Vehicle control systems, methods, electronic devices and media

Abstract

This application provides a vehicle control system, method, electronic device, and medium, relating to the field of vehicle technology. The vehicle control system includes a lateral control module and a longitudinal control module; wherein the lateral control module includes a first wheel steering angle control module, and the longitudinal control module includes a speed tracking control module and an acceleration compensation module. The first wheel steering angle control module obtains the first wheel steering angle control quantity of the first vehicle based on a path tracking control problem, where the first wheel is either the front wheel or the rear wheel; the speed tracking control module obtains the desired acceleration of the first vehicle based on a speed tracking control problem; the acceleration compensation module uses a nonlinear disturbance observer to estimate the value of the longitudinal control disturbance parameter based on the actual acceleration and the desired acceleration, and obtains the acceleration control quantity of the first vehicle accordingly. This application can balance the control performance and robustness of vehicle control.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle control system, method, electronic device and medium. Background Technology

[0002] Control performance and robustness are both important factors affecting the effectiveness of vehicle control. For example, the uncertainty of the external environment in which the vehicle is located affects the longitudinal control performance of the vehicle. Therefore, it is necessary to take both control performance and robustness into account when designing vehicle control. Summary of the Invention

[0003] This application provides a vehicle control system, method, electronic device, and medium that can balance the control performance and robustness of vehicle control.

[0004] In a first aspect, embodiments of this application provide a vehicle control system, including: a vehicle lateral control module and a vehicle longitudinal control module; wherein the vehicle lateral control module includes a first wheel steering angle control module, and the vehicle longitudinal control module includes a speed tracking control module and an acceleration compensation module; the first wheel steering angle control module is used to solve a path tracking control problem for tracking a planned path, and obtain a first wheel steering angle control quantity for a first vehicle, wherein the first wheel is one of the front wheel and the rear wheel; wherein the planned path includes a reference pose at a series of future time points, and the reference pose includes at least one of a reference lateral position and a reference heading angle; the speed tracking control module... The system is used to solve the speed tracking control problem for tracking the planned speed and obtain the desired acceleration of the first vehicle; wherein the planned speed includes reference speeds at a series of future time points; the acceleration compensation module is used to use a nonlinear disturbance observer to solve a first function based on the actual acceleration and the desired acceleration of the first vehicle to obtain the value of the longitudinal control disturbance parameter; and obtain the acceleration control quantity of the first vehicle based on the value of the longitudinal control disturbance parameter and the desired acceleration of the first vehicle; wherein the first function includes the correlation between the desired acceleration of the vehicle, the actual acceleration of the vehicle, the longitudinal control disturbance parameter and the calibration parameter of the nonlinear disturbance observer.

[0005] In this embodiment, the first wheel steering angle control module and the speed tracking control module are independent of the vehicle chassis interface. They meet the control performance requirements through a designed high-performance control law to achieve precise control of path tracking and speed tracking. The acceleration compensation module, on the other hand, depends on the vehicle chassis interface and meets the requirements for resisting unknown interference through a designed robust control law to ensure the robustness of vehicle control. In this way, the vehicle control system can balance control performance and robustness.

[0006] By using a nonlinear disturbance observer to perform real-time estimation and feedforward compensation of the longitudinal control disturbance term, the vehicle control can still exhibit high performance with "fast convergence, high accuracy, and strong robustness" even when there are uncertainties in the external environment of the vehicle and the modeling mechanism of the nominal vehicle control model.

[0007] Optionally, the vehicle lateral control module further includes a second wheel steering angle control module; the second wheel steering angle control module is used to obtain the second wheel steering angle control amount of the first vehicle based on the correlation between the front wheel steering angle and the rear wheel steering angle of the first vehicle and the first wheel steering angle control amount of the first vehicle, wherein the second wheel is the other of the front wheel and the rear wheel; wherein the correlation is obtained based on the maximum value of the front wheel steering angle and the maximum value of the rear wheel steering angle of the first vehicle.

[0008] The second wheel steering angle control module relies on the vehicle chassis interface and uses a robust control law to meet the requirements of resisting unknown interference, so as to ensure the robustness of the vehicle's lateral control.

[0009] Optionally, the first function is obtained based on the longitudinal control model, which includes the vehicle's desired acceleration, the vehicle's actual acceleration, and the correlation between the longitudinal control disturbance parameters and the parameters of the first-order inertial element.

[0010] By using a nonlinear disturbance observer to perform real-time estimation and feedforward compensation of the longitudinal control disturbance term, the burden on the speed tracking control module can be reduced.

[0011] Optionally, the acceleration compensation module is used to use a tracking differentiator to solve a second function based on the actual speed of the first vehicle to obtain the actual acceleration of the first vehicle; wherein the second function includes the correlation between the actual speed of the vehicle, the actual acceleration of the vehicle, and the calibration parameters of the tracking differentiator.

[0012] By using a tracking differentiator to estimate the vehicle's actual acceleration based on its actual speed, the vehicle's actual acceleration can be accurately obtained.

[0013] Optionally, solving the speed tracking control problem for tracking the planned speed includes: solving a third function using a fast terminal sliding mode control law; wherein the third function is obtained from a fast terminal sliding mode surface including a speed tracking error, which describes the error between the reference speed and the vehicle speed predicted based on the desired acceleration.

[0014] The fast terminal sliding mode control law can bring the speed tracking error to zero within a finite time, enabling the vehicle longitudinal control to exhibit high performance with "fast convergence, high accuracy and strong robustness".

[0015] Optionally, solving the path tracking control problem for tracking the planned path includes: solving a quadratic programming problem, wherein the quadratic programming problem is obtained by transforming the path tracking control problem, and the constraints of the quadratic programming problem are obtained based on the vehicle kinematic equations and the Lagrange function.

[0016] The path tracking control problem can be transformed from a nonlinear optimization problem into a linear quadratic programming problem with optimization. The first wheel steering angle control value can be obtained by solving the quadratic programming problem.

[0017] Optionally, any solution result includes state variables reflecting the vehicle's pose and the first wheel's steering angle at a series of future time points, control variables reflecting the first wheel's steering angular velocity, and the values ​​of Lagrange multipliers. Solving the quadratic programming problem includes: if there is a solution result from the previous iteration, then solving the quadratic programming problem based on the solution result from the previous iteration to obtain the state variable increment; obtaining the solution result for the current iteration based on the obtained state variable increment and the solution result from the previous iteration; if the set conditions are met, then the control variable for the first wheel's steering angle of the first vehicle is the first wheel's steering angle at the next moment obtained in the current iteration; the set conditions include that the absolute value of the obtained state variable increment is not greater than a set tolerance; if the set conditions are not met, then the steps for solving the quadratic programming problem are executed again.

[0018] By iteratively solving the quadratic programming problem based on the increment of the solution results obtained from two adjacent iterations, and by limiting the increment of the state variable at the end of the iteration to be sufficiently small, the accurate acquisition of the first vehicle steering angle control variable can be supported.

[0019] Secondly, embodiments of this application provide a vehicle control method, comprising: solving a path tracking control problem for tracking a planned path to obtain a first wheel steering angle control quantity for a first vehicle, wherein the first wheel is one of a front wheel and a rear wheel; wherein the planned path includes a reference pose at a series of future time points, and the reference pose includes at least one of a reference lateral position and a reference heading angle; solving a speed tracking control problem for tracking a planned speed to obtain a desired acceleration of the first vehicle; wherein the planned speed includes a reference speed at a series of future time points; using a nonlinear disturbance observer, solving a first function based on the actual acceleration and the desired acceleration of the first vehicle to obtain a value of a longitudinal control disturbance parameter; and obtaining an acceleration control quantity for the first vehicle based on the value of the longitudinal control disturbance parameter and the desired acceleration of the first vehicle; wherein the first function includes the correlation between the desired acceleration of the vehicle, the actual acceleration of the vehicle, the longitudinal control disturbance parameter, and the calibration parameters of the nonlinear disturbance observer.

[0020] Thirdly, embodiments of this application provide an electronic chip, including: a processor for executing computer program instructions stored in a memory, wherein when the computer program instructions are executed by the processor, the electronic chip is triggered to perform the method as described in any of the first aspects.

[0021] Fourthly, embodiments of this application provide an electronic device including at least one processor and a memory coupled together. The memory is used to store computer program instructions, and the processor is used to execute the computer program instructions. When the computer program instructions are executed by the processor, the electronic device is triggered to perform a method as described in any of the first aspects.

[0022] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method as described in any of the first aspects.

[0023] In a sixth aspect, embodiments of this application provide a computer program product, which includes a computer program that, when run on a computer, causes the computer to perform the method as described in any of the first aspects.

[0024] The technical effects of the aforementioned aspects can be referenced from each other, and will not be elaborated further here. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below.

[0026] Figure 1 A block diagram of a vehicle control system provided in this application embodiment;

[0027] Figure 2 A block diagram of another vehicle control system provided in this application embodiment;

[0028] Figure 3 A schematic diagram illustrating a vehicle kinematic model provided in an embodiment of this application;

[0029] Figure 4 This is a schematic diagram illustrating the path tracking control problem provided in the embodiments of this application;

[0030] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0031] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0032] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0033] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0034] It should be understood that the term "at least one" as used in this document refers to one or more, and "more than one" refers to two or more. The term "and / or" as used in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Additionally, the character " / " in this document generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0035] It should be understood that although the terms "first," "second," etc., may be used to describe the set thresholds in the embodiments of this application, these set thresholds should not be limited to these terms. These terms are only used to distinguish the set thresholds from each other. For example, without departing from the scope of the embodiments of this application, the first set threshold may also be referred to as the second set threshold, and similarly, the second set threshold may also be referred to as the first set threshold.

[0036] refer to Figure 1 One embodiment of this application provides a vehicle control system for controlling a vehicle 100. The vehicle control system has a decoupled design for lateral control and longitudinal control. The longitudinal control module includes a fast terminal sliding mode control law 101, a tracking differentiator 1021 and a nonlinear disturbance observer 1022. The lateral control module includes a top-level lateral control module 103 and a bottom-level lateral allocation module 104.

[0037] For example, vehicle 100 can be a four-wheel steering vehicle, a front-wheel steering vehicle, a rear-wheel steering vehicle, a steer-by-wire vehicle, or a non-steer-by-wire vehicle. Taking a front-wheel steering vehicle as an example, compared to a front-wheel steering vehicle (where the front wheels are the drive wheels), a four-wheel steering vehicle can simultaneously control the front and rear wheels to achieve steering actions by adding rear wheel steering angle control. This can improve the vehicle's maneuverability during parking and driving. For example, a four-wheel steering vehicle can more easily complete parking operations in narrow environments.

[0038] Figure 1 The vehicle control system shown is designed based on a hierarchical architecture, which decouples the lateral and longitudinal control, the front and rear wheel control in the lateral control, and the control performance and robustness indicators in the longitudinal control.

[0039] Figure 1 In the vehicle control system shown, the top-level lateral control module 103 solves for the front wheel steering angle control quantity that enables path tracking control based on the planned path; the bottom-level lateral allocation module 104 solves for the rear wheel steering angle control quantity based on the front wheel steering angle control quantity output by the top-level lateral control module 103 and the correlation between the front and rear wheel steering angles.

[0040] The planned path can include the vehicle's reference lateral position y at a series of future time points. ref and reference heading angle Lateral control of a vehicle can be achieved by controlling the steering angles of its front and rear wheels.

[0041] In one implementation, the top-level lateral control module 103 can calculate the desired front wheel steering angle based on the tracking error of the planned path (such as lateral position error and heading angle error).

[0042] The top-level lateral control module 103 is independent of the vehicle chassis interface. It uses high-performance control laws to meet control performance requirements and ensure vehicle lateral control performance. The bottom-level lateral distribution module 104 depends on the vehicle chassis interface. It uses robust control laws to meet the requirements of resisting unknown interference and ensure the robustness of vehicle lateral control.

[0043] By defining the vehicle's lateral control strategy as a hierarchical architecture that decouples the calculation process of the desired front wheel steering angle and the desired rear wheel steering angle, the closed-loop control effect of the front and rear wheel steering angles in a four-wheel steering vehicle during parking can be made more human-like, safer, and more comfortable.

[0044] Figure 1 In the vehicle control system shown, the fast terminal sliding mode control law 101, based on the planned speed, solves for the desired acceleration α that enables speed tracking control. cThe fast terminal sliding mode control law 101 is used to achieve the purpose of tracking the planned speed, so as to ensure the longitudinal control performance of the vehicle.

[0045] The planned speed can include the vehicle's reference speed v at a series of future time points. ref By controlling the vehicle's acceleration, longitudinal control of the vehicle can be achieved.

[0046] In one implementation, the fast terminal sliding mode control law 101 can calculate the desired vehicle acceleration based on the tracking error of the planned speed.

[0047] Figure 1 In the vehicle control system shown, the tracking differentiator 1021 estimates the vehicle's actual acceleration (i.e., the estimated value of the vehicle's actual acceleration) based on the vehicle's actual speed. The nonlinear disturbance observer 1022 estimates the longitudinal control disturbance term d in real time based on the actual vehicle acceleration estimated by the tracking differentiator 1021 and the reference acceleration obtained by solving the fast terminal sliding mode control law 101, thus obtaining the disturbance estimate. Then, the acceleration control quantity is obtained by feedforward compensation of the desired acceleration based on the disturbance estimate d. That is, the tracking differentiator 1021 and the nonlinear disturbance observer 1022 are combined to realize the estimation and feedforward compensation of vehicle control disturbances, so as to ensure the robustness of vehicle longitudinal control.

[0048] For example, the longitudinal control disturbance term may include uncertainties in the external environment of the vehicle and uncertainties in the modeling mechanism of the nominal model used to design the vehicle controller. The nonlinear disturbance observer 1022 can reduce the burden of the fast terminal sliding mode control law and improve the longitudinal control performance of the vehicle by performing real-time estimation and feedforward compensation of the longitudinal control disturbance term.

[0049] By limiting the vehicle's longitudinal control strategy to real-time estimation and feedforward compensation of the longitudinal control disturbance term using a nonlinear disturbance observer, the vehicle control can still exhibit high performance with "fast convergence, high accuracy, and strong robustness" even when there are uncertainties in the external environment of the vehicle and the modeling mechanism of the vehicle control nominal model.

[0050] The fast terminal sliding mode control law 101 is independent of the vehicle chassis interface. It meets the control performance requirements by designing a high-performance control law to ensure the longitudinal control performance of the vehicle. The acceleration compensation module, composed of the tracking differentiator 1021 and the nonlinear disturbance observer 1022, depends on the vehicle chassis interface. It meets the requirements for resisting unknown disturbances by designing a robust control law to ensure the robustness of the longitudinal control of the vehicle.

[0051] The vehicle control system can achieve vehicle driving control by decoupling the lateral and longitudinal directions of the vehicle.

[0052] in this way, Figure 1 The vehicle control system shown can have at least the following characteristics:

[0053] (1) The fast terminal sliding mode control law and the top-level lateral control module are independent of the vehicle chassis interface. The high-performance control law is designed to meet the control performance requirements, so as to achieve precise control of path tracking and speed tracking. The acceleration compensation module and the bottom-level lateral distribution module depend on the vehicle chassis interface. The robust control law is designed to meet the requirements of resisting unknown interference, so as to ensure the robustness of vehicle control. In this way, the vehicle control system can take into account both control performance and robustness.

[0054] (2) By decoupling lateral control and longitudinal control through a hierarchical architecture, and decoupling the two control quantities of front wheel steering angle and rear wheel steering angle in vehicle lateral control, and decoupling control performance and robustness indicators in vehicle longitudinal control, the vehicle control exhibits high performance with "fast convergence, high precision and strong robustness".

[0055] (3) During the driving process of a four-wheel steering vehicle, the control commands for the front and rear wheel steering angles exhibit a complex nonlinear relationship with the vehicle's yaw motion, which affects the vehicle's dynamic response characteristics. Figure 1 The embodiment shown decouples the front and rear wheel controls and uses a designed robust control law to address the impact of this complex nonlinear relationship on the vehicle's dynamic response characteristics, thus improving the robustness of the vehicle's lateral control.

[0056] During vehicle operation, the relationship between the drive and braking control commands and the vehicle's longitudinal motion exhibits a complex nonlinearity. Furthermore, the uncertainties of the external environment in which the vehicle operates, as well as the uncertainties in the nominal modeling mechanism used to design the vehicle controller, also affect the vehicle's dynamic response characteristics. Figure 1 The embodiment shown decouples control performance and robustness indices in vehicle longitudinal control and uses a nonlinear disturbance observer to perform real-time estimation and feedforward compensation of longitudinal control disturbance terms. This addresses the impact of complex nonlinear relationships and uncertainties on vehicle dynamic response characteristics and improves the robustness of vehicle longitudinal control.

[0057] Thus, the vehicle is based on Figure 1 The vehicle control system shown is capable of nonlinear robust vehicle control, thereby enabling precise tracking and control of the planned trajectory and planned speed.

[0058] (4) Through the layered architecture design, the vehicle control requirements of "easy to implement, easy to maintain, easy to expand and easy to upgrade" are met.

[0059] (5) The vehicle control system has backward compatibility, meaning it can be applied not only to the vehicle control of four-wheel steering vehicles but also to the vehicle control of front-wheel steering vehicles. Therefore, it can serve as the basis for building a "high-performance, platform-based, and vehicle-model-decoupled" general vehicle control architecture.

[0060] (6) Figure 1 When the vehicle control system shown is applied to the parking scenario of a four-wheel steering vehicle, the four-wheel steering vehicle can synchronously control the front and rear wheels to achieve steering actions based on the vehicle control system, and can accurately track and control the parking planning path, so that the four-wheel steering vehicle has better maneuverability during the parking process and can more easily complete the parking operation in narrow spaces.

[0061] refer to Figure 2 This application provides a vehicle control system 200, which includes a vehicle lateral control module 210 and a vehicle longitudinal control module 220. The vehicle lateral control module 210 includes a first wheel steering angle control module 211, and the vehicle longitudinal control module 220 includes a speed tracking control module 221 and an acceleration compensation module 222.

[0062] Feasibly, the vehicle control system 200 is applicable to vehicle control scenarios such as parking and driving. During parking, the vehicle is in a low-speed, large-angle movement state, and the vehicle control system 200 supports accurate and stable parking in parking scenarios.

[0063] Feasibly, the vehicle control system 200 is applicable to vehicles such as front-wheel steering vehicles, rear-wheel steering vehicles, and four-wheel steering vehicles. Specifically, when the vehicle control system 200 is used to control a front-wheel steering vehicle, the first wheel is the front wheel; when the vehicle control system 200 is used to control a rear-wheel steering vehicle, the first wheel is the rear wheel. When the vehicle control system 200 is used to control a four-wheel steering vehicle, the first wheel can be the front wheel.

[0064] In the vehicle control system 200, the first wheel steering angle control module 211 solves the path tracking control problem for tracking the planned path, and obtains the first wheel steering angle control quantity of the first vehicle. The first wheel is one of the front wheel and the rear wheel. The planned path includes reference poses at a series of future time points, and the reference poses include at least one of reference lateral position and reference heading angle.

[0065] In one embodiment, the first wheel steering angle control module 211 may be the aforementioned top-level lateral control module 103.

[0066] Feasibly, the path tracking control problem is used to track a planned path by controlling the steering angle of the first wheel of a first vehicle. The steering angle of the first wheel of the first vehicle can be controlled based on the first wheel steering angle control value obtained from solving the path tracking control problem.

[0067] In one embodiment, a path tracking control problem including a first wheel steering angle control quantity and a speed tracking control problem including a reference acceleration can be established based on the vehicle coordinate system. In this embodiment, the lateral and longitudinal directions can be the lateral and longitudinal directions based on the vehicle coordinate system.

[0068] In one embodiment, the constraints of the path tracking control problem can be obtained from the vehicle control dynamics model, the maximum / minimum values ​​of the wheel steering angle, etc.

[0069] Taking the first wheel as the front wheel as an example, in one embodiment, the path tracking control problem can be solved repeatedly to obtain the front wheel steering angle control quantity that meets the path tracking requirements. The path tracking requirement can be that the difference between the predicted trajectory and the planned path meets the requirements (i.e., controlling the vehicle to travel along the planned path laterally). The predicted trajectory is the vehicle's predicted trajectory over a future period of time when the vehicle is controlled using the solved front wheel steering angle control quantity.

[0070] If the front wheel steering angle obtained in the current iteration does not meet the path tracking requirements, the next iteration of the path tracking control problem can be executed based on the front wheel steering angle obtained in the current iteration. This ensures that as the number of iterations increases, the obtained front wheel steering angle control value will be more likely to meet the path tracking requirements. In this way, the path tracking control problem can be solved repeatedly until a front wheel steering angle that meets the path tracking requirements is obtained. This front wheel steering angle is then used as the front wheel steering angle control value to control the vehicle's front wheel steering angle for lateral control.

[0071] The reference pose in the planned path can include a reference lateral position and / or a reference heading angle. By using the reference lateral position and / or reference heading angle as a reference to solve the front wheel steering angle, and using the solved front wheel steering angle to control the vehicle, the vehicle can achieve the effect of lateral control that tracks the planned path.

[0072] The planned path can include reference poses at a series of future time points, such as the reference poses at steps 0 to N. Based on the vehicle's current state variables (including pose) and control variables (including front wheel steering angle), the vehicle's state variables at the next time step can be predicted. Thus, in any given iteration, the state variables and control variables at steps 0 to N can be predicted sequentially, and the resulting series of state variables reflects the predicted trajectory.

[0073] In the vehicle control system 200, the speed tracking control module 221 solves the speed tracking control problem for tracking the planned speed to obtain the expected acceleration of the first vehicle; wherein, the planned speed includes the reference speed at a series of future time points.

[0074] In one embodiment, the speed tracking control module 221 can be the aforementioned fast terminal sliding mode control law 101.

[0075] Feasibly, the speed tracking control problem can be used to track a planned speed by controlling the acceleration of a first vehicle. The acceleration of the first vehicle can be controlled based on the desired acceleration obtained from solving the speed tracking control problem, after acceleration disturbance compensation.

[0076] In the vehicle control system 200, the acceleration compensation module 222 uses a nonlinear disturbance observer to solve a first function based on the actual acceleration and the desired acceleration of the first vehicle to obtain the value of the longitudinal control disturbance parameter; based on the value of the longitudinal control disturbance parameter and the desired acceleration of the first vehicle, it obtains the acceleration control quantity of the first vehicle; wherein, the first function includes the correlation between the desired acceleration of the vehicle, the actual acceleration of the vehicle, the longitudinal control disturbance parameter and the calibration parameter of the nonlinear disturbance observer.

[0077] The vehicle longitudinal control module can control the acceleration of the first vehicle based on the obtained acceleration control value.

[0078] In one embodiment, the value of the longitudinal control disturbance parameter can be added to the desired acceleration of the first vehicle to obtain the acceleration control amount of the first vehicle.

[0079] In one embodiment, the vehicle's speed can be collected from onboard sensors, i.e., the actual speed of the vehicle, and the actual acceleration of the vehicle can be estimated based on the actual speed of the vehicle.

[0080] In one feasible implementation, the longitudinal control disturbance term can represent a combination of disturbance factors such as the uncertainty of the external environment in which the vehicle is located, the uncertainty of the modeling mechanism of the nominal model used to design the vehicle controller, and the complex nonlinear relationship between the drive and braking control commands and the longitudinal motion of the vehicle.

[0081] By using a nonlinear disturbance observer to estimate the values ​​of longitudinal control disturbance parameters in real time based on the actual and desired acceleration, and then performing feedforward compensation for acceleration accordingly, an acceleration control quantity that meets the speed tracking requirement can be obtained. The speed tracking requirement can be defined as the difference between the predicted and planned speed meeting a certain requirement (i.e., controlling the vehicle to travel at the planned speed longitudinally), where the predicted speed is the vehicle's predicted speed over a future period when the vehicle is controlled using the solved acceleration control quantity.

[0082] Since the acceleration control quantity obtained after compensation eliminates the influence of longitudinal control disturbance terms on the longitudinal control of the vehicle, the acceleration control quantity can better meet the speed tracking requirements compared with the reference acceleration, thus ensuring the longitudinal control effect of the vehicle.

[0083] To ensure that the vehicle control system 200 is applicable not only to four-wheel steering vehicles but also backward compatible to front-wheel or rear-wheel steering vehicles, in one embodiment of this application, reference is made to... Figure 2 The vehicle lateral control module 210 also includes a second wheel steering angle control module 212; the second wheel steering angle control module 212 obtains the second wheel steering angle control amount of the first vehicle based on the correlation between the front wheel steering angle and the rear wheel steering angle of the first vehicle and the first wheel steering angle control amount of the first vehicle, wherein the second wheel is the other of the front wheel and the rear wheel; wherein the correlation is obtained based on the maximum value of the front wheel steering angle and the maximum value of the rear wheel steering angle of the first vehicle.

[0084] The steering angle of the second wheel of the first vehicle can be controlled based on the obtained second wheel steering angle control value.

[0085] In one embodiment, the second wheel steering angle control module 212 can be the aforementioned underlying lateral allocation module 104.

[0086] After the top-level lateral control module obtains the front wheel steering angle control value, the bottom-level lateral distribution module can calculate and output the rear wheel steering angle control value accordingly, thereby achieving perfect decoupled control of the front wheel steering angle and the rear wheel steering angle of a four-wheel steering vehicle.

[0087] The second wheel steering angle control module relies on the vehicle chassis interface and uses a robust control law to meet the requirements of resisting unknown interference, so as to ensure the robustness of the vehicle's lateral control.

[0088] The vehicle control system 200 can achieve a reasonable allocation of the steering angle control quantity of the front and rear wheels by decoupling the steering angles of the front and rear wheels and combining the correlation between the steering angles of the front and rear wheels, thereby improving the accuracy of lateral control.

[0089] refer to Figure 3 The schematic diagram shown reflects the vehicle kinematics model of a four-wheel steering vehicle 301. In one embodiment, the rear wheel steering angle δ of the vehicle can be defined. r With front wheel steering angle δ f The action is performed and satisfies the following equation (1).

[0090] L f tanδ r =L r tanδ f (1)

[0091] In equation (1), L f and L r These are the distances from point A on a four-wheel steering vehicle to the front and rear axles, respectively.

[0092] In equation (1),

[0093] Among them, the maximum steering angles of the front and rear wheels of a four-wheel steering vehicle are δ fmax and δ rmax L is the wheelbase of a four-wheel steering vehicle.

[0094] As can be seen from equation (1), the bottom-level lateral distribution module can calculate the output rear wheel steering angle control quantity based on the front wheel steering angle control quantity output by the top-level lateral control module by comprehensively considering the maximum value constraint of the front and rear wheel steering angles of the four-wheel steering vehicle.

[0095] For example, the relationship between the front and rear wheel steering angles of the first vehicle can be expressed as Equation (1). Then, by substituting the first wheel steering angle control quantity output by the first wheel steering angle control module 211 into Equation (1), the second wheel steering angle control quantity of the first vehicle can be obtained.

[0096] In one embodiment of this application, solving the path tracking control problem for tracking the planned path includes: solving a quadratic programming problem, wherein the quadratic programming problem is obtained by transforming the path tracking control problem, and the constraints of the quadratic programming problem are obtained based on the vehicle kinematics equations and the Lagrange function.

[0097] The path tracking control problem is used to determine the first wheel steering angle control value that satisfies the path tracking requirement. Based on the vehicle kinematic equations and the Lagrangian function, the path tracking control problem can be transformed into a quadratic programming problem. The first wheel steering angle control value is obtained by iteratively solving the quadratic programming problem. In other words, the path tracking control problem can be transformed from a nonlinear optimization problem into a linear quadratic programming problem with optimization, and the first wheel steering angle control value is obtained by solving the quadratic programming problem.

[0098] In one embodiment of this application, any solution result includes state variables reflecting the vehicle pose and the first wheel steering angle at a series of future time points, control variables reflecting the first wheel steering angular velocity, and the values ​​of Lagrange multipliers. Based on this, the step of solving the quadratic programming problem may include:

[0099] If the solution result of the previous loop exists, then the quadratic programming problem is solved based on the solution result of the previous loop to obtain the state variable increment; according to the obtained state variable increment and the solution result of the previous loop, the solution result of the current loop is obtained; if the set conditions are met, then the control quantity of the first wheel steering angle of the first vehicle is the first wheel steering angle of the next moment obtained in the current loop; the set conditions include that the absolute value of the obtained state variable increment is not greater than the set tolerance.

[0100] In the process of iteratively solving the quadratic programming problem, the actual lateral position of the vehicle at future time can be the prediction result of the vehicle's lateral position, and the actual heading angle of the vehicle at future time can be the prediction result of the vehicle's heading angle.

[0101] Since the solution process in the first loop does not have the solution result from the previous loop, the first loop solution process can be performed based on the preset initial information. Subsequent loop solutions are all based on the solution result of the previous loop (i.e., the (k+1)th loop solution is performed based on the solution result of the kth loop).

[0102] The state increment is the difference between the state variables obtained from solving the quadratic programming problem in two consecutive iterations. By always performing iterations based on the results of the previous iteration, the solution results can better meet the path tracking requirements as the number of iterations increases.

[0103] If the absolute value of the increment of the state variable is not greater than the set tolerance, it means that the difference between the state variables obtained by the two adjacent loops is small, that is, the state variables obtained by the two adjacent loops are similar, so there is no need to continue the loop, and the loop solution process can be terminated.

[0104] Conversely, if the absolute value of the state variable increment is greater than the set tolerance, it indicates that the state variables obtained from two adjacent iterations differ significantly, so the iteration needs to continue until the tolerance requirement is met. Thus, in one embodiment of this application, if the set conditions are not met, the steps for solving the quadratic programming problem are executed again.

[0105] By iteratively solving the quadratic programming problem based on the increment of the solution results obtained from two adjacent iterations, and by limiting the increment of the state variable at the end of the iteration to be sufficiently small, the accurate acquisition of the first vehicle steering angle control variable can be supported.

[0106] The following example illustrates a feasible implementation process for the first wheel steering angle control module 211 to solve for the front wheel steering angle control quantity, using the first wheel as the front wheel, the vehicle as a steerable four-wheel drive car, and the planned path as a planned parking path. The related technical implementation is also applicable to embodiments of vehicles with the first wheel as the rear wheel, vehicles with front or rear wheel steering, vehicles turning on the spot, and vehicles with a planned driving path.

[0107] refer to Figure 3 Using x a y a and The pose of point A on a steer-by-wire four-wheel steering vehicle (i.e., the lateral and longitudinal positions of point A in the OXY coordinate system, and the heading angle of the steer-by-wire four-wheel steering vehicle) is represented by v. a The speed of point A on a steerable four-wheel drive vehicle is represented by ω. f Let represent the steering angular velocity of the front wheel of a steerable four-wheel drive vehicle. Based on the instantaneous center of velocity theorem, the kinematic equation of the steerable four-wheel drive vehicle described by equation (2) can be established.

[0108]

[0109] Based on equation (2), the system state vector is defined as follows: The control vector is u = [ω f ] T Therefore, equation (2) can be rewritten as equation (3).

[0110]

[0111] By discretizing equation (3) using the fourth-order Runge-Kutta integral method, equation (4) can be obtained.

[0112]

[0113] In equation (4), x(k) represents the state vector at step k (or the state vector at the kth time point in the future), and x(k+1) represents the state vector at step k+1.

[0114] The calculation of step size h, coefficient K1, coefficient K2, coefficient K3 and coefficient K4 in equation (4) is shown in equation (5).

[0115]

[0116] In equation (5), t f N and N represent the prediction duration and the number of discrete points for model prediction control, respectively.

[0117] Combining equations (4) and (5), it can be seen that the state vector at step (k+1) can be predicted based on the state vector and control vector at step k.

[0118] refer to Figure 4 By considering the maximum / minimum constraints of the front wheel steering angle and the maximum / minimum constraints of the front wheel steering angular velocity in a steer-by-wire four-wheel steering vehicle, and in the vehicle coordinate system Ax v y vLet 401 be the planned path to be tracked. Based on equation (4), the top-level lateral control problem (i.e., the path tracking control problem) of the steerable four-wheel steering vehicle can be transformed into a nonlinear optimization problem described by equation (6).

[0119]

[0120] In equation (6), ω fmax denoted as ω0, where ω0 is the maximum value of the front wheel steering angular velocity of the steerable four-wheel steering vehicle; Q and R are both weighting coefficients; x0 is the initial state vector of the system.

[0121] The nonlinear optimization problem described by equation (6) is abstracted into the nonlinear optimization problem described by equation (7).

[0122]

[0123] The constraint conditions for the nonlinear optimization problem described by equation (7) are given by equation (8).

[0124]

[0125] In equation (8), L(x,λ,μ) is the Lagrangian function of the nonlinear optimization problem, and can be expressed as equation (9).

[0126]

[0127] Using {x k ,λ k ,μ k} represents the result of the k-th iteration of the solution to the nonlinear optimization problem, and uses {x k+1 ,λ k+1 ,μ k+1} represents the result of the (k+1)th iteration of the solution to the nonlinear optimization problem, and the iteration increment δ is shown in equation (10).

[0128]

[0129] Using Taylor's formula in {x k ,λ k ,μ k If we make a second-order approximation of the Lagrange function described by equation (9) at}, then we have equation (11).

[0130]

[0131] In equation (11) and They can be represented as equation (12) and equation (13) respectively.

[0132]

[0133]

[0134] Among them, g k A ek A ik a k c k and Z k They can be expressed as equations (14) to (19).

[0135]

[0136]

[0137]

[0138] a k =[a1(x k a2(x) k … a p (x k )] T (17)

[0139] c k =[c1(x k c2(x) k ) … c q (x k )] T (18)

[0140]

[0141] By taking the derivative of equation (11) with respect to the cyclic increment δ, we can obtain equation (20).

[0142]

[0143] make Combining equations (20), (10), (12), and (13), we can obtain equation (21).

[0144]

[0145] Based on equation (21), the constraints of the original optimization problem described by equation (8) are approximated by a first-order linear approximation, resulting in equation (22).

[0146]

[0147] Using equation (21) Combined with equation (22), the constraints of the original optimization problem described by equation (8) are approximated as equation (23).

[0148]

[0149] Equation (23) can be transformed into an equivalent quadratic programming problem, as shown in Equation (24).

[0150]

[0151] The quadratic programming problem described by equation (24) can be represented as a linear band constraint problem. Solving this linear band constraint problem iteratively can yield the optimal solution for the front wheel steering angle control.

[0152] In one embodiment, the quadratic programming problem solved by the first wheel steering angle control module 211 can be Equation (24), and the constraints of the quadratic programming problem are obtained according to the vehicle kinematics equation described by Equation (2) and the Lagrange function described by Equation (9).

[0153] Based on the above, the solution steps for nonlinear optimization problems may include the following steps 1.1 to 1.6.

[0154] Step 1.1: Initialize the initial loop result of the variables to be solved in the nonlinear optimization problem as {x0, λ0, μ0}, and initialize Z0 = I. n Initialize the tolerance ε. n It is an identity matrix.

[0155] Step 1.2, using equations (14) to (19), calculate g. k A ek A ik a k c k and Z k .

[0156] Step 1.3: Based on the calculation results of Step 1.2, solve equation (24) to obtain δ. x δ x δ represents the state increment in the cyclic increment.

[0157] Based on the calculation results of step 1.2, the equality constraint A in formula (24) is calculated. ek δ x =-a k Multiple δ values ​​that satisfy the equality constraint can be obtained. x , and thus the multiple δ x Substituting the functions into equation (24) respectively If multiple values ​​are obtained, the minimum δ value used in calculating these multiple values ​​is selected. x The δ obtained as a solution to equation (24) x .

[0158] Step 1.4: Calculate the scaling factor α using the line search method. kScaling factor α k It may show a gradual downward trend.

[0159] Step 1.5, calculate x k+1 =x k +α k δ x .

[0160] The δ obtained in step 1.3 x α obtained in step 1.4 k Substituting the state vector obtained from the previous iteration (i.e., the kth iteration) into x k+1 =x k +α k δ x The state vector of the vehicle in the current iteration (i.e., the (k+1)th iteration) can be calculated.

[0161] When step 1.5 is executed for the first time, let x0 represent the state vector obtained from the previous loop solution of the vehicle.

[0162] Step 1.6, if ||δ x If ||≤ε, output the x calculated in step 1.5. k+1 Then stop the loop; otherwise, set k = k + 1 and jump to step 1.2 to perform another loop. Repeat this process until the δ that meets the tolerance requirement is obtained. x .

[0163] Each iteration of the solution yields a state vector that includes the front wheel steering angle at a series of future time points. Therefore, the output state vector x obtained from the (k+1)th iteration can be taken as the state vector. k+1 The front wheel steering angle at the next time point is used as the front wheel steering angle control value obtained by solving.

[0164] In one feasible implementation, step 1.3 can use the activation set method to solve equation (24) to obtain δ. x .

[0165] Comparing the constraints described by equation (8) and equation (23), it can be seen that equation (23) is a first-order approximation of the constraints described by equation (8). Therefore, solving the problem described by equation (24) in the (k+1)th iteration is equivalent to solving the optimal solution of the approximate problem of equation (7). Using the activation set method to solve the problem described by equation (24), equation (24) is rewritten as equation (25).

[0166]

[0167] Based on the above, the calculation process of the activation set method may include the following steps 2.1 to 2.6.

[0168] Step 2.1, initialize the loop start point and working set to δ. x,0 And W0, such that i∈W0, we have Make have

[0169] Step 2.2, assume the working set for the m-th iteration is W. m Solving the equality-constrained optimization problem described by equation (30) yields s m .

[0170] The following equation (26) exists.

[0171]

[0172] Equation (26) can be further simplified to Equation (27).

[0173]

[0174] In equation (27), Y m =Z k δ x,m +g k .

[0175] The optimality condition of the optimization equation (27) is equation (28).

[0176]

[0177] In equation (28), λ i ,i∈W m Let L(s,λ) be a Lagrange multiplier and L(s,λ) be a Lagrange function that can be expressed as equation (29).

[0178]

[0179] Further simplification of equation (29) yields equation (30).

[0180]

[0181] Solving equation (30) will yield s m .

[0182] Step 2.3, based on the s calculated in step 2.2 m If s m =0, proceed to step 2.4, if s m If ≠0, proceed to step 2.5.

[0183] Step 2.4, calculate the Lagrange multiplier λ. i ,i∈W m If λ i≥0, i∈W m If ∩I, then the optimal solution δ is output. x,m And stop the loop solution; otherwise, update the working set W. m+1 =W m / {i|arg(λ i <0, i∈W m ∩I)},δ x,m+1 =δ x,m Then proceed to step 2.2.

[0184] Step 2.5, Calculate the step size And calculate δ x,m+1 =δ x,m +β m s m If it exists Update working set W m+1 =W m ∪{i}, and proceed to step 2.6; otherwise, W k+1 =W k Then proceed to step 2.6;

[0185] Step 2.6: Set m = m + 1 and jump to step 2.2.

[0186] Steps 2.2 to 2.6 can be executed repeatedly until the loop termination condition described in step 2.4 is met. At this point, the loop can be terminated and the δ obtained in the current iteration can be output. x,m .

[0187] In one embodiment of this application, the first function is obtained based on a longitudinal control model, which includes the vehicle's desired acceleration, the vehicle's actual acceleration, and the correlation between longitudinal control disturbance parameters and first-order inertial element parameters.

[0188] The longitudinal response characteristics of a vehicle can be approximated as a first-order inertial element to design a longitudinal control model for the vehicle. Then, a nonlinear disturbance observer can be used to estimate the values ​​of the longitudinal control disturbance parameters.

[0189] By using a nonlinear disturbance observer to perform real-time estimation and feedforward compensation of the uncertainties in the parking external environment and the modeling mechanism of the nominal control model, the burden on the speed tracking control module can be reduced.

[0190] In one embodiment, the acceleration compensation module 222 may use the aforementioned nonlinear disturbance observer to estimate the uncertainty of the parking external environment and the modeling mechanism of the nominal control model.

[0191] The following example illustrates a feasible implementation process for the acceleration compensation module 222 to solve longitudinal control disturbance parameters, using a steerable four-wheel drive vehicle as an example and a planned parking path as an example. The related technical implementation is also applicable to embodiments where the vehicle is a front-wheel or rear-wheel steering vehicle and the planned driving path is a planned driving path.

[0192] The longitudinal control disturbance parameter d is used to represent the uncertainty of the parking external environment and the modeling mechanism of the nominal control model, and If the longitudinal response characteristics of a steerable four-wheel drive vehicle are approximated as a first-order inertial element, then the nominal model of the longitudinal control of a steerable four-wheel drive vehicle for parking can be expressed as equation (31).

[0193]

[0194] In equation (31), v and a are the actual vehicle speed and actual acceleration of the steer-by-wire four-wheel steering vehicle, respectively; a c τ represents the desired acceleration of a steerable four-wheel drive vehicle; τ is the parameter of the first-order inertial element.

[0195] For the longitudinal control model described by equation (31), a nonlinear disturbance observer described by equation (32) can be designed to estimate the uncertainty of the parking external environment and the modeling mechanism of the nominal control model.

[0196]

[0197] In the formula, is the calibration parameter of the nonlinear disturbance observer, and z is an intermediate variable.

[0198] At that time, the nonlinear disturbance observer described by equation (32) is stable, and (That is, the error between the actual value of the disturbance and the estimated value of the disturbance is small enough), so that the longitudinal control disturbance parameters can be accurately estimated to support the accurate acquisition of the acceleration control quantity, which is beneficial to improving the accuracy of vehicle longitudinal tracking.

[0199] In one embodiment, the first function described above can be the function shown in equation (32) obtained from the longitudinal control model described by equation (31). To accurately obtain the acceleration control quantity, the first function can be limited to include the calibration parameters of the nonlinear disturbance observer, and its value can be a value that can stabilize the nonlinear disturbance observer.

[0200] Below, based on equations (33) to (36), we will... Then, the stability of the nonlinear disturbance observer described by equation (32) is verified.

[0201] For the nonlinear disturbance observer of equation (32), the Lyapunov function is defined as equation (33).

[0202]

[0203] Differentiating the Lyapunov function described by equation (33) yields equation (34).

[0204]

[0205] Estimate the longitudinal control disturbance parameter d in equation (34) (For example, it can be reflected as an estimation term of the uncertainty of the parking external environment and the modeling mechanism of the nominal control model) Taking the derivative, we can obtain equation (35).

[0206]

[0207] Substituting equation (35) into equation (34), we obtain equation (36).

[0208]

[0209] From equation (36), it can be seen that if The nonlinear disturbance observer described by equation (34) is stable and has

[0210] By using a stable nonlinear disturbance observer for disturbance estimation, it is helpful to accurately estimate the impact of disturbance on longitudinal control. Then, based on the estimated disturbance amount, the reference acceleration is fed forward and the vehicle acceleration is controlled, which can support the vehicle's accurate tracking of the planned speed in the longitudinal direction.

[0211] In one embodiment of this application, the acceleration compensation module uses a tracking differentiator to solve a second function based on the actual speed of the first vehicle to obtain the actual acceleration of the first vehicle; wherein, the second function includes the correlation between the actual speed of the vehicle, the actual acceleration of the vehicle, and the calibration parameters of the tracking differentiator.

[0212] Vehicles can be equipped with sensors to sense vehicle speed. The actual speed of the vehicle can be obtained from the data collected in real time by the sensors, and this actual speed is used as input to a tracking differentiator. The tracking differentiator can then estimate the actual acceleration of the vehicle using its actual speed. By using the tracking differentiator to estimate the actual acceleration based on the vehicle's actual speed, accurate acquisition of the vehicle's actual acceleration can be achieved.

[0213] In one embodiment, the acceleration compensation module 222 may use the aforementioned tracking differentiator 1021 to estimate the actual acceleration of the vehicle.

[0214] The actual vehicle acceleration estimated by the tracking differentiator can be used as input to the nonlinear disturbance observer. The nonlinear disturbance observer then combines the actual acceleration output by the tracking differentiator to estimate the values ​​of the longitudinal control disturbance parameters in real time.

[0215] In one feasible implementation, a tracking differentiator as described in equation (37) can be designed.

[0216]

[0217] In equation (37), Q > 0, b0 > 0, b1 > 0, and c > 1 are all calibration parameters of the tracking differentiator. This is an estimate of the vehicle's actual acceleration.

[0218] In one embodiment of this application, solving the speed tracking control problem for tracking the planned speed includes: solving a third function using a fast terminal sliding mode control law; wherein the third function is obtained based on a fast terminal sliding mode surface including a speed tracking error, the speed tracking error being used to describe the error between a reference speed and a vehicle speed predicted based on the desired acceleration.

[0219] Based on the vehicle's current actual speed and desired acceleration, the vehicle's speed at future moments can be predicted. If the error between the predicted speed and the reference speed (i.e., the speed tracking error) is small, the longitudinal tracking effect for the planned speed can be considered good. Thus, a fast terminal sliding surface can be constructed based on the speed tracking error, and the desired acceleration that meets the speed tracking requirements can be solved using a fast terminal sliding surface control law.

[0220] The fast terminal sliding mode control law can bring the speed tracking error to zero within a finite time, enabling the vehicle longitudinal control to exhibit high performance with "fast convergence, high accuracy and strong robustness".

[0221] In one embodiment, the velocity tracking control module 221 can use the fast terminal sliding mode control law 101 described above to solve for the reference acceleration.

[0222] In one feasible implementation, the fast terminal sliding mode control law described by equation (39) can be designed based on the fast terminal sliding mode surface described by equation (38).

[0223] Using s=vv ref The velocity tracking error is represented by the fast terminal sliding surface described by the velocity tracking error definition (38).

[0224]

[0225] In equation (38), P > 0, q > 0, m and n are both positive odd numbers, and n > m.

[0226] Using the fast terminal sliding surface described by equation (38), the vehicle longitudinal control model described by equation (31) is designed with a fast terminal sliding control law as described by equation (39), so that the closed-loop system composed of equations (31) and (39) is stable in finite time.

[0227]

[0228] In equation (39), k1>0, k2>0, k3>ξ and 0<η<1 are all parameters of the fast terminal sliding mode control law.

[0229] Below, we will use equations (40) to (49) to prove that the closed-loop system formed by equations (31) and (39) is stable in finite time.

[0230] The Lyapunov function is defined as equation (40).

[0231]

[0232] Differentiating equation (40) yields equation (41).

[0233]

[0234] Substituting the fast terminal sliding mode control law of equation (39) into equation (41), we can obtain equation (42).

[0235]

[0236]

[0237] Further simplification of equation (43) yields equation (44).

[0238]

[0239] As can be seen from equation (44), the closed-loop system formed by equations (31) and (39) converges to σ = 0 in finite time.

[0240] Under the premise that the closed-loop system composed of equations (31) and (39) converges to σ = 0 in finite time, equation (45) can be obtained from the fast terminal sliding surface defined by equation (38).

[0241]

[0242] Differentiating equation (45) yields equation (46).

[0243]

[0244] Divide both sides of equation (46) by s m / n And perform variable substitution y = s1-m / n Equation (47) can be obtained.

[0245]

[0246] In equation (47),

[0247] Solving equation (47) yields equation (48).

[0248]

[0249] Let y = 0, we can get equation (49).

[0250]

[0251] When y = 0, s = 0. Therefore, the velocity tracking error converges to s = 0 in finite time. In summary, the closed-loop system composed of equations (31) and (39) is finite-time stable.

[0252] It is evident that since the fast terminal sliding mode control law can bring the speed tracking error to zero within a finite time, by using the fast terminal sliding mode control law to solve for the reference acceleration and controlling the vehicle's acceleration based on this reference acceleration, the vehicle's longitudinal control can exhibit high performance with "fast convergence, high accuracy, and strong robustness".

[0253] This application provides a vehicle control method, comprising: solving a path tracking control problem for tracking a planned path to obtain a first wheel steering angle control quantity for a first vehicle, wherein the first wheel is one of the front wheel and the rear wheel; wherein the planned path includes a reference pose at a series of future time points, and the reference pose includes at least one of a reference lateral position and a reference heading angle; solving a speed tracking control problem for tracking a planned speed to obtain a desired acceleration of the first vehicle; wherein the planned speed includes a reference speed at a series of future time points; using a nonlinear disturbance observer, solving a first function based on the actual acceleration and the desired acceleration of the first vehicle to obtain the value of a longitudinal control disturbance parameter; and obtaining an acceleration control quantity for the first vehicle based on the value of the longitudinal control disturbance parameter and the desired acceleration of the first vehicle; wherein the first function includes the correlation between the desired acceleration of the vehicle, the actual acceleration of the vehicle, the longitudinal control disturbance parameter, and the calibration parameters of the nonlinear disturbance observer.

[0254] One embodiment of this application provides an electronic chip, including: a processor for executing computer program instructions stored in a memory, wherein when the computer program instructions are executed by the processor, the electronic chip is triggered to execute the method described in any embodiment of this application.

[0255] One embodiment of this application provides an electronic device including at least one processor and a memory coupled together. The memory is used to store computer program instructions, and the processor is used to execute the computer program instructions. When the computer program instructions are executed by the processor, the electronic device is triggered to execute the method described in any embodiment of this application.

[0256] One embodiment of this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the methods described in any embodiment of this application.

[0257] One embodiment of this application provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to perform the methods described in any embodiment of this application.

[0258] Figure 5 This is a schematic diagram of a computer device provided according to one embodiment of this application. Figure 5 As shown, the computer device 20 in this embodiment includes a processor 21 and a memory 22. The memory 22 stores a computer program 23 that can run on the processor 21. When the computer program 23 is executed by the processor 21, it implements the steps in the method embodiments of this application. To avoid repetition, these steps are not described in detail here. Alternatively, when the computer program 23 is executed by the processor 21, it implements the functions of each model / unit in the device embodiments of this application. To avoid repetition, these functions are not described in detail here.

[0259] Computer device 20 includes, but is not limited to, processor 21 and memory 22. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 20 and does not constitute a limitation on computer device 20. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0260] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or it can be any conventional processor.

[0261] The memory 22 can be an internal storage unit of the computer device 20, such as a hard disk or RAM of the computer device 20. The memory 22 can also be an external storage device of the computer device 20, such as a plug-in hard disk, Smart Media (SM) card, Secure Digital (SD) card, or FlashCard equipped on the computer device 20. Furthermore, the memory 22 can include both internal and external storage units of the computer device 20. The memory 22 is used to store the computer program 23 and other programs and data required by the computer device. The memory 22 can also be used to temporarily store data that has been output or will be output.

[0262] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0263] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0264] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0265] An integrated unit implemented as a software functional unit can be stored in a computer-readable storage medium. This software functional unit, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0266] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0267] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0268] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments of this application can be implemented using electronic hardware, computer software, or a combination of electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0269] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the same or similar parts between the various embodiments of this application can be referred to mutually. For example, the specific working processes of the systems, devices, and units described in the embodiments of this application can be referred to the corresponding processes in the method embodiments of this application, and will not be repeated here.

[0270] The above description is merely a specific embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A vehicle control system, characterized in that, include: Vehicle lateral control module and vehicle longitudinal control module; The vehicle lateral control module includes a first wheel steering angle control module, and the vehicle longitudinal control module includes a speed tracking control module and an acceleration compensation module. The first wheel steering angle control module is used to solve the path tracking control problem for tracking the planned path and obtain the first wheel steering angle control quantity of the first vehicle, wherein the first wheel is one of the front wheel and the rear wheel; wherein the planned path includes a reference pose at a series of future time points, and the reference pose includes at least one of a reference lateral position and a reference heading angle. The speed tracking control module is used to solve the speed tracking control problem for tracking the planned speed and obtain the expected acceleration of the first vehicle; wherein, the planned speed includes reference speeds at a series of future time points; The acceleration compensation module is used to use a nonlinear disturbance observer to solve a first function based on the actual acceleration and the desired acceleration of the first vehicle to obtain the value of the longitudinal control disturbance parameter; and to obtain the acceleration control quantity of the first vehicle based on the value of the longitudinal control disturbance parameter and the desired acceleration of the first vehicle; wherein, the first function includes the correlation between the desired acceleration of the vehicle, the actual acceleration of the vehicle, the longitudinal control disturbance parameter and the calibration parameter of the nonlinear disturbance observer; The vehicle lateral control module also includes a second wheel steering angle control module; The second wheel steering angle control module is used to obtain the second wheel steering angle control amount of the first vehicle based on the correlation between the front wheel steering angle and the rear wheel steering angle of the first vehicle and the first wheel steering angle control amount of the first vehicle, wherein the second wheel is the other one of the front wheel and the rear wheel; The correlation is obtained based on the maximum value of the front wheel steering angle of the first vehicle and the maximum value of the rear wheel steering angle of the first vehicle.

2. The vehicle control system according to claim 1, characterized in that, The first function is obtained based on the longitudinal control model, which includes the vehicle's desired acceleration, the vehicle's actual acceleration, and the correlation between the longitudinal control disturbance parameters and the first-order inertial element parameters.

3. The vehicle control system according to claim 1, characterized in that, The acceleration compensation module is used to use a tracking differentiator to solve a second function based on the actual speed of the first vehicle to obtain the actual acceleration of the first vehicle. The second function includes the correlation between the vehicle's actual speed, the vehicle's actual acceleration, and the calibration parameters of the tracking differentiator.

4. The vehicle control system according to claim 1, characterized in that, The solution to the speed tracking control problem for tracking the planned speed includes: solving the third function using a fast terminal sliding mode control law; The third function is obtained based on a fast terminal sliding surface including a speed tracking error, which describes the error between the reference speed and the vehicle speed predicted based on the desired acceleration.

5. The vehicle control system according to claim 1, characterized in that, The solution to the path tracking control problem used to track the planned path includes: Solving a quadratic programming problem, wherein the quadratic programming problem is obtained by transforming the path tracking control problem, and the constraints of the quadratic programming problem are obtained based on the vehicle kinematic equations and the Lagrange function.

6. The vehicle control system according to claim 5, characterized in that, Each solution result includes state variables reflecting the vehicle's pose and the first wheel's steering angle at a series of future time points, control variables reflecting the first wheel's steering angular velocity, and the values ​​of the Lagrange multipliers. The solution to the quadratic programming problem includes: If the solution result of the previous iteration exists, then based on the solution result of the previous iteration, solve the quadratic programming problem to obtain the state variable increment; Based on the obtained state variable increment and the solution result of the previous loop, obtain the solution result of the current loop; If the set conditions are met, the first wheel steering angle control quantity of the first vehicle is the first wheel steering angle obtained at the next moment in the current cycle; the set conditions include that the absolute value of the obtained state variable increment is not greater than the set tolerance. If the set conditions are not met, the steps to solve the quadratic programming problem are executed again.

7. A vehicle control method, characterized in that, include: Solve the path tracking control problem for tracking the planned path to obtain the first wheel steering angle control value of the first vehicle, where the first wheel is one of the front wheel and the rear wheel; wherein, the planned path includes a reference pose at a series of future time points, and the reference pose includes at least one of a reference lateral position and a reference heading angle; Solve the speed tracking control problem for tracking the planned speed to obtain the desired acceleration of the first vehicle; wherein the planned speed includes reference speeds at a series of future time points; Using a nonlinear disturbance observer, a first function is solved based on the actual acceleration and the desired acceleration of the first vehicle to obtain the value of the longitudinal control disturbance parameter; based on the value of the longitudinal control disturbance parameter and the desired acceleration of the first vehicle, the acceleration control quantity of the first vehicle is obtained; wherein, the first function includes the correlation between the desired acceleration of the vehicle, the actual acceleration of the vehicle, the longitudinal control disturbance parameter and the calibration parameter of the nonlinear disturbance observer; The method further includes: Based on the correlation between the front wheel steering angle and the rear wheel steering angle of the first vehicle, and the first wheel steering angle control amount of the first vehicle, the second wheel steering angle control amount of the first vehicle is obtained, where the second wheel is the other one of the front wheel and the rear wheel; The correlation is obtained based on the maximum value of the front wheel steering angle of the first vehicle and the maximum value of the rear wheel steering angle of the first vehicle.

8. An electronic device, characterized in that, The electronic device includes at least one processor coupled to a memory for storing computer program instructions and for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the method as described in claim 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in claim 7.

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