Vehicle path tracking control method, vehicle and storage medium

Abstract

The embodiment of the invention provides a vehicle path tracking control method, a vehicle and a storage medium, and the method comprises the steps: predicting an expected front wheel steering angle of the vehicle at the next moment based on a navigation planning path and an operation state of the vehicle at the current moment; predicting an expected yaw velocity of the vehicle at the next moment based on the kinetic model of the vehicle and the expected front wheel steering angle; second-order sliding mode control is carried out based on the error between the expected yaw velocity and the yaw velocity at the current moment, a front wheel steering angle compensation value of the vehicle is determined, and the front wheel steering angle compensation value is used for compensating a front wheel steering angle of the vehicle so as to adjust the error; and determining a front wheel steering angle control value of the vehicle based on the expected front wheel steering angle and the front wheel steering angle compensation value, the front wheel steering angle control value being used for controlling the vehicle to adjust the front wheel steering angle. The technical problem of low accuracy and stability of vehicle path tracking control in the prior art is solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of vehicle technology and automatic driving, in particular to a vehicle path tracking control method, a vehicle and a storage medium. BACKGROUND

[0002] With the development of automatic driving technology, vehicle path tracking control has become an important technology to achieve safe, efficient and comfortable autonomous driving. However, the traditional path tracking control method faces many challenges in complex and uncertain dynamic environments, and it is difficult to meet the requirements of high precision and high stability. As a nonlinear control method with strong robustness, sliding mode control can directly design a controller for a nonlinear system, avoiding complex linearization process. However, the high-frequency chattering problem of steering angle caused by the inherent discontinuous switching control of sliding mode control will significantly affect the service life and ride comfort of the actuator, will trigger unstable modes, and will endanger the safety of vehicle driving, that is, the accuracy and stability of vehicle path tracking control in related technologies are low.

[0003] At present, there is no good solution to the above problems. SUMMARY

[0004] Embodiments of the present application provide a vehicle path tracking control method, a vehicle and a storage medium to at least solve the technical problem of low accuracy and stability of vehicle path tracking control in related technologies.

[0005] According to an aspect of embodiments of the present application, a vehicle path tracking control method is provided, comprising: predicting a desired front wheel steering angle of a vehicle at a next time based on a navigation planning path and a running state of the vehicle at a current time, wherein the running state at least includes a yaw rate at the current time; predicting a desired yaw rate of the vehicle at the next time based on a dynamics model of the vehicle and the desired front wheel steering angle, wherein the dynamics model is used to represent the correlation between the desired yaw rate and the desired front wheel steering angle; performing second-order sliding mode control based on the error between the desired yaw rate and the yaw rate at the current time to determine a front wheel steering angle compensation value of the vehicle, wherein the front wheel steering angle compensation value is used to compensate the front wheel steering angle of the vehicle to adjust the error; determining a front wheel steering angle control value of the vehicle based on the desired front wheel steering angle and the front wheel steering angle compensation value, wherein the front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle.

[0006] In the foregoing embodiments of the present application, the second-order sliding mode control is performed based on an error between the expected yaw rate and the current yaw rate, and a front wheel steering angle compensation value of the vehicle is determined, including: constructing a sliding surface based on the error between the expected yaw rate and the current yaw rate; performing the second-order sliding mode control based on the sliding surface to construct a control law, wherein the control law is used to control a speed of a driving system of the vehicle to reach the sliding surface; and adjusting parameters of a dynamic model based on the control law to obtain the front wheel steering angle compensation value.

[0007] In the foregoing embodiments of the present application, the second-order sliding mode control is performed based on the sliding surface to construct the control law, including: determining a derivative of the sliding surface; determining a first control law term based on a target power of the derivative, a sign of the derivative and a first control gain; determining a second control law term based on the derivative, the sign of the derivative and a second control gain; and obtaining the control law based on the first control law term and the second control law term.

[0008] In the foregoing embodiments of the present application, the parameters of the dynamic model are adjusted based on the control law to obtain the front wheel steering angle compensation value, including: constructing an objective function based on the sliding surface; performing stability analysis on the driving system of the vehicle by using the objective function to obtain an analysis result, wherein the analysis result is used to represent whether the driving system is in a stable state; and in response to the analysis result representing that the driving system is in the stable state, adjusting the parameters of the dynamic model based on the control law to obtain the front wheel steering angle compensation value.

[0009] In the foregoing embodiments of the present application, the expected front wheel steering angle of the vehicle at the next time is predicted based on a navigation planning path and a running state of the vehicle at the current time, including: determining a longitudinal displacement and a lateral displacement of the vehicle at the next time based on the navigation planning path and the running state; and determining the expected front wheel steering angle based on a motion geometry relationship of the vehicle, the longitudinal displacement and the lateral displacement.

[0010] In the foregoing embodiments of the present application, the running state further includes a vehicle speed, a heading angle and a vehicle position at the current time; and the longitudinal displacement and the lateral displacement of the vehicle at the next time are determined based on the navigation planning path and the running state, including: determining the longitudinal displacement based on the vehicle speed and a preset time length, wherein the preset time length is a time length from the current time to the next time; determining a preview point at the next time from the navigation planning path based on the running state; and determining the lateral displacement based on a lateral error between the vehicle position and the preview point.

[0011] In the above embodiments of the present application, the determining the expected front wheel steering angle based on the motion geometry relationship, the longitudinal displacement and the lateral displacement includes: determining a yaw rate at the next time based on the motion geometry relationship, the longitudinal displacement and the lateral displacement; and determining the expected front wheel steering angle based on the yaw rate at the next time and a preset mapping relationship, where the preset mapping relationship is used to represent a mapping relationship between the yaw rate at the next time and the expected front wheel steering angle.

[0012] In the above embodiments of the present application, the method further includes: obtaining a dynamic parameter of the vehicle; and constructing a dynamic model based on a vehicle speed in the running state, the dynamic parameter and the motion geometry relationship of the vehicle.

[0013] According to another aspect of the embodiments of the present application, a vehicle path tracking control device is further provided, which includes: a first prediction module configured to predict an expected front wheel steering angle of the vehicle at a next time based on a navigation planning path and a running state of the vehicle at a current time, where the running state at least includes a yaw rate at the current time; a second prediction module configured to predict an expected yaw rate of the vehicle at the next time based on a dynamic model of the vehicle and the expected front wheel steering angle, where the dynamic model is used to represent an association relationship between the expected yaw rate and the expected front wheel steering angle; a first determination module configured to determine a front wheel steering angle compensation value of the vehicle based on a second order sliding mode control of an error between the expected yaw rate and the yaw rate at the current time, where the front wheel steering angle compensation value is used to compensate the front wheel steering angle of the vehicle to adjust the error; and a second determination module configured to determine a front wheel steering angle control value of the vehicle based on the expected front wheel steering angle and the front wheel steering angle compensation value, where the front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle.

[0014] According to another aspect of the embodiments of the present application, a vehicle is further provided, which includes: a memory storing an executable program; and a processor configured to run the program, where the program is executed to perform the method in the embodiments of the present application when the program is run.

[0015] According to another aspect of the embodiments of the present application, a computer readable storage medium is further provided, which includes a stored executable program, where the computer readable storage medium is controlled to perform the method in the embodiments of the present application when the executable program is run.

[0016] According to another aspect of the embodiments of the present application, a computer program product is further provided, which includes a computer program, where the computer program is executed by a processor to implement the method in the embodiments of the present application.

[0017] According to a further aspect of the embodiments of the present application, a computer program product is also provided, which comprises a non-volatile computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the method in the embodiments of the present application.

[0018] According to a further aspect of the embodiments of the present application, a computer program is also provided, which is executed by a processor to implement the method in the embodiments of the present application.

[0019] In the embodiments of the present application, based on the navigation planning path and the running state of the vehicle at the current time, the expected front wheel steering angle of the vehicle at the next time is predicted, wherein the running state at least comprises the yaw rate at the current time; based on the dynamics model of the vehicle and the expected front wheel steering angle, the expected yaw rate of the vehicle at the next time is predicted, wherein the dynamics model is used to represent the correlation between the expected yaw rate and the expected front wheel steering angle; based on the error between the expected yaw rate and the yaw rate at the current time, the second-order sliding mode control is performed to determine the front wheel steering angle compensation value of the vehicle, wherein the front wheel steering angle compensation value is used to compensate the front wheel steering angle of the vehicle to adjust the error; based on the expected front wheel steering angle and the front wheel steering angle compensation value, the front wheel steering angle control value of the vehicle is determined, wherein the front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle. It is easy to note that the process of predicting the expected front wheel steering angle in the present application utilizes the real-time state information of the vehicle, and the expected yaw rate is predicted by using the dynamics model, so that the control strategy can be more suitable for the actual physical characteristics of the vehicle, and the stability of path tracking can be significantly improved. The front wheel steering angle compensation value is determined by combining dynamics and kinematics, and the front wheel steering angle control value is obtained, which comprehensively considers the requirements of vehicle dynamic prediction and real-time error correction, improves the control strategy of the front wheel steering angle of the vehicle, and makes the front wheel steering angle of the vehicle more suitable for the actual driving requirements. Since the front wheel steering angle compensation value is combined, not only the tracking error of the vehicle is adjusted, but also the smoothness of the steering process is improved through the control algorithm, unnecessary steering angle mutations are reduced, and thus the technical problems of low accuracy and stability of vehicle path tracking control in the related art are solved. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which are included to provide a further understanding of the present application and constitute a part of this application, illustrate certain illustrative embodiments of the present application and together with the description serve to explain the present application. In the drawings:

[0021] Figure 1 is a flowchart of a vehicle path tracking control method according to an embodiment of the present application;

[0022] Figure 2is a schematic diagram of a vehicle path tracking control process according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a vehicle path prediction model according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of a vehicle dynamics model according to an embodiment of the present invention;

[0025] Figure 5 2 is a schematic diagram of a vehicle path tracking control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present application, an embodiment of a vehicle path tracking control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] In this embodiment, a vehicle path tracking control method is provided. Figure 1 is a flow chart of a vehicle path tracking control method according to an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0030] Step S102 , based on the navigation planning path and operating status of the vehicle at the current moment, predict the expected front wheel steering angle of the vehicle at the next moment.

[0031] The operating state at least includes: the yaw angular velocity at the current moment.

[0032] The above-mentioned vehicles may refer to various types of vehicles with autonomous driving functions. The vehicles can be equipped with various sensors, actuators and controllers to achieve autonomous driving. The vehicles can automatically adjust their motion status through the built-in control system to achieve the predetermined driving path and destination.

[0033] The above-mentioned current moment can refer to the time point when processing is being carried out in the control loop. In the autonomous driving system, the control loop runs at a sampling frequency. Each sampling period can obtain vehicle status information, evaluate the current operating status, and make decisions.

[0034] The above-mentioned navigation planning path may refer to a preferred or feasible path generated by a path planning module of an autonomous driving system. The navigation planning path may indicate the route for a vehicle to move from its current position to its target position. The navigation planning path may be pre-set on a map or generated in real time during driving. Factors such as road conditions and traffic conditions may be considered and are not limited here.

[0035] The aforementioned operating state can refer to various physical and operational parameters of the vehicle at the current moment, including at least yaw rate. The operating state can also include vehicle speed, heading angle, and vehicle position. This state information can serve as data for control algorithms to adjust vehicle behavior.

[0036] The yaw rate may refer to the speed at which a vehicle rotates around its axis perpendicular to the ground, and may indicate the degree to which the vehicle is turning or steering. The yaw rate may affect the vehicle's handling stability and safety.

[0037] The above-mentioned next moment may refer to the time point of the next control cycle after the current moment. The control algorithm may predict or calculate the requirements of the next moment based on the information of the current moment to adjust the behavior of the vehicle in advance.

[0038] The above-mentioned expected front wheel steering angle may refer to the ideal steering angle calculated by the controller at the next moment in order to make the vehicle travel along the navigation planned path. The expected front wheel steering angle may be used as the deviation angle of the vehicle's front wheels relative to the vehicle's straight-line driving direction to guide the vehicle's steering.

[0039] As an optional implementation, in the path tracking control of the autonomous vehicle, the navigation planning path and the running state of the vehicle at the current time can be obtained, so as to calculate the desired front wheel steering angle required by the vehicle at the next time in order to maintain path tracking. The running state here can include the yaw rate reflecting the dynamic characteristics of the vehicle turning, and other running states of the vehicle can also be collected, for example, can include but are not limited to vehicle speed, heading angle, vehicle position, and current yaw rate, etc. Optionally, the desired position that the vehicle is expected to reach at the next time can be determined based on the navigation planning path. And the predicted position that the vehicle is expected to reach at the next time can be determined based on the motion state of the vehicle. Then based on the deviation between the desired position and the predicted position, the desired yaw rate of the vehicle can be determined to ensure that the vehicle can accurately reach the desired position at the next time.

[0040] In a preferred embodiment, the navigation planning path of the vehicle can be used to identify the preview point that the vehicle should approach or reach at the next time. Then, the relevant displacement information of the vehicle from the current center of mass position to the preview point at the next time can be calculated through the motion geometry of the vehicle. The relevant displacement error between the vehicle and the preview point can be evaluated, which helps to quantify the degree of deviation of the vehicle from the planning path, and then the vehicle control is required. Finally, the expected front wheel steering angle can be determined by combining the vehicle running state and the pre-relevant displacement error and other information, using geometric and kinematic principles.

[0041] In the above process, by combining the navigation planning path and the current running state, including the yaw rate, the ideal steering action of the vehicle at the next time can be more accurately predicted, thereby effectively guiding the control strategy of the vehicle, avoiding the problems of lagging response and over-adjustment. By predicting the expected front wheel steering angle, the controller can be more proactive in path tracking, and the control strategy can be adjusted in time according to the preview point and the current vehicle state, ensuring that the vehicle can follow the planning path and also maintain good dynamic stability.

[0042] Step S104, predicting the desired yaw rate of the vehicle at the next time based on the dynamics model of the vehicle and the expected front wheel steering angle.

[0043] The dynamics model is used to represent the relationship between the desired yaw rate and the expected front wheel steering angle.

[0044] The above dynamics model can refer to the description of the motion characteristics of the vehicle. The dynamics model can include the mass, moment of inertia, tire cornering characteristics, and other factors that affect the motion of the vehicle. The dynamics model can be used to predict the future motion state of the vehicle under a given control input.

[0045] The above-mentioned expected yaw rate can refer to an ideal yaw rate that the vehicle should have at the next moment according to the navigation planning path and the dynamic model of the vehicle. The expected yaw rate can be a target value set to ensure that the vehicle can accurately follow the predetermined path.

[0046] As an optional implementation, in the path tracking control of the autonomous vehicle, the dynamic characteristics of the vehicle can be combined with the control target to predict the expected yaw rate that the vehicle should reach at the next moment, thereby guiding the steering control of the vehicle. The expected yaw rate of the vehicle at the next moment can be predicted based on the dynamic model of the vehicle and the expected front wheel steering angle obtained above. Specifically, a dynamic model of the vehicle can be constructed, which can focus on the lateral and longitudinal motion of the vehicle. The dynamic model can also include key parameters such as tire cornering characteristics, vehicle mass, moment of inertia, etc. to reflect the basic physical characteristics of the vehicle, which are important references for subsequent design of the front wheel steering angle compensation controller. Then, the expected front wheel steering angle obtained above can be used as a control input, combined with other parameters in the dynamic model, such as the distance from the front axle to the center of mass, the distance from the rear axle to the center of mass, the overall mass and moment of inertia of the vehicle, to predict the expected yaw rate at the next moment through the calculation of the dynamic model.

[0047] Optionally, the implementation of the prediction process can be regarded as a process of determining a differential equation that can describe the dynamic response of the vehicle under a given steering angle. In the prediction process, it can also be considered that the vehicle center of mass cornering angle is small in most cases, and the vehicle speed remains relatively stable. The dynamic model can be reasonably simplified to convert complex dynamic response into an easy-to-calculate formula. This step utilizes the kinematics and geometric relationships of the vehicle to ensure the accuracy and real-time performance of the prediction.

[0048] In the above process, the expected yaw rate at the next moment can be accurately predicted based on the dynamic characteristics of the vehicle, which helps to achieve high-precision path tracking. The expected yaw rate obtained can reflect the requirements on how the vehicle should turn to remain on the planned path, and also takes into account the actual physical limitations of the vehicle and the road conditions, making the control strategy more reasonable and effective. The prediction based on the dynamic model can better handle the dynamic response of the vehicle under different speeds and different road conditions, enhancing the robustness of the control.

[0049] In step S106, a second-order sliding mode control is performed based on the error between the expected yaw rate and the yaw rate at the current moment to determine the front wheel steering angle compensation value of the vehicle.

[0050] The front wheel steering angle compensation value is used to compensate for the front wheel steering angle of the vehicle to adjust the error.

[0051] The second-order sliding mode control mentioned above can refer to a control strategy for quickly reaching the desired state of the vehicle, so that the vehicle can maintain stability even in the presence of disturbances. The second-order sliding mode control can make the motion state of the vehicle approach or reach the specified steady motion state in a finite time by designing a proper sliding surface and control law.

[0052] The front wheel steering angle compensation value mentioned above can refer to an additional steering angle adjustment value calculated by a front wheel steering angle compensation controller in the second-order sliding mode control to reduce the error between the actual yaw rate of the vehicle and the desired yaw rate. The front wheel steering angle compensation value can be used to compensate for the deficiency or excess of the vehicle during steering to more accurately control the motion of the vehicle.

[0053] The front wheel steering angle mentioned above can refer to the angle of the front wheel of the vehicle relative to the forward direction of the vehicle. By adjusting the front wheel steering angle, the direction and path of the vehicle can be changed.

[0054] As an optional implementation, a second-order sliding mode control strategy can be used to calculate the front wheel steering angle compensation value of the vehicle based on the difference between the desired yaw rate and the current yaw rate of the vehicle. Specifically, the front wheel steering angle compensation value of the vehicle can be determined by constructing a sliding surface and a control law in combination with the dynamics model of the vehicle. The sliding surface can be a virtual surface between the ideal state and the actual state of the vehicle, which can be used to measure whether the yaw rate of the vehicle matches the desired value. The front wheel steering angle compensation value of the vehicle can reflect the angle that needs to be increased or decreased based on the current front wheel steering angle, so as to reduce the yaw rate error, improve the path tracking performance of the vehicle, reduce the chattering phenomenon in the control process, and enhance the smoothness and robustness of the control system.

[0055] In the above process, the improved second-order sliding mode control strategy can effectively reduce the path tracking error of the vehicle, reduce the chattering in the control process, improve the comfort and safety of the vehicle, and effectively improve the overall performance of the sliding mode control system, so that the vehicle can still maintain stable and accurate path tracking when facing dynamic changes and external disturbances.

[0056] Step S108, determining the front wheel steering angle control value of the vehicle based on the desired front wheel steering angle and the front wheel steering angle compensation value.

[0057] The front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle.

[0058] The front-wheel steering angle control value described above may refer to the front-wheel steering angle value ultimately determined by the vehicle controller. The front-wheel steering angle control value combines the desired front-wheel steering angle and the front-wheel steering angle compensation value. The front-wheel steering angle control value may be sent to the vehicle's steering actuator to actually adjust the vehicle's front-wheel steering, thereby achieving path tracking.

[0059] As an optional implementation, during the path-following control process of an autonomous vehicle, the desired front-wheel steering angle and the front-wheel steering angle compensation value can be combined. For example, the desired front-wheel steering angle and the front-wheel steering angle compensation value can be added together or weighted together to generate the front-wheel steering angle control value used to actually control the vehicle's steering. This process includes responding to the desired steering requirements and also takes into account compensation for vehicle dynamic deviations, ensuring that the vehicle accurately tracks the planned path while maintaining good driving stability. The controller can add the desired front-wheel steering angle and the front-wheel steering angle compensation value to obtain the front-wheel steering angle control value, which guides the vehicle's steering system to make appropriate adjustments. The calculated front-wheel steering angle control value can be sent to the vehicle's steering actuator, such as the electric power steering system, for precise steering control. The actuator can adjust the direction of the vehicle's front wheels based on the control command to ensure that the vehicle follows the planned path while also correcting for deviations in the dynamic response.

[0060] By combining the desired front-wheel steering angle with the front-wheel steering angle compensation value in this process, the controller can fine-tune the vehicle's actual steering maneuvers, improving its ability to track the planned path. This allows the vehicle to maintain high tracking accuracy even under complex driving conditions. The incorporation of the front-wheel steering angle compensation value not only adjusts the vehicle's tracking error but also improves the smoothness of the steering process through the control algorithm, reducing unnecessary sudden changes in steering angles. This improves driving comfort and reduces user discomfort.

[0061] In the embodiment of the present application, based on the navigation planning path and the running state of the vehicle at the current time, the expected front wheel steering angle of the vehicle at the next time is predicted, wherein the running state at least includes the yaw rate at the current time; based on the dynamics model of the vehicle and the expected front wheel steering angle, the expected yaw rate of the vehicle at the next time is predicted, wherein the dynamics model is used to represent the correlation between the expected yaw rate and the expected front wheel steering angle; based on the error between the expected yaw rate and the yaw rate at the current time, the second-order sliding mode control is performed to determine the front wheel steering angle compensation value of the vehicle, wherein the front wheel steering angle compensation value is used to compensate the front wheel steering angle of the vehicle to adjust the error; based on the expected front wheel steering angle and the front wheel steering angle compensation value, the front wheel steering angle control value of the vehicle is determined, wherein the front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle. It is easy to note that the process of predicting the expected front wheel steering angle in the present application utilizes the real-time state information of the vehicle, and the expected yaw rate is predicted by using the dynamics model, so that the control strategy can be more suitable for the actual physical characteristics of the vehicle, and the stability of path tracking can be significantly improved. The front wheel steering angle compensation value is determined by combining dynamics and kinematics, and the front wheel steering angle control value is obtained, which comprehensively considers the requirements of vehicle dynamic prediction and real-time error correction, improves the control strategy of the front wheel steering angle of the vehicle, and makes the front wheel steering angle of the vehicle more suitable for actual driving requirements. Since the front wheel steering angle compensation value is combined, not only the tracking error of the vehicle is adjusted, but also the smoothness of the steering process is improved through the control algorithm, unnecessary steering angle mutations are reduced, and thus the technical problems of low accuracy and stability of vehicle path tracking control in the related art are solved.

[0062] In the above embodiment of the present application, the second-order sliding mode control is performed based on the error between the expected yaw rate and the yaw rate at the current time to determine the front wheel steering angle compensation value of the vehicle, including: based on the error between the expected yaw rate and the yaw rate at the current time, a sliding surface is constructed; based on the sliding surface, the second-order sliding mode control is performed to construct a control law, wherein the control law is used to control the speed of the driving system of the vehicle to reach the sliding surface; based on the control law, the parameters of the dynamics model are adjusted to obtain the front wheel steering angle compensation value.

[0063] The above-mentioned sliding surface can refer to a virtual surface designed in the sliding mode control strategy. In vehicle path tracking control, the sliding surface can be defined as a function of the error between the expected yaw rate and the current yaw rate. When the system state is on the sliding surface, theoretically, the system will move along the predetermined sliding surface, and finally reach the expected control performance.

[0064] The control law mentioned above can refer to a rule or formula in the control strategy for guiding the change of system state. The control law can reflect how the controller adjusts the control quantity according to the current state of the system and the environmental input. In the sliding mode control system, the control law can be used to drive the system state to quickly and stably reach the sliding mode surface and remain on the sliding mode surface. For second-order sliding mode control, the control law can contain a positive proportional term of the rate of change of the system state and a sign function term of the sliding mode surface.

[0065] As an optional implementation, the sliding mode surface can be designed based on the error between the desired yaw rate of the vehicle and the current yaw rate. The designed sliding mode surface can ensure that the system state can quickly converge to the sliding mode surface, while avoiding chattering caused by sharp changes in control input. Then, the control law can be constructed to ensure that the vehicle driving system can tend to the sliding mode surface at an appropriate speed. The control law takes into account both the approaching speed of the sliding mode surface and the suppression of the change of the system state. Optionally, the present application can also use a nonlinear integral sliding mode surface and an improved control law of Super-Twisting algorithm. Finally, based on the designed control law, the parameters of the vehicle dynamics model can be adjusted to obtain a front wheel steering angle compensation value. The obtained front wheel steering angle compensation value can be used to adjust the dynamic response of the vehicle, so that the yaw rate of the vehicle is closer to the desired value, thereby reducing the path tracking error.

[0066] In the above process, the yaw rate of the vehicle can be more quickly close to the desired value through the control law with fast convergence, thereby reducing the deviation of the vehicle in the path tracking process, ensuring that the vehicle can travel more accurately along the planned path, and the improved control law design makes the control system have stronger resistance to unknown disturbances and model uncertainties, enhancing the driving stability of the vehicle in complex environments.

[0067] In the above embodiments of the present application, the second-order sliding mode control is performed based on the sliding mode surface, and the control law is constructed, including: determining the derivative of the sliding mode surface; determining the first control law term based on the target power of the derivative, the sign of the derivative and the first control gain; determining the second control law term based on the derivative, the sign of the derivative and the second control gain; and obtaining the control law based on the first control law term and the second control law term.

[0068] The target power mentioned above can refer to a parameter used to adjust the smoothness and control speed of the control input in the control law design. The target power can be introduced into the first term of the control law to smooth the control process and reduce chattering. The selection of the target power will affect the shape of the control law and the convergence speed of the system. A smaller power value will result in a smoother control signal, but the convergence speed will be slower. A larger power value will speed up the convergence of the system, but will increase the discontinuity of the control signal.

[0069] The above-mentioned symbol can refer to a function (sgn()) and the like. In the sliding mode control, it can be used to quickly determine the direction of the control signal to drive the system state to converge to the sliding surface.

[0070] The above-mentioned first control gain can refer to a parameter that affects the convergence speed and robustness of the sliding mode control in the control law design. The greater the first control gain, the faster the system converges to the sliding surface, but it can cause a sharp change in the control signal.

[0071] The above-mentioned first control law term can refer to a control law term that plays a stabilizing role in the control law. It can quickly adjust the control direction through a sign function to ensure that the system state quickly approaches the sliding surface.

[0072] The above-mentioned second control gain can refer to a parameter that affects the control effect in the control law design and forms the second term of the control law.

[0073] The above-mentioned second control law term can refer to a control law term that accelerates the convergence of the system state to the sliding surface through the action of a nonlinear power function in the control law. The second control law term helps to reduce the chattering phenomenon in the control process.

[0074] As an optional implementation, the process of constructing the control law can be performed through the following steps. Specifically, the derivative of the sliding surface, i.e., the rate of change of the sliding surface, can be calculated. This step helps to understand the trend of the system state converging to the sliding surface. Then, based on the target power of the change of the derivative of the sliding surface, the sign of the derivative, and the first control gain, the first control law term can be determined. The first control law term can ensure that the control signal can be quickly adjusted according to the direction of the sliding surface to accelerate the approach to the sliding surface. Next, based on the derivative of the sliding surface, the sign of the derivative, and the second control gain, the second control law term can be determined. Through the second control law term, the control law can adapt to different sizes and directions of the sliding surface to achieve smooth and fast convergence. Finally, the first control law term and the second control law term can be combined to obtain the control law. The obtained control law can quickly drive the vehicle state to converge to the desired sliding surface, and through the introduction of the nonlinear power function, it can reduce the chattering in the control process.

[0075] In the above process, through the first control law term, a quick response can be achieved to shorten the time to reach the sliding surface, which helps the vehicle to adjust the path tracking strategy in real time in a dynamic environment. The power function design of the second control law term effectively smooths the control signal and reduces high-frequency chattering, which can improve the stability and ride comfort of the control system. The gain adjustment of the first control gain and the second control gain, as well as the consideration of the derivative of the sliding surface, enhances the resistance of the control system to external disturbances, and can maintain good path tracking performance under non-ideal conditions.

[0076] In the above embodiments of the present application, the parameters of the dynamics model are adjusted based on the control law to obtain the front wheel steering angle compensation value, including: constructing a target function based on the sliding surface; performing stability analysis on the drive system of the vehicle using the target function to obtain an analysis result, wherein the analysis result is used to represent whether the drive system is in a stable state; in response to the analysis result representing that the drive system is in a stable state, adjusting the parameters of the dynamics model based on the control law to obtain the front wheel steering angle compensation value.

[0077] The target function described above can be a function used to evaluate the performance of a control system, and can be used to minimize errors or maximize system performance indicators. The target function can be designed to evaluate whether the sliding surface-based control strategy can enable the vehicle drive system to stably track the desired path. The form of the target function can include the value of the sliding surface, the rate of change of the sliding surface or higher-order derivatives, and penalty terms such as control energy consumption or quantification of chattering degree. The form of the target function can be set according to actual needs, which is not limited here.

[0078] The analysis result described above can be a result about the stability of the vehicle drive system obtained by analyzing the target function. The analysis result can be used to determine whether the current control strategy can ensure that the vehicle remains stable during path tracking. That is, whether the changes in dynamic parameters such as the yaw rate and lateral displacement of the vehicle are within a controllable range and do not cause the vehicle to lose control or deviate from the planned path.

[0079] The stable state described above can mean that the control system can return to or remain in a specific state after being disturbed. For a vehicle path tracking control system, the stable state can reflect that when the vehicle is subjected to external disturbances such as changes in road conditions, wind resistance, etc., the vehicle can quickly recover to a state consistent with the desired path by adjusting the front wheel steering angle.

[0080] As an optional implementation, a target function can be constructed based on the sliding surface. The target function can reflect the influence of the control law on the stability of the control system. The target function can include the square of the sliding surface and the derivative of the sliding surface in order to quantify the degree of deviation of the control system from the sliding surface. Then, the drive system of the vehicle can be analyzed for stability using the constructed target function. This step can use Lyapunov stability algorithm. By proving that the derivative of the target function is less than or equal to zero, the stable convergence of the control system is ensured. If the result of the stability analysis indicates that the control system is in a stable state, i.e., the derivative of the target function is less than or equal to zero, the control law can be adjusted. The adjustment process can include adjusting certain parameters in the dynamics model to ensure that the front wheel steering angle compensation value can further improve the tracking performance of the control system while maintaining the stability of the control system.

[0081] In the above process, by constructing the objective function and performing stability analysis, it can be ensured that the control strategy of the vehicle remains effective under various driving conditions, and the vehicle can stably track the planned path, avoiding vehicle instability caused by improper control. On the premise that the analysis result indicates that the control system is stable, parameter adjustment of the control law can further improve the control effect, improve the accuracy and response speed of path tracking, and make the vehicle more flexible and accurate in dynamic environment.

[0082] In the above embodiments of the present application, the expected front wheel steering angle of the vehicle at the next moment is predicted based on the navigation planning path and the running state of the vehicle at the current moment, including: determining the longitudinal displacement and lateral displacement of the vehicle at the next moment based on the navigation planning path and the running state; determining the expected front wheel steering angle based on the motion geometry relationship of the vehicle, the longitudinal displacement and the lateral displacement.

[0083] The above-mentioned longitudinal displacement can refer to the displacement change of the vehicle in the driving direction, such as the forward direction of the vehicle. The longitudinal displacement can be related to the vehicle speed and the preview time.

[0084] The above-mentioned lateral displacement can refer to the displacement change of the vehicle perpendicular to the driving direction. The lateral displacement can reflect the degree of deviation of the vehicle from the planned path.

[0085] The above-mentioned motion geometry relationship can refer to the geometric relationship between various dynamic parameters during vehicle driving. In path tracking control, the motion geometry relationship can be reflected in the correlation between vehicle speed, steering angle and lateral displacement. For example, the rate of change of vehicle lateral displacement is related to vehicle speed and heading angle deviation, and the heading angle deviation is related to the front wheel steering angle. By establishing the geometric relationship between these parameters, the steering action of the vehicle can be predicted and adjusted to ensure that the vehicle stably and accurately tracks the planned path.

[0086] As an optional implementation, the position of the driver's preview point at the next moment can be predicted according to the vehicle running state (such as vehicle speed, heading angle, center of mass position, etc.) at the current moment and the navigation planning path. The longitudinal displacement can be calculated based on the vehicle speed and the preview time. At the same time, the lateral displacement can be obtained based on the lateral error between the vehicle and the preview point. Then, the longitudinal displacement and the lateral displacement can be taken as inputs based on the motion geometry relationship of the vehicle. Combined with the heading angle deviation of the vehicle at the current moment and the vehicle speed, the expected yaw rate of the vehicle at the next moment is calculated by formula. Further, the expected front wheel steering angle can be finally determined according to the established vehicle dynamics model, such as the mapping relationship between the front wheel steering angle and the yaw rate.

[0087] In the above process, by accurately predicting the longitudinal displacement and lateral displacement and combining the motion geometry of the vehicle, the expected front wheel steering angle can be accurately calculated, thereby significantly improving the accuracy of vehicle path tracking, ensuring that the vehicle stably and accurately follows the planned path in a dynamic environment. In the above process, the preview behavior of a skilled driver is simulated, i.e., the current speed, heading angle and preview time are used to predict the future position, which helps the vehicle to adjust the steering strategy in advance, avoids control lag and improves the forward-looking nature of driving decisions.

[0088] In the above embodiments of the present application, the operating state further includes the vehicle speed, the heading angle and the vehicle position at the current time; based on the navigation planning path and the operating state, the longitudinal displacement and the lateral displacement of the vehicle at the next time are determined, including: based on the vehicle speed and a preset time length, the longitudinal displacement is determined, wherein the preset time length is the time length from the current time to the next time; based on the operating state, a preview point at the next time is determined from the navigation planning path; based on the lateral error between the vehicle position and the preview point, the lateral displacement is determined.

[0089] The vehicle speed mentioned above can refer to the actual driving speed of the vehicle at a certain time.

[0090] The heading angle mentioned above can refer to the angle between the direction of the vehicle center of mass in the coordinate system and the driving direction of the vehicle. The heading angle can be used to describe the direction of the vehicle.

[0091] The vehicle position mentioned above can refer to the position of the vehicle center of mass in the fixed coordinate system. The vehicle position can be represented by latitude and longitude, horizontal-longitudinal coordinates, etc.

[0092] The preset time length mentioned above can refer to the time interval from the current time to the next time. The preset time length can be used to predict the future state of the vehicle.

[0093] The preview point mentioned above can be a position point on the navigation planning path, which is predicted based on the current speed of the vehicle and the preview time. The preview point can be used to calculate the ideal position of the vehicle at the next time, and can be used as a reference point in path tracking control.

[0094] The lateral error mentioned above can refer to the lateral distance between the actual position of the vehicle center of mass and the preview point. The lateral error can reflect the degree of deviation of the vehicle from the planned path.

[0095] As an optional implementation, the longitudinal displacement of the vehicle at the next time instant can be calculated based on the current vehicle speed and the preset time length, reflecting the distance that the vehicle advances along the driving direction within the predetermined time. And the preview point at the next time instant can be found from the navigation planning path according to the current heading angle, position and preset time length of the vehicle. The selection of the preview point can be based on the motion state of the vehicle and the path planning, ensuring the forward-looking nature of the path tracking. Then, the lateral error between the vehicle center of mass position and the preview point can be measured or calculated, and the lateral error is used to determine the lateral displacement at the next time instant. The lateral displacement can reflect the lateral distance that the vehicle deviates from the path, which is helpful for path tracking control.

[0096] In the above process, by considering the current speed, heading angle and position of the vehicle, combined with the determination of the preset time length and the preview point, the longitudinal and lateral displacements of the vehicle at the next time instant can be accurately predicted. Accurate input data is provided for path tracking control, improving the accuracy of path tracking. The introduction of the preview point allows the controller to calculate the displacement and error at the next time instant in advance, making the control strategy more forward-looking, enabling timely adjustment of the driving direction of the vehicle, avoiding deviation from the planned path, and enhancing the dynamic response capability of the vehicle.

[0097] In the above embodiments of the present application, based on the motion geometry relationship, the longitudinal displacement and the lateral displacement, the expected front wheel steering angle is determined, including: based on the motion geometry relationship, the longitudinal displacement and the lateral displacement, the yaw rate at the next time instant is determined; based on the yaw rate at the next time instant and the preset mapping relationship, the expected front wheel steering angle is determined, wherein the preset mapping relationship is used to represent the mapping relationship between the yaw rate at the next time instant and the expected front wheel steering angle.

[0098] The above-mentioned preset mapping relationship can refer to a function or algorithm that is pre-set and used to describe the relationship between the yaw rate at the next time instant of the vehicle and the expected front wheel steering angle. In the control of autonomous vehicles, the preset mapping relationship can be derived based on experimental data or theoretical analysis, and is used to guide the controller to adjust the front wheel steering angle according to the expected dynamic performance (such as yaw rate), so as to achieve smooth and accurate driving of the vehicle.

[0099] As an optional implementation, the expected yaw rate of the vehicle at the next time instant can be calculated using the motion geometry relationship of the vehicle, combined with the predicted longitudinal displacement and lateral displacement. This process can involve understanding and analysis of the motion characteristics of the vehicle, and the displacement is converted into angular velocity through a formula, so as to facilitate the formulation of subsequent control strategies. Then, the expected front wheel steering angle can be determined by querying the preset mapping relationship or applying a corresponding function. The preset mapping relationship takes into account the dynamic characteristics of the vehicle, such as the cornering characteristics of the tires and the steering response of the vehicle, ensuring that the adjustment of the steering angle can guide the vehicle to travel according to the expected yaw rate.

[0100] In the above process, through the application of the yaw rate prediction based on the motion geometry and the preset mapping relationship, the control strategy can more finely adjust the front wheel steering angle to adapt to the dynamic changes of the vehicle, effectively handle the path tracking problem of the vehicle under complex situations such as turning and obstacle avoidance, and improve the accuracy and flexibility of control.

[0101] In the above embodiments of the present application, the method further comprises: obtaining the dynamic parameters of the vehicle; and constructing a dynamic model based on the vehicle speed in the running state, the dynamic parameters, and the motion geometry relationship of the vehicle.

[0102] The dynamic parameters mentioned above can refer to physical quantities and characteristic parameters used to describe the motion characteristics of the vehicle in the vehicle dynamics model. The dynamic parameters can include but are not limited to vehicle mass, moment of inertia, tire cornering stiffness, distance from front and rear axles to center of mass, etc. The dynamic parameters can reflect the physical properties and dynamic performance of the vehicle, which helps to accurately predict and control the dynamic behavior of the vehicle.

[0103] The motion geometry relationship mentioned above can refer to the relationship between the geometric position changes of various parts of the vehicle (such as front wheels, rear wheels, and vehicle body) and the dynamic performance of the vehicle during vehicle driving. In the path tracking control of the vehicle, the motion geometry relationship can include how to use vehicle speed, steering angle, side slip angle, etc. to describe the dynamic characteristics such as yaw rate and lateral displacement of the vehicle.

[0104] As an optional implementation, the dynamic parameters of the vehicle, such as vehicle mass, moment of inertia, tire cornering stiffness, and distance from front and rear axles to center of mass, can be accurately obtained through specifications provided by the vehicle manufacturer or on-site testing. Then, a dynamic model reflecting the dynamic characteristics of the vehicle can be constructed based on the vehicle speed, the obtained dynamic parameters, and the motion geometry relationship of the vehicle. A two-dimensional dynamic model of the vehicle can be established, which can include the description of vehicle yaw rate and side slip angle, as well as the relationship between vehicle yaw rate, side slip angle, and front wheel steering angle. The accuracy of the dynamic parameters and the rationality of the motion geometry relationship can affect the accuracy of the constructed dynamic model.

[0105] In the above process, the constructed accurate dynamic model can more accurately predict the dynamic response of the vehicle under given control input. By considering the actual dynamic characteristics of the vehicle, the control strategy can better cope with uncertain factors such as road condition changes and load differences, ensuring that the vehicle can stably track the path under various working conditions.

[0106] The technical solutions proposed in the application are described below in combination with an optional embodiment. The application proposes a vehicle path tracking control method based on an improved second-order sliding mode, which can effectively suppress chattering and improve the accuracy of vehicle path tracking and driving stability. The vehicle path tracking control method based on the improved second-order sliding mode proposed in the application can first design a vehicle desired front wheel steering angle controller, and then design a front wheel steering angle compensation controller based on the improved second-order sliding mode according to the vehicle state. The front wheel steering angle compensation value and the desired front wheel steering angle jointly act on the vehicle path tracking control.

[0107] The application designs a vehicle desired front wheel steering angle controller based on path prediction. Skilled vehicle drivers can perceive the motion state of the vehicle and predict the driving position after a period of time according to the driving environment. When designing a vehicle path tracking controller, the driving path can also be predicted. Path prediction can be that the vehicle predicts the position relationship between itself and the expected path at the next moment according to the expected path at the current moment and its own motion state, so as to calculate the desired front wheel steering angle.

[0108] Figure 2 is a schematic diagram of a vehicle path tracking control process according to an embodiment of the application. As shown in Figure 2 , in the vehicle path tracking control process, first, ideal front wheel steering angle control can be performed based on the navigation planning path and the vehicle centroid side slip angle β provided by the vehicle model to obtain the desired front wheel steering angle δ f . Then, the vehicle reference model can be used to generate the expected yaw rate γ d based on the vehicle speed V and the desired front wheel steering angle δ f provided by the vehicle model. Then, the front wheel steering angle compensation value Δδ fs can be generated based on the vehicle yaw rate γ and the expected yaw rate γ d provided by the vehicle model. Finally, the desired front wheel steering angle δ f and the front wheel steering angle compensation value Δδ fs are added to obtain the front wheel steering angle control value, which acts on the vehicle model.

[0109] Figure 3 is a schematic diagram of a vehicle path prediction model according to an embodiment of the application. As shown in Figure 3OXY is a global coordinate system fixed on the ground. xy is a coordinate system fixed on the vehicle center of mass. M is the center of mass position of the vehicle. ψ is the current heading angle of the vehicle. H is the center of the vehicle path. θ is the center angle of the vehicle circular path. R is the vehicle trajectory radius. A is the predicted center of mass position of the vehicle after driving for tp seconds. P is the preview point on the target path. xMC and yMC are the longitudinal displacement and lateral displacement in the vehicle coordinate system, respectively. ePC is the lateral error of the vehicle and the preview point. eAψ is the deviation of the current heading angle of the vehicle and the heading angle after tp seconds. epψ is the deviation of the current heading angle of the vehicle and the heading angle of the preview point. V is the current speed of the vehicle, V x is the longitudinal displacement of the vehicle, V y is the lateral displacement of the vehicle, represents the chord angle of the vehicle circular path, and β is the side slip angle of the vehicle center of mass.

[0110] From the above vehicle path prediction model, we have:

[0111]

[0112] wherein, the chord angle of the vehicle circular path can be represented as,

[0113] Thus, we have:

[0114]

[0115] According to the vehicle motion geometry, we have:

[0116]

[0117] Assuming that the preview time of the driver, i.e., the preset time, is tp seconds, and the vehicle travels at a constant speed V. After tp seconds, if the actual position C of the vehicle center of mass coincides with the expected position P, then e pψ = e Aψ , y AC = e PC . Assuming that the vehicle is in uniform circular motion, the center angle θ of the circular path is the angle turned over in tp seconds, thus θ = γt p . During the actual driving of the vehicle, the longitudinal displacement of the vehicle is much larger than the lateral displacement, i.e., V x is much larger than V y , and V x ≈ V, then the longitudinal displacement of the vehicle is x MC = Vt p . Thus, we have:

[0118]

[0119] The vehicle expected yaw rate predicted according to the path, i.e., the yaw rate at the next moment, can be:

[0120]

[0121] According to the relationship between the vehicle expected yaw rate and the front wheel steering angle, the expected front wheel steering angle is ultimately obtained as:

[0122]

[0123] Next, a desired yaw rate reference model can be established. A vehicle dynamics model (2-DOF) is established.

[0124] Figure 4 is a schematic diagram of a vehicle dynamics model according to an embodiment of the present application. As shown in Figure 4 , OXY is a global coordinate system fixed to the ground. zxy is a coordinate system fixed at the center of mass of the vehicle. The z point is at the center of mass position. The x axis is along the driving direction of the vehicle. The y axis is perpendicular to the x axis of the vehicle and points to the left side of the driver. The parameter interpretation of the dynamics model (2-DOF) can be as follows: δ f may represent the front wheel steering angle, v f may represent the front axle midpoint speed, v fl may represent the front wheel longitudinal speed, v fc may represent the front wheel lateral speed, α f may represent the front wheel side slip angle, α r may represent the rear wheel side slip angle, F fl may represent the front wheel longitudinal force, F fc may represent the front wheel lateral force, F rl may represent the rear wheel longitudinal force, F rc may represent the rear wheel lateral force, l f may represent the distance from the front axle to the center of mass, l r may represent the distance from the rear axle to the center of mass, V x may represent the vehicle longitudinal speed, V y may represent the vehicle lateral speed, γmay represent the vehicle yaw rate.

[0125] According to Newton's second law, the dynamic equilibrium equation of the vehicle can be obtained as:

[0126]

[0127] where m can represent the vehicle mass; Iz can represent the moment of inertia.

[0128] Considering the tire side slip characteristics acting in the linear range, the lateral forces of the front and rear wheels are:

[0129]

[0130] wherein C fc may represent the cornering stiffness of the front tire; C rc may represent the cornering stiffness of the rear tire.

[0131] According to the kinematics and geometric relations of the vehicle, we have:

[0132]

[0133] By combining equations (7) to (9), the linear two-degree-of-freedom dynamic mathematical model of the vehicle can be obtained as:

[0134]

[0135] Since the value of the vehicle's mass center side slip angle β is very small, if the vehicle's driving speed V remains constant, then V x = V cos β = V, and V y = V sin β = Vβ. Since β = V y / V x , we have

[0136] Equation (10) can be transformed into:

[0137]

[0138] Let When the vehicle is in steady-state driving, the expected yaw rate is:

[0139]

[0140] wherein, L = l f + l r .

[0141] According to equation (13), considering the driving road conditions of the vehicle, the expected yaw rate of the vehicle is:

[0142]

[0143] Next, a front wheel steering angle compensation controller based on an improved second-order sliding mode can be designed. Since the sliding mode control system has the chattering phenomenon, the traditional reaching law replaces the sign function with a saturation function, which is a way to weaken the chattering phenomenon. However, in the process of use, the overall robustness of the sliding mode control system is weak because the function of the saturation function is given priority. The present application proposes a second-order sliding mode controller based on a nonlinear integral sliding surface and a Super-Twisting algorithm, which not only improves the speed of the control system to reach the sliding surface, but also suppresses the chattering phenomenon.

[0144] Let η = s, then a first-order system can be obtained:

[0145]

[0146] The control law of the system is:

[0147]

[0148] In the formula, k1, k2 and n are all positive real numbers. Here, k1 can be a first control gain, and k2 can be a first control gain.

[0149] The power of the first term |s| of the Super-Twisting algorithm is replaced by a variable parameter, that is, a target power. The constant speed reaching law of the second integral term is replaced by a power reaching law with a power of 1.

[0150] In the case where the parameters k1 and k2 are the same and the initial positions of the sliding mode surface phase trajectories are the same, the spiral number of the improved Super-Twisting algorithm is obviously less than that of the Super-Twisting algorithm. The speed of the phase trajectory tending to the stable point is faster than that of the Super-Twisting algorithm, so that the control system can quickly converge to the sliding mode surface from an arbitrary state.

[0151] To ensure the stability and safety of the vehicle, the application compensates the front wheel steering angle to ensure that the vehicle yaw angular velocity tracks the expected value, and then formula (12) is rewritten as:

[0152]

[0153] The method can integrate the advantages of the nonlinear integral sliding mode surface and the Super-Twisting algorithm, and simultaneously solve formulas (15)-(17) to obtain the expression of the front wheel steering angle compensation value:

[0154]

[0155] The stability of the designed controller is analyzed, and a Lyapunov function, that is, an objective function, is selected:

[0156]

[0157] The derivative of formula (19) is taken, and is substituted to obtain:

[0158]

[0159] Formula (17) is substituted into formula (20) to obtain:

[0160]

[0161] Substituting equation (18) into equation (21) gives:

[0162]

[0163] Since k1, k2 and n are positive real numbers,

[0164] Therefore, the designed second-order sliding mode control system is stable, the control law (18) can track the desired yaw rate, and the vehicle can stably track the driving path.

[0165] Finally, the output vehicle front wheel steering angle control value can be calculated. The vehicle expected front wheel steering angle based on path prediction and the front wheel steering angle compensation value based on the improved second-order sliding mode are combined to calculate the vehicle front wheel steering angle control value.

[0166] The application designs a vehicle expected front wheel steering angle controller. Then, an improved second-order sliding mode based front wheel steering angle compensation controller is designed according to the vehicle state. The front wheel steering angle compensation value and the expected front wheel steering angle jointly act on the vehicle path tracking control. Finally, the actual front wheel steering angle can make the vehicle stably track the driving path while ensuring the path tracking accuracy. The application designs an improved second-order sliding mode based front wheel steering angle compensation controller, which combines a nonlinear integral sliding mode surface and an improved Super-Twisting algorithm. The improved Super-Twisting algorithm can not only improve the speed of the control system reaching the sliding mode surface, but also suppress the chattering phenomenon, thereby improving the accuracy and real-time performance of the vehicle path tracking control.

[0167] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant national and regional laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0168] According to another aspect of the embodiment of the application, a vehicle path tracking control device is also provided, which can execute the vehicle path tracking control method of the above-mentioned embodiment. The specific implementation method and preferred application scenario are the same as those of the above-mentioned embodiment, and will not be repeated here.

[0169] Figure 5 FIG. 1 is a schematic diagram of a vehicle path tracking control device according to an embodiment of the application, which is used to realize the vehicle path tracking control method of the above-mentioned embodiment. Figure 5 ​As shown, the apparatus comprises the following: a first prediction module 502, a second prediction module 504, a first determination module 506, and a second determination module 508.

[0170] The first prediction module 502 is configured to predict an expected front wheel steering angle of the vehicle at a next time instant based on a navigation planning path and a running state of the vehicle at a current time instant, wherein the running state at least comprises a yaw rate at the current time instant; the second prediction module 504 is configured to predict an expected yaw rate of the vehicle at the next time instant based on a dynamics model of the vehicle and the expected front wheel steering angle, wherein the dynamics model is used to represent a correlation between the expected yaw rate and the expected front wheel steering angle; the first determination module 506 is configured to determine a front wheel steering angle compensation value of the vehicle based on a second order sliding mode control on an error between the expected yaw rate and the yaw rate at the current time instant, wherein the front wheel steering angle compensation value is used to compensate the front wheel steering angle of the vehicle to adjust the error; and the second determination module 508 is configured to determine a front wheel steering angle control value of the vehicle based on the expected front wheel steering angle and the front wheel steering angle compensation value, wherein the front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle.

[0171] The first determination module is further configured to construct a sliding surface based on the error between the expected yaw rate and the yaw rate at the current time instant; construct a control law based on the second order sliding mode control on the sliding surface, wherein the control law is used to control a speed of a drive system of the vehicle to reach the sliding surface; and adjust parameters of the dynamics model based on the control law to obtain the front wheel steering angle compensation value.

[0172] The first determination module is further configured to determine a derivative of the sliding surface; determine a first control law term based on a target power of the derivative, a sign of the derivative, and a first control gain; determine a second control law term based on the derivative, the sign of the derivative, and a second control gain; and obtain the control law based on the first control law term and the second control law term.

[0173] The first determination module is further configured to construct an objective function based on the sliding surface; perform a stability analysis on the drive system of the vehicle by using the objective function to obtain an analysis result, wherein the analysis result is used to represent whether the drive system is in a stable state; and in response to the analysis result representing that the drive system is in the stable state, adjust the parameters of the dynamics model based on the control law to obtain the front wheel steering angle compensation value.

[0174] The first prediction module is further configured to determine a longitudinal displacement and a lateral displacement of the vehicle at the next time instant based on the navigation planning path and the running state; and determine the expected front wheel steering angle based on a motion geometry relationship of the vehicle, the longitudinal displacement, and the lateral displacement.

[0175] The running state further includes a vehicle speed, a heading angle and a vehicle position at a current time; the first prediction module is further configured to determine a longitudinal displacement based on the vehicle speed and a preset time length, the preset time length being a time length from the current time to a next time; determine a preview point at the next time from the navigation planning path based on the running state; and determine a lateral displacement based on a lateral error between the vehicle position and the preview point.

[0176] The first prediction module is further configured to determine a yaw rate at the next time based on the motion geometry relationship, the longitudinal displacement and the lateral displacement; and determine an expected front wheel steering angle based on the yaw rate at the next time and a preset mapping relationship, the preset mapping relationship being used to represent a mapping relationship between the yaw rate at the next time and the expected front wheel steering angle.

[0177] The second prediction module is further configured to acquire a dynamic parameter of the vehicle; and construct a dynamic model based on the vehicle speed in the running state, the dynamic parameter and a motion geometry relationship of the vehicle.

[0178] Embodiments of the present application further provide a vehicle, comprising a memory storing an executable program; and a processor configured to execute the program, wherein the program is executed to perform the method in any of the embodiments of the present application.

[0179] Embodiments of the present application further provide a computer readable storage medium, comprising a stored executable program, wherein the executable program is executed to control a device where the computer readable storage medium is located to perform the method in any of the embodiments of the present application.

[0180] Embodiments of the present application further provide a computer program product, comprising a computer program, the computer program being executed by a processor to implement the method in any of the embodiments of the present application.

[0181] Embodiments of the present application further provide a computer program product, comprising a non-volatile computer readable storage medium, the non-volatile computer readable storage medium being used to store a computer program, the computer program being executed by a processor to implement the method in any of the embodiments of the present application.

[0182] Embodiments of the present application further provide a computer program, the computer program being executed by a processor to implement the method in any of the embodiments of the present application.

[0183] In the above embodiments of the present application, the description of each embodiment has its own focus, and the part not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0184] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0185] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0186] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0187] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0188] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled persons in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A vehicle path tracking control method, characterized in that: include: Predicting an expected front wheel steering angle of the vehicle at a next moment based on a navigation planning path and an operating state of the vehicle at a current moment, wherein the operating state includes at least: a yaw angular velocity at the current moment; predicting an expected yaw rate of the vehicle at a next moment based on a dynamic model of the vehicle and the expected front wheel steering angle, wherein the dynamic model is used to characterize a correlation between the expected yaw rate and the expected front wheel steering angle; performing second-order sliding mode control based on an error between the desired yaw rate and the current yaw rate to determine a front wheel steering angle compensation value for the vehicle, wherein the front wheel steering angle compensation value is used to compensate the front wheel steering angle of the vehicle to adjust the error; A front wheel steering angle control value of the vehicle is determined based on the desired front wheel steering angle and the front wheel steering angle compensation value, wherein the front wheel steering angle control value is used to control the vehicle to adjust the front wheel steering angle.

2. The method according to claim 1, characterized in that The performing second-order sliding mode control based on the error between the desired yaw rate and the current yaw rate to determine the front wheel steering angle compensation value of the vehicle includes: constructing a sliding mode surface based on an error between the desired yaw rate and the current yaw rate; Performing second-order sliding mode control based on the sliding mode surface to construct a control law, wherein the control law is used to control the speed at which the drive system of the vehicle reaches the sliding mode surface; The parameters of the dynamic model are adjusted based on the control law to obtain the front wheel steering angle compensation value.

3. The method according to claim 2, characterized in that The second-order sliding mode control is performed based on the sliding mode surface to construct a control law, including: determining a derivative of the sliding surface; determining a first control law term based on a target power of the derivative, a sign of the derivative, and a first control gain; determining a second control law term based on the derivative, the sign of the derivative, and a second control gain; The control law is obtained based on the first control law term and the second control law term.

4. The method according to claim 2, characterized in that The adjusting the parameters of the dynamic model based on the control law to obtain the front wheel steering angle compensation value includes: constructing an objective function based on the sliding surface; Performing a stability analysis on the vehicle's drive system using the objective function to obtain an analysis result, wherein the analysis result is used to indicate whether the drive system is in a stable state; In response to the analysis result indicating that the drive system is in the stable state, the parameters of the dynamic model are adjusted based on the control law to obtain the front wheel steering angle compensation value.

5. The method according to any one of claims 1 to 4, characterized in that The method of predicting an expected front wheel steering angle of the vehicle at a next moment based on the navigation planning path and the operating state of the vehicle at a current moment includes: Determining the longitudinal displacement and lateral displacement of the vehicle at the next moment based on the navigation planning path and the operating state; The desired front wheel steering angle is determined based on the vehicle's kinematic geometry, the longitudinal displacement, and the lateral displacement.

6. The method according to claim 5, characterized in that The operating state further includes: the vehicle speed, heading angle, and vehicle position at the current moment; and determining the longitudinal displacement and lateral displacement of the vehicle at the next moment based on the navigation planning path and the operating state includes: Determining the longitudinal displacement based on the vehicle speed and a preset time duration, wherein the preset time duration is a time duration from the current moment to the next moment; Based on the operating state, determining the preview point at the next moment from the navigation planning path; The lateral displacement is determined based on a lateral error between the vehicle position and the preview point.

7. The method according to claim 5, characterized in that The determining the desired front wheel steering angle based on the vehicle's kinematic geometry, the longitudinal displacement, and the lateral displacement includes: determining the yaw rate at the next moment based on the motion geometric relationship, the longitudinal displacement, and the lateral displacement; The expected front wheel steering angle is determined based on the yaw angular velocity at the next moment and a preset mapping relationship, wherein the preset mapping relationship is used to represent a mapping relationship between the yaw angular velocity at the next moment and the expected front wheel steering angle.

8. The method according to any one of claims 1 to 4, characterized in that The method further comprises: obtaining dynamic parameters of the vehicle; The dynamic model is constructed based on the vehicle speed in the operating state, the dynamic parameters and the motion geometry of the vehicle.

9. A vehicle, characterized in that: include: a memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 8 when running.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 8.

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