

[0084] Example 1

[0085] like Figure 1As shown, this embodiment proposes a trajectory tracking control method for an unmanned vehicle, more specifically, a design of a backstepping trajectory tracking controller based on error integration. The method mainly consists of a core controller module group and an input and output module. By reflecting the error integral of the parameters under the kinematic model in the entire tracking process of the vehicle, the accuracy and robustness of the automatic driving vehicle in tracking the preset trajectory in various complex situations are improved. The overall structural block diagram of the present invention is shown in FIG. Figure 2 The method includes the following steps:

[0086] S1. Input the current state information of the unmanned vehicle, including position, speed, yaw angle, and preset reference trajectory information; the present invention considers using the relevant parameters of the preset reference trajectory as the input and output module of the controller, and the trajectory can be expressed on the kinematic model.

[0087] S2. Based on the kinematic model of the unmanned vehicle, calculate the attitude error between the current state and the reference trajectory, including speed error, yaw angle error, longitudinal error and lateral error.

[0088] S3. Introduce the integral term of the attitude error into the design of the virtual control quantity, introduce auxiliary virtual input to compensate for the speed error and yaw angle error, and generate the compensation control quantity.

[0089] S4. Based on the compensation control quantity, a multi-error compensation mechanism is designed in combination with the Lyapunov function to generate primary control instructions.

[0090] S5. Based on the preliminary control instruction, the motion state of the rear wheel is adjusted through the rear wheel feedback control mechanism to generate the final control instruction.

[0091] S6. Output the final control command to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking.

[0092] Step S2 includes: The kinematic model of a two-wheeled bicycle is a mathematical model that describes the relationship between the motion state of the bicycle in a plane and the control input and output. Different from traditional four-wheeled vehicles, bicycles have unique dynamic characteristics, especially in terms of steering, balance and stability control. The two-wheeled bicycle model proposed in the present invention is as follows: Figure 3 As shown, the kinematic model of the vehicle is determined as follows:

[0093] .

[0094] in, and Indicates the horizontal and vertical coordinates of the vehicle’s current position, Indicates the current yaw angle, Indicates the current speed of the vehicle. represents the desired vehicle speed, Indicates the length of the vehicle, represents the steering angle of the front wheels; the vehicle posture P, which describes the motion of the autonomous vehicle, can be expressed as: .

[0095] The reference posture As input to the vehicle controller, the length of the vehicle Steering angle of the front wheels As the output of the controller, , where and are the horizontal and vertical coordinates of the reference trajectory, is the yaw angle of the reference trajectory; calculate the attitude error between the current state and the reference trajectory as follows:

[0096] .

[0097] in, represents the speed error, represents the yaw angle error, represents the longitudinal error, Indicates lateral error.

[0098] Step S3 includes: calculating the integral term of the yaw angle error to obtain the original rate of change of the reference trajectory steering angle , as follows:

[0099] .

[0100] in, Indicates the steering angle error in the time interval The integral value within , are two undetermined constants, Represents the integral gain of the yaw angle error;

[0101] Integral error As the last term, we get the improvement rate of yaw angle error , the improvement rate is used to adjust the convergence speed of the yaw angle error, as shown in the following formula:

[0102] .

[0103] It can be understood that the steps S3 and S4 of the present invention disclose the controller design process based on the backstepping method. Specifically, a nonlinear adaptive backoff controller is introduced for the input (current speed) and (steering angle) trajectory tracking. In order to improve the error convergence accuracy and ensure robustness in different scenarios, an auxiliary virtual input is introduced to compensate and .

[0104] Step S4 includes designing a multi-error compensation mechanism in combination with the Lyapunov function to generate a primary control instruction for compensating the current speed of the vehicle, including:

[0105] Using a superimposable neurodynamic model, we can know the speed of the vehicle Virtual input and yaw rate It is expressed as:

[0106] .

[0107] in, are two unknown positive constants that can be adjusted in the above dynamic model. The input value of the yaw rate, is the input value of the steering angle.

[0108] According to the kinematic model of the vehicle, and The relationship between can be expressed as:

[0109] .

[0110] because The parameters do not match , so we consider designing Lyapunov stable functions separately. By combining (5), The derivative of is expressed as:

[0111] .

[0112] The Lyapunov candidate function is selected as:

[0113] .

[0114] .

[0115] in, is a positive constant, in order to satisfy , then the current speed of the vehicle after compensation is As follows:

[0116] .

[0117] Next, the focus shifts to solving the yaw rate error. In order to ensure that the system has strong anti-interference ability and improve the robustness of the algorithm, the integral error term is also considered. and the integral term Step S4 includes designing a multi-error compensation mechanism in combination with the Lyapunov function to generate a primary control instruction for compensating the current steering angle of the vehicle, including:

[0118] Considering the integral error term and the integral term , select the Lyapunov candidate function as:

[0119] .

[0120] According to the Lyapunov candidate function, the compensated steering angle is generated, and the compensated steering angle is used to adjust the steering accuracy of the vehicle. The expression is:

[0121] .

[0122] in, , , is a positive constant to be determined, the steering angle after compensation The expression is:

[0123] .

[0124] Further preferably, the method further comprises: in order to improve the tracking accuracy along the reference trajectory, incorporating the auxiliary virtual input corresponding to the lateral and longitudinal displacements into the backstepping control system. The state equation of the improved control system can be expressed as:

[0125] .

[0126] in, All of them are positive numbers, and the Lyapunov candidate function is selected as:

[0127] .

[0128] Select virtual inputs related to lateral and longitudinal displacements and for:

[0129] .

[0130] in, , is a normal number, replace , replace , the current speed of the vehicle can be reconstructed as follows:

[0131] .

[0132] Reconstruct the current speed of the vehicle based on the virtual input :

[0133] .

[0134] In order to enable the vehicle to converge to the reference trajectory in a shorter distance and thus minimize resource consumption in practical situations, we adopt rear wheel feedback control. This method involves designing the steering angle error , to compensate for the original steering angle .

[0135] Step S5 includes:

[0136] Design steering angle error , to compensate for the original steering angle , as follows:

[0137] .

[0138] in, is the yaw rate, which is intended to be compensated by the rear wheel feedback algorithm in the standard controller;

[0139] To facilitate calculation , using the F coordinate as the basis for rear wheel feedback control. The F coordinate uses the center line of the road as a reference, and its tangent vector and normal vector as coordinate axes, such as Figure 4 The vehicle normal vector error is calculated using the F coordinate system, and the compensation control input is designed to obtain the steering angle control law U2c, as shown below:

[0140] .

[0141] In the formula, is the linear velocity of the vehicle, is the displacement error in the normal direction, and P is the curvature of the trajectory. Then, we choose the Lyapunov candidate function as:

[0142] .

[0143] in, is a normal number. In order to satisfy Designed to subtract positive terms from the critical value ,in is a positive constant. We can obtain:

[0144] .

[0145] By adopting this method, it can be proved that the designed compensation term guarantees the convergence of the control system. Can be interpreted as a virtual input Therefore, Redefined as , whose expression is:

[0146] .

[0147] This input is incorporated into the original steering angle expression as a compensation term. Finally, the control law of the steering angle is obtained as follows:

[0148] .

[0149] In the above-mentioned unmanned vehicle trajectory tracking control scheme of the present invention, the primary control instruction and the final control instruction are two key steps in the control process. First, based on the kinematic model of the unmanned vehicle, the system calculates the attitude error (including speed error, yaw angle error, longitudinal error and lateral error) between the current state and the reference trajectory, and generates the compensation control amount by introducing the integral error compensation mechanism. Combined with the multi-error compensation mechanism designed by the Lyapunov function, the system generates the primary control instruction, which is mainly used to preliminarily adjust the speed and steering angle of the unmanned vehicle to reduce the attitude error and improve the tracking accuracy. Subsequently, the system further adjusts the motion state of the rear wheel through the rear wheel feedback control mechanism to generate the final control instruction. The final control instruction further optimizes the trajectory tracking performance of the vehicle on the basis of the primary control instruction, ensuring that the vehicle can quickly converge to the target trajectory in a complex environment and maintain a stable tracking effect. Through this staged control strategy, the present invention significantly improves the trajectory tracking accuracy and robustness of the unmanned vehicle under complex road conditions.

[0150] Example 2

[0151] This embodiment proposes an unmanned vehicle trajectory tracking control system, which is applied to execute any method of the above embodiment 1. The system includes an input module, a backstepping controller and an output module:

[0152] The input module is used to obtain the current state information of the unmanned vehicle, including position, speed, yaw angle and preset reference trajectory information;

[0153] The backstepping controller also includes:

[0154] The error calculation module is used to calculate the attitude error between the current state and the reference trajectory, including longitudinal error, lateral error, velocity error and yaw angle error;

[0155] A compensation control module is used to design a virtual control amount based on the error calculation result and to compensate for the error by introducing an auxiliary virtual input;

[0156] A control command generation module is used to generate primary control commands based on the compensation control amount and in combination with the Lyapunov function, and to generate final control commands through a rear wheel feedback control mechanism;

[0157] The output module is used to output the final control instructions to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking.

[0158] Example 3

[0159] This embodiment proposes an unmanned vehicle, including the unmanned vehicle trajectory tracking control system as in Embodiment 2, and performs automatic driving under the control of the control system.

[0160] The method proposed in the present invention is further described below in combination with simulation and experiment.

[0161] After the controller design is completed, in order to study the performance of the proposed decision-making planning scheme, the feasibility of the proposed method will be evaluated through simulation and experiments, thereby providing a proof of the effectiveness of the basic theoretical framework. Next, the unmanned vehicle trajectory tracking situation is simulated using MATLAB / Simulink software. Finally, the Quanser platform autonomous driving car (QCar) is used for experimental verification.

[0162] In the present invention, the following scenarios are designed:

[0163] .

[0164] Set the initial posture P(0) of the autonomous driving vehicle to , the initial point of the reference trajectory q(0) is calculated as The main physical parameters of the vehicle are shown in Table 1. The tuning coefficients of the Lyapunov function Set them to 10.3, 18, 20, 20, 1 respectively.

[0165]

[0166] Table 1 Selection of vehicle related parameters

[0167] visible Figure 5-Figure 8 , Figure 5 The red line in the middle represents the reference trajectory, while the actual trajectory of the vehicle under the proposed control method (P-Con) is drawn with an orange dotted line. Controllers from other literature are selected for comparison and are marked as Con1 and Con2. The tracking parameters are compared. When the initial point is located at the origin of the coordinate system, all three control methods show controllable tracking performance. However, as Figure 6The tracking results show that the overshoot of the controller of the present invention is significantly smaller than that of Con1, and even only one-third of that of Con2. In general, under simulation conditions, this method has significant advantages in reference trajectory tracking.

[0168] To further verify the practicality of the proposed lane change trajectory planning scheme, an experimental test was conducted on a 1 / 10 scale model car QCar on the Quanser platform. The effectiveness of the scheme was verified by comparing the response of QCar with the expected characteristics of the trajectory based on the proposed decision-making planning scheme. The architecture of the experimental setup is shown in Figure 2. Fig. 9 shown.

[0169] Taking into account the actual road conditions and hardware limitations, the pre-set unmanned vehicle motion trajectory on the QCar platform is redefined as follows:

[0170] .

[0171] The motion of the QCar is controlled by a drive and steering servo motor based on these inputs. Unlike the simulation setting with an initial position (0,0), we set the QCar to an initial position (2,0). By considering a more challenging scenario, our goal is to further evaluate the effectiveness of the method under initial tracking errors. Figure 10-13 The trajectory tracking results are shown. The results show that incorporating rear wheel feedback control significantly improves the vehicle's ability to converge to the reference trajectory, both in terms of time required and distance traveled. These data also clearly show that the proposed method outperforms Con1 and Con2 in this regard.

[0172] It is worth noting that Fig.12 As shown by the yellow line in the middle, the error in the yaw angle remains relatively stable at 2.6 seconds when the vehicle approaches the first corner. This stability is attributed to the slight difference between the direction of the vehicle when it reaches the target trajectory and the direction required for the turn. However, in subsequent steering operations, the vehicle experienced saturation of the steering angle, resulting in significant deviations in the integrated values ​​of the steering angle at 3.6 seconds, 7 seconds, and 9.4 seconds. However, the controller proposed in the present invention exhibits a strong adjustment capability that can effectively mitigate these errors. It ensures accurate trajectory tracking by correcting deviations in a timely manner and maintaining consistent performance.

[0173] In addition to ensuring trajectory tracking accuracy and error convergence, it is important to verify that the vehicle's motion parameters remain within hardware constraints. The results confirm that both inputs remain within acceptable ranges, satisfying both the hardware constraints and the desired trajectory. Both simulations and experiments demonstrate the stable tracking performance of the proposed backstepping controller.

[0174] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A trajectory tracking control method for an unmanned vehicle, characterized in that: The steps include: S1. Input the current state information of the unmanned vehicle, including position, speed, yaw angle, and preset reference trajectory information; S2. Based on the kinematic model of the unmanned vehicle, calculate the attitude error between the current state and the reference trajectory, including speed error, yaw angle error, longitudinal error and lateral error; S3. Introducing the integral term of the attitude error into the design of the virtual control amount, introducing an auxiliary virtual input to compensate for the speed error and yaw angle error, and generating a compensation control amount; S4. Based on the compensation control amount, a multi-error compensation mechanism is designed in combination with the Lyapunov function to generate a primary control instruction; S5. Based on the primary control instruction, the motion state of the rear wheel is adjusted through the rear wheel feedback control mechanism to generate a final control instruction; S6. Output the final control instruction to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking.

2. The unmanned vehicle trajectory tracking control method according to claim 1, characterized in that: Step S2 includes: The kinematic model of the vehicle is determined as follows: ； in, and Indicates the horizontal and vertical coordinates of the vehicle’s current position, Indicates the current yaw angle, Indicates the current speed of the vehicle. Indicates the length of the vehicle, Indicates the steering angle of the front wheels; The reference posture As input to the vehicle controller, the length of the vehicle Steering angle of the front wheels As the output of the controller, , where and are the horizontal and vertical coordinates of the reference trajectory, is the yaw angle of the reference trajectory; Calculate the attitude error between the current state and the reference trajectory as follows: ； in, represents the desired vehicle speed, represents the speed error, represents the yaw angle error, represents the longitudinal error, Indicates lateral error.

3. The unmanned vehicle trajectory tracking control method according to claim 1, characterized in that: Step S3 includes: Calculate the integral term of the yaw angle error to obtain the original rate of change of the reference trajectory steering angle , as follows: ； in, Indicates the steering angle error in the time interval The integral value within , are two undetermined constants, Represents the integral gain of the yaw angle error; Integral error As the last term, we get the improvement rate of yaw angle error , the improvement rate is used to adjust the convergence speed of the yaw angle error, as shown in the following formula: 。 4. The unmanned vehicle trajectory tracking control method according to claim 1, characterized in that: Step S4 includes designing a multi-error compensation mechanism in combination with the Lyapunov function to generate a primary control instruction for compensating the current speed of the vehicle, including: Determine the current vehicle speed based on a superimposable dynamic model ; ； in, is an adjustable constant in the above dynamic model, is the actual input of vehicle speed; The Lyapunov candidate function is selected as: ； ； in, is a positive constant, in order to satisfy , then the current speed of the vehicle after compensation is As follows: 。 5. The unmanned vehicle trajectory tracking control method according to claim 1, characterized in that: Step S4 includes designing a multi-error compensation mechanism in combination with the Lyapunov function to generate a primary control instruction for compensating the current steering angle of the vehicle, including: Considering the integral error term and the integral term , select the Lyapunov candidate function as: ； According to the Lyapunov candidate function, a compensated steering angle is generated, and the compensated steering angle is used to adjust the steering accuracy of the vehicle, and obtain The expression is: ； in, , , is a positive constant to be determined, the steering angle after compensation The expression is: 。 6. The unmanned vehicle trajectory tracking control method according to claim 1, characterized in that: The method further comprises: The auxiliary virtual inputs corresponding to the lateral and longitudinal displacements are incorporated into the backstepping control system, and the state equation of the improved control system is obtained: ； in, All of them are positive numbers, and the Lyapunov candidate function is selected as: ； Select virtual inputs related to lateral and longitudinal displacements and for: ； in, , , is a normal number, use replace , replace , the current speed of the vehicle can be reconstructed as follows: ； Reconstruct the current speed of the vehicle based on the virtual input : 。 7. The unmanned vehicle trajectory tracking control method according to claim 1, characterized in that: Step S5 includes: Design steering angle error , to compensate for the original steering angle , as follows: ； in, is the yaw rate, which is intended to be compensated by the rear wheel feedback algorithm in the standard controller; The vehicle normal vector error is calculated using the F coordinate system, and the compensation control input is designed to obtain the control law of the steering angle. , as follows: ； in, is the compensation amount for the vehicle steering angle.

8. An unmanned vehicle trajectory tracking control system, applied to execute the method according to any one of claims 1 to 7, characterized in that: The system includes an input module, a backstepping controller and an output module: The input module is used to obtain the current state information of the unmanned vehicle, including position, speed, yaw angle and preset reference trajectory information; The backstepping controller also includes: The error calculation module is used to calculate the attitude error between the current state and the reference trajectory, including longitudinal error, lateral error, velocity error and yaw angle error; A compensation control module is used to design a virtual control amount based on the error calculation result and to compensate for the error by introducing an auxiliary virtual input; A control command generation module is used to generate primary control commands based on the compensation control amount and in combination with the Lyapunov function, and to generate final control commands through a rear wheel feedback control mechanism; The output module is used to output the final control instruction to control the speed and steering angle of the unmanned vehicle to perform trajectory tracking.

9. An unmanned vehicle, characterized in that: It includes the unmanned vehicle trajectory tracking control system as described in claim 8, and performs automatic driving under the control of the control system.

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