Transverse control method and device of vehicle, vehicle and storage medium

Abstract

The invention relates to a transverse control method and device of a vehicle, the vehicle and a storage medium, and the method comprises the steps that at least one piece of information of positioning information, planning information, a transverse error, a transverse rate, a side slip angle error and a side slip angle rate of the vehicle is obtained; establishing an error dynamic model meeting a preset uncertainty condition according to the at least one piece of information, and obtaining a state transition matrix, a control transition matrix and an uncertainty transition matrix of the vehicle according to the error dynamic model; and calculating a preset equation based on the state transition matrix, the control transition matrix and the uncertainty transition matrix, and calculating a controller gain of the vehicle according to the preset equation so as to control lateral movement of the vehicle by using the controller gain. Therefore, the problem of poor performance under some extreme working conditions due to certain response lag, low control precision and poor control stability during transverse control of the vehicle in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle lateral control method, device, vehicle and storage medium. Background Art

[0002] Vehicle mass information is important information required for the lateral control of intelligent vehicles and has a significant impact on steering scenarios. However, vehicle mass information cannot be accurately obtained in real time. On the other hand, the accuracy of lateral control error is an important indicator for evaluating vehicle safety. A smaller lateral error represents better lateral safety of intelligent vehicles. Uncertainty or changes in vehicle mass may cause the accuracy of lateral control to deteriorate, or even cause instability and lead to unsafe behavior. A typical example is the operation process of a sanitation vehicle. The water tank changes from full to empty. During this process, the mass of the sanitation vehicle changes greatly. In order to maintain normal operation, the lateral control performance is challenged.

[0003] In the related technology, model reference adaptive control and integral compensation control can be introduced to perform feedforward compensation on the vehicle to ensure system stability and performance.

[0004] However, in the related technologies, there is a certain response lag, low control accuracy and poor control stability when the vehicle is controlled laterally, resulting in poor performance under certain extreme conditions, which urgently needs to be improved. Summary of the invention

[0005] The present application provides a vehicle lateral control method, device, vehicle and storage medium to solve the problems in the related art that when the vehicle is lateral controlled, there is a certain response lag, low control accuracy, poor control stability, etc., which leads to poor performance under certain extreme conditions.

[0006] A first aspect of the present application provides a method for lateral control of a vehicle, comprising the following steps: obtaining at least one piece of information among the vehicle's positioning information, planning information, lateral error, lateral rate, sideslip angle error and sideslip angle rate; establishing an error dynamics model that satisfies a preset uncertainty condition based on the at least one piece of information, and obtaining a state transfer matrix, a control transfer matrix and an uncertainty transfer matrix of the vehicle based on the error dynamics model; calculating a preset equation based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and calculating the controller gain of the vehicle based on the preset equation, so as to use the controller gain to control the lateral movement of the vehicle.

[0007] The embodiments of the present application can obtain accurate positioning information and detailed planning information of the vehicle, so that the controller can adjust the vehicle behavior more accurately. The embodiments of the present application can calculate the controller gain of the vehicle and control the lateral movement of the vehicle based on this, which can not only significantly improve the control accuracy and stability of the vehicle, but also enhance the robustness and applicability of the overall system.

[0008] Optionally, in one embodiment of the present application, the calculation of the preset equation based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix includes: discretizing the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix to generate a discretized state transfer matrix, a discretized control transfer matrix and a discretized uncertainty transfer matrix; establishing a discrete Lyapunov equation based on the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix, and converting the discrete Lyapunov equation into a Bellman equation to calculate the preset equation using the Bellman equation.

[0009] The embodiments of the present application can discretize the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, establish a discrete Lyapunov equation and perform equation calculations, thereby simplifying numerical calculations, improving computational efficiency, and further achieving efficient and reliable vehicle lateral control.

[0010] Optionally, in one embodiment of the present application, the calculating of the controller gain of the vehicle based on the preset equation includes: obtaining the initialization control gain and initialization uncertainty gain of the vehicle based on the preset equation; iteratively calculating a matrix to generate uncertainty parameters that meet preset conditions; and calculating the controller gain of the vehicle based on the initialization control gain, the uncertainty gain and the uncertainty parameters.

[0011] The embodiment of the present application can obtain the initialization control gain and initialization uncertainty gain of the vehicle, iteratively calculate the matrix to generate uncertainty parameters under the worst conditions, thereby achieving effective control of the lateral movement of the vehicle, improving control accuracy, and reducing unstable behavior.

[0012] Optionally, in one embodiment of the present application, the formula for iterative calculation matrix is:

[0013]

[0014] in, is the discretized state transfer matrix, is the discretized control transfer matrix, is the uncertainty transfer matrix of the discretization, K i is the control gain at the i-th iteration, Mi is the uncertainty gain at the i-th iteration, and R is the parameter to be designed.

[0015] The embodiments of the present application can improve the calculation efficiency according to the calculation formula, further ensure the lateral control accuracy of the vehicle, and ensure the lateral control of the input state stability.

[0016] Optionally, in one embodiment of the present application, the use of the controller gain to control the lateral movement of the vehicle includes: generating a lateral control instruction for the vehicle based on the controller gain; adjusting relevant parameters of the vehicle in response to the lateral control instruction to generate an adjusted torque and an adjusted steering wheel angle; and controlling the lateral movement of the vehicle based on the adjusted torque and the adjusted steering wheel angle.

[0017] The embodiment of the present application can adjust the vehicle's torque and steering wheel angle to control the vehicle's lateral movement according to the adjusted torque and the adjusted steering wheel angle, thereby reducing the deviation between the actual driving trajectory and the expected path and improving the vehicle's handling accuracy.

[0018] A second aspect of the present application provides a lateral control device for a vehicle, comprising: an acquisition module for acquiring at least one of the vehicle's positioning information, planning information, lateral error, lateral rate, sideslip angle error and sideslip angle rate; an establishment module for establishing an error dynamics model that meets preset uncertainty conditions based on the at least one information, and acquiring a state transfer matrix, a control transfer matrix and an uncertainty transfer matrix of the vehicle based on the error dynamics model; a control module for calculating a preset equation based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and calculating the controller gain of the vehicle based on the preset equation, so as to control the lateral movement of the vehicle using the controller gain.

[0019] Optionally, in one embodiment of the present application, the control module includes: a generation unit for discretizing the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix to generate a discretized state transfer matrix, a discretized control transfer matrix and a discretized uncertainty transfer matrix; a conversion unit for establishing a discrete Lyapunov equation based on the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix, and converting the discrete Lyapunov equation into a Bellman equation to calculate the preset equation using the Bellman equation.

[0020] Optionally, in one embodiment of the present application, the control module also includes: an acquisition unit, used to acquire the initialization control gain and initialization uncertainty gain of the vehicle based on the preset equation; an iterative calculation unit, used to iteratively calculate the matrix to generate uncertainty parameters that meet preset conditions; and a calculation unit, used to calculate the controller gain of the vehicle based on the initialization control gain, the uncertainty gain and the uncertainty parameter.

[0021] Optionally, in one embodiment of the present application, the formula for iterative calculation matrix is:

[0022]

[0023] in, is the discretized state transfer matrix, is the discretized control transfer matrix, is the uncertainty transfer matrix of the discretization, K i is the control gain at the i-th iteration, M i is the uncertainty gain at the i-th iteration, and R is the parameter to be designed.

[0024] Optionally, in one embodiment of the present application, the control module also includes: an instruction generation unit, used to generate a lateral control instruction of the vehicle according to the controller gain; an adjustment unit, used to adjust relevant parameters of the vehicle in response to the lateral control instruction to generate an adjusted torque and an adjusted steering wheel angle; a control unit, used to control the lateral movement of the vehicle according to the adjusted torque and the adjusted steering wheel angle.

[0025] A third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lateral control method of the vehicle as described in the above embodiment.

[0026] A fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the above-mentioned vehicle lateral control method.

[0027] The embodiment of the present application can first establish lateral error dynamics with matching uncertainty, and then calculate a Lyapunov equation through a value iteration scheme based on the input state stability principle. Finally, a robust controller is obtained through a min-max algorithm. Compared with other types of robust controllers, there is no need to know the maximum upper bound of uncertainty in advance, and since there is no need to measure the vehicle mass in real time, it can be applied to various types of vehicles and working conditions. The controller obtained by the min-max algorithm is robust, reduces the occurrence of unstable behavior, increases control accuracy, and ensures lateral control with input state stability. As a result, the problem of a certain response lag and low control accuracy in lateral control of the vehicle in related technologies, which leads to poor performance under certain extreme working conditions, is solved.

[0028] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A flow chart of a lateral control method of a vehicle provided according to an embodiment of the present application;

[0031] Figure 2 is a flow chart of a lateral control method of a vehicle according to an embodiment of the present application;

[0032] Figure 3 A schematic diagram of the structure of a lateral control device for a vehicle provided according to an embodiment of the present application;

[0033] Figure 4 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0035] The following describes the lateral control method, device, vehicle and storage medium of the vehicle of the embodiment of the present application with reference to the accompanying drawings. In view of the related art mentioned in the above background technology, when the vehicle is in lateral control, there is a certain response lag and low control accuracy, which leads to poor performance under certain extreme working conditions. The present application provides a lateral control method for a vehicle, in which a lateral error dynamics with matching uncertainty can be first established, and then a Lyapunov equation is calculated through a value iteration scheme according to the input state stability principle. Finally, a robust controller is obtained through a min-max algorithm. Compared with other types of robust controllers, there is no need to know the maximum upper bound of uncertainty in advance, and since there is no need to measure the vehicle mass in real time, it can be applied to various types of vehicles and working conditions. The controller obtained by the min-max algorithm is robust, reduces the occurrence of unstable behavior, increases control accuracy, and ensures lateral control with input state stability. Therefore, the problem that the vehicle has a certain response lag and low control accuracy when it is in lateral control, which leads to poor performance under certain extreme working conditions, etc., is solved in the related art.

[0036] Specifically, Figure 1 A schematic flow chart of a lateral control method for a vehicle provided in an embodiment of the present application.

[0037] like Figure 1 As shown, the lateral control method of the vehicle includes the following steps:

[0038] In step S101, at least one of the positioning information, planning information, lateral error, lateral rate, sideslip angle error and sideslip angle rate of the vehicle is obtained.

[0039] It can be understood that, in the embodiment of the present application, the positioning information of the vehicle may be the current position information of the vehicle, the planning information may be the relevant information of the vehicle's driving route, the lateral error may be the deviation in the lateral direction between the actual driving trajectory of the vehicle and its predetermined path, the lateral rate may be the speed at which the lateral error changes over time, the sideslip angle error may be the angular difference between the actual orientation of the vehicle and the expected orientation, and the sideslip angle rate may be the rate of change of the sideslip angle over time.

[0040] During the actual implementation process, the embodiments of the present application can obtain accurate positioning information and detailed planning information of the vehicle, so that the controller can adjust the vehicle behavior more accurately, and monitor the vehicle's position and the lateral error, lateral rate, sideslip angle error and sideslip angle rate relative to the predetermined path in real time, so as to promptly detect and correct deviations, increase control accuracy, thereby avoiding potential safety risks, improving vehicle safety, and enhancing the user's driving experience.

[0041] In step S102, an error dynamics model that meets a preset uncertainty condition is established according to at least one item of information, and a state transfer matrix, a control transfer matrix and an uncertainty transfer matrix of the vehicle are obtained according to the error dynamics model.

[0042] It can be understood that the error dynamics model that meets the preset uncertainty condition in the embodiment of the present application can be an error dynamics model with uncertainty.

[0043] In the actual implementation process, the embodiment of the present application can establish an error dynamics model with uncertainty based on the above information, wherein the dynamics equation is as follows:

[0044]

[0045] Among them, A is the state transfer matrix, B is the control transfer matrix, L is the uncertainty transfer matrix, X is the vector, e1 is the lateral error, is the lateral velocity, e2 is the sideslip angle error, is the slip angle rate, m is the mass, w is the matching uncertainty parameter, which indicates the influence of the change of mass m on the B matrix, C f and C r is the stiffness coefficient of the front and rear wheels, l f and l r is the distance from the front wheel and rear wheel to the center of mass, I z is the moment of inertia about the z-axis.

[0046] Furthermore, the embodiments of the present application can obtain the vehicle's state transfer matrix, control transfer matrix and uncertainty transfer matrix based on the error dynamics model. The above matrices provide support for the subsequent calculation of controller gains, thereby significantly improving the vehicle's control accuracy and stability.

[0047] It should be noted that the preset uncertainty condition can be set by those skilled in the art according to actual conditions and is not specifically limited here.

[0048] In step S103, a preset equation is calculated based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and the controller gain of the vehicle is calculated according to the preset equation to control the lateral movement of the vehicle using the controller gain.

[0049] It can be understood that the preset equation in the embodiment of the present application can be a converted discrete Lyapunov equation; the controller gain in the embodiment of the present application can be a proportional coefficient used in the controller to adjust the relationship between input and output.

[0050] Among them, the embodiments of the present application can calculate preset equations based on the state transfer matrix, control transfer matrix and uncertainty transfer matrix, and calculate the vehicle's controller gain according to the preset equations, and control the lateral movement of the vehicle based on this, which can not only significantly improve the control accuracy and stability of the vehicle, but also enhance the robustness and applicability of the overall system.

[0051] The embodiments of the present application do not need to know the maximum upper bound of uncertainty in advance, and since there is no need to measure the vehicle mass in real time, they can be applied to various types of vehicles and working conditions. The controller derived by the min-max algorithm is robust, reduces the occurrence of unstable behavior, and increases control accuracy.

[0052] Optionally, in one embodiment of the present application, a preset equation is calculated based on a state transfer matrix, a control transfer matrix and an uncertainty transfer matrix, including: discretizing the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix to generate a discretized state transfer matrix, a discretized control transfer matrix and a discretized uncertainty transfer matrix; establishing a discrete Lyapunov equation based on the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix, and converting the discrete Lyapunov equation into a Bellman equation to calculate the preset equation using the Bellman equation.

[0053] It can be understood that the embodiments of the present application can discretize the above dynamics, thereby simplifying numerical calculations, improving calculation efficiency, and further achieving efficient and reliable vehicle lateral control.

[0054] In the actual implementation process, the embodiment of the present application can discretize the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and use Represents the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix. Based on the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix, the discrete Lyapunov equation is established:

[0055]

[0056] Convert the discrete Lyapunov equation to the Bellman equation:

[0057]

[0058] The above functions satisfy:

[0059]

[0060] So the Lyapunov equation is transformed into:

[0061]

[0062] Optionally, in one embodiment of the present application, the controller gain of the vehicle is calculated based on a preset equation, including: based on the preset equation, obtaining the initialization control gain and initialization uncertainty gain of the vehicle; iteratively calculating the matrix to generate uncertainty parameters that meet preset conditions; and calculating the controller gain of the vehicle based on the initialization control gain, uncertainty gain and uncertainty parameters.

[0063] It can be understood that the uncertainty parameter that meets the preset conditions in the embodiment of the present application may be the uncertainty parameter in the worst case.

[0064] During the actual implementation process, the embodiment of the present application can obtain the vehicle's initialization control gain K and initialization uncertainty gain M based on a preset equation, iteratively calculate the matrix P, calculate the uncertainty parameters in the worst case, obtain a robust controller through the minmax algorithm, and calculate the vehicle's controller gain based on the initialization control gain K, uncertainty gain M and uncertainty parameters.

[0065] Among them, the min max algorithm is applied:

[0066]

[0067] The controller structure is obtained:

[0068]

[0069] Among them, gamma is the parameter to be set, R is the parameter to be designed, and P is calculated according to the value iteration method:

[0070]

[0071] in, is the discretized state transfer matrix, is the discretized control transfer matrix, is the discretized uncertainty transfer matrix, K i is the control gain at the i-th iteration, M i is the uncertainty gain at the i-th iteration, and R is the parameter to be designed.

[0072] The embodiment of the present application can obtain the initialization control gain and initialization uncertainty gain of the vehicle, iteratively calculate the matrix to generate uncertainty parameters under the worst conditions, thereby achieving effective control of the lateral movement of the vehicle, improving control accuracy, and reducing unstable behavior.

[0073] Optionally, in one embodiment of the present application, the lateral movement of the vehicle is controlled using the controller gain, including: generating a lateral control instruction for the vehicle based on the controller gain; adjusting relevant parameters of the vehicle in response to the lateral control instruction to generate an adjusted torque and an adjusted steering wheel angle; and controlling the lateral movement of the vehicle based on the adjusted torque and the adjusted steering wheel angle.

[0074] It can be understood that the lateral motion instructions in the embodiment of the present application can be a series of operation instructions calculated by the controller based on the current vehicle state (such as lateral error, sideslip angle error, etc.) and preset targets, which are intended to guide the vehicle to travel along a predetermined path.

[0075] As a possible implementation method, the embodiment of the present application can more accurately calculate the required lateral motion command based on the controller gain, and adjust the vehicle's torque and steering wheel angle in response to the lateral motion command to control the vehicle's lateral motion based on the adjusted torque and the adjusted steering wheel angle, thereby reducing the deviation between the actual driving trajectory and the expected path and improving the vehicle's handling accuracy.

[0076] The embodiments of the present application do not need to know the maximum upper bound of uncertainty in advance, and because there is no need to measure the vehicle mass in real time, it can be applicable to various types of vehicles and working conditions. The controller derived by the min max algorithm is robust, reduces the occurrence of unstable behavior, increases control accuracy, reduces unnecessary shaking, and provides passengers with a more comfortable riding experience.

[0077] Specifically, it can be combined Figure 2 As shown, the working principle of the lateral control method of the vehicle in the embodiment of the present application is described in detail with a specific embodiment.

[0078] like Figure 2 As shown, the embodiments of the present application may include:

[0079] 1: Obtain positioning and planning information, calculate lateral error and its rate, sideslip angle error and its rate; 2: Obtain A, B, L matrices; 3: Discretization processing; 4: Controller calculation.

[0080] According to the lateral control method of the vehicle proposed in the embodiment of the present application, the lateral error dynamics with matching uncertainty can be first established, and then a Lyapunov equation can be calculated through a value iteration scheme based on the input state stability principle. Finally, a robust controller is obtained through a min-max algorithm. Compared with other types of robust controllers, there is no need to know the maximum upper bound of uncertainty in advance, and since there is no need to measure the vehicle mass in real time, it can be applied to various types of vehicles and working conditions. The controller obtained by the min-max algorithm is robust, reduces the occurrence of unstable behavior, and increases control accuracy. As a result, the problem in the related art that the vehicle has a certain response lag and low control accuracy when it is in lateral control, resulting in poor performance under certain extreme conditions, is solved.

[0081] Next, the lateral control device for a vehicle proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0082] Figure 3 It is a schematic diagram of the structure of a lateral control device for a vehicle according to an embodiment of the present application.

[0083] like Figure 3 As shown, the lateral control device 10 of the vehicle includes: an acquisition module 100 , a creation module 200 and a control module 300 .

[0084] Specifically, the acquisition module 100 is used to acquire at least one of the following information: positioning information, planning information, lateral error, lateral rate, sideslip angle error and sideslip angle rate of the vehicle.

[0085] Establishing module 200, used to establish an error dynamics model that meets preset uncertainty conditions according to at least one information, and obtain the state transfer matrix, control transfer matrix and uncertainty transfer matrix of the vehicle according to the error dynamics model.

[0086] The control module 300 is used to calculate a preset equation based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and calculate the controller gain of the vehicle according to the preset equation to control the lateral movement of the vehicle using the controller gain.

[0087] Optionally, in one embodiment of the present application, the control module 300 includes: a generation unit and a conversion unit.

[0088] Among them, the generating unit is used to discretize the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix to generate a discretized state transfer matrix, a discretized control transfer matrix and a discretized uncertainty transfer matrix.

[0089] The conversion unit is used to establish a discrete Lyapunov equation based on a discretized state transfer matrix, a discretized control transfer matrix and a discretized uncertainty transfer matrix, and convert the discrete Lyapunov equation into a Bellman equation so as to calculate a preset equation using the Bellman equation.

[0090] Optionally, in one embodiment of the present application, the control module 300 further includes: an acquisition unit, an iterative calculation unit and a calculation unit.

[0091] The acquisition unit is used to acquire the initialization control gain and initialization uncertainty gain of the vehicle based on a preset equation.

[0092] The iterative calculation unit is used for iteratively calculating the matrix to generate uncertainty parameters that meet preset conditions.

[0093] The calculation unit is used to calculate the controller gain of the vehicle according to the initialization control gain, the uncertainty gain and the uncertainty parameter.

[0094] Optionally, in one embodiment of the present application, the formula for iteratively calculating the matrix is:

[0095]

[0096] in, is the discretized state transfer matrix, is the discretized control transfer matrix, is the discretized uncertainty transfer matrix, K i is the control gain at the i-th iteration, M i is the uncertainty gain at the i-th iteration, and R is the parameter to be designed.

[0097] Optionally, in one embodiment of the present application, the control module 300 further includes: an instruction generating unit, an adjusting unit and a control unit.

[0098] Wherein, the instruction generation unit is used to generate a lateral control instruction of the vehicle according to the controller gain.

[0099] The adjustment unit is used for adjusting relevant parameters of the vehicle in response to the lateral control instruction to generate an adjusted torque and an adjusted steering wheel angle.

[0100] A control unit is used to control the lateral movement of the vehicle based on the adjusted torque and the adjusted steering wheel angle.

[0101] It should be noted that the above explanation of the embodiment of the vehicle lateral control method is also applicable to the vehicle lateral control device of this embodiment, which will not be repeated here.

[0102] According to the lateral control device of the vehicle proposed in the embodiment of the present application, the lateral error dynamics with matching uncertainty can be first established, and then a Lyapunov equation can be calculated through a value iteration scheme based on the input state stability principle. Finally, a robust controller is obtained through a min-max algorithm. Compared with other types of robust controllers, there is no need to know the maximum upper bound of uncertainty in advance, and since there is no need to measure the vehicle mass in real time, it can be applied to various types of vehicles and working conditions. The controller obtained by the min-max algorithm is robust, reduces the occurrence of unstable behavior, and increases control accuracy. As a result, the problem in the related art that the vehicle has a certain response lag and low control accuracy when it is in lateral control, resulting in poor performance under certain extreme conditions, is solved.

[0103] Figure 4 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0104] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0105] When the processor 402 executes the program, the lateral control method of the vehicle provided in the above embodiment is implemented.

[0106] Furthermore, the vehicle also includes:

[0107] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0108] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0109] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0112] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0113] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned lateral control method of the vehicle is implemented.

[0114] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0115] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0116] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0118] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0120] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A lateral control method for a vehicle, characterized in that: The following steps are involved: Acquiring at least one of the following information: positioning information, planning information, lateral error, lateral rate, sideslip angle error and sideslip angle rate of the vehicle; Establishing an error dynamics model that satisfies a preset uncertainty condition according to the at least one item of information, and acquiring a state transfer matrix, a control transfer matrix, and an uncertainty transfer matrix of the vehicle according to the error dynamics model; A preset equation is calculated based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and a controller gain of the vehicle is calculated according to the preset equation to control the lateral movement of the vehicle using the controller gain.

2. The method according to claim 1, characterized in that The calculating of the preset equation based on the state transfer matrix, the control matrix and the uncertainty transfer matrix includes: Discretizing the state transfer matrix, the control transfer matrix, and the uncertainty transfer matrix to generate a discretized state transfer matrix, a discretized control transfer matrix, and a discretized uncertainty transfer matrix; Based on the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix, a discrete Lyapunov equation is established, and the discrete Lyapunov equation is converted into a Bellman equation so as to calculate the preset equation using the Bellman equation.

3. The method according to claim 2, characterized in that The calculating the controller gain of the vehicle based on the preset equation comprises: Based on the preset equation, obtaining an initialization control gain and an initialization uncertainty gain of the vehicle; Iteratively calculate the matrix to generate uncertainty parameters that meet preset conditions; A controller gain of the vehicle is calculated based on the initialization control gain, the uncertainty gain, and the uncertainty parameter.

4. The method according to claim 3, characterized in that: The formula for the iterative calculation matrix is: in, is the discretized state transfer matrix, is the discretized control transfer matrix, is the uncertainty transfer matrix of the discretization, K i is the control gain at the i-th iteration, M i is the uncertainty gain at the i-th iteration, and R is the parameter to be designed.

5. The method according to claim 1, characterized in that The controlling the lateral movement of the vehicle using the controller gain comprises: generating a lateral control command for the vehicle according to the controller gain; In response to the lateral control command, adjusting relevant parameters of the vehicle to generate an adjusted torque and an adjusted steering wheel angle; A lateral movement of the vehicle is controlled based on the adjusted torque and the adjusted steering wheel angle.

6. A lateral control device for a vehicle, characterized in that: include: An acquisition module, used to acquire at least one of the following information: positioning information, planning information, lateral error, lateral rate, sideslip angle error and sideslip angle rate of the vehicle; An establishing module, configured to establish an error dynamics model satisfying a preset uncertainty condition according to the at least one item of information, and to obtain a state transfer matrix, a control transfer matrix and an uncertainty transfer matrix of the vehicle according to the error dynamics model; A control module is used to calculate a preset equation based on the state transfer matrix, the control transfer matrix and the uncertainty transfer matrix, and calculate the controller gain of the vehicle according to the preset equation to control the lateral movement of the vehicle using the controller gain.

7. The device according to claim 6, characterized in that The control module comprises: A generating unit, configured to discretize the state transfer matrix, the control transfer matrix, and the uncertainty transfer matrix to generate a discretized state transfer matrix, a discretized control transfer matrix, and a discretized uncertainty transfer matrix; A conversion unit is used to establish a discrete Lyapunov equation based on the discretized state transfer matrix, the discretized control transfer matrix and the discretized uncertainty transfer matrix, and convert the discrete Lyapunov equation into a Bellman equation to calculate the preset equation using the Bellman equation.

8. The device according to claim 7, characterized in that The control module also includes: An acquisition unit, configured to acquire an initialization control gain and an initialization uncertainty gain of the vehicle based on the preset equation; An iterative calculation unit, used for iteratively calculating the matrix to generate uncertainty parameters that meet preset conditions; A calculation unit is used to calculate the controller gain of the vehicle based on the initialization control gain, the uncertainty gain and the uncertainty parameter.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lateral control method of a vehicle as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the lateral control method of a vehicle as described in any one of claims 1 to 5.

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