Stability control method and device based on disturbance self-adaption, equipment and medium

Abstract

The invention discloses a stability control method, device and equipment based on disturbance self-adaption and a medium, and relates to the technical field of vehicle intelligent control. The method comprises the steps that the yaw velocity, the side slip angle and control information of a vehicle are obtained, and the vehicle disturbance quantity is calculated according to the environment disturbance quantity and the vehicle state disturbance quantity; determining a self-adaptive prediction step number and a single-step prediction time domain related to the prediction control degree according to the calculation performance of the vehicle and the calculated vehicle disturbance quantity; constructing a vehicle two-degree-of-freedom linear system, performing multi-step iteration to obtain a multi-step state transition equation, and constructing an optimization task based on the multi-step state transition equation, the determined prediction step number and the single-step prediction time domain; the optimal solution of the optimization task is solved, the optimal control quantity is obtained, then the four-wheel additional yawing moment is obtained and output, and vehicle stability control is achieved. The stability control effect of the vehicle can be effectively guaranteed.

Description

Technical Field

[0001] This application relates to the field of vehicle intelligent control technology, specifically to a stability control method, device, equipment, and medium based on disturbance adaptation. Background Technology

[0002] Vehicle stability control refers to the ability of a vehicle to travel in the direction (straight or turning) given by the driver through the steering system and steering wheels, and to resist external disturbances (uneven road surface, crosswinds, uneven loading of cargo or passengers) and maintain stable driving.

[0003] Currently, for four-wheel independent drive vehicles, when performing stability control based on stability control algorithms, adaptive control is rarely performed according to the degree of disturbance, resulting in poor vehicle stability control performance. Summary of the Invention

[0004] This application provides a disturbance-adaptive stability control method, device, equipment, and medium that can effectively ensure the stability control effect of a vehicle.

[0005] In a first aspect, embodiments of this application provide a stability control method based on disturbance adaptation, the stability control method based on disturbance adaptation includes: The vehicle's yaw rate, center of gravity sideslip angle, and handling information are acquired to calculate the vehicle disturbance based on environmental disturbance and vehicle state disturbance. Based on the vehicle's computational performance and the calculated vehicle disturbance, the adaptive prediction step number and single-step prediction time domain related to the predictive control level are determined. A two-degree-of-freedom linear system of a vehicle is constructed and multi-step iterations are performed to obtain a multi-step state transition equation. An optimization task is then constructed based on the multi-step state transition equation, the determined number of prediction steps, and the single-step prediction time domain. Solve for the optimal solution of the optimization task to obtain the optimal control quantity, and then obtain and output the additional yaw moment of the four wheels to achieve vehicle stability control.

[0006] In conjunction with the first aspect, in one implementation, the acquisition of the vehicle's yaw rate, center of gravity sideslip angle, and handling information to calculate the vehicle disturbance based on environmental disturbances and vehicle state disturbances specifically includes: Based on the vehicle's yaw rate and sideslip angle, the vehicle's state disturbance is calculated, specifically:

[0007] in, express The vehicle state disturbance at any given time. , This represents the adjustable state weight parameter. This represents the adjustable perturbation weight parameter. express The rate of change of the yaw rate at time t, express The rate of change of the centroid sideslip angle at time t; Based on the vehicle's handling information, the environmental disturbance is calculated, specifically:

[0008] in, This indicates the amount of environmental disturbance. This represents the adjustable perturbation weight parameter. express The rate of change of the front wheel steering angle at any given moment; Based on the calculated vehicle state disturbance and environmental disturbance, the vehicle disturbance is calculated, specifically:

[0009] in, Indicates the amount of vehicle disturbance, which affects , Normalization is performed to ensure that vehicle disturbances remain within a fixed range.

[0010] In conjunction with the first aspect, in one implementation, determining the adaptive prediction step number and single-step prediction time domain related to the predictive control level based on the vehicle's computational performance and the calculated vehicle disturbance amount specifically includes: The maximum number of prediction steps is determined based on the computing performance of the onboard chip configured in the vehicle. By combining the determined maximum prediction steps, the calculated vehicle disturbance, and the aiming time required for the vehicle to maintain stability in the current environment, the adaptive prediction steps and the correspondence between the single-step prediction time domain and the predictive control level are determined. Specifically:

[0011]

[0012] in, Indicates the number of prediction steps. Indicates the maximum number of prediction steps. This indicates the amount of vehicle disturbance. Indicates single-step prediction in the time domain. This indicates the anticipation time required for the vehicle to maintain stability in the current environment. This indicates the rounding up operation.

[0013] In conjunction with the first aspect, in one implementation, the construction of a two-degree-of-freedom linear system of the vehicle and the multi-step iteration to obtain a multi-step state transition equation, wherein the construction of the two-degree-of-freedom linear system of the vehicle specifically includes: Construct a two-degree-of-freedom model for vehicle stability:

[0014]

[0015] in, This indicates the rate of change of the vehicle's center of gravity sideslip angle. This indicates the lateral force on the left front wheel. This indicates the lateral force on the right front wheel. This indicates the lateral force on the left rear wheel. This indicates the lateral force on the right rear wheel. Indicates the steering angle of the vehicle's front wheels. Indicates the overall mass of the vehicle. Indicates the longitudinal speed of the vehicle. This indicates the yaw rate of the vehicle. This represents the rate of change of the vehicle's yaw rate. Indicates the distance from the front axle to the center of gravity. This indicates the distance from the rear axle to the center of mass. It represents half of the wheelbase. This indicates the additional yaw moment of the entire vehicle. Indicates the vehicle's moment of inertia; Constructing a tire force model:

[0016] in, Represents the tire force model. Indicates the lateral stiffness of the tire. Indicates the tire slip angle; Construct a tire slip angle estimation model:

[0017]

[0018] in, Indicates the front wheel slip angle. Indicates the lateral speed of the vehicle. Indicates the longitudinal speed of the vehicle. Indicates the rear wheel slip angle; Based on the constructed two-degree-of-freedom vehicle stability model, tire force model, and tire slip angle estimation model, the two-degree-of-freedom linear system of the vehicle is obtained:

[0019]

[0020]

[0021]

[0022] in, This represents a state group that includes the rate of change of the vehicle's center of gravity sideslip angle. and the rate of change of the vehicle's yaw rate Two states, This indicates a state group that includes the vehicle's center of gravity sideslip angle. and the vehicle's yaw rate Two states, , , , , , Represents the parameters of a linear system. This represents the control quantity, specifically the additional yaw moment of the entire vehicle. , Indicates the lateral force of the tire. This indicates the lateral force of the left front tire. This indicates the lateral force on the right front tire. This indicates the lateral force of the left rear tire. This indicates the lateral force on the right rear tire. Indicates the slip angle of the left front tire. Indicates the slip angle of the right front tire. Indicates the slip angle of the left rear tire. This indicates the slip angle of the right rear tire.

[0023] In conjunction with the first aspect, in one implementation, the construction of a two-degree-of-freedom linear system of the vehicle and the multi-step iteration to obtain a multi-step state transition equation specifically includes: Based on the two-degree-of-freedom linear system of the vehicle, multi-step iteration is performed to obtain the multi-step state transition equation. ; in, Indicates the future The state group matrix in the time domain at each time step Indicates the future The control matrix in the time domain at each time step This represents the control input coefficients of the multi-step state transition equation. These represent the initial state coefficients of the multi-step state transition equation. This represents the initial state matrix of the vehicle system, specifically:

[0024]

[0025]

[0026]

[0027]

[0028] in, Indicates the first The state group at each moment Indicates the first The control quantity at each moment, i.e. , This represents the state group at the initial moment. Denotes the state transition coefficients of a single-step state transition equation. This represents the control input coefficients of the single-step state transition equation. express of Power of 1 Represents the identity matrix. This indicates the time domain for single-step prediction.

[0029] In conjunction with the first aspect, in one implementation, the step of constructing the optimization task based on the multi-step state transition equation and the determined number of prediction steps and single-step prediction time domain specifically includes: For the obtained multi-step state transition equation, the step size is set to a fixed number of prediction steps. ,make The single-step prediction time domain is set to a defined single-step prediction time domain. To obtain the future The state group matrix at each time step ; Optimized task build:

[0030]

[0031] in, Represents the calculation of the magnitude of a vector. Indicates the future The expected state matrix at each time step. Indicates the future Control matrix for each time step This represents the minimum value of the control quantity matrix. This represents the maximum value of the control quantity matrix.

[0032] In conjunction with the first aspect, in one implementation, the step of solving the optimal solution of the optimization task to obtain the optimal control quantity and then obtaining and outputting the four-wheel additional yaw moment specifically includes: The optimal solution obtained by solving the optimization task is:

[0033] in, This represents the optimal solution. This represents the optimal control quantity. Indicates matrix transpose; Take the optimal control quantity at the first time step If the output is performed, the additional yaw moment of the four wheels is:

[0034]

[0035] in, This represents the expected additional yaw moment of the entire vehicle at the first moment. This indicates the additional yaw moment of the four wheels.

[0036] Secondly, embodiments of this application provide a disturbance-adaptive stability control device, the disturbance-adaptive stability control device comprising: The calculation module is used to acquire the vehicle's yaw rate, center of gravity sideslip angle, and control information, so as to calculate the vehicle disturbance based on the environmental disturbance and the vehicle state disturbance. The determination module is used to determine the adaptive prediction steps and single-step prediction time domain related to the degree of predictive control, based on the vehicle's computational performance and the calculated vehicle disturbance. The module is used to construct a two-degree-of-freedom linear system of a vehicle and perform multi-step iterations to obtain multi-step state transition equations. Based on the multi-step state transition equations and the determined number of prediction steps and single-step prediction time domain, an optimization task is constructed. The execution module is used to solve the optimal solution of the optimization task, obtain the optimal control quantity, and then obtain and output the additional yaw moment of the four wheels to achieve vehicle stability control.

[0037] Thirdly, embodiments of this application provide a disturbance-adaptive stability control device, which includes a processor, a memory, and a disturbance-adaptive stability control program stored in the memory and executable by the processor. When the disturbance-adaptive stability control program is executed by the processor, it implements the steps of the disturbance-adaptive stability control method described above.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing a disturbance-adaptive stability control program, wherein when the disturbance-adaptive stability control program is executed by a processor, it implements the steps of the disturbance-adaptive stability control method described above.

[0039] The beneficial effects of the technical solutions provided in this application include: Based on the disturbance conditions under lateral conditions, the control algorithm is adapted to the disturbance level. Under stable vehicle conditions with low disturbance, the prediction period of the control algorithm is increased while the prediction step size is reduced. Under unstable vehicle conditions with large disturbance, the prediction period of the control algorithm is shortened while the prediction step size is increased, effectively ensuring the stability control effect of the vehicle. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the disturbance-adaptive stability control method of this application; Figure 2 This is a schematic diagram of the functional modules of the disturbance-adaptive stability control device of this application; Figure 3 This is a schematic diagram of the hardware structure of the disturbance-adaptive stability control device of this application. Detailed Implementation

[0041] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0043] In a first aspect, embodiments of this application provide a disturbance-adaptive stability control method to solve the stability control problem of a four-wheel independent drive vehicle during motion.

[0044] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the disturbance-adaptive stability control method of this application. Figure 1 As shown, the stability control method based on disturbance adaptation includes: S1: Acquire the vehicle's yaw rate, center of gravity sideslip angle, and handling information to calculate the vehicle disturbance based on environmental disturbance and vehicle state disturbance. S2: Based on the vehicle's computational performance and the calculated vehicle disturbance, determine the adaptive prediction step number and single-step prediction time domain related to the predictive control level. S3: Construct a two-degree-of-freedom linear system for the vehicle and perform multi-step iterations to obtain multi-step state transition equations. Based on the multi-step state transition equations, the determined number of prediction steps, and the single-step prediction time domain, construct an optimization task. S4: Solve for the optimal solution of the optimization task to obtain the optimal control quantity, and then obtain the additional yaw moment of the four wheels and output it to achieve vehicle stability control.

[0045] Regarding the calculation of vehicle disturbances, it should be noted that in lateral conditions, yaw rate and sideslip angle are typically used as state variables to measure vehicle stability. In lateral conditions, only the steering wheel angle needs to be considered. Specifically, the disturbance conditions can be defined based on two types of information. The first type is the vehicle's controlled state variable, whose rate of change directly reflects the magnitude of the disturbance. The diversity of disturbances does not need to be considered, as they will ultimately be reflected in the rate of change of the vehicle's controlled state variable, although there is a certain lag. The second type is environmental disturbance information, including changes in road conditions and driver operation. The vehicle model described in this application does not have relevant environmental sensors and preprocessing algorithms; therefore, only driver operation information is considered.

[0046] Furthermore, in one embodiment, the vehicle's yaw rate, center of gravity sideslip angle, and handling information are acquired to calculate the vehicle disturbance based on environmental disturbance and vehicle state disturbance, specifically including: S101: Calculate the vehicle's state disturbance based on the vehicle's yaw rate and sideslip angle. Specifically:

[0047] in, express The vehicle state disturbance at any given time. , This represents the adjustable state weight parameter. This represents the adjustable perturbation weight parameter. express The rate of change of the yaw rate at time t, express The rate of change of the centroid sideslip angle at time t; S102: Based on the vehicle's control information, calculate the environmental disturbance amount, specifically:

[0048] in, This indicates the amount of environmental disturbance. This represents the adjustable perturbation weight parameter. express The rate of change of the front wheel steering angle at any given moment; S103: Based on the calculated vehicle state disturbance and environmental disturbance, calculate the vehicle disturbance. Specifically:

[0049] in, Indicates the amount of vehicle disturbance, which affects , Normalization is performed to ensure that vehicle disturbances remain within a fixed range.

[0050] Among them, for those through the , After normalization to ensure that the vehicle disturbance is within a fixed range, it can be expressed as:

[0051]

[0052]

[0053] .

[0054] It should be noted that for adaptive disturbance control using correlated model predictive control, the number of prediction steps in model predictive control... and single-step prediction time domain These are two important parameters for improving the effectiveness of predictive control. When the number of prediction steps... The larger the value, the better the single-step prediction time domain. The smaller the number of prediction steps, the higher the accuracy of the model's predictions, but the greater the computational cost; when the number of prediction steps... The smaller the value, the better the single-step prediction time domain. The smaller the value, the lower the accuracy of the model's predictions and the less computation is required.

[0055] Furthermore, in one embodiment, based on the vehicle's computational performance and the calculated vehicle disturbance, an adaptive number of prediction steps and a single-step prediction time domain related to the predictive control level are determined, specifically including: S201: Determine the maximum number of prediction steps based on the computing performance of the onboard chip configured in the vehicle; Specifically, the maximum number of prediction steps when the vehicle control chip runs the stability control method (algorithm) described in this application is first determined. The maximum number of prediction steps is an empirical value, which is affected by the algorithm complexity and the computing power allocated to the algorithm by the chip, and can be set in advance. S202: Combining the determined maximum prediction steps, the calculated vehicle disturbance, and the aiming time required for the vehicle to maintain stability in the current environment, determine the adaptive prediction steps and the correspondence between the single-step prediction time domain and the predictive control level. Specifically:

[0056]

[0057] in, Indicates the number of prediction steps. Indicates the maximum number of prediction steps. This indicates the amount of vehicle disturbance. Indicates single-step prediction in the time domain. This indicates the anticipation time required for the vehicle to maintain stability in the current environment. It is an empirical value derived from operating condition testing. This indicates the rounding up operation.

[0058] Furthermore, in one embodiment, a two-degree-of-freedom linear system of the vehicle is constructed and multi-step iterations are performed to obtain a multi-step state transition equation. Specifically, the construction of the two-degree-of-freedom linear system of the vehicle includes: S301: Constructing a two-degree-of-freedom model for vehicle stability:

[0059]

[0060] in, This indicates the rate of change of the vehicle's center of gravity sideslip angle. This indicates the lateral force on the left front wheel. This indicates the lateral force on the right front wheel. This indicates the lateral force on the left rear wheel. This indicates the lateral force on the right rear wheel. Indicates the steering angle of the vehicle's front wheels. Indicates the overall mass of the vehicle. Indicates the longitudinal speed of the vehicle. This indicates the yaw rate of the vehicle. This represents the rate of change of the vehicle's yaw rate. Indicates the distance from the front axle to the center of gravity. This indicates the distance from the rear axle to the center of mass. It represents half of the wheelbase. This indicates the additional yaw moment of the entire vehicle. Indicates the vehicle's moment of inertia; S302: Constructing a tire force model:

[0061] in, Represents the tire force model. Indicates the lateral stiffness of the tire. Indicates the tire slip angle; S303: Constructing a tire slip angle estimation model:

[0062]

[0063] in, Indicates the front wheel slip angle. Indicates the lateral speed of the vehicle. Indicates the longitudinal speed of the vehicle. Indicates the rear wheel slip angle; S304: Based on the constructed two-degree-of-freedom vehicle stability model, tire force model, and tire slip angle estimation model, the two-degree-of-freedom linear system of the vehicle is obtained:

[0064]

[0065]

[0066]

[0067] in, This represents a state group that includes the rate of change of the vehicle's center of gravity sideslip angle. and the rate of change of the vehicle's yaw rate Two states, This indicates a state group that includes the vehicle's center of gravity sideslip angle. and the vehicle's yaw rate Two states, , , , , , Represents the parameters of a linear system. This represents the control quantity, specifically the additional yaw moment of the entire vehicle. , Indicates the lateral force of the tire. This indicates the lateral force of the left front tire. This indicates the lateral force on the right front tire. This indicates the lateral force of the left rear tire. This indicates the lateral force on the right rear tire. Indicates the slip angle of the left front tire. Indicates the slip angle of the right front tire. Indicates the slip angle of the left rear tire. This indicates the slip angle of the right rear tire.

[0068] Further:

[0069]

[0070]

[0071] in, Indicates the vehicle's center of gravity and front wheelbase. Indicates wheelbase. Indicates the wheelbase from the vehicle's center of gravity. Indicates the lateral stiffness of the left front wheel. Indicates the lateral stiffness of the right front wheel. Indicates the lateral stiffness of the left rear wheel. This indicates the lateral stiffness of the right rear wheel.

[0072] Furthermore, in one embodiment, a two-degree-of-freedom linear system of the vehicle is constructed and multi-step iterations are performed to obtain multi-step state transition equations, specifically including: Based on the two-degree-of-freedom linear system of the vehicle, multi-step iteration is performed to obtain the multi-step state transition equation. That is, multi-step state transition equations can be obtained by performing multi-step iterations based on the two-degree-of-freedom linear system of the vehicle. in, Indicates the future The state group matrix in the time domain at each time step Indicates the future The control matrix in the time domain at each time step This represents the control input coefficients of the multi-step state transition equation. These represent the initial state coefficients of the multi-step state transition equation. This represents the initial state matrix of the vehicle system, specifically:

[0073]

[0074]

[0075]

[0076]

[0077] in, Indicates the first The state group at each moment Indicates the first The control quantity at each moment, i.e. , This represents the state group at the initial moment. Denotes the state transition coefficients of a single-step state transition equation. This represents the control input coefficients of the single-step state transition equation. express of Power of 1 Represents the identity matrix. This represents the time domain for single-step prediction. The multiplication rules for abstract matrices follow the rules for real matrix multiplication, and the multiplication of each element in the matrix is ​​a matrix multiplication.

[0078] Furthermore, in one embodiment, an optimization task is constructed based on the multi-step state transition equation, the determined number of prediction steps, and the single-step prediction time domain, specifically including: S401: For the obtained multi-step state transition equation, set the step size to a predetermined number of prediction steps. ,make The single-step prediction time domain is set to a defined single-step prediction time domain. To obtain the future The state group matrix at each time step ; That is, an optimization task is constructed based on adaptive parameters. For a multi-step state transition equation, the step size is taken as the number of prediction steps. , that is to say The single-step prediction time domain is taken as the defined single-step prediction time domain. Thus, one can obtain the future. The state group matrix at each time step ; S402: Optimized task construction:

[0079]

[0080] in, Represents the calculation of the magnitude of a vector. Indicates the future The expected state matrix at each time step. Indicates the future Control matrix for each time step This represents the minimum value of the control quantity matrix. This represents the maximum value of the control quantity matrix.

[0081] Furthermore, in one embodiment, solving for the optimal solution to the optimization task yields the optimal control quantity, which in turn generates and outputs the additional yaw torque for the four wheels. Specifically, this includes: S411: The optimal solution obtained by solving the optimization task is:

[0082] in, This represents the optimal solution. This represents the optimal control quantity. Indicates matrix transpose; S412: Take the optimal control quantity at the first time step. If the output is performed, the additional yaw moment of the four wheels is:

[0083]

[0084] in, This represents the expected additional yaw moment of the entire vehicle at the first moment. This indicates the additional yaw moment of the four wheels.

[0085] The disturbance-adaptive stability control method of this application adapts the control algorithm based on the disturbance level under lateral operating conditions. Under stable vehicle conditions with low disturbance, the prediction period of the control algorithm is increased while the prediction step size is reduced. Under unstable vehicle conditions with large disturbance, the prediction period of the control algorithm is shortened while the prediction step size is increased, effectively ensuring the stability control effect of the vehicle.

[0086] Secondly, embodiments of this application also provide a stability control device based on disturbance adaptation.

[0087] In one embodiment, reference is made to Figure 2 , Figure 2 This is a schematic diagram of the functional modules of the disturbance-adaptive stability control device of this application. Figure 2 As shown, the stability control device based on disturbance adaptation includes: a calculation module, a determination module, a construction module, and an execution module.

[0088] The calculation module acquires the vehicle's yaw rate, center of gravity sideslip angle, and handling information to calculate the vehicle disturbance based on environmental and vehicle state disturbances. The determination module determines the adaptive prediction steps and single-step prediction time domain related to the predictive control level based on the vehicle's computational performance and the calculated vehicle disturbance. The construction module constructs a two-degree-of-freedom linear system for the vehicle and performs multi-step iterations to obtain multi-step state transition equations. Based on these multi-step state transition equations and the determined prediction steps and single-step prediction time domain, an optimization task is constructed. The execution module solves the optimal solution of the optimization task to obtain the optimal control quantity, which in turn generates and outputs the four-wheel additional yaw moment, thus achieving vehicle stability control.

[0089] Thirdly, embodiments of this application provide a disturbance-adaptive stability control device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0090] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the disturbance-adaptive stability control device involved in the embodiments of this application. In the embodiments of this application, the disturbance-adaptive stability control device may include a processor, a memory, a communication interface, and a communication bus.

[0091] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0092] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the disturbance-adaptive stability control device, as well as interfaces used for interconnecting the disturbance-adaptive stability control device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0093] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0094] The processor can be a general-purpose processor, which can call a disturbance-adaptive stability control program stored in memory and execute the disturbance-adaptive stability control method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the disturbance-adaptive stability control program is called can be referred to in the various embodiments of the disturbance-adaptive stability control method of this application, and will not be repeated here.

[0095] Those skilled in the art will understand that Figure 3 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0096] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0097] The present application has a computer-readable storage medium storing a disturbance-adaptive stability control program, wherein when the disturbance-adaptive stability control program is executed by a processor, it implements the steps of the disturbance-adaptive stability control method described above.

[0098] The method implemented when the disturbance-adaptive stability control program is executed can be referred to in various embodiments of the disturbance-adaptive stability control method of this application, and will not be repeated here.

[0099] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0100] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0101] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0102] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0104] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A stability control method based on disturbance adaptation, characterized in that, The disturbance-adaptive stability control method includes: The vehicle's yaw rate, center of gravity sideslip angle, and handling information are acquired to calculate the vehicle disturbance based on environmental disturbance and vehicle state disturbance. Based on the vehicle's computational performance and the calculated vehicle disturbance, the adaptive prediction step number and single-step prediction time domain related to the predictive control level are determined. A two-degree-of-freedom linear system of a vehicle is constructed and multi-step iterations are performed to obtain a multi-step state transition equation. An optimization task is then constructed based on the multi-step state transition equation, the determined number of prediction steps, and the single-step prediction time domain. Solve for the optimal solution of the optimization task to obtain the optimal control quantity, and then obtain and output the additional yaw moment of the four wheels to achieve vehicle stability control.

2. The stability control method based on disturbance adaptation as described in claim 1, characterized in that, The acquisition of the vehicle's yaw rate, center of gravity sideslip angle, and handling information, in order to calculate the vehicle disturbance based on environmental disturbances and vehicle state disturbances, specifically includes: Based on the vehicle's yaw rate and sideslip angle, the vehicle's state disturbance is calculated, specifically: in, express The vehicle state disturbance at any given time. , This represents the adjustable state weight parameter. This represents the adjustable perturbation weight parameter. express The rate of change of the yaw rate at time t, express The rate of change of the centroid sideslip angle at time t; Based on the vehicle's handling information, the environmental disturbance is calculated, specifically: in, This indicates the amount of environmental disturbance. This represents the adjustable perturbation weight parameter. express The rate of change of the front wheel steering angle at any given moment; Based on the calculated vehicle state disturbance and environmental disturbance, the vehicle disturbance is calculated, specifically: in, Indicates the amount of vehicle disturbance, which affects , Normalization is performed to ensure that vehicle disturbances remain within a fixed range.

3. The stability control method based on disturbance adaptation as described in claim 1, characterized in that, The step of determining the adaptive prediction step number and single-step prediction time domain related to the predictive control level based on the vehicle's computational performance and the calculated vehicle disturbance amount specifically includes: The maximum number of prediction steps is determined based on the computing performance of the onboard chip configured in the vehicle. By combining the determined maximum prediction steps, the calculated vehicle disturbance, and the aiming time required for the vehicle to maintain stability in the current environment, the adaptive prediction steps and the correspondence between the single-step prediction time domain and the predictive control level are determined. Specifically: in, Indicates the number of prediction steps. Indicates the maximum number of prediction steps. This indicates the amount of vehicle disturbance. Indicates single-step prediction in the time domain. This indicates the anticipation time required for the vehicle to maintain stability in the current environment. This indicates the rounding up operation.

4. The stability control method based on disturbance adaptation as described in claim 1, characterized in that, The construction of a two-degree-of-freedom linear system for the vehicle and the subsequent multi-step iteration to obtain the multi-step state transition equations, specifically include the following: Construct a two-degree-of-freedom model for vehicle stability: in, This indicates the rate of change of the vehicle's center of gravity sideslip angle. This indicates the lateral force on the left front wheel. This indicates the lateral force on the right front wheel. This indicates the lateral force on the left rear wheel. This indicates the lateral force on the right rear wheel. Indicates the steering angle of the vehicle's front wheels. Indicates the overall mass of the vehicle. Indicates the longitudinal speed of the vehicle. This indicates the yaw rate of the vehicle. This represents the rate of change of the vehicle's yaw rate. Indicates the distance from the front axle to the center of gravity. This indicates the distance from the rear axle to the center of mass. It represents half of the wheelbase. This indicates the additional yaw moment of the entire vehicle. Indicates the vehicle's moment of inertia; Constructing a tire force model: in, Represents the tire force model. Indicates the lateral stiffness of the tire. Indicates the tire slip angle; Construct a tire slip angle estimation model: in, Indicates the front wheel slip angle. Indicates the lateral speed of the vehicle. Indicates the longitudinal speed of the vehicle. Indicates the rear wheel slip angle; Based on the constructed two-degree-of-freedom vehicle stability model, tire force model, and tire slip angle estimation model, the two-degree-of-freedom linear system of the vehicle is obtained: in, This represents a state group that includes the rate of change of the vehicle's center of gravity sideslip angle. and the rate of change of the vehicle's yaw rate Two states, This indicates a state group that includes the vehicle's center of gravity sideslip angle. and the vehicle's yaw rate Two states, , , , , , Represents the parameters of a linear system. This represents the control quantity, specifically the additional yaw moment of the entire vehicle. , Indicates the lateral force of the tire. This indicates the lateral force of the left front tire. This indicates the lateral force on the right front tire. This indicates the lateral force of the left rear tire. This indicates the lateral force on the right rear tire. Indicates the slip angle of the left front tire. Indicates the slip angle of the right front tire. Indicates the slip angle of the left rear tire. This indicates the slip angle of the right rear tire.

5. The stability control method based on disturbance adaptation as described in claim 4, characterized in that, The construction of the two-degree-of-freedom linear system of the vehicle and the multi-step iteration to obtain the multi-step state transition equations specifically include: Based on the two-degree-of-freedom linear system of the vehicle, multi-step iteration is performed to obtain the multi-step state transition equation. ; in, Indicates the future The state group matrix in the time domain at each time step Indicates the future The control matrix in the time domain at each time step This represents the control input coefficients of the multi-step state transition equation. These represent the initial state coefficients of the multi-step state transition equation. This represents the initial state matrix of the vehicle system, specifically: in, Indicates the first The state group at each moment Indicates the first The control quantity at each moment, i.e. , This represents the state group at the initial moment. Denotes the state transition coefficients of a single-step state transition equation. This represents the control input coefficients of the single-step state transition equation. express of Power of 1 Represents the identity matrix. This indicates the time domain for single-step prediction.

6. The stability control method based on disturbance adaptation as described in claim 5, characterized in that, The optimization task, based on the multi-step state transition equation and the determined number of prediction steps and single-step prediction time domain, specifically includes: For the obtained multi-step state transition equation, the step size is set to a fixed number of prediction steps. ,make The single-step prediction time domain is set to a defined single-step prediction time domain. To obtain the future The state group matrix at each time step ; Optimized task build: in, Represents the calculation of the magnitude of a vector. Indicates the future The expected state matrix at each time step. Indicates the future Control matrix for each time step This represents the minimum value of the control quantity matrix. This represents the maximum value of the control quantity matrix.

7. The stability control method based on disturbance adaptation as described in claim 6, characterized in that, The process of finding the optimal solution to the optimization task, obtaining the optimal control quantity, and then obtaining and outputting the additional yaw moment of the four wheels specifically includes: The optimal solution obtained by solving the optimization task is: in, This represents the optimal solution. This represents the optimal control quantity. Indicates matrix transpose; Take the optimal control quantity at the first time step If the output is performed, the additional yaw moment of the four wheels is: in, This represents the expected additional yaw moment of the entire vehicle at the first moment. This indicates the additional yaw moment of the four wheels.

8. A stability control device based on disturbance adaptation, characterized in that, The disturbance-adaptive stability control device includes: The calculation module is used to acquire the vehicle's yaw rate, center of gravity sideslip angle, and control information, so as to calculate the vehicle disturbance based on the environmental disturbance and the vehicle state disturbance. The determination module is used to determine the adaptive prediction steps and single-step prediction time domain related to the degree of predictive control, based on the vehicle's computational performance and the calculated vehicle disturbance. The module is used to construct a two-degree-of-freedom linear system of a vehicle and perform multi-step iterations to obtain multi-step state transition equations. Based on the multi-step state transition equations and the determined number of prediction steps and single-step prediction time domain, an optimization task is constructed. The execution module is used to solve the optimal solution of the optimization task, obtain the optimal control quantity, and then obtain and output the additional yaw moment of the four wheels to achieve vehicle stability control.

9. A stability control device based on disturbance adaptation, characterized in that, The disturbance-adaptive stability control device includes a processor, a memory, and a disturbance-adaptive stability control program stored in the memory and executable by the processor, wherein when the disturbance-adaptive stability control program is executed by the processor, it implements the steps of the disturbance-adaptive stability control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a disturbance-adaptive stability control program, wherein when the disturbance-adaptive stability control program is executed by a processor, it implements the steps of the disturbance-adaptive stability control method as described in any one of claims 1 to 7.

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