A robust cooperative control method for vehicle yaw stability of distributed corner module electric drive vehicle

Abstract

The application relates to a kind of distributed corner module electric drive vehicle yaw stability robust cooperative control methods.The method comprises: obtaining the driving state data of corner module vehicle;According to lateral stability constraint condition and lateral dynamics model, nominal control model is constructed, and nominal control model is the part related to determining parameter in lateral dynamics model, wherein the target of lateral stability constraint condition is the error of yaw angular velocity and the error of lateral velocity tends to zero;Uncertainty control model is constructed, and uncertainty control model is the part related to uncertain parameter in lateral dynamics model;Driving state data is input into nominal control model and uncertainty control model, and yaw moment control parameter is determined;Based on yaw moment control parameter and rear wheel steering control parameter, stability cooperative control is carried out on corner module vehicle.By using the method, the yaw stability of corner module vehicle can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a robust cooperative control method for lateral stability of a distributed corner module electric drive vehicle. BACKGROUND

[0002] A corner module of a distributed electric drive chassis is defined as a composite functional unit integrating driving, braking, steering and suspension systems (drive-brake-steer-suspension), and a distributed corner module chassis is defined as a distributed electric drive chassis equipped with multiple corner module units, thereby canceling the physical constraints between the wheels of a traditional vehicle, integrating driving, braking, steering and suspension systems in an independently controllable corner module, and effectively improving the degree of freedom of wheel control. However, the physical constraints between the wheels of a distributed corner module vehicle can easily lead to lateral instability in extreme conditions such as emergency obstacle avoidance or sharp turns.

[0003] In the conventional technology, a vehicle is cooperatively controlled by combining an active rollover protection system (ARS) and a dynamic cruise control (DYC).

[0004] However, the conventional cooperative control method still has the problem of poor lateral stability when used to control a distributed corner module vehicle. SUMMARY

[0005] Therefore, it is necessary to provide a robust cooperative control method for lateral stability of a distributed corner module electric drive vehicle to improve the lateral stability of the vehicle.

[0006] In a first aspect, the present application provides a robust cooperative control method for lateral stability of a distributed corner module electric drive vehicle, comprising:

[0007] The method comprises:

[0008] obtaining driving state data of a corner module vehicle;

[0009] constructing a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a deterministic parameter, wherein the target of the lateral stability constraint condition is that the error of yaw rate and the error of lateral velocity tend to zero;

[0010] constructing an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter;

[0011] inputting the driving state data into the nominal control model and the uncertainty control model to determine a yaw moment control parameter;

[0012] performing stability cooperative control on the cornering module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter.

[0013] In one of the embodiments, before the inputting the driving state data into the lateral dynamics model and determining the nominal control model according to the lateral stability constraint condition, the method further comprises:

[0014] taking a front wheel steering linear two-degree-of-freedom vehicle model as a reference model;

[0015] determining the lateral stability constraint condition based on the reference model, the expected value of the yaw rate and the expected value of the lateral velocity.

[0016] In one of the embodiments, the stability control comprises four-wheel torque distribution control, and the performing stability cooperative control on the cornering module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter comprises:

[0017] determining a longitudinal vehicle speed control parameter according to a longitudinal vehicle speed constraint condition and a longitudinal dynamics model;

[0018] performing four-wheel torque distribution control on the cornering module vehicle based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, and performing rear wheel steering control on the cornering module vehicle based on the rear wheel steering control parameter.

[0019] In one of the embodiments, the inputting the driving state data into the nominal control model and the uncertainty control model and determining the yaw moment control parameter comprises:

[0020] obtaining a rear wheel steering control parameter;

[0021] inputting the rear wheel steering control parameter and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

[0022] In one of the embodiments, the obtaining the rear wheel steering control parameter comprises:

[0023] determining the rear wheel steering control parameter according to a preset steady-state turning constraint condition and the lateral dynamics model; wherein the steady-state turning constraint condition is used to limit lateral acceleration and yaw angular acceleration.

[0024] In one of the embodiments, before the inputting the driving state data into the lateral dynamics model and determining the nominal control model according to the lateral stability constraint condition, the method comprises:

[0025] A dynamics equation for describing lateral and yaw motion of the vehicle is established based on relationships between total mass of the vehicle, longitudinal and lateral speeds, yaw rate, total lateral force of front and rear tires, front and rear tire steering angles, moment of inertia, distances from the mass center to front and rear axles, and additional yaw moment;

[0026] A linear relationship between the tire lateral force and the tire slip angle is obtained;

[0027] The lateral dynamics model is established based on the dynamics equation and the linear relationship.

[0028] In a second aspect, the present application further provides a distributed corner module electrically driven vehicle yaw stability robust cooperative control device, comprising:

[0029] A first obtaining module is configured to obtain driving state data of a corner module vehicle;

[0030] A first constructing module is configured to construct a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a certain parameter, wherein the target of the lateral stability constraint condition is that errors of yaw rate and lateral speed tend to be zero;

[0031] A second constructing module is configured to construct an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter;

[0032] A first determining module is configured to input the driving state data into the nominal control model and the uncertainty control model, and determine a yaw moment control parameter;

[0033] A control module is configured to perform stability cooperative control on the corner module vehicle based on the yaw moment control parameter and a rear wheel steering control parameter.

[0034] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0035] Driving state data of a corner module vehicle is obtained;

[0036] A nominal control model is constructed according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a certain parameter, wherein the target of the lateral stability constraint condition is that errors of yaw rate and lateral speed tend to be zero;

[0037] An uncertainty control model is constructed, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter;

[0038] inputting the driving state data into the nominal control model and the uncertainty control model to determine a yaw moment control parameter;

[0039] performing stability collaborative control on the corner module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter.

[0040] In a fourth aspect, the present application further provides a computer readable storage medium, having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0041] obtaining driving state data of a corner module vehicle;

[0042] constructing a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a determined parameter, wherein the target of the lateral stability constraint condition is that errors of yaw angular velocity and lateral velocity tend to zero;

[0043] constructing an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter;

[0044] inputting the driving state data into the nominal control model and the uncertainty control model to determine a yaw moment control parameter;

[0045] performing stability collaborative control on the corner module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter.

[0046] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, the computer program being executed by a processor to implement the following steps:

[0047] obtaining driving state data of a corner module vehicle;

[0048] constructing a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a determined parameter, wherein the target of the lateral stability constraint condition is that errors of yaw angular velocity and lateral velocity tend to zero;

[0049] constructing an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter;

[0050] inputting the driving state data into the nominal control model and the uncertainty control model to determine a yaw moment control parameter;

[0051] Based on the yaw moment control parameter and the rear wheel steering control parameter, the corner module vehicle is subjected to stability collaborative control.

[0052] The above-mentioned distributed corner module electric drive vehicle yaw stability robust collaborative control method obtains the driving state data of the corner module vehicle; a nominal control model is constructed according to the lateral stability constraint condition and the lateral dynamics model, and the nominal control model is the part related to the determined parameters in the lateral dynamics model, wherein the target of the lateral stability constraint condition is that the error of the yaw rate and the error of the lateral velocity tend to zero; an uncertainty control model is constructed, and the uncertainty control model is the part related to the uncertain parameters in the lateral dynamics model; the driving state data is input into the nominal control model and the uncertainty control model to determine the yaw moment control parameter; and based on the yaw moment control parameter and the rear wheel steering control parameter, the corner module vehicle is subjected to stability collaborative control. The yaw moment control parameter is determined in combination with the uncertain part in the driving state of the vehicle, the robustness when responding to the parameter uncertainty and external disturbance of the vehicle is improved, the error of the yaw rate and the side slip angle of the mass center are effectively reduced, in addition, while the control moment is applied to the four wheels of the vehicle, the active rear wheel steering control adjusts the rear wheel angle in real time to ensure that the vehicle maintains the steady turning state during turning, effectively reduces the error of the yaw rate and the lateral velocity, and improves the yaw stability of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0054] Figure 1 An application environment diagram of the distributed corner module electric drive vehicle yaw stability robust collaborative control method in one embodiment;

[0055] Figure 2 A flowchart of the distributed corner module electric drive vehicle yaw stability robust collaborative control method in one embodiment;

[0056] Figure 3 A flowchart of the distributed corner module electric drive vehicle yaw stability robust collaborative control method in another embodiment;

[0057] Figure 4 A flowchart of the distributed corner module electric drive vehicle yaw stability robust collaborative control method in another embodiment;

[0058] Figure 5Flow chart of the robust cooperative control method for distributed corner module electric drive vehicle yaw stability in another embodiment;

[0059] Figure 6 Flow chart of the robust cooperative control method for distributed corner module electric drive vehicle yaw stability in another embodiment;

[0060] Figure 7 Analysis diagram of vehicle dynamics;

[0061] Figure 8 Diagram of the control algorithm model;

[0062] Figure 9 Time domain response curve diagram of yaw rate under different control strategies;

[0063] Figure 10 Time domain response curve diagram of yaw rate error under different control strategies;

[0064] Figure 11 Time domain response curve diagram of center of mass side slip angle under different control strategies;

[0065] Figure 12 Control input diagram of rear wheel steering angle;

[0066] Figure 13 Control input diagram of four-wheel torque;

[0067] Figure 14 Vehicle speed tracking curve diagram;

[0068] Figure 15 Time domain response curve diagram of yaw rate under different control strategies;

[0069] Figure 16 Time domain response curve diagram of yaw rate error under different control strategies;

[0070] Figure 17 Time domain response curve diagram of center of mass side slip angle under different control strategies;

[0071] Figure 18 Control input diagram of rear wheel steering angle;

[0072] Figure 19 Control input diagram of four-wheel torque;

[0073] Figure 20 Vehicle speed tracking curve diagram;

[0074] Figure 21This is a flowchart illustrating the robust cooperative control method for yaw stability of a distributed corner module electric drive vehicle in another embodiment.

[0075] Figure 22 This is a structural block diagram of a distributed corner module electric drive vehicle yaw stability robust cooperative control device in one embodiment.

[0076] Figure 23 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0078] The distributed corner module electric drive vehicle yaw stability robust cooperative control method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the vehicle monitoring system 102 communicates with the controller 104 via a network. The controller 104 acquires the driving status data of the corner module vehicle collected by the vehicle monitoring system 102, and determines the yaw moment control parameters and rear wheel steering control parameters based on the driving status data and a preset dynamic model. The vehicle monitoring system 102 may include multiple sensors for monitoring driving status, as well as driver status monitoring equipment; the controller 104 may be an on-board controller or other terminal.

[0079] In one embodiment, such as Figure 2 As shown, a robust cooperative control method for yaw stability of a distributed corner module electric drive vehicle is provided, which is then applied to... Figure 1 Taking the controller in the example, the explanation includes:

[0080] S201, Obtain the driving status data of the corner module vehicle.

[0081] The driving status data may include the vehicle's lateral speed, longitudinal speed, front wheel steering angle, vehicle yaw rate, and center of gravity sideslip angle.

[0082] In this embodiment of the application, the vehicle monitoring system may include multiple sensors for monitoring driving status, and a driver status monitoring device. The multiple sensors for monitoring driving status can collect the vehicle's lateral speed, longitudinal speed, vehicle yaw rate, center of gravity sideslip angle, etc., and the driver status monitoring device can collect the front wheel steering angle and target vehicle speed.

[0083] S202, constructing a nominal control model according to the lateral stability constraint condition and the lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to the determined parameters, wherein the target of the lateral stability constraint condition is that the error of the yaw rate and the error of the lateral velocity tend to be zero.

[0084] In the embodiments of the present application, a lateral dynamics model of the corner module vehicle is constructed in advance, which can be shown as formula 1:

[0085] (Formula 1)

[0086] wherein, is an uncertain parameter representing the possible boundary of uncertainty; is a control input representing an externally applied control force; is a velocity vector; is an acceleration vector; M0 represents the inertia of the vehicle in the lateral and yaw directions; C0 represents the force generated by the speed and rotational motion of the vehicle during turning; G0 represents the gravity acting on the vehicle in the vertical direction.

[0087] It should be noted that, considering the strong nonlinearity and complexity of the vehicle system, the vehicle dynamics model derived based on assumptions cannot completely and accurately describe the dynamic behavior of the actual vehicle. For example, the matrix in formula 1 not only includes model parameters, but also covers uncertain factors such as crosswind influence and model error. Similarly, the system parameters in the matrix , such as vehicle weight, moment of inertia of the vehicle around the z-axis of the center of mass, front tire cornering stiffness, rear tire cornering stiffness, etc., are not only difficult to accurately estimate, but also often change over time. Therefore, the matrix in the model is decomposed into a nominal part and a time-varying uncertainty part , and the decomposed parameter matrix can be represented as formula 2:

[0088] (Formula 2)

[0089] Optionally, the matrix , , . Thus, the following can be obtained: .

[0090] In the embodiments of the present application, it is assumed that for each , , there is ; and each constraint condition , at least one solution exists. Further, the lateral dynamics model with lateral stability constraint is solved according to the virtual work theorem, to obtain a constrained force of a minimum norm analytical solution satisfying the D'Alembert's principle, as shown in equation 3, and the constrained force is taken as a nominal control model:

[0091] (equation 3)

[0092] wherein, , and the symbol represents the Moore-Penrose generalized inverse of a matrix.

[0093] S203, an uncertainty control model is constructed, and the uncertainty control model is a part of the lateral dynamics model related to the uncertain parameters.

[0094] It should be noted that, since the angular module vehicle in actual driving will inevitably exist uncertainty , and the initial state of the vehicle may not satisfy the constraint condition, it is necessary to construct an adaptive robust control for the uncertainty and initial error of the system.

[0095] In the embodiments of the present application, the uncertainty control model includes an initial error control model and a robust control model .

[0096] Optionally, it is assumed that each , is full rank, that is, invertible. If , there is a constant which may be unknown, and for each , : wherein, represents the minimum eigenvalue of the matrix . It should be noted that this assumption limits the influence of the uncertainty on within a certain one-way limit, and the control direction will not change due to the parameter disturbance of . In the case where there is no uncertainty, , then , and , so may be selected.

[0097] In the embodiments of the present application, the initial error control model can be as shown in equation 4:

[0098] (equation 4)

[0099] wherein, is an adjustable parameter, is a given matrix of appropriate dimension, where , represents the lateral stability constraint following error. The control is still asymptotically convergent in the presence of initial errors.

[0100] Optionally, for all , there exists an unknown constant vector and a known function such that equation 5 holds:

[0101] equation 5

[0102] where denotes the two-norm of the matrix and equation 6 holds:

[0103] equation 6

[0104] Optionally, based on equation 5 and equation 6 above, for each , a linear decomposition can be made with respect to and there exists a function such that equation 7 holds:

[0105] equation 7

[0106] It is shown that the multi-source uncertainty of the vehicle system can be described by a function and the function is a parameterization of the worst-case effect of the uncertainty on the system. The vector is related to the boundary of the uncertainty where , the equation can be converted to equation 8:

[0107] equation 8

[0108] where , is an unknown scalar constant. In the actual operation of the vehicle, the exact value of the uncertainty boundary is difficult to obtain and the vector is unknown. Therefore, in order to estimate , a leakage-type adaptive law is constructed based on the adaptive parameter vector : where is a scalar constant and the initial value is a strictly positive value.

[0109] In the embodiments of the present application, the robust control model may be shown as formula 9-11:

[0110] (formula 9)

[0111] (formula 10)

[0112] (formula 11)

[0113] wherein, is a scalar constant. Let , wherein represents the estimation error of .

[0114] Optionally, under the above conditions, can guarantee satisfy the uniform boundedness and the uniform ultimate boundedness. The uniform boundedness is that for any , there is a , such that when , for all , there is always ; the uniform ultimate boundedness is that for any , if , there is a , such that for any , when , there is , wherein .

[0115] Optionally, the determination process of the nominal control model and the uncertainty control model related to the longitudinal vehicle speed control is the same as the determination process of the nominal control model and the uncertainty control model described above.

[0116] S204, input the driving state data to the nominal control model and the uncertainty control model, and determine the yaw moment control parameter.

[0117] In the embodiments of the present application, the nominal control model and the uncertainty control model can be shown as formula 12:

[0118] (formula 12)

[0119] wherein, is related to the uncertainty boundary.

[0120] In the embodiment of the present application, the driving state data is input into formula 12 to determine the yaw moment control parameter, and the driving state data is input into the longitudinal vehicle speed control related nominal control model and uncertainty control model to determine the longitudinal vehicle speed control parameter.

[0121] S205, based on the yaw moment control parameter and the rear wheel steering control parameter, performing stability collaborative control on the diagonal module vehicle.

[0122] In the embodiment of the present application, based on the yaw moment control parameter, the torque control parameter for torque control of the diagonal module vehicle is determined, so that the vehicle of the diagonal module vehicle is controlled according to the torque control parameter; and the diagonal module vehicle is controlled based on the rear wheel steering control parameter.

[0123] Optionally, the torque control parameter for torque control of the diagonal module vehicle can be determined according to the yaw moment control parameter; or the torque control parameter for torque control of the diagonal module vehicle can be determined according to the yaw moment control parameter and the rear wheel steering control parameter.

[0124] In the above-mentioned distributed diagonal module electric drive vehicle yaw stability robust collaborative control method, the driving state data of the diagonal module vehicle is obtained; a nominal control model is constructed according to the lateral stability constraint condition and the lateral dynamics model, and the nominal control model is the part related to the determined parameter in the lateral dynamics model, wherein the target of the lateral stability constraint condition is that the error of the yaw angular velocity and the error of the lateral velocity tend to zero; an uncertainty control model is constructed, and the uncertainty control model is the part related to the uncertain parameter in the lateral dynamics model; the driving state data is input into the nominal control model and the uncertainty control model to determine the yaw moment control parameter; and based on the yaw moment control parameter and the rear wheel steering control parameter, the stability collaborative control is performed on the diagonal module vehicle. The yaw moment control parameter is determined in combination with the uncertain part in the vehicle driving state, which improves the robustness when dealing with the vehicle parameter uncertainty and external disturbance, effectively reduces the yaw angular velocity error and the mass center side slip angle, and in addition, while the control torque is applied to the four wheels of the vehicle, the active rear wheel steering control adjusts the rear wheel angle in real time to ensure that the vehicle maintains a steady state turning state during turning, effectively reduces the error of the yaw angular velocity and the lateral velocity, and improves the yaw stability of the vehicle.

[0125] In one embodiment, before S202, as shown in Figure 3 the above-mentioned distributed diagonal module electric drive vehicle yaw stability robust collaborative control method further comprises:

[0126] S206, taking the front wheel steering linear two-degree-of-freedom vehicle model as a reference model.

[0127] It should be noted that in the vehicle lateral stability analysis and control, the yaw rate and the mass center side slip angle are the key dynamic indexes for evaluating the vehicle lateral stability, the front wheel steering linear two-degree-of-freedom vehicle model can accurately represent the vehicle lateral dynamic behavior, and reveal the time-varying characteristics of the yaw rate and the mass center side slip angle under dynamic working conditions. Therefore, the front wheel steering two-degree-of-freedom vehicle model is selected as a reference model to calculate the vehicle steady-state expected value, and the dynamic equation thereof is shown in formula 13:

[0128] (Formula 13)

[0129] wherein, is the vehicle yaw rate, is the mass center side slip angle.

[0130] In the embodiment of the present application, in order to ensure that the steady-state gain of the yaw rate remains stable and close to the ideal value under various working conditions, the expected value is defined by the steady-state response of the reference model, and the expression of the steady-state yaw rate is shown in formula 14:

[0131] (Formula 14)

[0132] wherein, K is a stability factor, and specifically: .

[0133] Considering that the vehicle yaw rate is limited by the tire state and the adhesion constraint of the actual driving surface, the expression of the expected yaw rate is shown in formula 15 after the steady-state yaw rate is corrected:

[0134] (Formula 15)

[0135] wherein, c is a safety factor, and is usually taken as 0.85. In the turning process of the vehicle, the mass center side slip angle is an important index for representing the vehicle lateral stability. A smaller mass center side slip angle usually means that the vehicle yaw stability is higher, and at the same time indicates that the coincidence degree of the actual driving trajectory and the expected trajectory is higher. Therefore, the expected mass center side slip angle at the vehicle mass center is assumed to be: The four-wheel steering two-degree-of-freedom vehicle dynamic model in the Lagrange form is adopted as the control model in the embodiment of the present application, and the state variable is defined as: Therefore, the expected lateral velocity is taken as one of the reference expected values. According to the definition of the vehicle mass center side slip angle: , the expected lateral velocity is:

[0136] S207, based on the reference model, the expected value of the yaw rate and the expected value of the lateral velocity, the lateral stability constraint condition is determined.

[0137] In the embodiments of the present application, the control target of the lateral stability constraint condition is that the error between the actual value and the expected value of the vehicle yaw rate and lateral velocity tends to zero, and the error can be specifically expressed as formula 16:

[0138] (Formula 16)

[0139] wherein, is the error between the actual value and the expected value of the lateral velocity, is the error between the actual value and the expected value of the yaw rate, is the actual value of the lateral velocity, is the expected value of the lateral velocity, is the actual value of the yaw rate, is the expected value of the yaw rate. The equation constraint satisfying the control target is established based on the above error, which is used as the solution condition of the control input. In order to ensure the yaw stability of the vehicle in the limit condition, that is, when , the yaw stability constraint as formula 17 is applied to the vehicle:

[0140] (Formula 17)

[0141] wherein, is a parameter for adjusting the convergence speed, is a parameter satisfying the initial condition, which is used to ensure that the system can smoothly transition to the control process.

[0142] Further, formula 17 can be converted into a matrix form as follows: wherein, , the matrix form is recorded as the first order form of the yaw stability equation constraint, and the second order form of the equation constraint can be expressed as: wherein .

[0143] In the above embodiments, the reference model is first constructed, and then the lateral stability constraint condition is obtained according to the reference model, which improves the accuracy of the lateral stability constraint condition compared with the constraint condition between and.

[0144] In one embodiment, an implementation of S205 is provided, as shown in Figure 4 the above "stability collaborative control of the corner module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter" includes:

[0145] S301, determining the longitudinal vehicle speed control parameter according to the longitudinal vehicle speed constraint condition and the longitudinal dynamics model.

[0146] In the embodiments of the present application, the longitudinal dynamics of the vehicle is affected by a number of nonlinear factors, among which the quadratic air resistance and the rolling resistance are the most important ones. To simplify the longitudinal dynamics model of the vehicle, the following assumptions are made: (1) the effects of lateral and vertical motions on the longitudinal dynamics are ignored; (2) the vehicle is assumed to be symmetrical and the load transfer between the front and rear axles is not considered; (3) no slip is assumed to exist between the tires and the road surface; and (4) the vehicle is assumed to travel on a flat road surface with a slope of zero.

[0147] Under the above assumptions, the longitudinal force of the vehicle can be divided into four main parts: the total driving / braking force , the air resistance , and the rolling resistance . The longitudinal dynamics equation of the vehicle is shown in Equation 18:

[0148] (Equation 18)

[0149] The total longitudinal driving / braking force of the vehicle is represented as shown in Equation 19:

[0150] (Equation 19)

[0151] wherein, is the driving / braking torque of the four wheels, wherein a positive value represents a driving torque and a negative value represents a braking torque; is the transmission efficiency; is the wheel transmission ratio of the in-wheel motor; is the effective radius of the wheel. Only the component of the air resistance in the driving direction is considered in the longitudinal dynamics of the vehicle, which is described by Equation 20:

[0152] (Equation 20)

[0153] wherein, is the air resistance coefficient, is the windward area, is the vehicle speed, is the air density. The rolling resistance is represented by a model that is proportional to the total mass of the vehicle: wherein g is the gravitational acceleration and f is the rolling resistance coefficient of the wheel.

[0154] By combining Equations 18-20, the longitudinal dynamics model of the vehicle is represented as: .

[0155] In the embodiments of the present application, the longitudinal dynamics model is converted into the form of the Lagrange dynamics equation as shown in Equation 21:

[0156] (Equation 21)

[0157] wherein, is an uncertain parameter representing the boundary of uncertainty; is a control input representing the externally applied control force; is a velocity vector; is an acceleration vector; M1 represents the inertia of the vehicle in the lateral and yaw directions; C1 represents the forces generated by the speed and rotational motion of the vehicle during cornering; G1 represents the gravitational force acting on the vehicle in the vertical direction.

[0158] In the embodiment of the present application, when the cooperative control is involved, the four-wheel torque is redistributed, which can cause significant fluctuations in the actual driving speed of the vehicle. In order to make the vehicle follow the expected longitudinal speed, the equation constraint of formula 22 is imposed on the longitudinal dynamics model of the vehicle:

[0159] (Formula 22)

[0160] wherein, v x is the longitudinal speed, x t is the longitudinal target speed, is a parameter satisfying the initial condition. Further, formula 6 can be converted into a first-order matrix form, wherein, , , and the derivative of the two sides of the first-order matrix with respect to time t is obtained, and the second-order matrix form of the longitudinal vehicle speed equation constraint is: wherein, .

[0161] In the embodiment, the longitudinal speed constraint condition is applied to the longitudinal dynamics model for solving, and the form state data is input into the longitudinal dynamics model to determine the longitudinal speed control parameter.

[0162] S302, based on the longitudinal speed control parameter and the yaw moment control parameter, the four-wheel torque distribution control of the corner module vehicle is performed, and based on the rear wheel steering control parameter, the rear wheel steering control of the corner module vehicle is performed.

[0163] In the embodiment of the present application, according to the yaw moment control parameter, the longitudinal speed control parameter and the rear wheel steering control parameter, the four-wheel control force of the corner module vehicle is determined, and then the four-wheel control torque is determined according to the four-wheel control force, so as to perform the four-wheel torque distribution control of the corner module vehicle according to the four-wheel control torque.

[0164] Optionally, the total longitudinal force and the additional yaw moment of the vehicle can be represented by formula 23:

[0165] (Formula 23)

[0166] wherein, is the wheel base, is the four-wheel drive force, i = fl, fr, rl, rr. Convert formula 23 to matrix form: wherein, , , .

[0167] Optionally, since generally, the higher the tire adhesion utilization rate, the closer the tire adhesion force to the limit, resulting in a decrease in stability margin. Therefore, by optimizing the tire adhesion utilization rate, the adhesion capacity of the tire can be fully utilized under the premise of ensuring that the vehicle maintains a reasonable stability margin, and the dynamic performance of the vehicle under extreme conditions can be improved. The torque determination can be: In actual application, considering the coupling relationship between the lateral force and the longitudinal force of the tire, and the distributed angle module vehicle cannot directly control the tire lateral force and in order to facilitate the calculation, the optimization objective function is simplified to minimize the adhesion utilization rate of the longitudinal force, and the simplified optimization objective function can be expressed as: .

[0168] Optionally, considering the hub motor performance constraint and the road adhesion condition constraint: then the optimization solution of the four-wheel drive force can be written as: wherein, In order to ensure the real-time performance of the optimization solution, and considering that the tire vertical load is difficult to accurately obtain in actual application, the influence of the vehicle suspension system is ignored, and formula 24 is used to calculate the tire vertical load:

[0169] (formula 24)

[0170] wherein, is the wheel base of the vehicle, is the height of the vehicle mass center, , is the longitudinal acceleration and lateral acceleration of the vehicle.

[0171] In the embodiments of the present application, the active rear wheel steering control adjusts the rear wheel steering angle in real time, and optimizes the dynamic response of the vehicle under the steering condition. The control strategy cooperates with the adaptive robust direct yaw moment control based on constraint dynamics, and further improves the yaw stability of the vehicle. The active rear wheel steering control is based on the lateral dynamics model to solve the ideal rear wheel steering angle value that meets the steady-state turning equation constraint, that is, and adjust the vehicle rear wheel steering angle to tend to the ideal value, so that the vehicle can dynamically adapt to the steady-state turning under different working conditions. The expression of the ideal rear wheel steering angle is formula 25:

[0172] (Formula 25)

[0173] Considering the physical limit of the rear wheel steering angle, the expression of the steady-state rear wheel steering angle is Formula 26:

[0174] (Formula 26)

[0175] wherein, is an ideal rear wheel steering angle, is a maximum rear wheel steering angle.

[0176] In the above application embodiment, the cornering module vehicle is stably controlled based on the yaw moment control parameter, the longitudinal vehicle speed control parameter and the rear wheel steering control parameter, so that the comprehensiveness and stability effect of the stability control are improved.

[0177] In one embodiment, an implementation of S204 is provided, as shown in Figure 5 The above "inputting the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter" includes:

[0178] S401, obtaining a rear wheel steering control parameter.

[0179] In the embodiment of the application, the rear wheel steering control parameter can be a preset parameter, or the lateral dynamics model can be solved according to a preset condition to obtain the rear wheel steering control parameter.

[0180] Optionally, the rear wheel steering control parameter is determined according to a preset steady-state turning constraint condition and a lateral dynamics model, wherein the steady-state turning constraint condition is used to limit the lateral acceleration and the yaw angular acceleration.

[0181] In the embodiment of the application, the steady-state turning constraint condition can be expressed as: wherein, is a lateral acceleration, is a yaw angular velocity. The steady-state turning constraint condition is applied to the lateral dynamics model to be solved to obtain the rear wheel steering control parameter as shown in Formula 25 and Formula 26.

[0182] S402, inputting the rear wheel steering control parameter and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

[0183] In the embodiment of the application, the driving state data and the rear wheel steering control parameter are inputted into Formula 12 to determine the yaw moment control parameter, and the driving state data is inputted into the nominal control model and the uncertainty control model related to the longitudinal vehicle speed control to determine the longitudinal vehicle speed control parameter.

[0184] In the above application examples, the yaw moment control parameter is determined in combination with the rear wheel steering control parameter, so that the accuracy of the yaw moment control parameter is improved.

[0185] In one embodiment, before S202, as shown in Figure 6 The driving angle module vehicle yaw stability robust cooperative control method further includes:

[0186] S208, based on the relationship between the total mass of the vehicle, the longitudinal and lateral speed, the yaw angular velocity, the total lateral force of the front and rear tires, the front and rear wheel steering angle, the moment of inertia, the distance from the mass center to the front and rear axles, and the additional yaw moment, a dynamic equation for describing the lateral and yaw motion of the vehicle is established.

[0187] In the embodiments of the present application, the analysis of vehicle dynamics is as shown in Figure 7 According to Newton's second law, the vehicle dynamics equation is as shown in formula 27:

[0188] (Formula 27)

[0189] Wherein, m is the total mass of the vehicle; v and u are the longitudinal and lateral speeds of the vehicle, respectively; is the yaw angular velocity of the vehicle; , are the total lateral forces of the front and rear tires of the vehicle, respectively; are the front and rear wheel steering angles of the vehicle, respectively; is the moment of inertia of the vehicle around the mass center z axis; are the distances from the mass center to the front and rear axles, respectively; is the additional yaw moment.

[0190] S209, a linear relationship between the tire lateral force and the tire side slip angle is obtained.

[0191] In the embodiments of the present application, the linear relationship between the tire lateral force and the side slip angle can be expressed as formula 28:

[0192] (Formula 28)

[0193] Wherein, are the front and rear tire side slip stiffness, respectively; are the front and rear tire side slip angles, respectively, .

[0194] S210, based on the dynamic equation and the linear relationship, a lateral dynamics model is established.

[0195] In the embodiments of the present application, the front and rear wheel steering angles of the vehicle during vehicle driving are very small, and can be approximately considered as Then, by combining equations 27 and 28, we can obtain a linear two-degree-of-freedom four-wheel steering vehicle dynamics model as shown in equation 29:

[0196] (Equation 29)

[0197] Furthermore, equation 29 can be transformed into the Lagrange form shown in equation 1, where:

[0198] (Formula 30)

[0199] (Equation 31)

[0200] Optionally, the CarSim-Matlab / Simulink co-simulation platform can be used for experiments. For example... Figure 8 As shown, a schematic diagram of the corresponding control algorithm model was established in Matlab / Simulink, and the vehicle model and related data provided by CarSim were integrated to complete the co-simulation. The vehicle model parameters used in the simulation are shown in Table 1. This paper selects the double lane change simulation condition to verify the yaw stability of the vehicle. To further verify the robustness of the control strategy, the parameters m and I in Equation 30 were adjusted. z C f C r For parameters that cannot be accurately collected in practice, a 5%–15% time-varying perturbation was applied, and a time-varying crosswind disturbance with an amplitude of 50 km / h was added to CarSim. To more intuitively demonstrate the effect of the proposed control strategy, three control strategies were set up for comparison. The first scheme is Sliding Mode Control for Direct Yaw Moment Control (SMC); the second scheme is Adaptive Robust Control (ARC); the third scheme is a combined control strategy of sliding mode control and active rear steering (ARS), i.e., SMC+ARS; and finally, the ARC+ARS in the embodiment of this application. The main parameters in ARC control are... =200, =0.5, k1=10, k2=5, The main parameter in sliding mode control is the sliding surface weighting coefficient. =0.5, reaching law coefficient =0.1, =15.

[0201] Table 1

[0202]

[0203] Exemplarily, Figures 9-14 The simulation results of key parameters for high-speed high adhesion double-lane-change working condition are shown in the entity diagram. In the high-speed high adhesion double-lane-change simulation working condition, the vehicle completes the double-lane-change operation at a speed of 100 km / h on a road surface with an adhesion coefficient of 0.8, which simulates the emergency behavior of the vehicle when driving at high speed on a dry asphalt road. Figures 9-11 The time-domain response curves of the yaw rate, the yaw rate error and the sideslip angle of the center of mass under different control strategies are shown respectively. These parameters are key dynamic indicators for representing the yaw stability of the vehicle. The simulation results show that the four control strategies can effectively track the expected values, and the yaw rate error and the sideslip angle of the center of mass are always controlled within a reasonable range. Figures 12-13 The control input of the vehicle, wherein the active rear wheel steering control strategy improves the stability of the vehicle by fine-tuning the rear wheel steering angle (the maximum steering angle is not more than 2°) at a small angle; the ARC control input coordinates the four-wheel torque to generate an additional yaw moment, and its response characteristic is smooth and the amplitude is reasonable (the four-wheel torque fluctuation range is controlled within ±500 N·m), which has good engineering realizability. Figure 14 The vehicle speed tracking curve in the figure shows that during the implementation of the yaw stability control process, the deviation between the actual vehicle speed and the target vehicle speed is always maintained within ±0.2 km / h, ensuring that the vehicle power performance does not appear significant loss.

[0204] Table 2 is the absolute maximum value and the root mean square value of the yaw stability indicators under different control strategies. These statistics provide an important basis for quantitatively evaluating the performance of each control strategy. From Figures 9-14 From the experimental data in Table 2 and Table 2, it can be seen that ARC can reduce the yaw rate error and the sideslip angle of the center of mass of the vehicle compared with SMC. This proves that the direct yaw moment control using ARC algorithm is more effective for vehicle yaw stability control. Further analysis shows that after the introduction of ARS and ARC for collaborative control, the system performance is further improved, and the absolute maximum value and the root mean square of the yaw rate error of the vehicle are reduced by 87.0%, 49.3%, 86.7% and 85.7%, 46.1%, 79.7% compared with the other three control strategies; the absolute maximum value and the root mean square of the sideslip angle of the center of mass are reduced by 46.6%, 40.7%, 43.8% and 43.5%, 31.2%, 34.4% respectively. These performance improvements reflect the superiority of the ARC+ARS collaborative control strategy.

[0205] Table 2

[0206]

[0207] From Figures 9-14As shown in Table 2, the experimental data demonstrates that ARC reduces the vehicle's yaw rate error and sideslip angle compared to SMC. This proves that direct yaw moment control using the ARC algorithm is more effective for vehicle yaw stability control. Further analysis shows that by introducing ARS and ARC for coordinated control, the system performance is further improved. The maximum absolute value and root mean square error of the vehicle's yaw rate error are reduced by 87.0%, 49.3%, and 86.7%, and 85.7%, 46.1%, and 79.7%, respectively, compared to the other three control strategies; the maximum absolute value and root mean square error of the sideslip angle are reduced by 46.6%, 40.7%, and 43.8%, and 43.5%, 31.2%, and 34.4%, respectively. These performance improvements demonstrate the superiority of the ARC+ARS coordinated control strategy.

[0208] The medium-speed low-adhesion condition simulates an emergency lane change scenario where a vehicle is traveling at a medium speed of 72 km / h on a wet and slippery road surface (road adhesion coefficient of 0.4). Figures 15-20 These are simulation results from a medium-speed, low-adhesion double-track-shifting test. Similar to the high-speed, high-adhesion test, Figures 15-17 The time-domain response curves of yaw rate, yaw rate error, and sideslip angle under different control strategies are presented. Simulation results show that the four control strategies can still effectively track the desired value under this condition, with the yaw rate error controlled within ±0.06 rad / s and the sideslip angle maintained within a safe range of ±0.06 rad. Figure 18 , 19 The corresponding control inputs are shown. Under this condition, the maximum steering angle of the active rear-wheel steering control strategy increases to 3°, but the torque amplitude of the four wheels of the ARC control strategy is reduced to within ±400 N·m, while still maintaining smooth response characteristics. Figure 20 The actual vehicle speed and the target vehicle speed are still within ±0.2 km / h, indicating that the vehicle's power performance is well maintained.

[0209] Table 3 lists the maximum absolute value and root mean square value of the yaw stability index under this operating condition. Data analysis shows that the ARC+ARS cooperative control strategy still exhibits the best performance: the maximum absolute value and root mean square value of the yaw rate error are reduced by 79.6%, 62.9%, and 80.3%, and 88.4%, 77.5%, and 88.9%, respectively, compared with other control strategies; the corresponding indices of the center of mass sideslip angle are reduced by 76.4%, 62.9%, and 19.6%, and 67.6%, 48.7%, and 15.2%, respectively. This result further verifies the adaptability and superiority of the ARC+ARS cooperative control strategy under different operating conditions.

[0210] Table 3

[0211]

[0212] In the embodiment of the present application, a lateral dynamics model is established according to the linear relationship between the vehicle motion state parameters, the tire lateral force and the tire side slip angle, thereby improving the accuracy of the lateral dynamics model.

[0213] In one embodiment, as shown in Figure 21 A complete distributed corner module electric drive vehicle yaw stability robust cooperative control method is provided, which comprises the following steps:

[0214] S1, obtaining driving state data of the corner module vehicle.

[0215] S2, establishing a dynamics equation for describing the lateral and yaw motion of the vehicle based on the relationship between the total mass of the vehicle, the longitudinal and lateral speed, the yaw angular velocity, the total lateral force of the front and rear tires, the front and rear tire steering angle, the moment of inertia, the distance from the mass center to the front and rear axles and the additional yaw moment.

[0216] S3, obtaining the linear relationship between the tire lateral force and the tire side slip angle.

[0217] S4, establishing a lateral dynamics model based on the dynamics equation and the linear relationship.

[0218] S5, taking the front wheel steering linear two-degree-of-freedom vehicle model as a reference model.

[0219] S6, determining a lateral stability constraint condition based on the reference model, the expected value of the yaw angular velocity and the expected value of the lateral speed.

[0220] S7, constructing a nominal control model according to the lateral stability constraint condition and the lateral dynamics model, the nominal control model being the part of the lateral dynamics model related to the determined parameters, wherein the target of the lateral stability constraint condition is that the error of the yaw angular velocity and the error of the lateral speed tend to zero.

[0221] S8, constructing an uncertainty control model, the uncertainty control model being the part of the lateral dynamics model related to the uncertain parameters.

[0222] S9, determining rear wheel steering control parameters according to the preset steady-state turning constraint condition and the lateral dynamics model, wherein the steady-state turning constraint condition is used to limit the lateral acceleration and the yaw angular acceleration.

[0223] S10, inputting the rear wheel steering control parameters and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameters.

[0224] S11, determining the longitudinal vehicle speed control parameters according to the longitudinal vehicle speed constraint condition and the longitudinal dynamics model.

[0225] S12, based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, performing four-wheel torque distribution control on the corner module vehicle, and based on the rear wheel steering control parameter, performing rear wheel steering control on the corner module vehicle.

[0226] In the above-mentioned robust cooperative control method for the corner module electrically driven vehicle, the driving state data of the corner module vehicle is obtained; a nominal control model is constructed according to the lateral stability constraint condition and the lateral dynamics model, the nominal control model being the part of the lateral dynamics model related to the determined parameters, wherein the target of the lateral stability constraint condition is that the error of the yaw rate and the error of the lateral velocity tend to zero; an uncertainty control model is constructed, the uncertainty control model being the part of the lateral dynamics model related to the uncertain parameters; the driving state data is input into the nominal control model and the uncertainty control model to determine the yaw moment control parameter; and the corner module vehicle is subjected to stability cooperative control based on the yaw moment control parameter and the rear wheel steering control parameter. The yaw moment control parameter is determined in combination with the uncertain part of the driving state of the vehicle, thereby improving the robustness in response to the parameter uncertainty of the vehicle and external disturbance, effectively reducing the error of the yaw rate and the side slip angle of the mass center, and in addition, while the control moment is applied to the four wheels of the vehicle, the active rear wheel steering control adjusts the rear wheel angle in real time to ensure that the vehicle maintains a steady state turning condition during turning, effectively reduces the error of the yaw rate and the lateral velocity, and improves the yaw stability of the vehicle.

[0227] It should be understood that, although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential but can be executed in rotation or alternation with at least part of other steps or stages in other steps.

[0228] Based on the same inventive concept, the embodiment of the present application further provides a distributed corner module electric drive vehicle yaw stability robust cooperative control device for implementing the distributed corner module electric drive vehicle yaw stability robust cooperative control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more distributed corner module electric drive vehicle yaw stability robust cooperative control device embodiments provided below can be referred to the limitations of the distributed corner module electric drive vehicle yaw stability robust cooperative control method described above, which will not be repeated here.

[0229] In one exemplary embodiment, as shown in Figure 22 A distributed corner module electric drive vehicle yaw stability robust cooperative control device is provided, comprising: a first acquisition module 10, a first construction module 11, a second construction module 12, a first determination module 13, and a control module 14, wherein:

[0230] The first acquisition module 10 is configured to acquire driving state data of the corner module vehicle.

[0231] The first construction module 11 is configured to construct a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to the determined parameters, wherein the target of the lateral stability constraint condition is that the error of the yaw rate and the error of the lateral velocity tend to zero.

[0232] The second construction module 12 is configured to construct an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to the uncertain parameters.

[0233] The first determination module 13 is configured to input the driving state data into the nominal control model and the uncertainty control model, and determine a yaw moment control parameter.

[0234] The control module 14 is configured to perform stability cooperative control on the corner module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter.

[0235] In one embodiment, the distributed corner module electric drive vehicle yaw stability robust cooperative control device further comprises:

[0236] A third construction module is configured to take a front wheel steering linear two-degree-of-freedom vehicle model as a reference model.

[0237] A second determination module is configured to determine a lateral stability constraint condition based on the reference model, the expected value of the yaw rate, and the expected value of the lateral velocity.

[0238] In one embodiment, the control module comprises a first determination unit and a control unit, wherein:

[0239] The first determining unit is configured to determine the longitudinal vehicle speed control parameter according to the longitudinal vehicle speed constraint condition and the longitudinal dynamics model.

[0240] The control unit is configured to perform four-wheel torque distribution control on the cornering module vehicle based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, and perform rear wheel steering control on the cornering module vehicle based on the rear wheel steering control parameter.

[0241] In an embodiment, the distributed cornering module electrically driven vehicle yaw stability robust cooperative control device described above further comprises:

[0242] The second obtaining module is configured to obtain the rear wheel steering control parameter.

[0243] The third determining module is configured to input the rear wheel steering control parameter and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

[0244] In an embodiment, the second obtaining module comprises a first determining unit configured to determine the rear wheel steering control parameter according to a preset steady-state turning constraint condition and a lateral dynamics model; wherein the steady-state turning constraint condition is configured to limit the lateral acceleration and the yaw angular acceleration.

[0245] In an embodiment, the distributed cornering module electrically driven vehicle yaw stability robust cooperative control device further comprises a first establishing module, a third obtaining module and a second establishing module, wherein:

[0246] The first establishing module is configured to establish a dynamics equation for describing the lateral and yaw motion of the vehicle based on the relationship between the total mass of the vehicle, the longitudinal and lateral speeds, the yaw angular speed, the total lateral force of the front and rear tires, the front and rear wheel steering angles, the moment of inertia, the distance from the mass center to the front and rear axles, and the additional yaw moment.

[0247] The third obtaining module is configured to obtain a linear relationship between the tire lateral force and the tire side slip angle.

[0248] The second establishing module is configured to establish a lateral dynamics model based on the dynamics equation and the linear relationship.

[0249] Each module in the distributed cornering module electrically driven vehicle yaw stability robust cooperative control device can be realized by software, hardware and a combination thereof, in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0250] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 23 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a robust cooperative control method for yaw stability of a distributed corner module electric drive vehicle. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0251] Those skilled in the art will understand that Figure 23 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0252] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0253] Acquire the driving status data of the vehicle in the corner module;

[0254] Based on the lateral stability constraints and the lateral dynamics model, a nominal control model is constructed. The nominal control model is the part of the lateral dynamics model that is related to the determined parameters. The objective of the lateral stability constraints is to make the errors of yaw rate and lateral velocity approach zero.

[0255] constructing an uncertainty control model, the uncertainty control model being for a part of the lateral dynamics model related to the uncertain parameters;

[0256] inputting the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter;

[0257] performing stability collaborative control on the cornering module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter.

[0258] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0259] taking the front wheel steering linear two-degree-of-freedom vehicle model as a reference model;

[0260] determining a lateral stability constraint condition based on the reference model, the expected value of the yaw rate and the expected value of the lateral velocity.

[0261] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0262] determining a longitudinal vehicle speed control parameter according to the longitudinal vehicle speed constraint condition and the longitudinal dynamics model;

[0263] performing four-wheel torque distribution control on the cornering module vehicle based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, and performing rear wheel steering control on the cornering module vehicle based on the rear wheel steering control parameter.

[0264] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0265] obtaining the rear wheel steering control parameter;

[0266] inputting the rear wheel steering control parameter and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

[0267] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0268] determining the rear wheel steering control parameter according to a preset steady-state turning constraint condition and the lateral dynamics model; wherein the steady-state turning constraint condition is used to limit the lateral acceleration and the yaw angular acceleration.

[0269] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0270] establishing a dynamics equation for describing lateral and yaw motion of the vehicle based on a relationship between the total mass of the vehicle, the longitudinal and lateral velocities, the yaw rate, the total lateral force of the front and rear tires, the front and rear wheel steering angles, the moment of inertia, the distance from the mass center to the front and rear axles and the additional yaw moment.

[0271] obtaining a linear relationship between tire lateral force and tire side slip angle;

[0272] establishing a lateral dynamics model based on the dynamics equation and the linear relationship.

[0273] In one embodiment, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor implements the following steps:

[0274] obtaining driving state data of the corner module vehicle;

[0275] constructing a nominal control model according to the lateral stability constraint condition and the lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to the determined parameters, wherein the target of the lateral stability constraint condition is that the error of the yaw rate and the error of the lateral velocity tend to zero;

[0276] constructing an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to the uncertain parameters;

[0277] inputting the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter;

[0278] performing stability collaborative control on the corner module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter.

[0279] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0280] taking the front wheel steering linear two-degree-of-freedom vehicle model as a reference model;

[0281] determining the lateral stability constraint condition based on the reference model, the expected value of the yaw rate and the expected value of the lateral velocity.

[0282] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0283] determining a longitudinal vehicle speed control parameter according to a longitudinal vehicle speed constraint condition and a longitudinal dynamics model;

[0284] performing four-wheel torque distribution control on the corner module vehicle based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, and performing rear wheel steering control on the corner module vehicle based on the rear wheel steering control parameter.

[0285] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0286] obtaining the rear wheel steering control parameter;

[0287] The rear wheel steering control parameter and the driving state data are input into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

[0288] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0289] The rear wheel steering control parameter is determined according to a preset steady-state turning constraint condition and a lateral dynamics model; wherein the steady-state turning constraint condition is used to limit the lateral acceleration and the yaw angular acceleration.

[0290] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0291] The dynamics equation for describing the lateral and yaw motion of the vehicle is established based on the relationship between the total mass of the vehicle, the longitudinal and lateral speed, the yaw angular speed, the total lateral force of the front and rear tires, the front and rear wheel steering angle, the moment of inertia, the distance from the mass center to the front and rear axles, and the additional yaw moment;

[0292] The linear relationship between the tire lateral force and the tire side slip angle is obtained;

[0293] The lateral dynamics model is established based on the dynamics equation and the linear relationship.

[0294] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by the processor, implements the following steps:

[0295] The driving state data of the corner module vehicle is obtained;

[0296] The nominal control model is constructed according to the lateral stability constraint condition and the lateral dynamics model, and the nominal control model is the part of the lateral dynamics model related to the determined parameter, wherein the target of the lateral stability constraint condition is that the error of the yaw angular speed and the error of the lateral speed tend to zero;

[0297] The uncertainty control model is constructed, and the uncertainty control model is the part of the lateral dynamics model related to the uncertain parameter;

[0298] The driving state data is input into the nominal control model and the uncertainty control model to determine the yaw moment control parameter;

[0299] The corner module vehicle is stably and cooperatively controlled based on the yaw moment control parameter and the rear wheel steering control parameter.

[0300] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0301] The front wheel steering linear two-degree-of-freedom vehicle model is taken as the reference model;

[0302] determine a lateral stability constraint condition based on the reference model, the expected value of the yaw rate and the expected value of the lateral velocity.

[0303] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0304] determine a longitudinal vehicle speed control parameter according to the longitudinal vehicle speed constraint condition and the longitudinal dynamics model;

[0305] perform four-wheel torque distribution control on the cornering module vehicle based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, and perform rear wheel steering control on the cornering module vehicle based on the rear wheel steering control parameter.

[0306] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0307] obtain the rear wheel steering control parameter;

[0308] input the rear wheel steering control parameter and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

[0309] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0310] determine the rear wheel steering control parameter according to a preset steady-state turning constraint condition and a lateral dynamics model; wherein the steady-state turning constraint condition is used to limit the lateral acceleration and the yaw angular acceleration.

[0311] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0312] establish a dynamics equation for describing lateral and yaw motion of the vehicle based on a relationship between the total mass of the vehicle, the longitudinal and lateral velocities, the yaw rate, the total lateral force of the front and rear tires, the front and rear wheel steering angles, the moment of inertia, the distance from the mass center to the front and rear axles, and the additional yaw moment;

[0313] obtain a linear relationship between the tire lateral force and the tire side slip angle;

[0314] establish a lateral dynamics model based on the dynamics equation and the linear relationship.

[0315] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0316] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0317] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A robust cooperative control method for electrically driven vehicle yaw stability of distributed corner modules, characterized in that, The method comprises: acquiring driving state data of the corner module vehicle; the driving state data comprises lateral velocity, longitudinal velocity, front wheel steering angle, vehicle yaw rate, and mass center side slip angle of the corner module vehicle; constructing a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a determined parameter, wherein the target of the lateral stability constraint condition is that errors of the yaw rate and the lateral velocity tend to be zero; constructing an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter; inputting the driving state data into the nominal control model and the uncertainty control model to determine a yaw moment control parameter; performing stability collaborative control on the corner module vehicle based on the yaw moment control parameter and a rear wheel steering control parameter.

2. The method of claim 1, wherein, Before the inputting the driving state data into the lateral dynamics model and determining the nominal control model according to the lateral stability constraint condition, the method further comprises: taking a front wheel steering linear two-degree-of-freedom vehicle model as a reference model; determining the lateral stability constraint condition based on the reference model, an expected value of the yaw rate, and an expected value of the lateral velocity.

3. The method of claim 1, wherein, The stability control comprises: four-wheel torque distribution control, the performing stability collaborative control on the corner module vehicle based on the yaw moment control parameter and the rear wheel steering control parameter, comprising: determining a longitudinal vehicle speed control parameter according to a longitudinal vehicle speed constraint condition and a longitudinal dynamics model; performing four-wheel torque distribution control on the corner module vehicle based on the longitudinal vehicle speed control parameter and the yaw moment control parameter, and performing rear wheel steering control on the corner module vehicle based on the rear wheel steering control parameter.

4. The method of claim 1, wherein, The inputting the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter comprises: acquiring a rear wheel steering control parameter; inputting the rear wheel steering control parameter and the driving state data into the nominal control model and the uncertainty control model to determine the yaw moment control parameter.

5. The method of claim 4, wherein, The acquiring the rear wheel steering control parameter comprises: determining the rear wheel steering control parameter according to a preset steady-state turning constraint condition and the lateral dynamics model; wherein the steady-state turning constraint condition is used for limiting lateral acceleration and yaw angular acceleration.

6. The method of claim 1, wherein, Before the inputting the driving state data into the lateral dynamics model and determining the nominal control model according to the lateral stability constraint condition, the method comprises: establishing a dynamics equation for describing lateral and yaw motion of the vehicle based on relationships between vehicle total mass, longitudinal and lateral velocity, yaw rate, front and rear tire total lateral force, front and rear wheel steering angle, moment of inertia, mass center to front and rear axle distance, and additional yaw moment; acquiring a linear relationship between tire lateral force and tire side slip angle; establishing the lateral dynamics model based on the dynamics equation and the linear relationship.

7. A robust cooperative control device for electrically driven vehicle yaw stability of distributed corner modules, characterized in that The device comprises: The first obtaining module is configured to obtain driving state data of the corner module vehicle; the driving state data comprises lateral velocity, longitudinal velocity, front wheel steering angle, vehicle yaw rate and center of mass side slip angle of the corner module vehicle; The first constructing module is configured to construct a nominal control model according to a lateral stability constraint condition and a lateral dynamics model, the nominal control model being a part of the lateral dynamics model related to a determined parameter, wherein the lateral stability constraint condition aims to make errors of the yaw rate and lateral velocity tend to zero; The second constructing module is configured to construct an uncertainty control model, the uncertainty control model being a part of the lateral dynamics model related to an uncertain parameter; The first determining module is configured to input the driving state data into the nominal control model and the uncertainty control model, and determine a yaw moment control parameter; The control module is configured to perform stability collaborative control on the corner module vehicle based on the yaw moment control parameter and a rear wheel steering control parameter.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

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