An adaptive control method for vehicle yaw stability considering dynamic multi-objective requirements

Abstract

The present invention belongs to the field of automobile technology, and specifically is a vehicle yaw stability adaptive control method considering dynamic multi-objective requirements. It includes the following steps: Step 1, using piecewise linear fitting technology to establish a vehicle dynamics model considering tire nonlinear characteristics, and characterizing the dynamic characteristics of the vehicle under different driving conditions; Step 2, using the improved two-line method to obtain the dynamic stability domain boundary in the phase plane, and obtain the stability domain, critical stability domain and unstable domain corresponding to the vehicle driving state; Step 3, using model predictive control theory to design a stability control strategy, introducing a dynamic multi-objective mapping function to quantitatively characterize the vehicle driving state, and dynamically adjusting the built-in parameters of the stability control strategy to match the multi-objective requirements of the vehicle for lateral stability, yaw maneuverability and actuator energy consumption under different driving conditions. The present invention quantitatively evaluates the stability degree of the vehicle under different driving conditions, effectively improves the comprehensive performance of the stability controller in terms of vehicle lateral stability, yaw maneuverability and actuator energy consumption, and effectively provides a set of solutions for the optimal integrated control of chassis dynamics of future intelligent vehicles.

Description

Technical Field

[0001] The invention belongs to the field of automobile technology, and in particular is a vehicle yaw stability adaptive control method considering dynamic multi-objective requirements. Background Art

[0002] A car is considered to be a highly nonlinear system. Especially when it is in high-speed turning or continuous lane changing on a medium or low adhesion road, it is prone to problems such as decreased yaw control and insufficient lateral stability. This makes ordinary drivers who lack a deep understanding of the characteristics of the car likely to take wrong control under extreme tension, resulting in further deterioration of the vehicle's yaw stability, and even causing serious traffic accidents such as vehicle tail-spinning and rollover. Therefore, vehicle stability control is particularly important for safe driving of cars.

[0003] In order to achieve vehicle stability control, it is first necessary to evaluate the vehicle's driving state in real time. Existing studies use theories such as the two-line method and simulated annealing intelligent optimization algorithm to extract the vehicle's stability domain boundary conditions in phase planes such as the center of mass slip angle-center of mass slip angular velocity, center of mass slip angle-yaw angular velocity, and front wheel-rear wheel slip angle to characterize the vehicle's driving state. However, in order to simplify the complexity of stability system design, most of these studies ignore the influence of factors such as road adhesion coefficient and vehicle driving speed on the vehicle's stability domain boundary conditions. They can only simply identify the vehicle's stable and unstable states, lack the ability to quantitatively characterize the vehicle's driving state, and are difficult to reasonably guide the design of stability control strategies.

[0004] Some advanced control methods such as sliding mode control, robust H∞ control, model predictive control, etc. have been applied in vehicle stability control. Compared with sliding mode control with system chattering and overly conservative robust control, MPC method has significant advantages in dealing with multi-objective and multi-constraint problems of complex systems. Although the existing MPC method has improved vehicle stability to a certain extent, most studies still consider fixed control requirements in the entire multi-objective and multi-constraint optimization process, rather than matching the vehicle's dynamic changing requirements for yaw maneuverability, lateral stability, actuator energy consumption, etc. in real time according to different driving states of the vehicle, which limits the potential of stability control strategies to a certain extent. Summary of the invention

[0005] To solve the above problems, the present invention provides a vehicle yaw stability adaptive control method taking into account dynamic multi-objective requirements, quantitatively evaluates the stability degree of the vehicle under different driving conditions, effectively improves the comprehensive performance of the stability controller in terms of vehicle lateral stability, yaw maneuverability and actuator energy consumption, and effectively provides a solution for the optimal integrated control of chassis dynamics of future smart cars.

[0006] The technical solution of the present invention is described as follows in conjunction with the accompanying drawings:

[0007] A vehicle yaw stability adaptive control method considering dynamic multi-objective requirements comprises the following steps:

[0008] Step 1: Use piecewise linear fitting technology to establish a vehicle dynamics model that takes into account the nonlinear characteristics of the tire to characterize the dynamic characteristics of the vehicle under different driving conditions;

[0009] Step 2: Using the improved two-line method to obtain the dynamic stability domain boundary in the phase plane, obtain the stability domain, critical stability domain and unstable domain corresponding to the vehicle driving state;

[0010] Step 3: Apply model predictive control theory to design a stability control strategy, introduce a dynamic multi-objective mapping function to quantitatively characterize the vehicle's driving state, and dynamically adjust the built-in parameters of the stability control strategy to match the vehicle's multi-objective requirements for lateral stability, yaw maneuverability, and actuator energy consumption under different driving conditions.

[0011] Furthermore, the specific method of step one is as follows:

[0012] 11) Assuming that the left and right sides of the vehicle are dynamically symmetrical, a two-degree-of-freedom vehicle dynamics model is established, which can be expressed as follows based on the small-angle approximation method:

[0013]

[0014] In the formula, m represents the vehicle mass; v x Indicates the longitudinal speed of the vehicle; represents the lateral acceleration of the vehicle; γ represents the yaw rate of the vehicle; Indicates the vehicle's yaw angular acceleration; F yf Indicates the total lateral force of the front axle tire; F yr Represents the total lateral force of the rear axle tire; I z represents the vehicle's yaw moment of inertia; l f Indicates the distance from the vehicle's center of mass to the front axle; l r Indicates the distance from the vehicle's center of mass to the rear axle; ΔM z represents the additional yaw moment;

[0015] 12) The tire lateral force is modeled using piecewise linear fitting technology, that is,

[0016]

[0017] In the formula, i represents the front axle or rear axle; f represents the front axle; r represents the rear axle; F yi Indicates the total lateral force of the tire; Indicates the variable cornering stiffness of the tire; C iIndicates the linear cornering stiffness of the tire; D i represents the first dimension coefficient of linear fitting; e i Represents the second dimension coefficient of linear fitting; α i Indicates tire slip angle; p i is the tire slip angle cutoff value;

[0018] 13) The tire slip angles of the front and rear wheels are expressed as:

[0019]

[0020] In the formula, α f Represents the front tire side slip angle; α r Indicates the side slip angle of the rear tire; v x represents the longitudinal speed of the vehicle; β represents the sideslip angle of the center of mass of the vehicle; δ represents the front wheel turning angle;

[0021] 14) Select the vehicle's center of mass sideslip angle and yaw rate as the system state quantity x = [β γ] T , the additional yaw moment is the system control input u=ΔM z , the simplified state space equation of the vehicle stability control system is

[0022]

[0023] In the formula, x represents the system state quantity; represents the system state quantity derivative; A represents the system state matrix; B represents the system control matrix; E represents the system disturbance matrix; u represents the system control input; δ f Indicates the front wheel turning angle; Indicates the variable cornering stiffness of the front axle tire; Represents the vehicle's yaw moment of inertia.

[0024] Furthermore, the specific method of step 2 is as follows:

[0025] 21) The vehicle's center of mass slip angle β and yaw rate γ, which characterize the vehicle's lateral stability and yaw maneuverability, are used as stability control targets; the desired center of mass slip angle β is set r is 0; the desired yaw rate is determined by the steering wheel angle δ d etc. for identification,

[0026]

[0027] In the formula, γ d represents the expected yaw rate before correction; C f Represents the linear cornering stiffness of the front axle tire; C r Represents the linear cornering stiffness of the rear axle tire; δ d Indicates the steering wheel angle;

[0028] Vehicle stability control needs to take into account the road adhesion constraint, so the expected yaw rate is corrected to

[0029]

[0030] In the formula, γ r represents the desired yaw rate; μ represents the road adhesion coefficient; g represents the gravitational acceleration; v x Indicates the longitudinal speed of the vehicle;

[0031] 22) Nonlinear vehicle dynamics model with given tire lateral force and different vehicle longitudinal speed v x , the initial values ​​of the sideslip angle β and the yaw rate γ, and an open-loop simulation is performed to obtain Phase plane diagram; The band area in the phase plane that converges at the saddle point represents the vehicle stability domain. The vehicle is prone to instability when driving outside the stability domain. The phase plane diagram is divided into stable and unstable regions, where the unstable region is represented as:

[0032]

[0033] Wherein, B1 represents the first parameter of the stability domain boundary; B2 represents the second parameter of the stability domain boundary; β represents the side slip angle of the center of mass of the vehicle; It represents the sideslip angular velocity of the vehicle's center of mass;

[0034] The original stable region boundary is shrunk inward by the characteristic coefficient q. New stable, critically stable and unstable region boundaries are obtained in the phase plane; the inner green area of ​​the inner boundary line is the stable area, expressed as:

[0035]

[0036] In the formula, q represents the characteristic coefficient;

[0037] The boundary between the stable region and the unstable region is the critical stable region, that is,

[0038]

[0039] Furthermore, the specific method of step three is as follows:

[0040] 31) The vehicle yaw stability controller is designed using model predictive control theory. The optimization goal is to adjust the vehicle driving state to track the expected yaw angular velocity γ r and the desired sideslip angle β r , while constraining the vehicle's additional yaw moment ΔM zsize to save actuator energy consumption; therefore, the optimization problem is transformed into a quadratic programming problem, namely

[0041]

[0042] Where J represents the target cost function; u(k) represents the system control input of the kth step; R represents the braking energy consumption weight factor; N represents the total control step; β(k) represents the vehicle center of mass side slip angle of the kth step; β r (k) represents the expected sideslip angle of the center of mass of the car at the kth step; Q1 represents the lateral stability weight factor; γ(k) represents the yaw rate of the car at the kth step; γ r (k) represents the expected yaw rate of the vehicle at the kth step; Q2 represents the maneuverability weight factor; x(k+1) represents the system state at the k+1th step; x(k) represents the system state at the kth step; T S represents the sampling time; I represents the identity matrix; A represents the system state matrix; B represents the system control matrix; E represents the system disturbance matrix; u(k) represents the system control input of the kth step; δ(k) represents the front wheel steering angle of the kth step; u lim Indicates the maximum threshold of additional yaw moment; β lim Indicates the maximum threshold of the vehicle's center of mass sideslip angle; γ lim Indicates the maximum threshold of the vehicle's yaw rate;

[0043] 32) Obtain the optimal control sequence by solving the quadratic programming problem, and take the first value in the sequence as the actual control input;

[0044] When the vehicle is traveling in the stable area, critical stability area, and unstable area divided by the dynamic stability domain monitoring module, there are differentiated dynamic multi-objective requirements, which are specifically described as follows: (1) When the vehicle is traveling in the stable area, it is not desired that the stability controller intervene to reduce the vehicle's yaw maneuverability and generate unnecessary actuator energy consumption; (2) When the vehicle enters the critical stability area, the stability controller begins to intervene and is required to consider the vehicle's yaw maneuverability, lateral stability, and actuator energy consumption at the same time; in addition, in order to avoid frequent intervention and exit of the controller affecting the control effect, the controller will exit control only after the vehicle's driving state returns to the stable area for a certain period of time; (3) As the vehicle's driving state gradually exceeds the boundary of the critical stability area, it is necessary to give priority to the demand for lateral stability;

[0045] Design the vehicle stability index s to quantitatively match the vehicle dynamic multi-objective requirements in different stability areas. When the vehicle is at the center of the stable region of the phase plane, the s value is set to 0; as the vehicle driving state moves away from the center of the stable region, the s value is gradually increased, and the s value at the boundary of the stable region is set to 1; when the vehicle driving state is in the critical stable region, the s value is further approached to the critical stable region boundary value s=2; in addition, when the vehicle driving state reaches the unstable region, the s value is set to be greater than 2;

[0046] 33) Based on the vehicle stability index s, a dynamic multi-objective mapping function is designed to adjust the lateral stability weight factor Q1, maneuverability weight factor Q2 and braking energy consumption weight factor R in the MPC controller to respond to the control requirements of the vehicle under different driving conditions; the designed dynamic multi-objective mapping function is

[0047] Q1=k1{q1+[min(s,2)] 2} (16)

[0048] Q2=k2{q2+[min(s,2)] 2} (17)

[0049] R=k3{q3+[min(s,2)] 2} (18)

[0050] Wherein, Q1 represents the lateral stability weight factor; Q2 represents the maneuverability weight factor; R represents the braking energy consumption weight factor; k1 represents the lateral stability gain coefficient; q1 represents the lateral stability bias coefficient; k2 represents the maneuverability gain coefficient; q2 represents the maneuverability bias coefficient; k3 represents the braking energy consumption gain coefficient; q3 represents the braking energy consumption bias coefficient;

[0051] As the s value increases, the Q1 value increases, and the Q2 and R values ​​decrease accordingly, and the controller gives more priority to ensuring the lateral stability of the vehicle;

[0052] 34) The additional yaw moment ΔM calculated by the yaw stability control module is converted into z Distribute; Considering that the additional yaw moment in the same direction as the vehicle's rotation direction can be generated by braking the inner rear wheel of the vehicle and the working efficiency is the highest, and the additional yaw moment in the opposite direction of the vehicle's rotation direction can be generated by braking the outer front wheel of the vehicle and the working efficiency is the highest, a single-wheel differential braking rule is formulated;

[0053] When the corresponding wheel needs to be differentially braked, the additional wheel cylinder braking pressure is:

[0054]

[0055] In the formula, ΔP fl Indicates the additional wheel cylinder brake pressure of the left front wheel; ΔP fr Indicates the additional wheel cylinder brake pressure of the right front wheel; ΔPrl Indicates the additional wheel cylinder brake pressure of the left rear wheel; ΔP rr Indicates the additional wheel cylinder brake pressure of the right rear wheel; r w is the wheel radius; ΔM z Indicates the additional yaw moment; D d is the vehicle wheelbase; δ f Indicates the front wheel turning angle; K bf Indicates the front wheel braking efficiency factor; K br Represents the rear wheel braking efficiency factor; J r is the wheel moment of inertia; Indicates the front wheel acceleration; Represents the rear wheel acceleration.

[0056] Furthermore, the single-wheel differential braking rules are as follows:

[0057] When the working condition is δ>0, the judgment criterion is: γ-γ r >|K|, the braked wheels are: the right front wheel;

[0058] When the working condition is δ>0, the judgment criterion is: γ-γ r When <-|K|, the braked wheels are: left rear wheel;

[0059] When the working condition is δ>0, the judgment criterion is: -|K|<γ-γ r When <|K|, the brake wheel is: no control;

[0060] When the working condition is δ<0, the judgment criterion is: γ-γ r >|K|, the braked wheels are: the left front wheel;

[0061] When the working condition is δ<0, the judgment criterion is: γ-γ r When <-|K|, the braked wheels are: right rear wheel;

[0062] When the working condition is δ<0, the judgment criterion is: -|K|<γ-γ r When <|K|, the brake wheel is: no control;

[0063] When the working condition is δ=0, the judgment criterion is: γ-γ r >|K|, the braked wheels are: the right front wheel;

[0064] When the working condition is δ=0, the judgment criterion is: γ-γ r When <-|K|, the braked wheels are: left rear wheel;

[0065] When the working condition is δ=0, the judgment criterion is: -|K|<γ-γ r When <|K|, the brake wheel is: no control;

[0066] Wherein, K is the control threshold of differential braking.

[0067] The beneficial effects of the present invention are:

[0068] 1) The vehicle dynamics model established by the present invention using piecewise linear fitting technology can better characterize the nonlinear dynamic characteristics of the vehicle under different driving conditions;

[0069] 2) The phase plane designed based on the improved two-line method can accurately obtain the boundary conditions of the vehicle stability region, critical stability region and unstable region;

[0070] 3) The present invention quantitatively characterizes the driving state of the vehicle through a dynamic multi-objective mapping function, effectively guides the built-in parameters of the model predictive control dynamic regulator controller, and matches the changing multi-objective requirements of the vehicle for lateral stability, yaw maneuverability, actuator energy consumption, etc. under different driving conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0072] Figure 1 Schematic diagram of the architecture of the vehicle yaw stability adaptive control method considering dynamic multi-objective requirements;

[0073] Figure 2 It is a schematic diagram of a two-degree-of-freedom vehicle dynamics model;

[0074] Figure 3 It is a schematic diagram of the piecewise linear fitting process of tire lateral force;

[0075] Figure 4 Based on the two-line method Schematic diagram of the division of the phase plane stability region;

[0076] Figure 5 Based on the improved two-line method Schematic diagram of the division of the phase plane stability region;

[0077] Figure 6 It is a schematic diagram of the relationship between the position of each stable area and the stability index s;

[0078] Figure 7 Schematic diagram of weight factor change;

[0079] Figure 8 It is a schematic diagram of the double lane shift reference path;

[0080] Fig. 9 It is a schematic diagram of the experimental curve of double lane-shifting condition. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] Embodiment 1

[0083] Figure 1 The figure shows the architecture of a vehicle yaw stability adaptive control method designed by the present invention considering dynamic multi-objective requirements, which is specifically described as follows: (1) The driving intention identification module determines the longitudinal velocity v of the vehicle according to the vehicle longitudinal velocity v. x 、Driver input steering angle δ d Equal signal identification of expected yaw rate γ r and the desired sideslip angle β r ; (2) In the dynamic stability domain monitoring module, the improved two-line method is used to Dynamically obtain the boundary conditions of stable, critically stable and unstable regions in the phase plane; (3) The stability control module uses the stability index s to match the control requirements of the vehicle under different driving conditions. On this basis, a dynamic multi-objective mapping function is designed to adjust the values ​​of built-in parameters such as Q1, Q2, R of the MPC-based stability controller, and output the additional yaw moment ΔM z Used to adjust the vehicle to stably follow the desired yaw rate γ r and the desired sideslip angle β r (4) The brake pressure distribution module uses differential braking logic to add the additional yaw moment ΔM z Decomposed into four additional wheel brake pressures ΔP i , transmitted to the target vehicle to achieve yaw stability control.

[0084] The following is a detailed description of the above steps:

[0085] A vehicle yaw stability adaptive control method considering dynamic multi-objective requirements comprises the following steps:

[0086] Step 1: Use piecewise linear fitting technology to establish a vehicle dynamics model that takes into account the nonlinear characteristics of the tire to characterize the dynamic characteristics of the vehicle under different driving conditions, as follows:

[0087] 11) This invention focuses on the influence of vehicle lateral and yaw motion on stability control; assuming that the left and right sides of the vehicle are dynamically symmetrical, the following is established: Figure 2The two-degree-of-freedom vehicle dynamics model shown is expressed as follows based on the small-angle approximation method:

[0088]

[0089] In the formula, m represents the vehicle mass; v x Indicates the longitudinal speed of the vehicle; represents the lateral acceleration of the vehicle; γ represents the yaw rate of the vehicle; Indicates the vehicle's yaw angular acceleration; F yf Indicates the total lateral force of the front axle tire; F yr Represents the total lateral force of the rear axle tire; I z represents the vehicle's yaw moment of inertia; l f Indicates the distance from the vehicle's center of mass to the front axle; l r Indicates the distance from the vehicle's center of mass to the rear axle; ΔM z represents the additional yaw moment;

[0090] 12) Traditional linear tire models are difficult to characterize the nonlinear characteristics of tire lateral force when the vehicle has a large lateral acceleration, while tire models with higher accuracy such as the "magic formula" tend to increase the complexity of stability control strategy design. Figure 3 The piecewise linear fitting technique shown in Figure 2 models the tire lateral force, i.e.

[0091]

[0092] In the formula, i represents the front axle or rear axle; f represents the front axle; r represents the rear axle; F yi Indicates the total lateral force of the tire; Indicates the variable cornering stiffness of the tire; C i Indicates the linear cornering stiffness of the tire; D i represents the first dimension coefficient of linear fitting; e i Represents the second dimension coefficient of linear fitting; α i Indicates tire slip angle; p i is the tire slip angle cutoff value;

[0093] 13) The tire slip angles of the front and rear wheels are expressed as:

[0094]

[0095] In the formula, α f Represents the front tire side slip angle; α r Indicates the side slip angle of the rear tire; v x represents the longitudinal speed of the vehicle; β represents the sideslip angle of the center of mass of the vehicle; δ represents the front wheel turning angle;

[0096] 14) Finally, the vehicle's center of mass sideslip angle and yaw rate are selected as the system state quantity x = [β γ] T , the additional yaw moment is the system control input u=ΔM z , the simplified state space equation of the vehicle stability control system is:

[0097]

[0098] In the formula, x represents the system state quantity; represents the system state quantity derivative; A represents the system state matrix; B represents the system control matrix; E represents the system disturbance matrix; u represents the system control input; δ f Indicates the front wheel turning angle; Indicates the variable cornering stiffness of the front axle tire; Represents the vehicle's yaw moment of inertia.

[0099] Step 2: Use the improved two-line method to obtain the dynamic stability domain boundary in the phase plane, and obtain the stable domain, critical stable domain and unstable domain corresponding to the vehicle driving state, as follows:

[0100] 21) The vehicle's center of mass slip angle β and yaw rate γ, which characterize the vehicle's lateral stability and yaw maneuverability, are used as stability control targets. On the one hand, when the vehicle's center of mass slip angle β is small, the vehicle has better lateral stability. The desired center of mass slip angle β is set to r is 0; on the other hand, the expected yaw rate reflects the driver’s requirement for the vehicle’s maneuverability, which can be expressed by the steering wheel angle δ d etc. for identification, namely:

[0101]

[0102] In the formula, γ d represents the expected yaw rate before correction; C f Represents the linear cornering stiffness of the front axle tire; C r Represents the linear cornering stiffness of the rear axle tire; δ d Indicates the steering wheel angle;

[0103] In fact, vehicle stability control needs to take into account the road adhesion constraint, so the expected yaw rate is corrected to:

[0104]

[0105] In the formula, γ r represents the desired yaw rate; μ represents the road adhesion coefficient; g represents the gravitational acceleration; v x Indicates the longitudinal speed of the vehicle;

[0106] 22) Given a nonlinear vehicle dynamics model based on the “magic formula” tire lateral force, different vehicle longitudinal speeds v x , the initial values ​​of the sideslip angle β and the yaw rate γ, and the open-loop simulation is performed to obtain the following Figure 4 shown Phase plane diagram; The band area in the phase plane that converges at the saddle point represents the vehicle stability domain. The vehicle is prone to instability when driving outside the stability domain. The phase plane diagram is divided into stable and unstable regions, where the unstable region is represented as:

[0107]

[0108] Wherein, B1 represents the first parameter of the stability domain boundary; B2 represents the second parameter of the stability domain boundary; β represents the side slip angle of the center of mass of the vehicle; It represents the sideslip angular velocity of the vehicle's center of mass;

[0109] Considering that intervening stability control too early or too late is not conducive to the comprehensive regulation of the vehicle in terms of lateral stability, yaw maneuverability, actuator energy consumption, etc., the present invention uses the characteristic coefficient q to Figure 4 The original stable domain boundary shrinks inwards. In the phase plane, we obtain Figure 5 The new stable, critically stable and unstable region boundaries are shown; the inner boundary line is the stable region, which is expressed as:

[0110]

[0111] In the formula, q represents the characteristic coefficient;

[0112] The boundary between the stable region and the unstable region is the critical stable region, that is,

[0113]

[0114] The unstable region is still expressed by equation (11), corresponding to Figure 5 Area outside the outer boundary line.

[0115] Step 3: Apply model predictive control theory to design a stability control strategy, introduce a dynamic multi-objective mapping function to quantitatively characterize the vehicle's driving state, and dynamically adjust the built-in parameters of the stability control strategy to match the vehicle's multi-objective requirements for lateral stability, yaw maneuverability, and actuator energy consumption under different driving conditions. The details are as follows:

[0116] 31) The vehicle yaw stability controller is designed using model predictive control theory. The optimization goal is to adjust the vehicle driving state to track the expected yaw angular velocity γ r and the desired sideslip angle β r, while constraining the vehicle's additional yaw moment ΔM z size to save actuator energy consumption; therefore, the optimization problem is transformed into a quadratic programming problem, namely

[0117]

[0118] Where J represents the target cost function; u(k) represents the system control input of the kth step; R represents the braking energy consumption weight factor; N represents the total control step; β(k) represents the vehicle center of mass side slip angle of the kth step; β r (k) represents the expected sideslip angle of the center of mass of the car at the kth step; Q1 represents the lateral stability weight factor; γ(k) represents the yaw rate of the car at the kth step; γ r (k) represents the expected yaw rate of the vehicle at the kth step; Q2 represents the maneuverability weight factor; x(k+1) represents the system state at the k+1th step; x(k) represents the system state at the kth step; T S represents the sampling time; I represents the identity matrix; A represents the system state matrix; B represents the system control matrix; E represents the system disturbance matrix; u(k) represents the system control input of the kth step; δ(k) represents the front wheel steering angle of the kth step; u lim Indicates the maximum threshold of additional yaw moment; β lim Indicates the maximum threshold of the vehicle's center of mass sideslip angle; γ lim Indicates the maximum threshold of the vehicle's yaw rate;

[0119] 32) Obtain the optimal control sequence by solving the quadratic programming problem, and take the first value in the sequence as the actual control input;

[0120] In fact, when the vehicle is driving in the stable area, critical stability area and unstable area divided by the dynamic stability domain monitoring module, it has differentiated dynamic multi-objective requirements as shown in Table 2, which are specifically explained as follows: (1) When the vehicle is driving in the stable area, it is not desired that the stability controller intervene to reduce the vehicle's yaw maneuverability and generate unnecessary actuator energy consumption; (2) When the vehicle enters the critical stability area, the stability controller begins to intervene and is required to consider the vehicle's yaw maneuverability, lateral stability and actuator energy consumption at the same time; in addition, in order to avoid frequent intervention and exit of the controller affecting the control effect, the controller will exit control only after the vehicle's driving state returns to the stable area for a certain period of time; (3) As the vehicle's driving state gradually exceeds the boundary of the critical stability area, it is necessary to give priority to the demand for lateral stability;

[0121] Table 2 Dynamic multi-objective requirements

[0122] Vehicle driving status Requirements Stable area Roll control↑ Critical stability region Lateral stability↑, yaw control↓, actuator energy consumption↓ Unstable area Lateral stability↑

[0123] To this end, the design Figure 6 The vehicle stability index s shown quantitatively matches the vehicle dynamic multi-objective requirements in different stability areas, where the green area is the stable area, the yellow area is the critical stability area, and the red area is the unstable area. When the vehicle is at the center of the stable region of the phase plane, the s value is set to 0; as the vehicle driving state moves away from the center of the stable region, the s value is gradually increased, and the s value at the boundary of the stable region is set to 1; when the vehicle driving state is in the critical stable region, the s value is further approached to the critical stable region boundary value s=2; in addition, when the vehicle driving state reaches the unstable region, the s value is set to be greater than 2.

[0124] 33) Based on the vehicle stability index s, a dynamic multi-objective mapping function is designed to adjust the lateral stability weight factor Q1, maneuverability weight factor Q2 and braking energy consumption weight factor R in the MPC controller to respond to the control requirements of the vehicle under different driving conditions; the designed dynamic multi-objective mapping function is

[0125] Q1=k1{q1+[min(s,2)] 2} (16)

[0126] Q2=k2{q2+[min(s,2)] 2} (17)

[0127] R=k3{q3+[min(s,2)] 2} (18)

[0128] Wherein, Q1 represents the lateral stability weight factor; Q2 represents the maneuverability weight factor; R represents the braking energy consumption weight factor; k1 represents the lateral stability gain coefficient; q1 represents the lateral stability bias coefficient; k2 represents the maneuverability gain coefficient; q2 represents the maneuverability bias coefficient; k3 represents the braking energy consumption gain coefficient; q3 represents the braking energy consumption bias coefficient;

[0129] The changing trend of each weight factor with s is as follows Figure 7 As shown, as the s value increases, the Q1 value increases, and the Q2 and R values ​​decrease accordingly, and the controller gives more priority to ensuring the lateral stability of the vehicle;

[0130] 34) The present invention uses a single-wheel differential braking technology to calculate the additional yaw moment ΔM calculated by the yaw stability control module. z Considering that braking the inner rear wheel of the vehicle can generate an additional yaw moment in the same direction as the vehicle's rotation direction with the highest working efficiency, and braking the outer front wheel of the vehicle can generate an additional yaw moment in the opposite direction of the vehicle's rotation direction with the highest working efficiency, a single-wheel differential braking rule is formulated as shown in Table 3, where K is the control threshold of differential braking.

[0131] Table 3 Stable region boundary parameters

[0132]

[0133]

[0134] When the corresponding wheel needs to be differentially braked, the additional wheel cylinder braking pressure is:

[0135]

[0136] In the formula, ΔP fl Indicates the additional wheel cylinder brake pressure of the left front wheel; ΔP fr Indicates the additional wheel cylinder brake pressure of the right front wheel; ΔP rl Indicates the additional wheel cylinder brake pressure of the left rear wheel; ΔP rr Indicates the additional wheel cylinder brake pressure of the right rear wheel; r w is the wheel radius; ΔM z Indicates the additional yaw moment; D d is the vehicle wheelbase; δ f Indicates the front wheel turning angle; K bf Indicates the front wheel braking efficiency factor; K br Represents the rear wheel braking efficiency factor; J r is the wheel moment of inertia; Indicates the front wheel acceleration; Represents the rear wheel acceleration.

[0137] Furthermore, the single-wheel differential braking rules are as follows:

[0138] When the working condition is δ>0, the judgment criterion is: γ-γ r >|K|, the braked wheels are: the right front wheel;

[0139] When the working condition is δ>0, the judgment criterion is: γ-γ r When <-|K|, the braked wheels are: left front wheel;

[0140] When the working condition is δ>0, the judgment criterion is: -|K|γ-γ r When <|K|, the brake wheel is: no control;

[0141] When the working condition is δ<0, the judgment criterion is: γ-γ r >|K|, the braked wheels are: the left front wheel;

[0142] When the working condition is δ<0, the judgment criterion is: γ-γ r When <-|K|, the braked wheels are: right front wheel;

[0143] When the working condition is δ<0, the judgment criterion is: -|K|γ-γ rWhen <|K|, the brake wheel is: no control;

[0144] When the working condition is δ=0, the judgment criterion is: γ-γ r >|K|, the braked wheels are: the right front wheel;

[0145] When the working condition is δ=0, the judgment criterion is: γ-γ r When <-|K|, the braked wheels are: left front wheel;

[0146] When the working condition is δ=0, the judgment criterion is: -|K|γ-γ r When <|K|, the brake wheel is: no control;

[0147] Wherein, K is the control threshold of differential braking.

[0148] Embodiment 2

[0149] A joint simulation platform based on MATLAB / Simulink and vehicle dynamics software CarSim was built to test the vehicle yaw stability adaptive control method designed in the present invention. The target vehicle was controlled to perform high-speed double lane change at a speed of 120 km / h on a road with a road adhesion of 0.5. The reference path of the double lane change is as follows: Figure 8 The test results of the double line shifting experiment are shown in Fig. 9 As shown in the figure. The target vehicle without yaw stability control has a sharp increase in the sideslip angle and yaw angular velocity after 3 seconds and then becomes unstable. However, under the action of the controller, the instability can be effectively avoided. The controller dynamically considers the multi-objective requirements of the vehicle's yaw controllability, lateral stability and actuator energy consumption during the period of 1.9 to 6 seconds. During the period of 1.9 to 4.5 seconds, s As the value increases, the lateral stability weight factor Q1 is increased accordingly, and the yaw control weight factor Q2 and the braking energy consumption weight factor are reduced. R , effectively suppressing the increase of the sideslip angle of the center of mass after 3.5s, giving priority to ensuring the lateral stability of the vehicle; the s value gradually decreases in the two periods of time after 4.5-5s and 6s, so controller A correspondingly reduces Q1 and increases Q2 and R. It can be seen that the yaw angular velocity follows better in these two periods, and the yaw maneuverability of the target vehicle is increased while ensuring lateral stability.

[0150] In summary, the present invention quantitatively evaluates the stability degree of the vehicle under different driving conditions, effectively improves the comprehensive performance of the stability controller in terms of vehicle lateral stability, yaw maneuverability, actuator energy consumption, etc., and effectively provides a solution for the optimal integrated control of chassis dynamics of future smart cars.

[0151] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the protection scope of the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention. These simple variations all belong to the protection scope of the present invention.

[0152] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0153] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A vehicle yaw stability adaptive control method considering dynamic multi-objective requirements, characterized in that: The following steps are involved: Step 1: Use piecewise linear fitting technology to establish a vehicle dynamics model that takes into account the nonlinear characteristics of the tire to characterize the dynamic characteristics of the vehicle under different driving conditions; Step 2: Using the improved two-line method to obtain the dynamic stability domain boundary in the phase plane, obtain the stability domain, critical stability domain and unstable domain corresponding to the vehicle driving state; Step 3: Apply model predictive control theory to design a stability control strategy, introduce a dynamic multi-objective mapping function to quantitatively characterize the vehicle's driving state, and dynamically adjust the built-in parameters of the stability control strategy to match the multi-objective requirements of lateral stability, yaw maneuverability, and actuator energy consumption under different driving conditions of the vehicle. The specific methods are as follows: 31) The vehicle yaw stability controller is designed using model predictive control theory. The optimization goal is to adjust the vehicle driving state to track the expected yaw angular velocity γ r and the desired sideslip angle β r , while constraining the vehicle's additional yaw moment ΔM z size to save actuator energy consumption; therefore, the optimization problem is transformed into a quadratic programming problem, namely Where J represents the target cost function; u(k) represents the system control input of the kth step; R represents the braking energy consumption weight factor; N represents the total control step; β(k) represents the vehicle center of mass side slip angle of the kth step; β r (k) represents the expected sideslip angle of the center of mass of the car at the kth step; Q1 represents the lateral stability weight factor; γ(k) represents the yaw rate of the car at the kth step; γ r (k) represents the expected yaw rate of the vehicle at the kth step; Q2 represents the maneuverability weight factor; x(k+1) represents the system state at the k+1th step; x(k) represents the system state at the kth step; T S represents the sampling time; I represents the identity matrix; A represents the system state matrix; B represents the system control matrix; E represents the system disturbance matrix; u(k) represents the system control input of the kth step; δ(k) represents the front wheel steering angle of the kth step; u lim Indicates the maximum threshold of additional yaw moment; β lim Indicates the maximum threshold of the vehicle's center of mass sideslip angle; γ lim Indicates the maximum threshold of the vehicle's yaw rate; 32) Obtain the optimal control sequence by solving the quadratic programming problem, and take the first value in the sequence as the actual control input; When the vehicle is traveling in the stable area, critical stability area, and unstable area divided by the dynamic stability domain monitoring module, there are differentiated dynamic multi-objective requirements, which are specifically described as follows: (1) When the vehicle is traveling in the stable area, it is not desired that the stability controller intervene to reduce the vehicle's yaw maneuverability and generate unnecessary actuator energy consumption; (2) When the vehicle enters the critical stability area, the stability controller begins to intervene and is required to consider the vehicle's yaw maneuverability, lateral stability, and actuator energy consumption at the same time; in addition, in order to avoid frequent intervention and exit of the controller affecting the control effect, the controller will exit control only after the vehicle's driving state returns to the stable area for a certain period of time; (3) As the vehicle's driving state gradually exceeds the boundary of the critical stability area, it is necessary to give priority to the demand for lateral stability; Design the vehicle stability index s to quantitatively match the vehicle dynamic multi-objective requirements in different stability areas. When the vehicle is at the center of the stable region of the phase plane, the s value is set to 0; as the vehicle driving state moves away from the center of the stable region, the s value is gradually increased, and the s value at the boundary of the stable region is set to 1; when the vehicle driving state is in the critical stable region, the s value is further approached to the critical stable region boundary value s=2; in addition, when the vehicle driving state reaches the unstable region, the s value is set to be greater than 2; 33) Based on the vehicle stability index s, a dynamic multi-objective mapping function is designed to adjust the lateral stability weight factor Q1, maneuverability weight factor Q2 and braking energy consumption weight factor R in the MPC controller to respond to the control requirements of the vehicle under different driving conditions; the designed dynamic multi-objective mapping function is Q1=k1{q1+[min(s,2)] 2 } (16) <h2 style=";text-align:left;direction:ltr">Q2 = k2 {q2 + [min(s,2)]<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr">} (17) R=k3{q3+[min(s,2)] 2 } (18) Wherein, Q1 represents the lateral stability weight factor; Q2 represents the maneuverability weight factor; R represents the braking energy consumption weight factor; k1 represents the lateral stability gain coefficient; q1 represents the lateral stability bias coefficient; k2 represents the maneuverability gain coefficient; q2 represents the maneuverability bias coefficient; k3 represents the braking energy consumption gain coefficient; q3 represents the braking energy consumption bias coefficient; As the s value increases, the Q1 value increases, and the Q2 and R values ​​decrease accordingly, and the controller gives more priority to ensuring the lateral stability of the vehicle; 34) The additional yaw moment ΔM calculated by the yaw stability control module is converted into z Distribute; Considering that the additional yaw moment in the same direction as the vehicle's rotation direction can be generated by braking the inner rear wheel of the vehicle and the working efficiency is the highest, and the additional yaw moment in the opposite direction of the vehicle's rotation direction can be generated by braking the outer front wheel of the vehicle and the working efficiency is the highest, a single-wheel differential braking rule is formulated; When the corresponding wheel needs to be differentially braked, the additional wheel cylinder braking pressure is: Where ΔP fl Indicates the additional wheel cylinder brake pressure of the left front wheel; ΔP fr Indicates the additional wheel cylinder brake pressure of the right front wheel; ΔP rl Indicates the additional wheel cylinder brake pressure of the left rear wheel; ΔP rr Indicates the additional wheel cylinder brake pressure of the right rear wheel; r w is the wheel radius; ΔM z Indicates the additional yaw moment; D d is the vehicle wheelbase; δ f Indicates the front wheel turning angle; K bf Indicates the front wheel braking efficiency factor; K br Represents the rear wheel braking efficiency factor; J r is the wheel moment of inertia; Indicates the front wheel acceleration; Represents the rear wheel acceleration; l f Represents the distance from the vehicle's center of mass to the front axle.

2. The vehicle yaw stability adaptive control method considering dynamic multi-objective requirements according to claim 1, characterized in that: The specific method of step one is as follows: 11) Assuming that the left and right sides of the vehicle are dynamically symmetrical, a two-degree-of-freedom vehicle dynamics model is established, which can be expressed as follows based on the small-angle approximation method: In the formula, m represents the vehicle mass; v x Indicates the longitudinal speed of the vehicle; represents the lateral acceleration of the vehicle; γ represents the yaw rate of the vehicle; Indicates the vehicle's yaw angular acceleration; F yf Indicates the total lateral force of the front axle tire; F yr Represents the total lateral force of the rear axle tire; I z represents the vehicle's yaw moment of inertia; l f Indicates the distance from the vehicle's center of mass to the front axle; l r Indicates the distance from the vehicle's center of mass to the rear axle; ΔM z represents the additional yaw moment; 12) The tire lateral force is modeled using piecewise linear fitting technology, that is, In the formula, i represents the front axle or rear axle; f represents the front axle; r represents the rear axle; F yi Indicates the total lateral force of the tire; Indicates the variable cornering stiffness of the tire; C i Indicates the linear cornering stiffness of the tire; D i represents the first dimension coefficient of linear fitting; e i Represents the second dimension coefficient of linear fitting; α i Indicates tire slip angle; p i is the tire slip angle cutoff value; 13) The tire slip angles of the front and rear wheels are expressed as: In the formula, α f Represents the front tire side slip angle; α r Indicates the side slip angle of the rear tire; v x represents the longitudinal speed of the vehicle; β represents the sideslip angle of the center of mass of the vehicle; δ f Indicates the front wheel turning angle; 14) Select the vehicle's center of mass sideslip angle and yaw rate as the system state quantity x = [β γ] T , the additional yaw moment is the system control input u=ΔM z , the simplified state space equation of the vehicle stability control system is: In the formula, x represents the system state quantity; represents the system state quantity derivative; A represents the system state matrix; B represents the system control matrix; E represents the system disturbance matrix; u represents the system control input; δ f Indicates the front wheel turning angle; Indicates the variable cornering stiffness of the front axle tire; Indicates the variable cornering stiffness of the rear axle tire.

3. The vehicle yaw stability adaptive control method considering dynamic multi-objective requirements according to claim 1, characterized in that: The specific method of step 2 is as follows: 21) The vehicle's center of mass slip angle β and yaw rate γ, which characterize the vehicle's lateral stability and yaw maneuverability, are used as stability control targets; the desired center of mass slip angle β is set r is 0; the desired yaw rate is determined by the steering wheel angle δ d Information is identified, that is In the formula, γ d represents the expected yaw rate before correction; C f Represents the linear cornering stiffness of the front axle tire; C r Represents the linear cornering stiffness of the rear axle tire; δ d Indicates the steering wheel angle; l f Indicates the distance from the vehicle's center of mass to the front axle; l r It represents the distance from the center of mass of the vehicle to the rear axle; m represents the mass of the vehicle; Vehicle stability control needs to take into account the road adhesion constraint, so the expected yaw rate is corrected to In the formula, γ r represents the desired yaw rate; μ represents the road adhesion coefficient; g represents the gravitational acceleration; v x Indicates the longitudinal speed of the vehicle; 22) Nonlinear vehicle dynamics model for given tire lateral force and different vehicle longitudinal speeds v x , the initial values ​​of the sideslip angle β and the yaw rate γ, and an open-loop simulation is performed to obtain Phase plane diagram; The band area in the phase plane that converges at the saddle point represents the vehicle stability domain. The vehicle is prone to instability when driving outside the stability domain. The phase plane diagram is divided into stable and unstable regions, where the unstable region is represented as: Wherein, B1 represents the first parameter of the stability domain boundary; B2 represents the second parameter of the stability domain boundary; β represents the side slip angle of the center of mass of the vehicle; It represents the sideslip angular velocity of the vehicle's center of mass; The original stable region boundary is shrunk inward by the characteristic coefficient q. New stable, critically stable and unstable region boundaries are obtained in the phase plane; the area within the contracted stable domain boundary is the stable region, which is expressed as: In the formula, q represents the characteristic coefficient; The boundary between the stable region and the unstable region is the critical stable region, that is, 4. The vehicle yaw stability adaptive control method considering dynamic multi-objective requirements according to claim 1, characterized in that: The specific rules for single-wheel differential braking are as follows: When the working condition is δ f >0, judgment criteria: γ-γ r >|K|, the braked wheels are: the right front wheel; When the working condition is δ f >0, judgment criteria: γ-γ r When <-|K|, the braked wheels are: left rear wheel; When the working condition is δ f >0, judgment criteria: -|K|<γ-γ r When <|K|, the brake wheel is: no control; When the working condition is δ f <0, judgment criteria: γ-γ r >|K|, the braked wheels are: the left front wheel; When the working condition is δ f <0, judgment criteria: γ-γ r When <-|K|, the braked wheels are: right rear wheel; When the working condition is δ f <0, judgment criteria: -|K|<γ-γ r When <|K|, the brake wheel is: no control; When the working condition is δ f =0, the judgment criteria is: γ-γ r >|K|, the braked wheels are: the right front wheel; When the working condition is δ f =0, judgment criteria: γ-γ r When <-|K|, the braked wheels are: left rear wheel; When the working condition is δ f =0, judgment criteria: -|K|<γ-γ r When <|K|, the brake wheel is: no control; Among them, K is the control threshold of differential braking; γ represents the yaw rate of the vehicle.

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