Dynamic stability control method and device of full-vector drive-by-wire vehicle and vehicle

Abstract

The invention relates to a full-vector drive-by-wire vehicle dynamic stability control method and device and a vehicle, and the method comprises the steps: simplifying a target vehicle into an actual dynamic model meeting a preset degree-of-freedom condition; a corresponding model prediction controller is established based on the actual dynamic model, and the model prediction controller and the reference trajectory of the target vehicle are combined to generate the left and right wheel turning angle control quantity of the actual dynamic model; based on the left and right wheel turning angle control quantity, a turning angle instruction of each wheel of the target vehicle is obtained, and the target vehicle is controlled based on the turning angle instructions, so that the target vehicle meets a preset stability condition in the driving process. Therefore, the technical problem that the characteristic of high controllable degree of freedom of the full-vector drive-by-wire vehicle and the control calculation efficiency are difficult to consider at the same time based on a traditional automobile framework in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle control, and particularly relates to a dynamic stability control method, device and vehicle for a fully vector-by-wire vehicle. Background Technique

[0002] A linear system refers to a system in which the input and output, state and external interference of the system satisfy a linear relationship, and it satisfies the superposition principle, that is, the system response caused by multiple inputs is equal to the superposition of the responses caused by each individual input. When the vehicle's actuators are in the linear working area, linear simplification of the vehicle system and the use of linear system control can make the analysis and design process simple and clear, the stability easy to verify, and the control law structure easy to implement.

[0003] For a vehicle system, linearization is more effective under weak non-linear conditions (such as low-speed straight driving). In the case of strong non-linearity or large parameter changes, non-linear control may be required. Non-linear control is suitable for dealing with systems with strong non-linear effects such as saturation, dead zone, and hysteresis. It is usually highly adaptable, has excellent performance, and good robustness. However, the analysis and design process of non-linear systems is relatively complex, the stability proof is difficult, and the implementation difficulty is large.

[0004] In the related art, based on the traditional vehicle architecture, it is difficult to simultaneously take into account the characteristics of high controllable degrees of freedom of a fully vector-by-wire vehicle and the computational efficiency of control. That is to say, if it is necessary to release the control performance of a fully vector-by-wire vehicle, more complex models are often required, and the control has high requirements for computational platforms such as in-vehicle chips; if fast and efficient calculation is required, simplified models of traditional vehicles are often used, thus the advantages of fully vector-by-wire vehicles cannot be exerted, and there is an urgent need for improvement. Summary of the Invention

[0005] The present application provides a dynamic stability control method, device and vehicle for a fully vector-by-wire vehicle to solve the technical problem that in the related art, based on the traditional vehicle architecture, it is difficult to simultaneously take into account the characteristics of high controllable degrees of freedom of a fully vector-by-wire vehicle and the computational efficiency of control.

[0006] The first aspect of the embodiments of the present application provides a dynamic stability control method for a fully vector-by-wire vehicle, including the following steps: simplifying a target vehicle into an actual dynamic model that meets preset degree-of-freedom conditions; establishing a corresponding model predictive controller based on the actual dynamic model to generate left and right wheel steering angle control quantities of the actual dynamic model by combining the model predictive controller and the reference trajectory of the target vehicle; obtaining a steering angle command for each wheel of the target vehicle based on the left and right wheel steering angle control quantities, and controlling the target vehicle based on the steering angle command so that the target vehicle meets preset stability conditions during driving.

[0007] Optionally, in an embodiment of the present application, the reduction of the target vehicle to an actual dynamic model that meets the preset degree-of-freedom condition includes: simplifying the structure of the target vehicle to obtain a simplified vehicle structure; simulating the simplified vehicle structure under a preset driving condition to obtain balance equations for corresponding longitudinal translation, lateral translation, and yaw motion; combining the balance equations for longitudinal translation, lateral translation, and yaw motion with the longitudinal force and lateral force of the tire to obtain an initial dynamic model; expanding the initial dynamic model based on the preset path constraint and stability constraint of the target vehicle to obtain an expanded dynamic model; and discretizing the expanded dynamic model to obtain the actual dynamic model.

[0008] Optionally, in an embodiment of the present application, the expression of the actual dynamic model is:

[0009]

[0010] where T represents the time period, A k represents the state matrix, B k represents the control matrix, C k represents the output matrix, I is the identity matrix, A t represents the state quantity transfer matrix of the actual dynamic model, B t represents the control quantity transfer matrix of the actual dynamic model, C t represents the output matrix in the continuous time domain, k represents the discrete time, and t represents the continuous time.

[0011] Optionally, in an embodiment of the present application, the establishment of a corresponding model predictive controller based on the actual dynamic model to generate the left and right wheel steering angle control quantities of the actual dynamic model in combination with the model predictive controller and the reference trajectory of the target vehicle includes: predicting the predicted output of the target vehicle within a preset time domain based on the actual dynamic model; calculating a loss function using the predicted output and the reference trajectory; constructing input constraints, increment constraints, and output quantity constraints for the actual dynamic model; and obtaining the model predictive controller by combining the loss function, the input constraints, the increment constraints, and the output quantity constraints.

[0012] Optionally, in an embodiment of the present application, obtaining the steering angle command for each wheel of the target vehicle based on the left and right wheel steering angle control quantities includes: constructing a steering angle constraint for the wheel based on the Ackermann steering geometry relationship; calculating the steering angle of each wheel using the steering angle constraint and the left and right wheel steering angle control quantities, and obtaining the steering angle command for each wheel based on the steering angle of each wheel.

[0013] Optionally, in an embodiment of the present application, the calculation expression of the steering angle of each wheel is:

[0014]

[0015] where δ l,i represents the steering angle of each left wheel, L i represents the position vector of the center of the i-th axle relative to the vehicle's center of mass, δ r represents the right wheel steering angle control quantity of the actual dynamic model, δ l represents the left wheel steering angle control quantity of the actual dynamic model, B represents the wheelbase of the target vehicle, and δ r,i represents the steering angle of each right wheel.

[0016] An embodiment of the second aspect of the present application provides a dynamic stability control device for a full-vector steer-by-wire vehicle, including: a simplification module for simplifying the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions; a generation module for establishing a corresponding model predictive controller based on the actual dynamic model, and combining the model predictive controller and the reference trajectory of the target vehicle to generate the left and right wheel steering angle control quantities of the actual dynamic model; a control module for obtaining the steering angle command of each wheel of the target vehicle based on the left and right wheel steering angle control quantities, and controlling the target vehicle based on the steering angle command so that the target vehicle meets the preset stability conditions during driving.

[0017] Optionally, in an embodiment of the present application, the simplification module includes: a simplification unit for simplifying the structure of the target vehicle to obtain a simplified vehicle structure; a simulation unit for simulating the simplified vehicle structure under a preset driving condition to obtain the balance equations of the corresponding longitudinal translation, lateral translation, and yaw motion; a first construction unit for combining the balance equations of the longitudinal translation, lateral translation, and yaw motion and the longitudinal and lateral forces of the tires to obtain an initial dynamic model; an expansion unit for expanding the initial dynamic model based on the preset path constraint and stability constraint of the target vehicle to obtain an expanded dynamic model; a processing unit for discretizing the expanded dynamic model to obtain the actual dynamic model.

[0018] Optionally, in an embodiment of the present application, the expression of the actual dynamic model is:

[0019]

[0020] where T represents the time period, A k represents the state matrix, B k represents the control matrix, C kdenotes the output matrix, I is the identity matrix, A t denotes the state transition matrix of the actual dynamic model, B t denotes the control transition matrix of the actual dynamic model, C t denotes the output matrix in the continuous time domain, k denotes the discrete time, and t denotes the continuous time.

[0021] Optionally, in an embodiment of the present application, the generating module includes: a prediction unit configured to predict a predicted output of the target vehicle within a preset time domain based on the actual dynamic model; a first calculation unit configured to calculate a loss function by using the predicted output and the reference trajectory; a second construction unit configured to construct input constraints, increment constraints, and output quantity constraints of the actual dynamic model; and a third construction unit configured to obtain the model predictive controller by combining the loss function, the input constraints, the increment constraints, and the output quantity constraints.

[0022] Optionally, in an embodiment of the present application, the control module includes: a fourth construction unit configured to construct a corner angle constraint of a wheel based on the Ackermann steering geometry; a second calculation unit configured to calculate a steering angle of each wheel by using the corner angle constraint and the left and right wheel corner control quantities, and obtain a corner angle command of each wheel based on the steering angle of each wheel.

[0023] Optionally, in an embodiment of the present application, the calculation expression of the steering angle of each wheel is:

[0024]

[0025] wherein, δ l,i denotes the steering angle of each left wheel, l i denotes the position vector of the center of the i-th axle relative to the vehicle mass center, δ r denotes the right wheel corner control quantity of the actual dynamic model, δ l denotes the left wheel corner control quantity of the actual dynamic model, B denotes the wheelbase of the target vehicle, δ r,i denotes the steering angle of each right wheel.

[0026] An embodiment of the third aspect of the present application provides a vehicle, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the dynamic stability control method of the full-vector by-wire vehicle as described in the above embodiment.

[0027] In a fourth aspect embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the dynamic stability control method of the full-vector by-wire vehicle as described in the above embodiments.

[0028] In a fifth aspect embodiment of the present application, a computer program product is provided, including a computer program. When the computer program is executed, it is used to implement the dynamic stability control method of the full-vector by-wire vehicle as above.

[0029] The embodiments of the present application can simplify the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions. The structure is simple and easy to solve, and at the same time, it can better express the dynamic characteristics of the full-vector by-wire vehicle. Furthermore, a corresponding model predictive controller is established according to the actual dynamic model to generate the left and right wheel steering angle control amounts of the actual dynamic model, and obtain the steering angle commands for each wheel of the target vehicle, so as to control the target vehicle based on the steering angle commands, enabling the target vehicle to meet the preset stability conditions during driving, realizing the stability control of the full-vector by-wire vehicle, and having relatively low requirements for computing power, improving the practicability and economy of the control algorithm. Thus, the technical problem in the related art that it is difficult to simultaneously take into account the characteristics of the high controllable degree of freedom of the full-vector by-wire vehicle and the computational efficiency of control based on the traditional vehicle architecture is solved.

[0030] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0032] Figure 1 is a flowchart of a dynamic stability control method for a full-vector by-wire vehicle according to an embodiment of the present application;

[0033] Figure 2 is a schematic diagram of a full-vector by-wire chassis composed of angular modules in the related art;

[0034] Figure 3 is a schematic diagram of a dynamic model of a segway according to an embodiment of the present application;

[0035] Figure 4 is a relationship diagram of the model steering angle and the actual steering angle of each wheel of a segway model according to an embodiment of the present application;

[0036] Figure 5 is a schematic diagram of the principle of a dynamic stability control method for a full-vector by-wire vehicle according to an embodiment of the present application;

[0037] Figure 6 Schematic diagram of stability control effect provided according to an embodiment of the present application;

[0038] Figure 7 Schematic diagram of path tracking effect provided according to an embodiment of the present application;

[0039] Figure 8 Schematic structural diagram of a dynamic stability control device for a full - vector by - wire vehicle provided according to an embodiment of the present application;

[0040] Figure 9 Schematic structural diagram of a vehicle provided according to an embodiment of the present application. Detailed implementation manners

[0041] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0042] The dynamic stability control method, device and vehicle of the full - vector by - wire vehicle according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the technical problem in the related art mentioned in the above - mentioned background art that it is difficult to simultaneously take into account the characteristics of high controllable degrees of freedom of the full - vector by - wire vehicle and the computational efficiency of control based on the traditional vehicle architecture, the present application provides a dynamic stability control method for a full - vector by - wire vehicle. In this method, the target vehicle can be simplified into an actual dynamic model that meets the preset degree - of - freedom conditions, which has a simple structure and is convenient for calculation, and can also better express the dynamic characteristics of the full - vector by - wire vehicle. Then, a corresponding model predictive controller is established based on the actual dynamic model to generate the left - and - right wheel steering angle control amounts of the actual dynamic model, and the steering angle command for each wheel of the target vehicle is obtained, so as to control the target vehicle based on the steering angle command, making the target vehicle meet the preset stability conditions during driving, realizing the stability control of the full - vector by - wire vehicle, and having a relatively low requirement for computing power, improving the practicability and economy of the control algorithm. Thus, the technical problem in the related art that it is difficult to simultaneously take into account the characteristics of high controllable degrees of freedom of the full - vector by - wire vehicle and the computational efficiency of control based on the traditional vehicle architecture is solved.

[0043] Specifically, Figure 1 It is a flow schematic diagram of a dynamic stability control method for a full - vector by - wire vehicle provided by an embodiment of the present application.

[0044] As Figure 1 shown, the dynamic stability control method for the full - vector by - wire vehicle includes the following steps:

[0045] In step S101, the target vehicle is simplified into an actual dynamic model that meets the preset degree-of-freedom conditions.

[0046] It can be understood that in recent years, the drive-by-wire chassis technology has been continuously developing. Starting from the initial electrification and basic drive-by-wire technologies (such as ESC, EPS, e-booster, etc.), it has developed to the integration and application of multi-motor drive architectures, rear-wheel steering (RWS), electronically controlled suspension and other technologies, and will further enter a new stage, that is, using in-wheel motors as the drive form, integrating technologies such as intelligent tires, active suspension, redundant actuators, and omnidirectional drive-by-wire systems to build a fully active control chassis system. It fully integrates functions such as drive, steering, braking, and suspension into a single wheel system, called a "corner module", making each wheel a fully autonomous and controllable execution unit, achieving a high degree of decoupling in the chassis structure and full-dimensional degrees of freedom in control. As Figure 2 shown, the chassis composed of such corner modules is called a full-vector drive-by-wire chassis.

[0047] The target vehicle in the embodiment of the present application can be a full-vector drive-by-wire chassis vehicle. This new architecture is particularly suitable for application scenarios with strict requirements for vehicle dynamic performance, such as high-level autonomous driving, extreme off-road conditions, and special operations. The full-vector drive-by-wire chassis can greatly enhance the mobility and adaptive ability of the vehicle under complex working conditions.

[0048] In order to solve the technical problem of being difficult to simultaneously consider the characteristics of high controllable degrees of freedom of the full-vector drive-by-wire vehicle and the computational efficiency of control, the embodiment of the present application can simplify the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions to release the control performance of the full-vector drive-by-wire vehicle.

[0049] Optionally, in an embodiment of the present application, simplifying the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions includes: simplifying the structure of the target vehicle to obtain a simplified vehicle structure; simulating the simplified vehicle structure under preset driving conditions to obtain the equilibrium equations of the corresponding longitudinal translation, lateral translation, and yaw motion; combining the equilibrium equations of the longitudinal translation, lateral translation, and yaw motion and the longitudinal and lateral forces of the tires to obtain an initial dynamic model; expanding the initial dynamic model based on the preset path constraint and stability constraint of the target vehicle to obtain an expanded dynamic model; discretizing the expanded dynamic model to obtain an actual dynamic model, where the expression of the actual dynamic model is:

[0050]

[0051] where, T represents the time period, A k represents the state matrix, B k represents the control matrix, Ck Represents the output matrix, I is the identity matrix, A t Represents the state transfer matrix of the actual dynamic model, B t Represents the control quantity transfer matrix of the actual dynamic model, C t represents the output matrix in the continuous time domain, k represents discrete time, and t represents continuous time.

[0052] In actual implementation, for stability control of a fully vectored, steer-by-wire vehicle, the control model should be simplified as much as possible to achieve higher computational efficiency in the control algorithm. Furthermore, the subject matter of this embodiment of the application is vehicle lateral stability control, and therefore, the primary focus is on the vehicle's longitudinal, lateral, and yaw dynamics.

[0053] Based on this, the embodiment of the present application can make the following assumptions about the vehicle dynamics model:

[0054] ① Assume that the target vehicle is traveling on a horizontal road and ignore the effect of the suspension. That is, the translation of the target vehicle along the z-axis, the rotation around the x-axis, and the rotation around the y-axis are all ignored.

[0055] ②The wheel turning angle is small and the tire lateral force is in the linear range.

[0056] ③ Ignore the front and rear load transfer of the target vehicle and assume that the dynamic characteristics of the coaxial wheels are the same, that is, the left and right wheels of the vehicle can be simplified into one left wheel and one right wheel respectively.

[0057] In this way, the vehicle can be simplified into a single virtual axis dynamic model, such as Figure 3 To distinguish it from the virtual axis used as a splicing unit in the full vehicle dynamics model, this model is referred to here as the balancing vehicle dynamics model. It is a dynamics model with two independently driven and steered wheels, and with lateral, longitudinal, and yaw degrees of freedom.

[0058] According to Newton's second law, the equilibrium equations of the longitudinal translation, lateral translation and yaw motion of the balancing vehicle dynamics model are obtained respectively.

[0059] The translation equation in the x-direction is:

[0060]

[0061] The translation equation in the y direction is:

[0062]

[0063] The rotation equation along the z-axis is:

[0064]

[0065] in,

[0066] ∑F x = N(F xl cosδ l + F xr cosδ r - F yl sinδ l - F yr sinδ r )(4)

[0067] ∑F y = N(F xl sinδ l + F xr sinδ r + F yl cosδ l + F yr cosδ r )(5)

[0068]

[0069] where N is the number of axles of the fully vectorial by - wire vehicle, B is the wheelbase of the fully vectorial by - wire vehicle. δ l and δ r are the left and right wheel angles of the initial dynamic model respectively, m is the vehicle mass, is the longitudinal acceleration, is the lateral vehicle speed, is the yaw rate, ∑F x is the resultant longitudinal force of the vehicle, F x is the longitudinal force of the vehicle, ∑F y is the resultant lateral force of the vehicle, F y is the lateral force of the vehicle, ∑M z is the total yaw moment of the vehicle, M z is the yaw moment of the vehicle, F xl and F xr are the longitudinal forces of the left and right tires, F yl and F yr are the lateral forces of the left and right tires.

[0070] Under the small - angle approximation of the wheel angle, the longitudinal and lateral forces of the tire can be expressed as:

[0071] F yl = - C cl α l F yr = - C cr α r (7)

[0072] F xl = C ll sl F xr = C lr s r (8)

[0073] where C cl and C cr are the cornering stiffnesses (positive values) of the left and right wheels respectively, and C ll and C lr are the longitudinal force coefficients of the left and right wheels respectively. α l and α r can be expressed as:

[0074]

[0075] In the small-angle approximation,

[0076] cosθ≈1, sinθ≈θ, tanθ≈θ (11)

[0077] In summary, the linearized dynamic model of the in-wheel motor electric vehicle, i.e., the initial dynamic model, can be obtained:

[0078]

[0079] where is the lateral acceleration, I z is the moment of inertia of the vehicle about the z-axis, is the yaw angular acceleration, s l is the slip ratio of the left wheel, and s r is the slip ratio of the right wheel.

[0080] For the path tracking and stability control objectives involved in the embodiments of the present application, the observable quantity needs to be selected as Therefore, the dynamic model is expanded from Equation (12) to Equation (13):

[0081]

[0082] For linear MPC (Model Predictive Control) control, linearize it to obtain the state equation as follows:

[0083]

[0084] where χ(t) is the state quantity, is the rate of change of the state quantity, and the control quantity u(t) =

[0085] [δ l , δ r T , and the observable quantity is ​Y is the abscissa of the target vehicle in the inertial coordinate system, and X is the ordinate of the target vehicle in the inertial coordinate system. A t represents the state transition matrix of the augmented dynamic model, B t represents the control transition matrix of the augmented dynamic model, C t represents the output matrix in the continuous time domain.

[0086]

[0087] Based on Equations (14) and (15), the embodiments of the present application use the Euler method to discretize the continuous model, obtaining the discrete state space equation:

[0088] χ(k + 1) = A k χ(k) + B k u(k)(19)

[0089] Y(k) = C k χ(k)(20)

[0090] where, A k , B k and C k represent the state matrix, the control matrix, and the output matrix respectively, and their specific forms are:

[0091]

[0092] where, T is the time period.

[0093] In step S102, a corresponding model predictive controller is established based on the actual dynamic model to generate the left and right wheel steering angle control quantities of the actual dynamic model by combining the model predictive controller and the reference trajectory of the target vehicle.

[0094] Furthermore, in the process of path tracking control, the embodiments of the present application need to predict the future state and output of the vehicle within a given prediction time domain to solve the optimal control quantity that minimizes the error between the predicted output and the desired trajectory (reference trajectory) at each control moment.

[0095] Optionally, in an embodiment of the present application, establishing a corresponding model predictive controller based on the actual dynamic model to generate the left and right wheel steering angle control quantities of the actual dynamic model by combining the model predictive controller and the reference trajectory of the target vehicle includes: predicting the predicted output of the target vehicle within a preset time domain based on the actual dynamic model; calculating the loss function using the predicted output and the reference trajectory; constructing the input constraint, the increment constraint, and the output quantity constraint of the actual dynamic model; and obtaining the model predictive controller by combining the loss function, the input constraint, the increment constraint, and the output quantity constraint.

[0096] Based on the discrete state space model (Equations (19) and (20)), embodiments of the present application can use the control increment Δu(k) = u(k) - u(k - 1) as the input variable and introduce the extended state variable:

[0097]

[0098] Then we have

[0099]

[0100] Wherein,

[0101]

[0102] Embodiments of the present application can set the prediction horizon length to N p , the control horizon is N c ≤N p , assuming that the control increment remains 0 outside the control horizon, that is, Δu(k + j) = 0, j > N c , then the control increment sequence is defined as:

[0103] Δu a (k)=[Δu T (k),Δu T (k + 1),…,Δu T (k + N c - 1)] T (25)

[0104] The predicted output sequence can be written as:

[0105]

[0106] Wherein,

[0107]

[0108] The goal of model predictive control is to minimize the error between the predicted output and the reference trajectory within the control horizon while suppressing the change amplitude of the control input. Therefore, the following loss function is defined:

[0109]

[0110] Writing the cost function in the standard quadratic form, we get:

[0111]

[0112] Wherein,

[0113] To ensure the safety and stability of vehicle control, constraints on the input, control increment, and output are introduced in the model predictive controller. Specifically as follows:

[0114] ① Control input constraint: The control input u(k) represents the front wheel angle of the vehicle. To ensure the feasibility of vehicle operation and consider the actual capabilities of the hardware actuator (such as the steering mechanism), its range must be restricted. Based on the small angle assumption made by the vehicle dynamics model and the capabilities of the actuator, the following boundaries are set:

[0115]

[0116] That is, the wheel angle is restricted to ±10°, and the front wheel deflection angle is controlled within a reasonable range, effectively avoiding skidding or vehicle instability caused by excessive angles and increasing lateral stability.

[0117] ② Control increment constraint: The control increment Δu(k) = u(k) - u(k - 1) represents the change in the input between two adjacent control times. Limiting its rate of change helps prevent the controller from generating overly aggressive control commands, reducing the actuator response burden and improving the smooth operation of the system. The following constraints are set:

[0118]

[0119] That is, the maximum allowable steering rate is ±3° / s, thus suppressing drastic angle changes and enhancing the stability of the control system.

[0120] ③ Output variable constraint: To further ensure the trajectory tracking accuracy and the safe operation of the vehicle, constraints are imposed on the system output variables (such as lateral position error and heading angle error). These constraints ensure that the vehicle state remains within the allowable range of the road:

[0121]

[0122] Among them, the first item is the lateral error limit, that is, the deviation of the target vehicle center relative to the lane centerline is controlled within [-0.3m, 0.3m]; the second item is the heading angle error limit, and the angle range of the vehicle deviating from the target heading is limited to [-30°, 30°]. This helps to avoid deviating from the lane or causing oscillations.

[0123] The above constraints are extended to the entire prediction and control interval in the time domain using the Kronecker product form. By constructing appropriate matrix inequalities, these constraints are unified into the linear inequalities of the optimization problem to achieve efficient solution and stable control.

[0124] Combining the above loss function and constraint conditions, the optimization problem of the path tracking MPC controller is as follows:

[0125]

[0126] Δu min ≤ Δu k ≤ Δu max (34)

[0127] U min ≤ AΔu k + U k ≤ U max (35)

[0128] y c,min ≤ y c ≤ y c,max (36)

[0129] The above optimization problem is in the standard quadratic programming form. The optimal control increment obtained by solving is Based on this, the MPC control quantity output at the next moment is obtained:

[0130] u(k) = u(k - 1)+Δu T* (k) (37)

[0131] In step S103, based on the control quantities of the left and right wheel steering angles, the steering angle commands of each wheel of the target vehicle are obtained, so as to control the target vehicle based on the steering angle commands, so that the target vehicle meets the preset stability conditions during driving.

[0132] As a possible implementation manner, the physical quantity corresponding to the control output of the MPC controller based on the self-balancing vehicle model is the left and right wheel steering angles of the self-balancing vehicle model (u = [δ l , δ r ) T . For a fully vector-by-wire vehicle, since the steering angle of each corner module is controllable, therefore, it is necessary to further design a strategy for outputting the steering angle of each wheel based on the left and right wheel steering angles of the self-balancing vehicle model, so as to obtain the steering angle commands of each wheel of the target vehicle based on the control quantities of the left and right wheel steering angles, and control the target vehicle based on the steering angle commands, so that the target vehicle meets the preset stability conditions during driving. Among them, the preset stability conditions can be set accordingly by those skilled in the art according to the actual situation.

[0133] Optionally, in an embodiment of the present application, obtaining the steering angle commands of each wheel of the target vehicle based on the control quantities of the left and right wheel steering angles includes: constructing the steering angle constraints of the wheels based on the Ackermann steering geometry relationship; using the steering angle constraints and the control quantities of the left and right wheel steering angles to calculate the steering angle of each wheel, and obtaining the steering angle commands of each wheel based on the steering angle of each wheel, where the calculation expression of the steering angle of each wheel is:

[0134]

[0135] Among them, δ l,i represents the steering angle of each left wheel, L i represents the position vector of the center of the i-th axle relative to the vehicle's center of mass, δ r represents the right wheel steering angle control quantity of the actual dynamic model, δ l represents the left wheel steering angle control quantity of the actual dynamic model, B represents the wheelbase of the target vehicle, δ r,i represents the steering angle of each right wheel.

[0136] The balance car model of the full-vector-by-wire vehicle and the corresponding vehicle steering angles are as Figure 4 shown. Its left wheel steering angle δ l and right wheel steering angle δ r intersect at a point, that is, the steering center. The steering angle of each wheel should satisfy the Ackermann steering geometry relationship, and based on this, the steering angle δ i of each wheel can be obtained:

[0137]

[0138] For example, for a two-axle full-vector-by-wire vehicle, there is

[0139]

[0140] According to this, the steering angle control quantity of each wheel can be obtained from u = [δ l , δ r . T The steering angle control quantity of each wheel can be obtained.

[0141] Combined with Figures 5 to 7 shown, a working principle of the dynamic stability control method for the full-vector-by-wire vehicle according to the embodiments of the present application will be elaborated in detail with an example.

[0142] As Figure 5 shown, the embodiments of the present application can establish a simplified dynamic model of the full-vector-by-wire vehicle. This dynamic model consists of a "balance car" formed by the left and right wheels, and the steering angles of the left and right wheels are controllable quantities. Based on the dynamic model, a model predictive controller is established. According to the vehicle reference trajectory, the left and right wheel steering angle control quantities of the dynamic model are generated. According to the left and right wheel steering angle control quantities of the dynamic model, the steering angle command of each wheel is obtained.

[0143] Based on the above process, the embodiments of the present application have carried out an example implementation verification through the Carsim simulation platform. The vehicle used is a three-axle full-vector-by-wire chassis vehicle, and the simulation is carried out under the longitudinal vehicle speeds of 40 km / h and 80 km / h respectively. The yaw rate and the center of mass side slip angle in the results are as Figure 6 shown, and the controller trajectory and the lateral tracking error are as Figure 7 shown.

[0144] Figure 6 (Sub - figure (a) is the yaw rate and sub - figure (b) is the sideslip angle of the center of mass) shows that during the control process, both the yaw rate and the sideslip angle of the center of mass of the vehicle change smoothly under the two working conditions of low speed and high speed, and the values of the yaw rate and the sideslip angle of the center of mass are small, indicating that the lateral stability control effect is good. Figure 7 (Sub - figure (a) is the tracking trajectory generated by the controller and sub - figure (b) is the lateral tracking error) shows that the controller can also achieve a good path - tracking effect and can achieve accurate path tracking under both low - speed and high - speed conditions.

[0145] Different from the related technologies, the embodiment of the present application proposes a dynamic model of a balancing vehicle, which has a simple structure and is easy to solve, and can also better express the dynamic characteristics of a fully - actuated steer - by - wire vehicle. Based on this model, a model - predictive controller is established, and then a corner - allocation strategy is added, which can achieve high - precision vehicle stability control.

[0146] At the same time, since the used dynamic model of the balancing vehicle is a simple three - degree - of - freedom linear model, the control has a low requirement for computing power, which improves the practicability and economy of the control algorithm.

[0147] According to the dynamic - stability control method of the fully - actuated steer - by - wire vehicle proposed in the embodiment of the present application, the target vehicle can be simplified into an actual dynamic model that meets the preset degree - of - freedom conditions. The structure is simple and easy to solve, and it can also better express the dynamic characteristics of the fully - actuated steer - by - wire vehicle. Then, a corresponding model - predictive controller is established based on the actual dynamic model to generate the left - and - right wheel corner control amounts of the actual dynamic model, and obtain the corner command of each wheel of the target vehicle, so as to control the target vehicle based on the corner command, make the target vehicle meet the preset stability conditions during driving, realize the stability control of the fully - actuated steer - by - wire vehicle, and has a low requirement for computing power, which improves the practicability and economy of the control algorithm. Thus, the technical problem in the related technologies that it is difficult to simultaneously take into account the characteristics of the high controllable degree - of - freedom of the fully - actuated steer - by - wire vehicle and the computational efficiency of the control based on the traditional vehicle architecture is solved.

[0148] Next, the dynamic - stability control device of the fully - actuated steer - by - wire vehicle proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0149] Figure 8 It is a block - diagram schematic of the dynamic - stability control device of the fully - actuated steer - by - wire vehicle in the embodiment of the present application.

[0150] As Figure 8 shown, the dynamic - stability control device 10 of the fully - actuated steer - by - wire vehicle includes: a simplification module 100, a generation module 200, and a control module 300.

[0151] Specifically, the simplification module 100 is used to simplify the target vehicle into an actual dynamic model that meets the preset degrees of freedom condition.

[0152] The generation module 200 is used to establish a corresponding model predictive controller based on the actual dynamic model, and combine the model predictive controller and the reference trajectory of the target vehicle to generate the left and right wheel steering angle control quantities of the actual dynamic model.

[0153] The control module 300 is used to obtain the steering angle command of each wheel of the target vehicle based on the left and right wheel steering angle control quantities, and control the target vehicle based on the steering angle command so that the target vehicle meets the preset stability condition during driving.

[0154] Optionally, in an embodiment of the present application, the simplification module 100 includes: a simplification unit, a simulation unit, a first construction unit, an expansion unit, and a processing unit.

[0155] Among them, the simplification unit is used to simplify the structure of the target vehicle to obtain a simplified vehicle structure.

[0156] The simulation unit is used to simulate the simplified vehicle structure under a preset driving condition to obtain the balance equations of the corresponding longitudinal translation, lateral translation, and yaw motion.

[0157] The first construction unit is used to combine the balance equations of longitudinal translation, lateral translation, and yaw motion and the longitudinal and lateral forces of the tires to obtain an initial dynamic model.

[0158] The expansion unit is used to expand the initial dynamic model based on the preset path constraint and stability constraint of the target vehicle to obtain an expanded dynamic model.

[0159] The processing unit is used to discretize the expanded dynamic model to obtain an actual dynamic model.

[0160] Optionally, in an embodiment of the present application, the expression of the actual dynamic model is:

[0161]

[0162] Among them, T represents the time period, A k represents the state matrix, B k represents the control matrix, C k represents the output matrix, I is the identity matrix, A t represents the state quantity transfer matrix of the actual dynamic model, B t represents the control quantity transfer matrix of the actual dynamic model, C t represents the output matrix in the continuous time domain, k represents the discrete time, and t represents the continuous time.

[0163] Optionally, in an embodiment of the present application, the generation module 200 includes: a prediction unit, a first calculation unit, a second construction unit, and a third construction unit.

[0164] Among them, the prediction unit is used to predict the prediction output of the target vehicle within a preset time domain based on the actual dynamic model.

[0165] The first calculation unit is used to calculate the loss function by using the prediction output and the reference trajectory.

[0166] The second construction unit is used to construct the input constraint, increment constraint, and output quantity constraint of the actual dynamic model.

[0167] The third construction unit is used to obtain the model predictive controller by combining the loss function, input constraint, increment constraint, and output quantity constraint.

[0168] Optionally, in an embodiment of the present application, the control module 300 includes: a fourth construction unit and a second calculation unit.

[0169] Among them, the fourth construction unit is used to construct the steering angle constraint of the wheels based on the Ackermann steering geometry relationship.

[0170] The second calculation unit is used to calculate the steering angle of each wheel by using the steering angle constraint and the steering angle control amounts of the left and right wheels, and obtain the steering angle command of each wheel based on the steering angle of each wheel.

[0171] Optionally, in an embodiment of the present application, the calculation expression of the steering angle of each wheel is:

[0172]

[0173] Among them, δ l,i represents the steering angle of each left wheel, L i represents the position vector of the center of the i-th axle relative to the vehicle centroid, δ r represents the right wheel steering angle control amount of the actual dynamic model, δ l represents the left wheel steering angle control amount of the actual dynamic model, B represents the wheelbase of the target vehicle, δ r,i represents the steering angle of each right wheel.

[0174] It should be noted that the foregoing explanation of the embodiment of the dynamic stability control method for the all-vector by-wire vehicle also applies to the dynamic stability control device of the all-vector by-wire vehicle in this embodiment, and will not be elaborated here.

[0175] The dynamic stability control device for a full-vector by-wire vehicle proposed according to the embodiments of the present application can simplify the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions. It has a simple structure, is convenient for calculation, and can also better express the dynamic characteristics of the full-vector by-wire vehicle. Furthermore, a corresponding model predictive controller can be established based on the actual dynamic model to generate the left and right wheel steering angle control quantities of the actual dynamic model, obtain the steering angle commands for each wheel of the target vehicle, and control the target vehicle based on the steering angle commands, so that the target vehicle meets the preset stability conditions during driving, realizing the stability control of the full-vector by-wire vehicle, and having a relatively low requirement for computing power, improving the practicability and economy of the control algorithm. Thus, it solves the technical problem in the related art that it is difficult to simultaneously take into account the characteristics of the high controllable degree of freedom of the full-vector by-wire vehicle and the computational efficiency of control based on the traditional vehicle architecture.

[0176] Figure 9 The structural schematic diagram of the vehicle provided by the embodiments of the present application. The vehicle may include:

[0177] A memory 901, a processor 902, and a computer program stored on the memory 901 and executable on the processor 902.

[0178] When the processor 902 executes the program, it implements the dynamic stability control method for the full-vector by-wire vehicle provided in the above embodiments.

[0179] Furthermore, the vehicle further includes:

[0180] A communication interface 903 for communication between the memory 901 and the processor 902.

[0181] The memory 901 is used to store a computer program executable on the processor 902.

[0182] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0183] If the memory 901, the processor 902, and the communication interface 903 are implemented independently, the communication interface 903, the memory 901, and the processor 902 can be interconnected via a bus to complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 only a thick line is used to represent it in Figure 9 , but it does not mean that there is only one bus or one type of bus.

[0184] Optionally, in a specific implementation, if the memory 901, the processor 902, and the communication interface 903 are integrated on a single chip, the memory 901, the processor 902, and the communication interface 903 can complete communication with each other through an internal interface.

[0185] The processor 902 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.

[0186] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned dynamic stability control method for a fully vector-by-wire vehicle.

[0187] The embodiments of the present application also provide a computer program product, including a computer program. When the computer program is executed by a processor, it implements the dynamic stability control method for a fully vector-by-wire vehicle provided by the embodiments of the present invention.

[0188] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this 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 can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0189] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0190] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0191] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0192] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, 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 in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0193] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0194] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, may exist separately as individual physical units, 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. When 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.

[0195] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, 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 should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A dynamic stability control method for a full-vector by-wire vehicle, characterized in that, Including the following steps: Simplify the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions; Based on the actual dynamic model, establish a corresponding model predictive controller, and combine the model predictive controller and the reference trajectory of the target vehicle to generate the left and right wheel steering angle control quantities of the actual dynamic model; Based on the left and right wheel steering angle control quantities, obtain the steering angle command of each wheel of the target vehicle, and control the target vehicle based on the steering angle command so that the target vehicle meets the preset stability conditions during driving.

2. The method according to claim 1, wherein The simplifying the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions includes: Simplify the structure of the target vehicle to obtain a simplified vehicle structure; Simulate the simplified vehicle structure under preset driving conditions to obtain the balance equations of the corresponding longitudinal translation, lateral translation, and yaw motion; Combine the balance equations of the longitudinal translation, lateral translation, and yaw motion and the longitudinal and lateral forces of the tires to obtain an initial dynamic model; Based on the preset path constraints and stability constraints of the target vehicle, expand the initial dynamic model to obtain an expanded dynamic model; Discretize the expanded dynamic model to obtain the actual dynamic model.

3. The method according to claim 2, wherein The expression of the actual dynamic model is: Among them, T represents the time period, A k represents the state matrix, B k represents the control matrix, C k represents the output matrix, I is the identity matrix, A t represents the state quantity transfer matrix of the actual dynamic model, B t represents the control quantity transfer matrix of the actual dynamic model, C t represents the output matrix in the continuous time domain, k represents the discrete time, and t represents the continuous time.

4. The method according to claim 1, wherein The establishing a corresponding model predictive controller based on the actual dynamic model, and combining the model predictive controller and the reference trajectory of the target vehicle to generate the left and right wheel steering angle control quantities of the actual dynamic model includes: Predict the predicted output of the target vehicle within a preset time domain based on the actual dynamic model; Calculate the loss function using the predicted output and the reference trajectory; Construct the input constraints, increment constraints, and output quantity constraints of the actual dynamic model; Combine the loss function, the input constraints, the increment constraints, and the output quantity constraints to obtain the model predictive controller.

5. The method according to claim 1, wherein The obtaining the steering angle command of each wheel of the target vehicle based on the left and right wheel steering angle control quantities includes: Construct the steering angle constraints of the wheels based on the Ackermann steering geometry relationship; Use the steering angle constraints and the left and right wheel steering angle control quantities to calculate the steering angle of each wheel, and obtain the steering angle command of each wheel based on the steering angle of each wheel.

6. The method according to claim 5, wherein The calculation expression of the steering angle of each wheel is: where, δ l,i represents the steering angle of each left wheel, L i represents the position vector of the center of the i-th axle relative to the vehicle's center of mass, δ r represents the right wheel steering angle control quantity of the actual dynamic model, δ l represents the left wheel steering angle control quantity of the actual dynamic model, B represents the wheelbase of the target vehicle, δ r,i represents the steering angle of each right wheel.

7. A dynamic stability control device for a full-vector by-wire vehicle, characterized in that Including: A simplification module for simplifying the target vehicle into an actual dynamic model that meets the preset degree-of-freedom conditions; A generation module for establishing a corresponding model predictive controller based on the actual dynamic model, and combining the model predictive controller and the reference trajectory of the target vehicle to generate the left and right wheel steering angle control quantities of the actual dynamic model; A control module for obtaining the steering angle command of each wheel of the target vehicle based on the left and right wheel steering angle control quantities, and controlling the target vehicle based on the steering angle command so that the target vehicle meets the preset stability conditions during driving.

8. A vehicle, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the dynamic stability control method for a full-vector by-wire vehicle according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the dynamic stability control method for a full-vector by-wire vehicle according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the dynamic stability control method for a full-vector by-wire vehicle according to any one of claims 1-6.

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