Stability control method and system for MPC with adaptive weight coefficients

Abstract

A stability control method and system for MPC with adaptive weight coefficients. The method comprises: using an MPC algorithm to calculate the wheel angular velocity of a vehicle within a prediction horizon; on the basis of the wheel angular velocity of the vehicle within the prediction horizon, calculating a slip ratio within the prediction horizon; on the basis of the slip ratio, an additional yaw moment, and the additional torque of four-wheel electric motors, constructing an objective function; and minimizing the objective function within the prediction horizon to obtain the optimal additional torque of the four-wheel electric motors. A constructed objective function has a slip ratio control objective and an energy consumption objective, such that when a vehicle slips, a control quantity can be controlled, and when the vehicle meets stability conditions, the output of the control quantity is reduced to lower actuation energy, thereby improving the stability and safety of the vehicle and reducing the energy consumption of the vehicle; and by combining stability indicators with slip ratio errors, parameters of a vehicle model predictive controller are adjusted in real time on the basis of the requirements of the current vehicle state, thereby improving the adaptability of vehicle yaw stability control.

Description

A stability control method and system of adaptive weight coefficient MPC Cross-reference to Related Applications

[0001] This application claims priority to Chinese Patent Application No. 202410857908.8, filed on June 28, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0002] The present application belongs to the technical field of vehicle stability control, and specifically relates to a stability control method and system of adaptive weight coefficient MPC. BACKGROUND

[0003] Road safety has always been an important topic in vehicle research. Studies have shown that many traffic accidents at medium and high speeds are related to vehicle instability. Reducing traffic accidents and improving vehicle stability have become an important content and direction of modern automobile industry development.

[0004] With the advent of the electric vehicle era, electric motor driven cars have become the norm. Electric motors are divided into centralized and distributed types. Distributed electric motors are both information units and fast-response control execution units for cars, and they achieve various dynamic control functions by independently controlling the drive / braking torque of the electric motor. Among them, torque vectoring control technology has realized the transformation of dynamic control from torque and slip rate control of each wheel to torque distribution control for the whole wheel, and has played a key role in vehicle direct yaw moment control.

[0005] Currently, in the field of yaw stability control of distributed drive vehicles, MPC (Model Predictive Control) algorithm is commonly used. The MPC torque vectoring control method based on kinematics does not rely on extremely accurate models, and uses a rolling optimization method to replace the global optimal solution with a local optimal solution, which has gained the favor of more and more users. This algorithm can effectively solve the optimization problem with constraints and is suitable for vehicle stability control considering multiple objectives.

[0006] However, in traditional MPC control strategies, the weight coefficients of each control objective are constant. When the actual scene changes, the corresponding control effect cannot be achieved, which makes the vehicle driving stability worse. SUMMARY

[0007] To improve the driving stability of distributed drive vehicles under different working conditions, for the lower controller, based on the tire kinematics model of the vehicle and the model predictive control (MPC, Model Predictive Control) theory, the present application proposes a stability control method and system of adaptive weight coefficient MPC.

[0008] A stability control method of adaptive weight coefficient MPC is provided to achieve one of the purposes of the present application, comprising:

[0009] A wheel angular velocity in a prediction domain of the vehicle is calculated by using the MPC algorithm;

[0010] A slip ratio in the prediction domain is calculated according to the wheel angular velocity of the vehicle in the prediction domain;

[0011] A target function is constructed according to the slip ratio, an additional yaw moment and an additional torque of the four-wheel motor;

[0012] The target function in the prediction domain is minimized to obtain an optimal additional torque of the four-wheel motor.

[0013] The optimal additional torque of the four-wheel motor is sent to the controlled vehicle, so as to complete the stability control of the vehicle.

[0014] Further, the weight coefficient of the target function is optimized, and the optimization method comprises:

[0015] A stability index of the vehicle at the current time is calculated based on the vehicle yaw angular velocity and the lateral velocity;

[0016] The stability index of the vehicle at the current time and the slip ratio error are selected as inputs of a fuzzy reasoning system; and the weight coefficient in the target function is corrected by using the fuzzy reasoning system.

[0017] A stability control system of adaptive weight coefficient MPC is provided to achieve the second purpose of the present application, comprising:

[0018] A wheel angular velocity calculation module is configured to calculate a wheel angular velocity in a prediction domain of the vehicle by using the MPC algorithm;

[0019] A slip ratio calculation module is configured to calculate a slip ratio in the prediction domain according to the wheel angular velocity of the vehicle in the prediction domain;

[0020] A target function construction module is configured to construct a target function according to the slip ratio, an additional yaw moment and an additional torque of the four-wheel motor;

[0021] An additional torque calculation module of the motor is configured to minimize the target function in the prediction domain to obtain an optimal additional torque of the four-wheel motor.

[0022] The beneficial effects of the present application include:

[0023] The target function model established in the application has a slip ratio control target and an energy consumption target, when the vehicle slips, the control amount of the slip ratio can be controlled accordingly, when the vehicle meets the stability condition, the output of the control amount can be reduced to reduce the actuation energy, improve the stability and safety of the vehicle, and the energy consumption of the vehicle is also improved;

[0024] The application is based on a vehicle stability index estimation method, which combines the stability index with the slip rate error, adjusts the parameters of the vehicle model predictive controller in real time according to the current vehicle state requirements, and further improves the adaptability of the vehicle yaw stability control. BRIEF DESCRIPTION OF DRAWINGS

[0025] Fig. 1 is a flowchart of the method technical solution of the application;

[0026] Fig. 2 is a flowchart of the drawn lateral stability region;

[0027] Fig. 3 is a schematic diagram of a nonlinear vehicle lateral dynamics model;

[0028] Fig. 4 is a drawn vehicle lateral stability region diagram;

[0029] Fig. 5 and Fig. 6 are schematic diagrams of the stability index calculation principle and calculation process, respectively;

[0030] Fig. 7 is a schematic diagram of the four-wheel slip rate curve in an adaptive weight coefficient simulation process without using the provided one;

[0031] Fig. 8 is a schematic diagram of the four-wheel slip rate curve in an adaptive weight coefficient simulation process provided in the technical solution of the application;

[0032] Fig. 9 is a schematic diagram of the lateral velocity curve in an adaptive weight coefficient simulation process without using and using the provided one in the technical solution of the application;

[0033] Fig. 10 is a schematic diagram of the yaw rate curve in an adaptive weight coefficient simulation process without using and using the provided one in the technical solution of the application;

[0034] Fig. 11 is a schematic diagram of the four-wheel additional torque curve in an adaptive weight coefficient simulation process without using the provided one in the technical solution of the application;

[0035] Fig. 12 is a schematic diagram of the four-wheel additional torque curve in an adaptive weight coefficient simulation process provided in the technical solution of the application. DETAILED DESCRIPTION

[0036] The following detailed description is used to explain the technical solution of the claims of the application, so that those skilled in the art can understand the claims. The protection scope of the application is not limited to the following specific implementation structure. The protection scope of the application also includes those skilled in the art who make technical solutions different from the following specific embodiments.

[0037] In the technical solution of the application, the subscripts i = 1, 2, 3, 4 respectively represent the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel.

[0038] Technical solution 1

[0039] A stability control method of adaptive weight coefficient MPC, the flow chart is as shown in figure 1, mainly includes the following steps:

[0040] Step 1, the kinematic model of the vehicle is established, and the optimal motor additional torque is solved by using model predictive control

[0041] Since the focus of the present application is the adaptive method of MPC weight coefficient, the optimal motor additional torque is solved for the lower controller, so the optimal additional yaw moment of the upper controller is already solved. By obtaining the real-time motion state of the vehicle, the established tire rotation dynamics model is used as the controlled object of the optimization distribution problem, the objective function of tracking the expected additional yaw moment, the expected tire slip ratio and the minimum motor additional torque is constructed, and the corresponding system constraints are established, the optimization distribution problem is solved, and the optimal motor additional torque vector distribution scheme of each tire is obtained.

[0042] Step 1.1, construct the tire rotation dynamics model

[0043] According to the model, the tire rotation dynamics equation is:

[0044] (1)

[0045] Wherein, is the angular acceleration of the wheel; is the total torque of the tire, and the calculation method is , wherein is the motor additional torque corresponding to each tire; T d is the driving / braking torque applied by the driver; F xi is the tire longitudinal force, which is calculated by the tire model shown in formula (4); R is the effective rotation radius of the tire; I ω is the moment of inertia of the tire.

[0046] Step 1.2, calculate the longitudinal velocity of the tire

[0047] The longitudinal velocity at the center of the tire is composed of the component of the vehicle mass center speed at the center of the tire and the component velocity generated by the vehicle yaw rate at the center of the tire, so the longitudinal velocity at the center of the tire is the vector sum of the component and the component velocity, and its expression is as follows:

[0048] (2)

[0049] Wherein V x 1, V x 2, V x 3, V x4 is the longitudinal velocity of the tire center of the four wheels in the tire coordinate system, d1 and d2 are the front wheel track and the rear wheel track respectively, a is the distance from the mass center to the front axle, and δ is the front wheel steering angle; V y represents the vehicle lateral velocity; and r represents the yaw angular velocity.

[0050] Step 1.3, calculating the longitudinal slip ratio

[0051] The tire slip ratio is calculated according to the following formula (3):

[0052] (3)

[0053] ω i is the angular velocity of each wheel, R is the effective rotation radius of the tire, V xi is the longitudinal velocity of each tire in the tire coordinate system.

[0054] Step 1.4, tire model

[0055] For the vehicle extreme working condition (such as the vehicle approaching instability under the extreme steering condition or the wheel appearing slip), in order to improve the model accuracy, the lateral force of the tire is described by using a nonlinear model. Since the Dugoff tire model needs fewer parameters, the tire characteristics can be well expressed without fitting the tire characteristics through a large amount of test data, and the tire force can be easily solved by programming. Therefore, the tire model in the technical solution is approximated to the following formula (4):

[0056] (4)

[0057] wherein F x is the front tire cornering force; F y is the rear tire cornering force; C x is the front tire longitudinal cornering stiffness; C y is the rear tire longitudinal cornering stiffness; μ is the current road adhesion coefficient; F z is the vertical load; σ is the longitudinal slip ratio; and α is the tire cornering angle, which can be calculated by the wheel dynamics equation shown in the following formula (5):

[0058] (5)

[0059] wherein α fl is the left front wheel cornering angle, α fr is the right front wheel cornering angle, α rl is the left rear wheel cornering angle, and α rr is the right rear wheel cornering angle.

[0060] The vertical load F z can be calculated by the following formula:

[0061] (6)

[0062] where F z 1 is the vertical load of the left front wheel, F z 2 is the vertical load of the right front wheel, F z 3 is the vertical load of the left rear wheel, F z 4 is the vertical load of the right rear wheel. In the formula, g is the gravity acceleration, h is the height of the center of mass, a x is the longitudinal acceleration, a v is the lateral acceleration.

[0063] Step 1.5, prediction of tire angular velocity

[0064] Discretize the tire rotation dynamics equation (1) to obtain the discrete tire rotation model:

[0065] (7)

[0066] T s is the discrete sampling time, T d is the driving / braking torque applied by the driver, F xi is the tire longitudinal force, R is the effective tire rotation radius; I ω is the tire rotation inertia.

[0067] In this definition, the state vector and the control vector of the discrete tire rotation model are respectively:

[0068]

[0069] where ω1~ω4 represent the angular velocities of the four wheels respectively; is the additional torque of the motor corresponding to each tire;

[0070] The state space equation of the system can then be obtained as follows:

[0071] (8)

[0072] where, , I is a four-order identity matrix, O4 is a four-order square matrix, is the tire force vector, is the driving / braking torque vector applied by the driver.

[0073] Taking the prediction time domain as n, the system state equation in the entire prediction time domain is as follows (9):

[0074] (9)

[0075] In the formula:

[0076] for the k+1~k+n moment of the four-wheel angular velocity;

[0077] for the k moment of the four-wheel angular velocity;

[0078] denotes the k~k+n-1 moment of the four-wheel motor additional torque; that is:

[0079]

[0080] Step 1.6, by the motor additional torque Calculate the additional yaw moment

[0081] (10)

[0082] wherein , d is the left and right wheelbase, and the left and right wheelbase is equal here.

[0083] Step 1.7, construct the objective function

[0084] The control target is selected as the additional yaw moment , the four-wheel corresponding motor additional torque , and the slip rate , which respectively reflect the vehicle dynamic control requirements of the steering, energy consumption, and anti-skid ability, and the objective function J is constructed:

[0085] (11)

[0086] wherein:

[0087]

[0088] refy(k+j) denotes the output reference value of the additional yaw moment and the slip rate λ in the k moment prediction domain at the jth moment; u(k+j-1) denotes the four-wheel corresponding motor additional torque in the k moment prediction domain at the jth moment; , , are the weight coefficients of the three control targets (the additional yaw moment , the slip rate , and the four-wheel corresponding motor additional torque ), reflecting the priority of each optimization target. The initial value of the weight coefficient is calibrated according to experience.

[0089] Step 1.8, construct the system constraints and model solution

[0090] In the actual control process, the constraints to be met are:

[0091] (12)

[0092] λ min , λ max are the upper and lower limit values of the tire slip ratio , to avoid incorrect solutions or no solution phenomenon in solving the optimization problem, the priority of the constraint is set to the lowest, indicating that the slip ratio is allowed to slightly exceed the constraint boundary in some extreme working conditions, therefore, the relaxation variable ∈ is added to the slip ratio constraint.

[0093] , the upper and lower limit values are set to , .

[0094] , is the upper and lower limit of the sum of the set motor additional torque , to avoid the vehicle unexpected acceleration or deceleration causing great disturbance to the driver driving, the sum of the motor additional torque is constrained, so that the system energy input is limited within a certain range.

[0095] The vehicle motor additional torque calculation problem is thus converted into a constrained objective function model, which can be converted into a quadratic programming problem and solved, and after solving the model in each MPC control period, the optimal control sequence (i.e. the optimal sequence of four-wheel motor additional torque in the prediction period ) is obtained, the first element in the optimal control sequence (i.e. the four-wheel motor additional torque at the next time) is applied to the control system, and the above operation is repeated at the next time to realize continuous control of the system.

[0096] In another technical solution, in order to optimize the weight coefficient in the objective function, so that the finally solved motor additional torque is more accurate, the following step 2 is further included:

[0097] Step 2, vehicle lateral stability index and slip ratio error calculation

[0098] Step 2.1, stability region drawing

[0099] A four-wheel vehicle lateral dynamics model is established, and the dynamics equation of the model is shown in Figure 3:

[0100] (13)

[0101] Where, r and V yrespectively denote the yaw rate and lateral velocity of the vehicle, and are the longitudinal and lateral coordinates of the designed stability region of the present application, respectively, is the derivative of the yaw rate r, is the derivative of the lateral velocity V y , m is the mass of the vehicle, and V x is the vehicle speed, is the side slip angle of the center of mass, F yfl and F yfr are the left and right lateral forces of the front wheels, respectively, F yrl and F yrr are the left and right lateral forces of the rear wheels, respectively, δ f is the front wheel steering angle, I z is the moment of inertia of the vehicle about the center of mass, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, and l s is half of the wheel track.

[0102] The tire model is a nonlinear Dugoff tire model, and the tire model is shown in equation (4) in step 1. The tire model is used to calculate the tire lateral force.

[0103] The vehicle lateral dynamics model shown in equation (13) and the tire model shown in equation (4) are locally linearized to obtain a vehicle lateral stability condition and a controllability condition, respectively, and then a stability region composed of the yaw rate r and the lateral velocity V y is drawn, and the specific method includes:

[0104] The subscript of the current working point is defined as o, equation (13) is linearized, and according to the current working point and the driver's front wheel steering angle input, the high-order terms of the Taylor expansion in the linearization process are discarded, and only the first-order term is retained to obtain a linearized model of the system:

[0105] (14)

[0106] wherein, and

[0107] , , are the lateral velocity V y , the yaw rate r, and the front wheel steering angle δ f , respectively, and the deviation variables of the working point, are the deviation variables of the working point of the lateral velocity V y and the yaw rate r, and A0 and B0 are system matrices obtained by expanding and taking partial derivatives of the wheel dynamics equation (5);

[0108] According to the stability criterion and controllability criterion of the state space equation, a stability condition shown in the following formula (15) is obtained:

[0109]

[0110] C afl 、C afr 、C arl 、C arr respectively represent the left front, right front, left rear and right rear tire cornering stiffness; the tire cornering stiffness here is obtained through the tire model (4), and describes real-time friction information, i.e., when the stability condition shown in the formula (14) is met, the system stability is met; V x is the vehicle longitudinal speed; l s is half of the wheel track; r0 represents the corresponding yaw rate of the current operating point; and L represents the wheelbase.

[0111] The controllability condition is:

[0112] (16)

[0113] According to the above steps, the stability discrimination condition shown in the formula (15) and the controllability discrimination condition shown in the formula (16) are obtained, the combination of the lateral speed V y and the yaw rate r of the vehicle that can occur is judged according to the stability condition shown in the formula (15) and the controllability condition shown in the formula (16), and the point set that meets the formula (15) and the formula (16) is taken out, and the stable region is drawn in the coordinate system with the lateral speed V y and the yaw rate r as the horizontal and vertical coordinates, and the stable boundary is formed by fitting the region boundary. The flow chart of drawing the lateral stability region is shown in FIG. 2, and the vehicle lateral stability region diagram is shown in FIG. 4.

[0114] Step 2.2, calculating the vehicle stability index

[0115] After obtaining the stable boundary, the stability of the vehicle needs to be judged in real time according to the state parameters of the vehicle at the current time: the vehicle longitudinal speed V x , the front wheel steering angle δ f , the current road adhesion coefficient μ and the corresponding stable boundary in the current state, and a quantitative index is given. In this definition, the stability index of the vehicle is K, and the calculation principle is as follows:

[0116] The point P v is defined as the state point corresponding to the current vehicle state, and according to the current yaw rate r of the vehicle, a straight line parallel to the horizontal axis is drawn through the state point P v corresponding to the current vehicle, and the left and right intersection points of the straight line and the stable boundary are P l and P rMeanwhile, the midpoint of the two intersection points is defined as P c =1 / 2(P l +P r ), and V y (P v ) represents the corresponding lateral velocity of point P v . At this time, the current state point P v of the vehicle corresponds to two cases, i.e., being in the stable region and being out of the stable region. The stability index K is calculated by the ratio of the distance (difference of the horizontal coordinates) between the current state point P v and the point P c in the X-axis direction and the distance between the midpoint and the boundary of the stable region in the X-axis direction, and the calculation method is as follows:

[0117] When the current state point P v of the vehicle is in the stable region (which can be determined by the stability criterion):

[0118] (17)

[0119] When the current state point P v of the vehicle is out of the stable region:

[0120] (18)

[0121] In addition, a gradient influence factor is designed. According to the size of the longitudinal coordinate yaw rate r of the stable region, the stability index K is accurately calculated near the extreme point of the yaw rate. As shown in FIG. 6, the gradient factor is designed as 0.5. When the yaw rate r of the actual state point P v of the vehicle is within the limits of the dashed lines I1 and L1, the normal stability index K is output. When the two limits are exceeded, the new stability index K is the normal calculated stability index multiplied by the gradient factor 0.5.

[0122] The dashed line I1 in the technical solution is a straight line passing through the midpoint of the boundary line ab (a and b are the points with the maximum horizontal coordinate and the maximum longitudinal coordinate on the boundary line of the stable region) shown in FIG. 5 and parallel to the horizontal axis, and L1 is a straight line passing through the midpoint of the boundary line cd (c and d are the points with the minimum horizontal coordinate and the minimum longitudinal coordinate on the boundary line of the stable region) and parallel to the horizontal axis, but is not limited thereto and can be set by the user. In principle, the more the current state point of the vehicle approaches the vertex b (the point with the maximum longitudinal coordinate in the stable region) or the vertex d (the point with the minimum longitudinal coordinate in the stable region), the less accurate the stability index value is, and the actual value is larger. Therefore, the gradient factor is designed to make the state point closer to the two vertices smaller, so that the stability index is more accurate.

[0123] In this case, only the first level gradient factor is preliminarily designed, and the limit and gradient factor can be further set accurately to the stability index K. In the technical solution, the range of the stability index K is [-2, 1], wherein (0, 1] indicates that the vehicle is in a stable region, the vehicle is in a stable state, [-2, 0] is in a non-stable region, indicates that the vehicle is unstable, and the stability index K less than -2 indicates that the vehicle is completely unstable. The calculation principle diagram and flowchart are shown in FIG. 5 and FIG. 6, respectively.

[0124] It should be noted that the division of the stable region shown in FIG. 4 is not fixed, but changes with the real-time changes of the driver's intention and road information. Among them, it will change with the changes of the vehicle speed, steering wheel angle and road adhesion coefficient.

[0125] Step 2.3, slip ratio error calculation

[0126] The calculated motor additional torque corresponding to the four wheels After being input into the vehicle model, the actual slip ratio of the four wheels is output, the actual slip ratio of the four wheels is subtracted from the ideal slip ratio of the four wheels, and the average value of the four differences is taken as one of the fuzzy control inputs. In this case, the ideal slip ratio of the four wheels is 0, so the slip ratio error S can be converted into the following formula (19):

[0127] (19)

[0128] ω is the wheel angular velocity, r is the effective tire rotation radius, and v is the tire longitudinal speed in the tire coordinate system

[0129] Step 3, fuzzy controller design and weight coefficient correction

[0130] Step 3.1, analysis of the influence of vehicle stability index K and slip ratio error S on the control system

[0131] In the control system, a larger weight indicates that the corresponding performance index is more important, and a smaller weight indicates that the corresponding performance index is relatively less important. According to the current state of the vehicle, the target to be controlled is determined, and by adjusting the weight coefficient, the priority of the system between different performance indexes can be flexibly changed, so that the system can better balance these different targets to achieve multi-objective optimization.

[0132] In the previous part, the stability boundary is estimated, and the vehicle lateral speed V yThe control region of the yaw rate r is divided according to the stability index K, and different regions correspond to different stable states of the vehicle. The divided region can be applied in the control system as a condition for switching the dynamic safety requirements. According to the stability index K, the current stable state of the vehicle can be determined, and accordingly, the safety requirements are changed, and the control target can also be adjusted according to the stability index K. When the stability index K ranges from 0 to 1, the expected main improvement is the handling performance of the vehicle, while considering the longitudinal stability and energy consumption. When the stability index K ranges from -2 to 0, the safety requirements of the lateral stability should be given priority. The slip error S of the actual and ideal slip rates of the vehicle tires directly reflects the control accuracy of the slip rate. By considering both, the corresponding control target weight coefficient in equation (11) can be controlled, and the system can be controlled.

[0133] Step 3.2, weight coefficient adaptive fuzzy control

[0134] According to the running condition of the vehicle, the weight coefficients of each optimization target in the target function equation (11) are corrected by using a fuzzy controller. In the specific optimization process, the weight coefficients of each optimization target in the target function at the current time are corrected according to the slip rate deviation E and the stability index K of the vehicle at the current time, and the two are taken as the inputs of the fuzzy controller. To adapt to the needs of complex working conditions, the output parameters of the fuzzy controller are not directly used as the weight coefficients of the MPC controller, but are used as the correction amount of the weight coefficients of the MPC controller online. The output is three adjustment factors of the tracking target weight coefficients, the weight coefficient of the expected additional yaw moment tracking is , the adjustment factor is , the weight coefficient of the expected slip rate tracking is , the adjustment factor is , and the weight coefficient of the system actuation energy suppression tracking (i.e. the additional torque of the motor, the smaller the additional torque, the more the actuation energy is suppressed) is , and the adjustment factor is .

[0135] When adjusting the weight coefficients, first, determine the value range of the input and output variables and fuzzify them. For the two input variables of the fuzzy controller, the domain of S is [-0.4, 0.4], and the domain of K is [-2, 1]. The fuzzy subsets are selected as 5, {NB, NS, Z, PS, PB}. The adjustment factors , and are selected according to the value size, and the five fuzzy subsets are {NB, NS, Z, PS, PB}. The Gaussian membership function is selected, and the fuzzy rules of the three adjustment factors are shown in Tables 1-3:

[0136] Table 1 Rule Table

[0137]

[0138] Table 2 Rule table

[0139]

[0140] Table 3 Rule table

[0141]

[0142] Step 3.3, correction of weight coefficients

[0143] To adapt to the needs of complex working conditions, the output parameters of the fuzzy controller are not directly used as the weight coefficients of the MPC controller, but are adjusted online as the correction of the weight coefficients of the MPC controller. According to the three adjustment factors output by the fuzzy rules, the correction formula of the three weight coefficients is:

[0144] (20)

[0145] In the formula, w1, w2 and w3 are the weight coefficients of the controller of the MPC algorithm before correction, respectively. , , are the weight coefficients of the controller of the MPC algorithm after correction, respectively, corresponding to the weight coefficients of the additional yaw moment , , in formula (11) in turn, respectively. .

[0146] Therefore, when the real-time state of the vehicle changes, the weight coefficients of the target function in the MPC controller can be automatically adjusted according to the changes of the stability index K and the slip rate error S through fuzzy control, so that the vehicle has good stability.

[0147] ​​A CarSim / Simulink joint simulation model is built, a low adhesion double lane shift condition with μ being 0.35 is set, the vehicle speed is 40 km / h, and in the joint simulation process, the MPC torque vector controller based on the adaptive weight coefficient is compared with the MPC torque vector controller based on the constant weight coefficient, FIG. 7 and FIG. 8 are comparison diagrams of four-wheel slip rate curves under the condition of non-adaptive weight coefficient and adaptive weight coefficient in the simulation condition, FIG. 9 is a comparison diagram of lateral velocity curves under the condition of non-adaptive weight coefficient and adaptive weight coefficient in the simulation condition, FIG. 10 is a comparison diagram of yaw rate curves under the condition of non-adaptive weight coefficient and adaptive weight coefficient in the simulation condition, FIG. 11 and FIG. 12 are comparison diagrams of four-wheel additional torque curves under the condition of non-adaptive weight coefficient and adaptive weight coefficient in the simulation condition. By comparing the simulation condition curves with and without the adaptive algorithm, it can be seen that the indicators are optimized, and the driving stability of the vehicle is improved.

[0148] Technical solution 2

[0149] An adaptive weight coefficient MPC stability control method, comprising the following steps:

[0150] The MPC algorithm is used to calculate the wheel angular velocity of the vehicle in the prediction domain;

[0151] The slip rate in the prediction domain is calculated according to the wheel angular velocity of the vehicle in the prediction domain;

[0152] The target function is constructed according to the slip rate, additional yaw moment and four-wheel motor additional torque;

[0153] The target function in the prediction domain is minimized to obtain the optimal four-wheel motor additional torque.

[0154] The optimal four-wheel motor additional torque is sent to the controlled vehicle, thereby completing the stability control of the vehicle.

[0155] In some technical solutions, the wheel angular velocity of the vehicle in the prediction domain is calculated according to the following formula:

[0156]

[0157] In the formula:

[0158] is the predicted wheel angular velocity at k+1~k+n time;

[0159] is the wheel angular velocity at k time;

[0160] represents the four-wheel motor additional torque at k~k+n-1 time;

[0161]

[0162] A = I; B = [I 4 O 4]; I is a four-order unit matrix, and O 4 is a four-order square matrix; ; is a driving / braking torque vector applied to the driver; is a tire force vector; n represents a prediction horizon;

[0163] In some embodiments, a target function in a prediction horizon is calculated according to the following formula:

[0164]

[0165] In the formula:

[0166]

[0167] refy(k+j) represents an additional yaw moment at the jth moment in the prediction horizon at the kth moment and the output reference value of the slip rate λ; u(k+j-1) represents the additional motor torque corresponding to the four wheels at the jth moment in the prediction horizon at the kth moment; , , are weight coefficients of the sum of the additional yaw moment , the slip rate , and the additional motor torque corresponding to the four wheels, respectively; subscript i represents the tire number.

[0168] In some embodiments, the calculation method of the vehicle slip rate comprises:

[0169] ;

[0170] In some embodiments, the calculation method of the vehicle additional yaw moment comprises:

[0171]

[0172] ω i is the wheel angular velocity, R is the effective rotation radius of the tire, V xi is the longitudinal speed of each tire in the tire coordinate system; is the additional motor torque; subscript i represents the tire number; , and d is the left and right wheel track.

[0173] In some embodiments, the target function is further constrained, and the constraint conditions comprise:

[0174]

[0175] λ min , λmax Tire slip ratio The upper and lower limits, ∈ are slack variables;

[0176] u represents the additional torque of the four-wheel motor. , These are the upper and lower limits of the motor's additional torque, respectively.

[0177] , The sum of the additional torque of the four-wheel motors The upper and lower limits.

[0178] In some technical solutions, the weight coefficients of the objective function are further optimized, and the optimization methods include:

[0179] The vehicle's stability index at the current moment is calculated based on its yaw rate and lateral velocity.

[0180] The vehicle's stability index and slip ratio error at the current moment are selected as inputs to the fuzzy inference system; the fuzzy inference system is used to correct the weight coefficients in the objective function; the weight coefficients include the weight coefficients for expected additional yaw moment tracking, the weight coefficients for expected slip ratio tracking, and the weight coefficients for system kinetic energy suppression tracking.

[0181] In some technical solutions, the methods for calculating the vehicle's stability index at the current moment include:

[0182] The lateral stability and controllability conditions of the vehicle are obtained based on the vehicle lateral dynamics model and the vehicle tire model, respectively.

[0183] In a two-dimensional coordinate system with the vehicle's lateral velocity as the abscissa and the vehicle's yaw rate as the ordinate, the stable region that satisfies the vehicle's lateral stability and controllability conditions is plotted.

[0184] The vehicle's stability index at the current moment is calculated based on the positional relationship between the state point corresponding to the vehicle's current lateral velocity and yaw rate in the two-dimensional coordinate system and the stable region.

[0185] In some technical solutions, the lateral stability conditions for vehicles include:

[0186]

[0187] In some technical solutions, the controllability condition is:

[0188]

[0189] In the formula:

[0190] C afl Cafr , C arl , C arr respectively represent the left front, right front, left rear, right rear tire cornering stiffness; V x is the vehicle longitudinal speed; l s is half of the wheel track; r0 represents the yaw rate of the working point; a and b respectively represent the distance from the mass center to the front axle, the distance from the mass center to the rear axle; m represents the vehicle mass; L represents the wheelbase.

[0191] In some technical solutions, the controllability condition is used to limit the cornering stiffness of the front wheels.

[0192] In some technical solutions, the controllability condition includes that the sum of the cornering stiffness of the front wheels is not equal to 0.

[0193] In some technical solutions, the calculation method of the stability index includes:

[0194] The current state point of the vehicle is set as P v in the two-dimensional coordinate system; the abscissa of P v is the current lateral speed of the vehicle, and the ordinate is the current yaw rate of the vehicle.

[0195] A straight line parallel to the horizontal axis is drawn through the state point P v in the two-dimensional coordinate system, and the left and right intersection points of the straight line and the boundary of the stable region are P l and P r , respectively; the midpoint of the two intersection points P l and P r is P c .

[0196] When the current state point P v of the vehicle is in the stable region:

[0197] If the corresponding lateral speed of the state point P v is greater than the corresponding lateral speed of P c :

[0198]

[0199] Otherwise:

[0200]

[0201] When the current state point P v of the vehicle is outside the stable region:

[0202] If the corresponding lateral speed of the state point P v is greater than the corresponding lateral speed of P r :

[0203] then​

[0204] Otherwise:

[0205]

[0206] wherein: V y (P v ), V y (P c ), V y (P l ), V y (P r ) respectively represent the corresponding lateral velocities of points P v , P c , P l and P r ; K represents a stability index;

[0207] In some embodiments, the method for calculating the stability index further comprises: when the current yaw rate of the vehicle is outside the set range, the weighted value of the stability index is the final stability index.

[0208] The method for determining the set range comprises:

[0209] The midpoint of the boundary line ab of the over-stable region is parallel to the horizontal axis, and the horizontal coordinate of the line is the upper limit of the set range;

[0210] The midpoint of the boundary line cd of the over-stable region is parallel to the horizontal axis, and the horizontal coordinate of the line is the lower limit of the set range;

[0211] wherein a and b are respectively the point with the maximum horizontal coordinate and the point with the maximum vertical coordinate on the boundary line of the stable region, and c and d are respectively the point with the minimum horizontal coordinate and the point with the minimum vertical coordinate on the boundary line of the stable region.

[0212] The current stability state of the vehicle can be determined according to the calculated stability index. In the present embodiment, the range of the stability index K is [-2, 1], wherein (0, 1] indicates that the vehicle is in the stable region and is in a stable state; [-2, 0] is in the non-stable region, indicating that the vehicle is unstable, and the stability index K is less than -2, indicating that the vehicle is completely unstable.

[0213] In some embodiments, the method for correcting the weight coefficient in the target function comprises:

[0214] The slip ratio error of the vehicle is calculated according to the wheel angular velocity, the wheel longitudinal velocity and the effective turning radius of the wheel;

[0215] The stability index and the slip ratio error of the vehicle at the current time are taken as input variables of the fuzzy controller, and adjustment factors of multiple weight coefficients are taken as output variables of the fuzzy controller;

[0216] The value range of the input and output variables is determined and is fuzzified;

[0217] Each weight coefficient is corrected according to the multiple adjustment factors output by the fuzzy controller.

[0218] In some technical solutions, the correction method comprises:

[0219]

[0220] wherein, , , are weight coefficients of the controller of the MPC algorithm before correction, respectively; , , are weight coefficients of the controller of the MPC algorithm after correction, corresponding to the additional yaw moment , the slip ratio , the sum of the additional torques of the motors corresponding to the four wheels in sequence, respectively.

[0221] Technical solution 3

[0222] An adaptive weight coefficient MPC stability control system comprises:

[0223] A wheel angular velocity calculation module is configured to calculate wheel angular velocities of a vehicle in a prediction domain by using an MPC algorithm;

[0224] A slip ratio calculation module is configured to calculate a slip ratio in the prediction domain according to the wheel angular velocities of the vehicle in the prediction domain;

[0225] A target function construction module is configured to construct a target function according to the slip ratio, the additional yaw moment and the additional torques of the motors of the four wheels;

[0226] A motor additional torque calculation module is configured to minimize the target function in the prediction domain to obtain optimal additional torques of the motors of the four wheels.

[0227] In some technical solutions, the wheel angular velocity calculation module comprises:

[0228] The wheel angular velocities of the vehicle in the prediction domain are calculated according to the following formula:

[0229]

[0230] wherein,

[0231] for the predicted wheel angular velocity at k+1~k+n time;

[0232] for the wheel angular velocity at k time;

[0233] denotes the four-wheel motor additional torque at k~k+n-1 time;

[0234]

[0235] A=I;B=[I 4 O4];I is a four-order unit matrix, and O4 is a four-order square matrix; ; is a driver-imposed driving / braking torque vector; is a tire force vector; n denotes a prediction time domain.

[0236] In some technical solutions, the target function in the prediction domain is calculated according to the following formula:

[0237]

[0238] In the formula:

[0239]

[0240] refy(k+j) denotes the additional yaw moment at the jth time in the prediction domain at k time and the output reference value of the slip rate λ; u(k+j-1) denotes the four-wheel corresponding motor additional torque at the jth time in the prediction domain at k time; , , are the weights of the sum of the additional yaw moment , the slip rate , and the four-wheel corresponding motor additional torque, respectively; subscript i denotes the tire number.

[0241] In some technical solutions, the calculation method of the stability index further includes:

[0242] When the current yaw rate of the vehicle is outside the set range, the stability index multiplied by the set gradient factor is the final stability index; the set method of the set range includes:

[0243] A straight line passing through the midpoint of the boundary line ab of the stable region and parallel to the horizontal axis, the horizontal coordinate of which is the upper limit of the set range; a straight line passing through the midpoint of the boundary line cd of the stable region and parallel to the horizontal axis, the horizontal coordinate of which is the lower limit of the set range; where a and b are the points with the largest horizontal coordinate and the largest vertical coordinate on the boundary line of the stable region, respectively, and c and d are the points with the smallest horizontal coordinate and the smallest vertical coordinate on the boundary line of the stable region, respectively.

[0244] In some technical solutions, methods for correcting the weight coefficients in the objective function include:

[0245] The slip ratio error of the vehicle is calculated based on the wheel angular velocity, wheel longitudinal velocity, and wheel effective rotation radius.

[0246] The vehicle's current stability index and slip ratio error are used as input variables for the fuzzy controller, and the adjustment factors of multiple weight coefficients are used as output variables for the fuzzy controller.

[0247] Determine the range of values ​​for the input and output variables, and then fuzzify them;

[0248] Each weight coefficient is adjusted based on multiple adjustment factors output by the fuzzy controller; the adjustment methods include:

[0249]

[0250] In the formula, , , These are the weight coefficients of the controller in the modified MPC algorithm; , , Each corresponds to an additional yaw moment in turn. slip ratio The sum of the additional torque of the motors corresponding to the four wheels The weighting coefficients.

[0251] It should be understood that the sequence number of each step in the above technical solution does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the technical solution of this application.

[0252] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A stability control method for adaptive weight coefficient MPC, comprising: The MPC algorithm is used to calculate the wheel angular velocity of the vehicle in the prediction domain; The slip ratio in the prediction domain is calculated based on the wheel angular velocity of the vehicle in the prediction domain. The objective function is constructed based on the slip ratio, the additional yaw moment, and the additional torque of the four-wheel motors. Minimize the objective function within the prediction domain to obtain the optimal additional torque of the four-wheel motor.

2. The stability control method for adaptive weight coefficient MPC as described in claim 1 further includes optimizing the weight coefficients of the objective function, wherein the optimization method includes: The vehicle's stability index at the current moment is calculated based on its yaw rate and lateral velocity. The vehicle's stability index and slip ratio error at the current moment are selected as the inputs to the fuzzy inference system; The weight coefficients in the objective function are corrected using a fuzzy inference system.

3. The stability control method for adaptive weight coefficient MPC as described in claim 2, wherein, The methods for calculating the vehicle's stability index at the current moment include: The lateral stability and controllability conditions of the vehicle are obtained based on the vehicle lateral dynamics model and the vehicle tire model, respectively. In a two-dimensional coordinate system with the vehicle's lateral velocity as the abscissa and the vehicle's yaw rate as the ordinate, the stable region that satisfies the vehicle's lateral stability and controllability conditions is plotted. The vehicle's stability index at the current moment is calculated based on the positional relationship between the state point corresponding to the vehicle's current lateral velocity and yaw rate in the two-dimensional coordinate system and the stable region.

4. The stability control method for adaptive weight coefficient MPC as described in claim 3, wherein, The methods for calculating stability indices include: In the two-dimensional coordinate system, the current state point of the vehicle is set as P. v ; Passing through state point P v Draw a straight line parallel to the horizontal axis in a two-dimensional coordinate system. The left and right intersection points of this line with the boundary of the stable region are P and P, respectively. l and P r The two intersection points P l and P r The midpoint is P. c ; When the vehicle's current state point P v When within a stable region: If state point P v The corresponding lateral velocity is greater than P. c The corresponding lateral velocity is then ； otherwise: ； When the vehicle's current state point P v When outside the stable region: If state point P v The corresponding lateral velocity is greater than P. r The corresponding lateral velocity is then ； otherwise: ； Where: V y (P v V y (P c V y (P l V y (P r ) represent points P respectively v P c P l and P r The corresponding lateral velocity; K represents the stability index.

5. The stability control method for adaptive weight coefficient MPC as described in claim 3 or 4, wherein, The methods for calculating stability indices also include: When the vehicle's current yaw rate is outside the set range, the value obtained by weighting the stability index is the final stability index.

6. The stability control method for adaptive weight coefficient MPC as described in claim 2, wherein, The methods for correcting the weight coefficients in the objective function include: The slip ratio error of the vehicle is calculated based on the wheel angular velocity, wheel longitudinal velocity, and wheel effective rotation radius. The vehicle's current stability index and slip ratio error are used as input variables for the fuzzy controller, and the adjustment factors of multiple weight coefficients are used as output variables for the fuzzy controller. Determine the range of values ​​for the input and output variables, and then fuzzify them; Each weight coefficient is corrected based on multiple adjustment factors output by the fuzzy controller.

7. The stability control method for adaptive weight coefficient MPC as described in claim 1, wherein, The methods for calculating the wheel angular velocity of a vehicle within the prediction domain include: The wheel angular velocity of the vehicle within the prediction domain is calculated using the following formula: ， In the formula: The predicted wheel angular velocity at times k+1 to k+n; Let be the wheel angular velocity at time k; This represents the additional torque of the four-wheel motor at times k~k+n-1; ， A=I; B=[I4 O4]; I is a fourth-order identity matrix, and O4 is a fourth-order square matrix; ； The vector of driving / braking torque applied to the driver; is the tire force vector; n represents the prediction time domain.

8. The stability control method for adaptive weight coefficient MPC as described in claim 1, wherein, It also includes calculating the objective function within the prediction domain according to the following formula: ， In the formula: ， refy(k+j) represents the additional yaw moment at time j within the prediction domain at time k. The output reference value of slip ratio λ; u(k+j-1) represents the additional motor torque corresponding to the four wheels at the j-th time in the prediction domain at time k; 、 、 These are the additional yaw moments. slip ratio The weighting coefficient of the sum of the additional torque of the motors corresponding to the four wheels; the subscript i indicates the tire number.

9. A stability control system for adaptive weight coefficient MPC, comprising: Wheel angular velocity calculation module: used to calculate the wheel angular velocity of the vehicle in the prediction domain using the MPC algorithm; Slip ratio calculation module: used to calculate the slip ratio in the prediction domain based on the wheel angular velocity of the vehicle in the prediction domain; Objective function construction module: used to construct the objective function based on slip ratio, additional yaw moment, and additional torque of the four-wheel motor; Motor additional torque calculation module: used to minimize the objective function within the prediction domain to obtain the optimal four-wheel motor additional torque.

10. The stability control system for adaptive weight coefficient MPC as described in claim 9 further includes a model optimization module for optimizing the weight coefficients of the objective function, wherein the optimization method includes: The vehicle's stability index at the current moment is calculated based on its yaw rate and lateral velocity. The vehicle's stability index and slip ratio error at the current moment are selected as the inputs to the fuzzy inference system; The weight coefficients in the objective function are corrected using a fuzzy inference system.

11. The stability control system of adaptive weight coefficient MPC as described in claim 9, wherein, The methods for calculating the vehicle's stability index at the current moment include: The lateral stability and controllability conditions of the vehicle are obtained based on the vehicle lateral dynamics model and the vehicle tire model, respectively. In a two-dimensional coordinate system with the vehicle's lateral velocity as the abscissa and the vehicle's yaw rate as the ordinate, the stable region that satisfies the vehicle's lateral stability and controllability conditions is plotted. The vehicle's stability index at the current moment is calculated based on the positional relationship between the state point corresponding to the vehicle's current lateral velocity and yaw rate in the two-dimensional coordinate system and the stable region.

12. The stability control system of adaptive weight coefficient MPC as described in any one of claims 9 to 10, wherein, The methods for calculating stability indices include: In the two-dimensional coordinate system, the current state point of the vehicle is set as P. v ; Passing through state point P v Draw a straight line parallel to the horizontal axis in a two-dimensional coordinate system. The left and right intersection points of this line with the boundary of the stable region are P and P, respectively. l and P r The two intersection points P l and P r The midpoint is P. c ; When the vehicle's current state point P v When within a stable region: If state point P v The corresponding lateral velocity is greater than P. c The corresponding lateral velocity is then ； otherwise: ； When the vehicle's current state point P v When outside the stable region: If state point P v The corresponding lateral velocity is greater than P. r The corresponding lateral velocity is then ； otherwise: ； Where: V y (P v V y (P c V y (P l V y (P r ) represent points P respectively v P c P l and P r The corresponding lateral velocity; K represents the stability index.

13. The stability control system for adaptive weight coefficient MPC as described in claim 11 or 12, wherein, The methods for calculating stability indices also include: When the vehicle's current yaw rate is outside the set range, the stability index is multiplied by the set gradient factor to obtain the final stability index.

14. The stability control system of adaptive weight coefficient MPC as described in claim 9, wherein, The methods for correcting the weight coefficients in the objective function include: The slip ratio error of the vehicle is calculated based on the wheel angular velocity, wheel longitudinal velocity, and wheel effective rotation radius. The vehicle's current stability index and slip ratio error are used as input variables for the fuzzy controller, and the adjustment factors of multiple weight coefficients are used as output variables for the fuzzy controller. Determine the range of values ​​for the input and output variables, and then fuzzify them; Each weight coefficient is corrected based on multiple adjustment factors output by the fuzzy controller.

15. The stability control system of adaptive weight coefficient MPC as described in claim 9, wherein, The wheel angular velocity calculation module includes: The angular velocity of the vehicle within the prediction domain is calculated using the following formula: ， In the formula: The predicted wheel angular velocity at times k+1 to k+n; Let be the wheel angular velocity at time k; This represents the additional torque of the four-wheel motor at times k~k+n-1; ， A=I; B=[I4 O4]; I is a fourth-order identity matrix, and O4 is a fourth-order square matrix; ； The vector of driving / braking torque applied to the driver; is the tire force vector; n represents the prediction time domain.

16. The stability control system for adaptive weight coefficient MPC as described in claim 9, wherein, This includes calculating the objective function within the prediction domain according to the following formula: ， In the formula: ， refy(k+j) represents the additional yaw moment at time j within the prediction domain at time k. The output reference value of slip ratio λ; u(k+j-1) represents the additional motor torque corresponding to the four wheels at the j-th time in the prediction domain at time k; 、 、 These are the additional yaw moments. slip ratio The weighting coefficient of the sum of the additional torque of the motors corresponding to the four wheels; the subscript i indicates the tire number.

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