Dynamic stability control methods, devices, and vehicles for fully vector-driven steerable vehicles

Abstract

This application relates to a method, device, and vehicle for dynamic stability control of a fully vector-driven steerable vehicle. The method includes: simplifying the target vehicle into an actual dynamic model that satisfies preset degrees of freedom conditions; establishing a corresponding model predictive controller based on the actual dynamic model, and generating 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 steering angle commands 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 these steering angle commands to ensure that the target vehicle meets preset stability conditions during operation. This solves the technical problem in related technologies where, based on traditional automotive architectures, it is difficult to simultaneously consider the high controllable degrees of freedom of fully vector-driven steerable vehicles and the computational efficiency of control.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a dynamic stability control method, device and vehicle for a fully vector-driven vehicle. Background Technology

[0002] A linear system is a system in which the inputs and outputs, states and external disturbances satisfy a linear relationship. It satisfies the superposition principle, meaning that the system response caused by multiple inputs is equal to the superposition of the responses caused by each individual input. When the actuators of a vehicle are in their linear operating regions, the vehicle system can be simplified linearly. Using linear system control simplifies the analysis and design process, makes stability easy to verify, and facilitates the implementation of the control law structure.

[0003] For vehicle systems, linearization is more effective under weakly nonlinear conditions (such as low-speed straight-line driving). In cases of strong nonlinearity or large parameter variations, nonlinear control may be necessary. Nonlinear control is suitable for systems exhibiting strong nonlinear effects such as saturation, dead zones, and hysteresis. It typically offers strong adaptability, superior performance, and good robustness. However, the analysis and design of nonlinear systems are complex, stability verification is difficult, and implementation is challenging.

[0004] In related technologies, based on traditional automotive architecture, it is difficult to simultaneously balance the high degree of controllability and computational efficiency of fully vector-driven steerable vehicles. In other words, to unleash the control performance of fully vector-driven steerable vehicles, more complex models are often required, placing higher demands on onboard chips and other computing platforms; conversely, if fast and efficient calculations are needed, simplified models of traditional vehicles are often required, thus failing to leverage the advantages of fully vector-driven steerable vehicles, which urgently needs improvement. Summary of the Invention

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

[0006] The first aspect of this application provides a dynamic stability control method for a fully vector-driven steerable vehicle, comprising the following steps: simplifying the target vehicle into an actual dynamic model that satisfies preset degrees of freedom conditions; establishing a corresponding model predictive controller based on the actual dynamic model, and generating 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 steering angle commands 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 commands so that the target vehicle satisfies preset stability conditions during driving.

[0007] Optionally, in one embodiment of this application, simplifying the target vehicle into an actual dynamic model that satisfies preset degrees of freedom 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 corresponding equilibrium equations for longitudinal translation, lateral translation, and yaw motion; combining the equilibrium equations for longitudinal translation, lateral translation, and yaw motion with the longitudinal and lateral forces of the tires to obtain an initial dynamic model; expanding the initial dynamic model based on preset path constraints and stability constraints 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 one embodiment of this application, the expression for the actual dynamic model is:

[0009]

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

[0011] Optionally, in one embodiment of this application, the step of establishing a corresponding model predictive controller based on the actual dynamics model to generate the left and right wheel steering angle control quantities of the actual dynamics model by combining the model predictive controller and the reference trajectory of the target vehicle includes: predicting the predicted output of the target vehicle in a preset time domain based on the actual dynamics model; calculating a loss function using the predicted output and the reference trajectory; constructing input constraints, incremental constraints, and output constraints of the actual dynamics model; and obtaining the model predictive controller by combining the loss function, the input constraints, the incremental constraints, and the output constraints.

[0012] Optionally, in one embodiment of this application, obtaining the steering angle command of each wheel of the target vehicle based on the left and right wheel steering angle control quantities includes: constructing wheel steering angle constraints based on Ackerman steering geometry; calculating the steering angle of each wheel using the steering angle constraints and the left and right wheel steering angle control quantities; and obtaining the steering angle command of each wheel based on the steering angle of each wheel.

[0013] Optionally, in one embodiment of this application, the formula for calculating the steering angle of each wheel is:

[0014]

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

[0016] A second aspect of this application provides a dynamic stability control device for a fully vector-driven steerable vehicle, comprising: a simplification module for simplifying a target vehicle into an actual dynamic model that satisfies preset degrees of freedom conditions; a generation module for establishing a corresponding model predictive controller based on the actual dynamic model, and generating 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; and a control module for obtaining steering angle commands 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 commands so that the target vehicle satisfies preset stability conditions during driving.

[0017] Optionally, in one embodiment of this 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 preset driving conditions to obtain the corresponding equilibrium equations for longitudinal translation, lateral translation, and yaw motion; a first construction unit for combining the equilibrium equations for longitudinal translation, lateral translation, and yaw motion with 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 preset path constraints and stability constraints of the target vehicle to obtain an expanded dynamic model; and a processing unit for discretizing the expanded dynamic model to obtain the actual dynamic model.

[0018] Optionally, in one embodiment of this application, the expression for the actual dynamic model is:

[0019]

[0020] Where T represents the time period, A k B represents the state matrix. k Represents the control matrix, C kLet I represent the output matrix, where I is the identity matrix and A is the output matrix. t B represents the state transition matrix of the actual dynamic model. t C represents the control transfer matrix of the actual dynamic model. t Let represent the output matrix in the continuous-time domain, where k represents discrete time and t represents continuous time.

[0021] Optionally, in one embodiment of this application, the generation module includes: a prediction unit, configured to predict the target vehicle's output in a preset time domain based on the actual dynamics model; a first calculation unit, configured to calculate a loss function using the predicted output and the reference trajectory; a second construction unit, configured to construct the input constraints, incremental constraints, and output constraints of the actual dynamics model; and a third construction unit, configured to combine the loss function, the input constraints, the incremental constraints, and the output constraints to obtain the model prediction controller.

[0022] Optionally, in one embodiment of this application, the control module includes: a fourth construction unit, used to construct the wheel angle constraints based on the Ackermann steering geometry; and a second calculation unit, used to calculate the steering angle of each wheel using the angle constraints and the left and right wheel angle control values, and to obtain the steering angle command of each wheel based on the steering angle of each wheel.

[0023] Optionally, in one embodiment of this application, the formula for calculating the steering angle of each wheel is:

[0024]

[0025] Where, δ l,i Indicates the steering angle of each left wheel, l i δ represents the position vector of the i-th axle center relative to the vehicle's center of mass. r δ represents the right wheel steering angle control value of the actual dynamic model. l δ represents the left wheel steering angle control value of the actual dynamic model, B represents the wheelbase of the target vehicle, and δ r,i This indicates the steering angle of each right-hand wheel.

[0026] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic stability control method for a fully vector-driven vehicle as described in the above embodiments.

[0027] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the dynamic stability control method for a fully vector-driven vehicle as described in the above embodiments.

[0028] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described method for dynamic stability control of a fully vector-driven vehicle.

[0029] This application's embodiments can simplify the target vehicle into an actual dynamic model that meets preset degrees of freedom conditions. The structure is simple, easy to solve, and can also better represent the dynamic characteristics of a fully vector-driven steerable vehicle. Then, a corresponding model predictive controller is established based on the actual dynamic model, generating the left and right wheel steering angle control quantities of the actual dynamic model, obtaining the steering angle command for each wheel of the target vehicle. Based on these steering angle commands, the target vehicle is controlled, ensuring it meets preset stability conditions during driving. This achieves stability control for fully vector-driven steerable vehicles with lower computational requirements, improving the practicality and economy of the control algorithm. Therefore, it solves the technical problem in related technologies where, based on traditional automotive architectures, it is difficult to simultaneously consider the high controllable degrees of freedom of fully vector-driven steerable vehicles and the computational efficiency of control.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[0032] Figure 1 This is a flowchart of a dynamic stability control method for a fully vector-driven vehicle according to an embodiment of this application;

[0033] Figure 2 This is a schematic diagram of a fully vector-guided chassis composed of corner modules in related technologies;

[0034] Figure 3 This is a schematic diagram of a self-balancing scooter dynamics model according to an embodiment of this application;

[0035] Figure 4 This is a diagram showing the relationship between the rotation angle of a self-balancing scooter model and the rotation angle of each actual wheel, according to one embodiment of this application.

[0036] Figure 5 This is a schematic diagram illustrating the principle of a dynamic stability control method for a fully vector-driven vehicle according to an embodiment of this application;

[0037] Figure 6 This is a schematic diagram illustrating the stability control effect according to an embodiment of this application;

[0038] Figure 7 This is a schematic diagram illustrating the path tracking effect according to an embodiment of this application;

[0039] Figure 8 This is a schematic diagram of the structure of a dynamic stability control device for a fully vector-driven vehicle according to an embodiment of this application;

[0040] Figure 9 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0041] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0042] The following description, with reference to the accompanying drawings, illustrates a dynamic stability control method, apparatus, and vehicle for a fully vector-driven steerable vehicle according to embodiments of this application. Addressing the technical problem mentioned in the background art—that, based on traditional automotive architectures, it is difficult to simultaneously consider the high controllable degrees of freedom of fully vector-driven steerable vehicles and the computational efficiency of control—this application provides a dynamic stability control method for fully vector-driven steerable vehicles. In this method, the target vehicle can be simplified into an actual dynamic model that satisfies preset degree-of-freedom conditions. This model is simple in structure, easy to solve, and can better represent the dynamic characteristics of the fully vector-driven steerable vehicle. Then, a corresponding model predictive controller is established based on the actual dynamic model, generating the left and right wheel steering angle control quantities of the actual dynamic model. This yields the steering angle command for each wheel of the target vehicle, which is then used to control the target vehicle, ensuring that the target vehicle meets preset stability conditions during driving. This achieves stability control for fully vector-driven steerable vehicles with lower computational requirements, improving the practicality and economy of the control algorithm. Therefore, this solves the technical problem in related technologies where, based on traditional automotive architectures, it is difficult to simultaneously consider the high controllable degrees of freedom of fully vector-driven steerable vehicles and the computational efficiency of control.

[0043] Specifically, Figure 1 This is a flowchart illustrating a dynamic stability control method for a fully vector-driven vehicle provided in an embodiment of this application.

[0044] like Figure 1 As shown, the dynamic stability control method for this fully vector-driven steerable vehicle includes the following steps:

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

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

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

[0048] To address the technical challenge of simultaneously balancing the high controllable degrees of freedom of a fully vector-driven vehicle with computational efficiency, embodiments of this application simplify the target vehicle into an actual dynamic model that satisfies preset degree-of-freedom conditions, thereby unleashing the control performance of the fully vector-driven vehicle.

[0049] Optionally, in one embodiment of this application, the target vehicle is simplified into an actual dynamic model that satisfies preset degrees of freedom conditions, including: 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 corresponding equilibrium equations for longitudinal translation, lateral translation, and yaw motion; combining the equilibrium equations for longitudinal translation, lateral translation, and yaw motion with the longitudinal and lateral forces of the tires to obtain an initial dynamic model; expanding the initial dynamic model based on preset path constraints and stability constraints of the target vehicle to obtain an expanded dynamic model; and discretizing the expanded dynamic model to obtain an actual dynamic model, wherein the expression of the actual dynamic model is:

[0050]

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

[0052] In practical implementation, for the stability control of fully vector-driven steerable vehicles, the control model should be simplified as much as possible to improve the computational efficiency of the control algorithm. Furthermore, the subject of this application is vehicle lateral stability control; therefore, it primarily focuses on the longitudinal, lateral, and yaw dynamics of the vehicle.

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

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

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

[0056] ③ Ignore the front and rear load transfer of the target vehicle and assume that the dynamic characteristics of the wheels on the same axle are the same. That is, the left and right wheels of the vehicle can be simplified to a left wheel and a right wheel, respectively.

[0057] In this way, the vehicle can be simplified to a single virtual axle dynamics model, such as Figure 3 As shown. To distinguish it from the virtual axis, which serves as a splicing unit in the overall vehicle dynamics model, it is referred to here as the self-balancing vehicle dynamics model. It is a dynamics model with two independently driven and steered wheels, possessing lateral, longitudinal, and yaw motion degrees of freedom.

[0058] Based on Newton's second law, the equilibrium equations for the longitudinal translation, lateral translation, and yaw motion of the self-balancing vehicle's dynamic model are obtained respectively.

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

[0060]

[0061] Translation equation in the y-direction:

[0062]

[0063] Equation of rotation along the z-axis:

[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 in the fully vector-guided steerable vehicle, and B is the wheelbase of the fully vector-guided steerable vehicle. δ l δ r Here, θ represents the left and right wheel steering angles of the initial dynamic model, and m represents the vehicle mass. For longitudinal acceleration, The lateral speed is the speed of the vehicle. Let F be the yaw rate. x For the resultant longitudinal force of the vehicle, F x For the longitudinal force of the vehicle, ∑F y For the resultant lateral force of the vehicle, F y For the lateral force of the vehicle, ∑M z M is the total yaw moment of the vehicle. z For the vehicle's yaw moment, F xl F xr F is the longitudinal force between the left and right tires. yl F yr This refers to the lateral force of the left and right tires.

[0070] Under a small-angle approximation of the wheel turning angle, the longitudinal force and lateral force 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] Among them, C cl C cr C represents the lateral stiffness (positive value) of the left and right wheels, respectively. ll C lr These are the longitudinal force coefficients of the left and right wheels, α. l α r It can be represented as:

[0074]

[0075] Under small angle approximation

[0076] cosθ≈1, sinθ≈θ, tanθ≈θ (11)

[0077] In summary, the linearized dynamic model of the self-balancing vehicle with full vector drive can be obtained, i.e., the initial dynamic model:

[0078]

[0079] in, For lateral acceleration, I z Let be the moment of inertia of the vehicle about the z-axis. Let s be the yaw acceleration. l The slip ratio of the left wheel is s. r This represents the slip ratio of the right wheel.

[0080] For the path tracking and stability control objectives involved in the embodiments of this application, the observations need to be selected as... Therefore, the dynamic model is expanded from equation (12) to equation (13):

[0081]

[0082] To use linear MPC (Model Predictive Control) control, we linearize it, resulting in the following state equations:

[0083]

[0084] Where χ(t) is the state variable, Let u(t) be the rate of change of the state variable, and u(t) be the control variable.

[0085] [δ l ,δ r ] T Observations are Y represents the x-coordinate of the target vehicle in the inertial coordinate system, and X represents the y-coordinate of the target vehicle in the inertial coordinate system. A t B represents the state transition matrix of the expanded dynamic model. t C represents the control transfer matrix of the extended dynamics model. t This represents the output matrix in the continuous time domain.

[0086]

[0087] Based on equations (14) and (15), this embodiment uses the Euler method to discretize the continuous model, obtaining the discrete state-space equations:

[0088] χ(k+1)=A k χ(k)+B k u(k)(19)

[0089] Y(k)=C k χ(k)(20)

[0090] Among them, A k B k and C k These represent the state matrix, control matrix, and output matrix, respectively, and their specific forms are as follows:

[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. By combining the model predictive controller and the reference trajectory of the target vehicle, the left and right wheel steering angle control quantities of the actual dynamic model are generated.

[0094] Furthermore, in the path tracking control process, it is necessary to predict the future state and output of the vehicle within a given prediction time domain so as 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 one embodiment of this application, a corresponding model predictive controller is established based on the actual dynamics model to generate the left and right wheel steering angle control quantities of the actual dynamics model by combining the model predictive controller and the reference trajectory of the target vehicle. This includes: predicting the target vehicle's predicted output in a preset time domain based on the actual dynamics model; calculating the loss function using the predicted output and the reference trajectory; constructing the input constraints, incremental constraints, and output constraints of the actual dynamics model; and obtaining the model predictive controller by combining the loss function, input constraints, incremental constraints, and output constraints.

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

[0097]

[0098] Then there is

[0099]

[0100] in,

[0101]

[0102] In this embodiment, the prediction time domain length can be set to N. p The control time domain is N c ≤N p Assume that the control increment remains 0 outside the control time domain, i.e., 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] in,

[0107]

[0108] The objective of model predictive control is to minimize the error between the predicted output and the reference trajectory in the control time domain, while suppressing the magnitude of changes in the control input. Therefore, the following loss function is defined:

[0109]

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

[0111]

[0112] in,

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

[0114] ① Control Input Constraints: The control input u(k) represents the front wheel steering angle of the vehicle. To ensure the feasibility of vehicle handling and consider the actual capabilities of hardware actuators (such as the steering mechanism), its range must be limited. Based on the small-angle assumptions made in the vehicle dynamics model and the capabilities of the actuators, the following boundaries are set:

[0115]

[0116] This means that the wheel turning angle is limited to ±10°, keeping the front wheel deflection angle within a reasonable range, effectively avoiding sideslip or vehicle instability caused by excessive turning angle, and increasing lateral stability.

[0117] ② Control Increment Constraint: The control increment Δu(k) = u(k) - u(k-1) represents the change in input between two adjacent control moments. Limiting its rate of change helps prevent the controller from generating overly aggressive control commands, reduces the actuator response burden, and improves the system's operational stability. The following constraints are set:

[0118]

[0119] That is, the maximum permissible steering speed is ±3° / s, thereby suppressing drastic changes in steering angle and improving the stability of the control system.

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

[0121]

[0122] The first parameter is the lateral error limit, which controls the deviation of the target vehicle's center from the lane centerline within [-0.3m, 0.3m]. The second parameter is the heading angle error limit, which limits the angle range of the vehicle's deviation from the target heading to [-30°, 30°]. This helps to avoid lane departure or causing oscillations.

[0123] The aforementioned 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] Based on the loss function and constraints above, the optimization problem of the path tracing 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 form of a standard quadratic programming problem. Solving for the optimal control increment yields... Based on this, the MPC control output for the next moment is obtained:

[0130] u(k)=u(k-1)+Δu T* (k) (37)

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

[0132] As one possible implementation, the physical quantity corresponding to the control output of the MPC controller based on the self-balancing scooter model is the left and right wheel rotation angle of the self-balancing scooter model (u = [δ... l ,δ r ] T For fully vector-driven drive-by-wire vehicles, since the turning angle of each corner module is controllable, it is necessary to further design a strategy based on the left and right wheel turning angles of the self-balancing vehicle model to output the turning angle of each wheel. This strategy aims to obtain the turning angle command for each wheel of the target vehicle based on the left and right wheel turning angle control quantities, and then control the target vehicle based on these turning angle commands, ensuring that the target vehicle meets preset stability conditions during operation. These preset stability conditions can be set by those skilled in the art according to actual conditions.

[0133] Optionally, in one embodiment of this 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 wheel steering angle constraints based on Ackerman steering geometry; calculating the steering angle of each wheel using the steering angle constraints 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, wherein the calculation expression for the steering angle of each wheel is:

[0134]

[0135] Where, δ l,i L represents the steering angle of each left wheel. i δ represents the position vector of the i-th axle center relative to the vehicle's center of mass. r δ represents the right wheel steering angle control value in the actual dynamic model. l δ represents the left wheel steering angle control value of the actual dynamic model, B represents the wheelbase of the target vehicle, and δ r,i This indicates the steering angle of each right-hand wheel.

[0136] The self-balancing scooter model of a fully vector-driven vehicle and the corresponding overall vehicle turning angle are as follows: Figure 4 As shown. Its left wheel steering angle δ l and right wheel steering angle δ r The wheels intersect at a single point, which is the steering center. The steering angle of each wheel should satisfy the Ackermann steering geometry, from which the steering angle δ of each wheel can be obtained. i :

[0137]

[0138] For example, for a two-axis fully vector-driven vehicle, there are

[0139]

[0140] Based on this, we can use u = [δ] l ,δ r ] T The steering angle control value for each wheel is obtained.

[0141] Combination Figures 5 to 7 The working principle of the full-vector drive-by-wire vehicle dynamic stability control method of this application is illustrated in detail with an example.

[0142] like Figure 5 As shown, this embodiment of the application can establish a simplified dynamic model of a fully vector-driven vehicle. This dynamic model consists of a "balanced vehicle" with left and right wheels, and the rotation angles of the left and right wheels are controllable quantities. A model predictive controller is established based on the dynamic model, and the rotation angle control quantities of the left and right wheels of the dynamic model are generated according to the vehicle reference trajectory. Based on the rotation angle control quantities of the left and right wheels of the dynamic model, the rotation angle command for each wheel is obtained.

[0143] Based on the above process, this application embodiment was implemented and verified using the Carsim simulation platform. The vehicle used was a three-axle, fully vector-driven steerable chassis vehicle. Simulations were conducted at longitudinal speeds of 40 km / h and 80 km / h, respectively. The yaw rate and sideslip angle in the results are as follows: Figure 6 As shown, the controller trajectory and lateral tracking error are as follows: Figure 7 As shown.

[0144] Figure 6 (Subplot (a) shows the yaw rate, and subplot (b) shows the sideslip angle.) It can be seen that during the control process, the yaw rate and sideslip angle of the vehicle change smoothly under both low and high speed conditions, and the values ​​of the yaw rate and sideslip angle are relatively small, indicating that the lateral stability control effect is good. Figure 7 (Subgraph (a) shows the tracking trajectory generated by the controller, and subgraph (b) shows the lateral tracking error.) This indicates that the controller can also achieve good path tracking performance, and can achieve accurate path tracking in both low-speed and high-speed conditions.

[0145] Unlike related technologies, this application proposes a dynamic model for a self-balancing vehicle. This model is simple in structure, easy to solve, and can effectively represent the dynamic characteristics of a fully vector-driven vehicle. A model predictive controller is established based on this model, along with a steering angle allocation strategy. This enables high-precision vehicle stability control.

[0146] Meanwhile, since the dynamic model of the self-balancing vehicle used is a simple three-degree-of-freedom linear model, the control requires less computing power, which improves the practicality and economy of the control algorithm.

[0147] The dynamic stability control method for a fully vector-driven steerable vehicle proposed in this application simplifies the target vehicle into an actual dynamic model that satisfies preset degrees of freedom conditions. This simplified model is easy to solve and effectively represents the dynamic characteristics of the fully vector-driven steerable vehicle. A corresponding model predictive controller is then established based on the actual dynamic model to generate the left and right wheel steering angle control quantities, obtaining the steering angle commands for each wheel of the target vehicle. These steering angle commands are then used to control the target vehicle, ensuring it meets preset stability conditions during operation. This achieves stability control for the fully vector-driven steerable vehicle while requiring less computational power, thus improving the practicality and economy of the control algorithm. This solves the technical problem in related technologies where, based on traditional automotive architectures, it is difficult to simultaneously consider the high controllable degrees of freedom of fully vector-driven steerable vehicles and the computational efficiency of control.

[0148] Next, referring to the accompanying drawings, a dynamic stability control device for a fully vector-driven vehicle according to an embodiment of this application is described.

[0149] Figure 8 This is a block diagram of the dynamic stability control device for a fully vector-driven vehicle according to an embodiment of this application.

[0150] like Figure 8 As shown, the dynamic stability control device 10 of the fully vector-driven 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 preset degrees of freedom conditions.

[0152] The generation module 200 is used to establish a corresponding model predictive controller based on the actual dynamic model, so as 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.

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

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

[0155] The simplification unit is used to simplify the structure of the target vehicle, resulting in a simplified vehicle structure.

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

[0157] The first building block is used to combine the equilibrium equations of longitudinal translation, lateral translation, and yaw motion with the longitudinal and lateral forces of the tire to obtain the initial dynamic model.

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

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

[0160] Optionally, in one embodiment of this application, the expression for the actual dynamic model is:

[0161]

[0162] Where T represents the time period, A k B represents the state matrix. k Represents the control matrix, C k Let I represent the output matrix, where I is the identity matrix and A is the output matrix. t B represents the state transition matrix of the actual dynamic model. t C represents the control transfer matrix of the actual dynamic model. t Let represent the output matrix in the continuous-time domain, where k represents discrete time and t represents continuous time.

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

[0164] The prediction unit is used to predict the target vehicle's output in a preset time domain based on the actual dynamic model.

[0165] The first computational unit is used to calculate the loss function using the predicted output and the reference trajectory.

[0166] The second building block is used to construct the input constraints, incremental constraints, and output constraints of the actual dynamic model.

[0167] The third building block is used to combine the loss function, input constraints, incremental constraints, and output constraints to obtain the model predictive controller.

[0168] Optionally, in one embodiment of this application, the control module 300 includes: a fourth building unit and a second computing unit.

[0169] The fourth building unit is used to construct the wheel's steering angle constraint based on the Ackermann steering geometry.

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

[0171] Optionally, in one embodiment of this application, the formula for calculating the steering angle of each wheel is:

[0172]

[0173] Where, δ l,i L represents the steering angle of each left wheel. i δ represents the position vector of the i-th axle center relative to the vehicle's center of mass. r δ represents the right wheel steering angle control value in the actual dynamic model. l δ represents the left wheel steering angle control value of the actual dynamic model, B represents the wheelbase of the target vehicle, and δ r,i This indicates the steering angle of each right-hand wheel.

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

[0175] The dynamic stability control device for a fully vector-driven steerable vehicle proposed in this application simplifies the target vehicle into an actual dynamic model that satisfies preset degrees of freedom conditions. This simplified model is easy to solve and effectively represents the dynamic characteristics of the fully vector-driven steerable vehicle. A corresponding model predictive controller is then established based on the actual dynamic model to generate the left and right wheel steering angle control quantities, obtaining the steering angle commands for each wheel of the target vehicle. These steering angle commands are then used to control the target vehicle, ensuring it meets preset stability conditions during operation. This achieves stability control for the fully vector-driven steerable vehicle while requiring less computational power, thus improving the practicality and economy of the control algorithm. This solves the technical problem in related technologies where, based on traditional automotive architectures, it is difficult to simultaneously consider the high controllable degrees of freedom of fully vector-driven steerable vehicles and the computational efficiency of control.

[0176] Figure 9 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0177] The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

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

[0179] Furthermore, the vehicle also includes:

[0180] Communication interface 903 is used for communication between memory 901 and processor 902.

[0181] The memory 901 is used to store computer programs that can run on the processor 902.

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

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

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

[0185] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0186] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for dynamic stability control of a fully vector-driven vehicle.

[0187] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the dynamic stability control method for a fully vector-driven vehicle provided in this embodiment of the invention.

[0188] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0189] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0190] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0191] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0192] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0193] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0194] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0195] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A dynamic stability control method for a fully vector-driven steerable vehicle, characterized in that, Includes the following steps: The target vehicle is simplified into an actual dynamic model that meets the preset degrees of freedom conditions; A corresponding model predictive controller is established based on the actual dynamic model, and the left and right wheel steering angle control quantities of the actual dynamic model are generated by combining the model predictive controller and the reference trajectory of the target vehicle. Based on the left and right wheel steering angle control values, the steering angle command of each wheel of the target vehicle is obtained, and the target vehicle is controlled based on the steering angle command so that the target vehicle meets the preset stability conditions during driving. The process of simplifying the target vehicle into a practical dynamic model that satisfies preset degrees of freedom includes: The structure of the target vehicle is simplified to obtain a simplified vehicle structure; The simplified vehicle structure was simulated under preset driving conditions to obtain the corresponding equilibrium equations for longitudinal translation, lateral translation, and yaw motion. By combining the equilibrium equations of the longitudinal translation, lateral translation, and yaw motion with the longitudinal and lateral forces of the tire, an initial dynamic model is obtained. Based on the preset path constraints and stability constraints of the target vehicle, the initial dynamic model is expanded to obtain the expanded dynamic model; The expanded dynamic model is discretized to obtain the actual dynamic model.

2. The method according to claim 1, characterized in that, The expression for the actual dynamic model is: ， Where T represents the time period. Represents the state matrix, Represents the control matrix. Indicates the output matrix. It is the identity matrix. This represents the state transition matrix of the actual dynamic model. This represents the control transfer matrix of the actual dynamic model. This represents the output matrix in the continuous-time domain. Represents discrete time. It indicates continuous time.

3. The method according to claim 1, characterized in that, The step of establishing a corresponding model predictive controller based on the actual dynamics 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 dynamics model, includes: Based on the actual dynamics model, predict the target vehicle's output in a preset time domain; The loss function is calculated using the predicted output and the reference trajectory; Construct the input constraints, incremental constraints, and output constraints of the actual dynamic model; The model prediction controller is obtained by combining the loss function, the input constraints, the incremental constraints, and the output constraints.

4. The method according to claim 1, characterized in that, The step of obtaining the steering angle command for each wheel of the target vehicle based on the left and right wheel steering angle control values ​​includes: The wheel's steering angle constraint is constructed based on the Ackermann steering geometry. Using the steering angle constraint and the steering angle control values ​​of the left and right wheels, the steering angle of each wheel is calculated, and the steering angle command of each wheel is obtained based on the steering angle of each wheel.

5. The method according to claim 4, characterized in that, The formula for calculating the steering angle of each wheel is: ， in, This indicates the steering angle of each left wheel. Indicates the first i The position vector of each axle center relative to the vehicle's center of mass This represents the right wheel steering angle control value of the actual dynamic model. This represents the left wheel steering angle control value of the actual dynamic model. This indicates the wheelbase of the target vehicle. This indicates the steering angle of each right-hand wheel.

6. A dynamic stability control device for a fully vector-driven steerable vehicle, characterized in that, include: The simplification module is used to simplify the target vehicle into an actual dynamic model that meets preset degrees of freedom conditions. The generation module is used to establish a corresponding model predictive controller based on the actual dynamic model, and 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. The control module is used to obtain the steering angle command of each wheel of the target vehicle based on the steering angle control values ​​of the left and right wheels, and to control the target vehicle based on the steering angle commands so that the target vehicle meets preset stability conditions during driving; wherein, The process of simplifying the target vehicle into a practical dynamic model that satisfies preset degrees of freedom includes: The structure of the target vehicle is simplified to obtain a simplified vehicle structure; The simplified vehicle structure was simulated under preset driving conditions to obtain the corresponding equilibrium equations for longitudinal translation, lateral translation, and yaw motion. By combining the equilibrium equations of the longitudinal translation, lateral translation, and yaw motion with the longitudinal and lateral forces of the tire, an initial dynamic model is obtained. Based on the preset path constraints and stability constraints of the target vehicle, the initial dynamic model is expanded to obtain the expanded dynamic model; The expanded dynamic model is discretized to obtain the actual dynamic model.

7. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the dynamic stability control method for a fully vector-driven vehicle as described in any one of claims 1-5.

8. 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 fully vector-driven vehicle as described in any one of claims 1-5.

9. 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 fully vector-driven vehicle as described in any one of claims 1-5.

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