An automobile intelligent chassis AFS / DYC collaborative controller and control method based on an extension theory

Abstract

The application discloses an automobile intelligent chassis AFS / DYC collaborative controller and control method based on an extension theory, innovatively introduces an extension theory method in vehicle chassis control technology, constructs an extension domain and a correlation degree function through coordination control of AFS and DYC, improves adaptability of vehicle dynamics characteristics and stability of system mode switching under complex working conditions, and can accurately cope with vehicle dynamics characteristic change and stability demand problems under complex working conditions on the premise of ensuring easy control of the system. The K-means clustering method is introduced, and the optimal partition problem of the classical domain, the extension domain and the non-domain is solved. The algorithm has fast iteration and strong global search capability, and avoids the shortcoming that the traditional method is easy to fall into local optimization. Meanwhile, the method simplifies the determination process of the boundary value, significantly improves the vehicle stability and control performance through clear division of the clustering result, and provides effective support for system optimization under complex dynamic conditions.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent automobile stability control, and particularly relates to an auxiliary driving vehicle chassis AFS / DYC body stability controller and a control method based on an extension theory. BACKGROUND

[0002] Intelligentization is one of the main directions of global automobile industry development and is becoming a strategic competition highland of the world's industrial powers. Stability control of the control execution link of the vehicle chassis and coordinated control between the chassis systems are the main direction and research difficulty of intelligentization. The active front wheel steering system (AFS) mainly applies an additional steering angle to the steering wheel to fine-tune the attitude of the vehicle, so that the AFS can control the stability of the vehicle on the premise of ensuring the driving state of the vehicle. However, when the lateral force of the vehicle tire is in a nonlinear range, the AFS system alone is difficult to achieve stability control. The direct yaw moment control (DYC) mainly distributes the additional yaw moment calculated by the controller to each wheel through the torque distribution module to control the stability of the vehicle. However, the DYC will affect the speed of the vehicle to some extent. In view of the above characteristics of AFS and DYC, the advantages and defects of the two systems can be complementary through coordinated control system to improve the stability of the vehicle, optimize the handling performance of the vehicle and ensure the driving safety.

[0003] For the AFS / DYC coordinated control problem, many scholars have proposed various methods for research and discussion: logic switching; fuzzy control; multi-agent method; proportional integral control (PI); etc. However, the above-mentioned coordinated control methods have some problems: the logic switching and fuzzy control may affect the control effect of the vehicle in the process of mode switching; the multi-agent method needs information interaction of the two systems, so the calculation is large and the control is complex; the PI control method only coordinates from the perspective of the system, so the coordination distribution is not enough. The extension theory proposed by Professor Cai Wen of China is an effective method for handling contradictions and has been widely applied in many fields. It has the advantages of clear logic, systematic method and strong adaptability, and can effectively solve the coordination problem between various subsystems in complex systems, and has important potential in solving the AFS / DYC chassis collaborative control problem.

[0004] The basic idea of the extension control theory: the extension theory refers to the law and method for using the extension theory to study and handle the contradictory problems in the control process, which is the process of describing the mutual conversion of "is" and "non" and the quantitative and qualitative changes, mainly including the extraction of characteristic quantity, two-dimensional extension set, correlation degree calculation, pattern recognition, control output calculation these processes. The extraction of characteristic quantity is mainly to extract the state quantity which needs to be focused on the system, and then the characteristic quantity is divided into regions according to the judgment condition of the system performance defined in advance, which is divided into classical domain, extension domain and non-domain, which respectively represents that the system is in stable state, stable boundary and unstable state. The following is the calculation of the correlation degree of the characteristic quantity, the state is monitored through the correlation function, so that the control process is smoothly transitioned in the classical domain, the extension domain and the non-domain, and the high-precision control region is expanded from the classical domain to the extension domain. Through this process, the real-time accurate monitoring and dynamic adjustment of the system state can be realized, so as to significantly improve the stability and control precision of the complex system. SUMMARY

[0005] In view of the problems existing in the existing intelligent vehicle chassis control technology, the application proposes a design of an auxiliary driving vehicle chassis AFS / DYC vehicle body stability controller under the framework of the extension theory, which applies the extension theory to the vehicle body stability control, so that the vehicle can quickly and accurately adjust the vehicle body attitude under various working conditions and keep stable.

[0006] The technical scheme of the application is: an auxiliary driving vehicle chassis AFS / DYC vehicle body stability control method based on the extension theory, comprising the following steps:

[0007] s1: establishing a two-degree-of-freedom dynamic model of the vehicle, and establishing a state space expression according to the vehicle dynamic model, wherein the state quantity is the lateral velocity v y of the vehicle and the yaw angular velocity ω of the vehicle. Based on the rolling optimization idea of the MPC control strategy, the additional front wheel angle Δδ f* and the additional yaw moment ΔM z* required to keep the vehicle body stable are calculated according to the constructed cost function.

[0008] s2: selection of extension theory characteristic quantity. The characteristic quantity is quantitatively described according to the energy method of the vehicle running state, specifically the RT energy ratio and the SY energy ratio.

[0009] s3: determination of the extension theory region boundary. The boundary is selected by using the K-means clustering method, the region is divided by clustering analysis, and finally the boundary of the extension partition is obtained, that is, RT1, RT2, SY1, SY2,

[0010] s4: select the real-time RT energy ratio of the vehicle and the change rate of the RT energy ratio and as characteristic quantities. According to the two selected characteristic quantities, a two-dimensional region is constructed, and the boundary RT1, RT2, is determined according to the K-means clustering method, and is divided into a classical domain, an extension domain and a non-domain, and the correlation function K(S) AFS is calculated. When K(S) AFS ≥ 1, the state of the vehicle is in the classical domain, the vehicle body is relatively stable, and the intervention of the AFS is not needed, so KA=0; when 0≤K(S) AFS <1, the vehicle is in the extension domain, the vehicle body state needs to be adjusted in time, and KA=K(S) AFS ; when K(S) AFS <0, the vehicle is in the non-domain, the vehicle body state is extremely unstable, and the AFS needs to be completely intervened, so KA=1.

[0011] s5: select the real-time SY energy ratio of the vehicle and the change rate of the SY energy ratio and as characteristic quantities. According to the two selected characteristic quantities, a two-dimensional region is constructed, and the boundary SY1, SY2, is determined according to the K-means clustering method, and is divided into a classical domain, an extension domain and a non-domain, and the correlation function K(S) DYC is calculated. When K(S) DYC ≥ 1, the state of the vehicle is in the classical domain, the vehicle body is relatively stable, and the intervention of the DYC is not needed, so KD=0; when 0≤K(S) DYC <1, the vehicle is in the extension domain, the vehicle body state needs to be adjusted in time, and KD=K(S) DYC ; when K(S) DYC <0, the vehicle is in the non-domain, the vehicle body state is extremely unstable, and the DYC needs to be completely intervened, so KD=1.

[0012] S6: finally obtain the output quantity Δδ f* of the extension cooperative controller. z* * KA and ΔM z* *KD.

[0013] Further, the specific method for establishing the vehicle state space equation of S1 is as follows:

[0014] A two-degree-of-freedom dynamic model is established as follows:

[0015]

[0016] Where m represents the mass of the vehicle, v x and v y represent the longitudinal vehicle speed and the lateral vehicle speed respectively, F yfF and Fyr are the lateral forces on the front and rear axles, respectively, generated by the slip angles of the front and rear wheels. z Let l be the moment of inertia of the vehicle about its center of mass. f , l r Let ΔM be the distance from the center of mass to the front and rear axes, respectively. z To add yaw moment;

[0017] Establish the lateral force model for the front and rear wheels:

[0018]

[0019] Where C f C r The lateral stiffness of the front and rear wheels are α and α, respectively. f and α r These are the slip angles of the front and rear wheels, respectively.

[0020] The slip angles of the front and rear wheels can be represented by the vehicle's state as follows:

[0021]

[0022] Where δ f This represents the total front wheel steering angle, which includes the front wheel steering angle δ input by the driver. fs and the additional front wheel steering angle Δδ f That is, δf = δ fs +Δδ f ,Δδ f Provided by the AFS system;

[0023] Define the state variable as the vehicle's lateral velocity v. y And yaw rate ω, used to measure vehicle stability, i.e.

[0024]

[0025] Define the control input as the total front wheel steering angle δ f and additional yaw moment ΔM z ;

[0026] Right now

[0027] According to the vehicle's lateral motion equation, we can obtain:

[0028]

[0029] According to the equation of motion of the vehicle's yaw, we can obtain:

[0030]

[0031] Rewrite the two equations in matrix form:

[0032]

[0033] where is the state vector; A is the system matrix, describing the dynamic behavior of the state variables, B1 and B2 are the influence matrices of the additional front wheel steering angle and the additional yaw moment on the state changes, respectively, and are as follows:

[0034]

[0035] Further, the design of the MPC controller of S1 is as follows:

[0036] The ideal reference state is set as:

[0037]

[0038] where the ideal yaw rate ω ref is expressed as:

[0039]

[0040] δ fs is the front wheel steering angle input by the driver, the ideal lateral velocity v yref is set to 0;

[0041] The cost function of the MPC is used to minimize the difference between the vehicle state and the ideal state, while controlling the size of the total front wheel steering angle and the additional yaw moment, and the cost function is defined as

[0042]

[0043] where Q represents the weight matrix of the state error, used to control the deviation of the lateral velocity and the yaw rate from the reference state; R1 is the control weight matrix of the total front wheel steering angle; and R2 is the control weight matrix of the additional yaw moment.

[0044] After the model is discretized, the MPC controller predicts the vehicle state in the future N steps at each time step by optimizing the cost function J, and calculates the optimal total front wheel steering angle δ f* and the additional yaw moment ΔM z* at each time, and the optimization problem can be solved in real time by a quadratic programming solver (QP);

[0045] According to the total front wheel steering angle δ f* , the optimal additional front wheel steering angle control input Δδ f* is calculated at each time: f* = δ fs - δ f*

[0046] and ΔM​z* as the input of the extension coordination control.

[0047] Further, the RT energy ratio in S2 represents the ratio of turning kinetic energy and forward kinetic energy, which can be specifically expressed by the formula:

[0048]

[0049] wherein is the inertia radius, β is the vehicle's mass center side slip angle, which can be generally considered tanβ≈β, m represents the mass of the vehicle, v represents the vehicle speed, and ω represents the vehicle's yaw rate, so the formula can be written as:

[0050]

[0051] The SY energy ratio represents the ratio of side slip kinetic energy and yaw kinetic energy, which can be specifically expressed by the formula:

[0052]

[0053] I z represents the moment of inertia of the vehicle around the z-axis.

[0054] Further, the implementation of S3 includes:

[0055] collecting the SY energy ratio when the vehicle is running, the change rate of the SY energy ratio the RT energy ratio, the change rate of the RT energy ratio data, ensuring that the data covers normal and abnormal states;

[0056] normalizing SY, RT, to scale the data between 0 and 1:

[0057]

[0058] wherein SY min , SY max respectively represent the minimum and maximum values of the SY energy ratio in all data, respectively represent the minimum and maximum values of the change rate of the SY energy ratio, RT min , RT max respectively represent the minimum and maximum values of the RT energy ratio in all data, respectively represent the minimum and maximum values of the change rate of the RT energy ratio;

[0059] The extension theory boundaries SY1, SY2, of SY and are determined below.

[0060] First, randomly select 3 data points containing SY and as initial centroids, respectively For each of the remaining data points Calculate its Euclidean distance from all centroids c k :

[0061]

[0062] Where SY ak and represent the two feature values of the kth centroid respectively;

[0063] According to the calculation results, each data point x i is assigned to the cluster corresponding to the centroid with the smallest distance, and after the assignment of data points in each cluster is completed, the average position of all data points in the cluster is calculated, and the centroid of the cluster is updated:

[0064]

[0065] Where N k represents the number of data points in the Kth cluster, S k represents all data points in the Kth cluster, and x j represents the jth data point coordinate in the Kth cluster.

[0066] Then repeat the above steps until the position of the centroid is stable, stop iteration; Finally, the stable centroid

[0067] The final boundary is:

[0068]

[0069] Similarly, determine the extension theory boundary RT1, RT2, of RT and

[0070] Further, in the S4, divide the boundary RT1, RT2, into classical domain, extension domain and non-domain as follows:

[0071] Classical domain: composed of the boundary determined by RT1 and ;

[0072] Extension domain: the boundary determined by RT2 and ;

[0073] Non-domain: the region beyond the above two boundaries.

[0074] The correlation function K(S) in S4 AFS To reflect the relationship between the current vehicle state and the AFS intervention requirement, specifically as follows:

[0075] Let the characteristic quantity RT of the current vehicle and In the two-dimensional region at P3 point, P3 point to the extension distance of the classical domain and the extension domain is ρ(P3,(P4, P1)) and ρ(P3,(P5, P2)) respectively, which is regarded as a piecewise function, the calculation result is as follows:

[0076]

[0077] The correlation function K(S) AFS Reflect the stability state in the whole control process, and finally the numerical value intuitively shows the gap between the current state and the best state, and the calculation formula is as follows:

[0078]

[0079] Wherein

[0080] D(P3,(P5, P2),(P4, P1)) = ρ(P3,(P5, P2))-ρ(P3,(P4, P1)) (23)

[0081] When K(S) AFS ≥1, the vehicle is in the classical domain, that is, in a relatively stable state, and does not need the intervention of AFS, at this time, set KA=0, that is, AFS does not participate;

[0082] When 0≤K(S) AFS <1, the vehicle is in the extension domain, that is, the state of the vehicle begins to deviate from the stable state, and needs to be adjusted to a certain extent, at this time, KA=K(S) AFS , indicating that AFS will partially intervene to help adjust the vehicle state;

[0083] If K(S) AFS <0, the vehicle enters the non-domain, which means that the vehicle is in an extremely unstable state, at this time, AFS needs to be fully involved to perform full state correction, at this time, set KA=1, indicating that AFS fully participates to ensure the safety and stability of the vehicle.

[0084] Further, the S5 divides the determined boundary SY1, SY2, Into the classical domain, the extension domain and the non-domain, and the specific division is as follows:

[0085] Classical domain: bounded by boundary values SY1 and The defined region represents a relatively stable state for the vehicle;

[0086] Extendable domain: defined by boundary values ​​SY2 and The defined area represents a transitional state in which the vehicle deviates from a stable state and requires adjustment.

[0087] Non-boundary: This means the vehicle is outside the boundary, indicating that the vehicle's state is unstable and requires emergency intervention.

[0088] Furthermore, the correlation function K(S) of S5... DYC The calculation method and the correlation function K(S) of step S4. AFS The calculation method is the same, and it is used to reflect the relationship between the vehicle's current state and the DYC system's intervention requirements, as follows:

[0089] When K(S) DYC When KD is ≥1, the vehicle is in the classical domain and the vehicle body is relatively stable. DYC intervention is not required. Therefore, KD = 0 is set, which means that DYC does not participate.

[0090] When 0 ≤ K(S) DYC When the value is less than 1, the vehicle is in the extension domain, indicating that the vehicle's state begins to deviate from the stable state and requires appropriate adjustment. At this time, the DYC system partially intervenes to help the vehicle adjust its state. Therefore, KD = K(S) is set. DYC That is, DYC intervenes to adjust based on the degree of deviation from the state;

[0091] When K(S) DYC When KD < 0, it indicates that the vehicle has entered a non-domain and is in an extremely unstable state. The vehicle's dynamic performance may be abnormally severe, requiring the full intervention of the DYC system to restore the vehicle's stability. Therefore, KD = 1 is set to indicate that the DYC system is fully involved to ensure vehicle safety.

[0092] The present invention also proposes an AFS / DYC cooperative controller for automotive intelligent chassis based on extension theory, which can execute the above-mentioned AFS / DYC cooperative control method for automotive intelligent chassis based on extension theory.

[0093] The beneficial effects of this invention are:

[0094] (1) This invention innovatively introduces the extension theory method into vehicle chassis control technology. By coordinating the control of AFS and DYC, an extension domain and correlation function are constructed, which improves the adaptability of vehicle dynamic characteristics under complex working conditions and the smoothness of system mode switching. Under the premise of ensuring that the system is easy to control, it can accurately deal with the problems of vehicle dynamic characteristics change and stability requirements under complex working conditions.

[0095] (2) The K-means clustering method is introduced to solve the optimal partition problem of the classical field, the extension field and the non-field. The algorithm has fast iteration and strong global search ability, avoiding the shortcoming of traditional methods that are easy to fall into local optimum. At the same time, the method simplifies the determination process of the boundary value, and through the clear division of the clustering result, the vehicle stability and control performance are significantly improved, which provides effective support for system optimization under complex dynamic conditions. BRIEF DESCRIPTION OF DRAWINGS

[0096] Figure 1 is a control framework schematic diagram

[0097] Figure 2 is a correlation function K(S) AFS Calculation schematic diagram

[0098] Figure 3 is a correlation function K(S) DYC Calculation schematic diagram DETAILED DESCRIPTION

[0099] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the embodiments and the drawings. The content mentioned in the embodiments is not a limitation of the present application.

[0100] The control method framework of the present application is shown in Figure 1 The control method includes the following steps:

[0101] Step 1: Establish the vehicle two-degree-of-freedom dynamics differential equation, considering the two degrees of freedom of lateral and yaw

[0102]

[0103] Where m represents the mass of the vehicle, v x and v y represent the longitudinal and lateral vehicle speeds, respectively, F yf , Fyr are the lateral forces of the front and rear axles, respectively, generated by the side slip angles of the front and rear wheels. I z is the moment of inertia of the vehicle around the center of mass, l f , l r are the distances from the center of mass to the front and rear axles, respectively. ΔM z is the additional yaw moment, and ω represents the yaw angular velocity.

[0104] By default, the lateral force of the tire is proportional to its side slip angle (in the approximate linear region), so the lateral forces of the front and rear wheels are:

[0105]

[0106] Where C f , C r are the side slip stiffness of the front and rear wheels, respectively, and αf and α r These are the slip angles of the front and rear wheels, respectively.

[0107] The slip angles of the front and rear wheels can be represented by the vehicle's state as follows:

[0108]

[0109] Where δ f This represents the total front wheel steering angle, which includes the front wheel steering angle δ input by the driver. fs and the additional front wheel steering angle Δδ f That is, δf = δ fs +Δδ f , and Δδ f Provided by the AFS system.

[0110] Define the state variable as the vehicle's lateral velocity v. y And yaw rate ω, used to measure vehicle stability.

[0111] Right now

[0112] Define the control input as the total front wheel steering angle δ f and additional yaw moment ΔM z .

[0113] Right now

[0114] According to the vehicle's lateral motion equation, we can obtain:

[0115]

[0116] According to the equation of motion of the vehicle's yaw, we can obtain:

[0117]

[0118] Rewrite the two equations in matrix form:

[0119]

[0120] in Let A be the state vector, B1 be the system matrix describing the dynamic behavior of the state variables, and B2 be the matrix showing the influence of the additional front wheel steering angle and the additional yaw moment on the state changes, as detailed below:

[0121]

[0122] The following section establishes the MPC controller. The purpose of the MPC controller is to control the total front wheel steering angle δ. f and additional yaw moment ΔM zThis causes the vehicle's yaw rate ω and lateral velocity v to... y To get as close as possible to the ideal stable state.

[0123] The ideal reference state is set as follows:

[0124]

[0125] Where the ideal yaw rate is ω ref It can be represented as:

[0126]

[0127] δ fs The front wheel steering angle input by the driver, and the ideal lateral speed v y,ref Set to 0, l represents the vehicle wheelbase, K u This is a stability factor.

[0128] The cost function of MPC is used to minimize the difference between the vehicle state and the ideal state, while controlling the total front wheel steering angle and the magnitude of the additional yaw moment. The cost function J is defined as follows:

[0129]

[0130] Where Q represents the weight matrix of the state error, used to control the deviation of the lateral velocity and yaw rate from the reference state; R1 is the control weight matrix of the total front wheel steering angle; R2 is the control weight matrix of the additional yaw moment; x(k) represents the real-time state matrix of the vehicle; u1(k) represents the real-time front wheel steering angle input of the vehicle; and u2(k) represents the real-time additional yaw moment input of the vehicle.

[0131] After discretizing the vehicle dynamics model, the MPC controller predicts the vehicle state for the next N steps within each time step by optimizing the cost function J, and calculates the optimal total front wheel steering angle δ at each moment. f* and additional yaw moment ΔM z* This optimization problem can be solved in real time using existing quadratic programming solvers (QP).

[0132] Then, the optimal control input for the front wheel steering angle at each moment can be calculated: Δδ f* =δ f* -δ fs .

[0133] Δδ f* and ΔM z* As an input to the extension coordination control section.

[0134] Step 2: Selection of Feature Quantities in Extension Theory

[0135] The energy method is used to quantitatively describe the driving state of the vehicle, specifically, the RT energy ratio and the SY energy ratio.

[0136] The RT energy ratio represents the ratio of the turning kinetic energy and the forward kinetic energy, and can be specifically expressed by the formula:

[0137]

[0138] wherein RKE represents the turning kinetic energy, TKE represents the forward kinetic energy, is the inertia radius, β is the mass center side slip angle of the vehicle, and tanβ≈β can be generally considered, m represents the mass of the vehicle, v represents the vehicle speed, and ω represents the yaw rate of the vehicle, and thus the formula can be written as:

[0139]

[0140] The SY energy ratio represents the ratio of the side slip kinetic energy and the yaw kinetic energy, and can be specifically expressed by the formula:

[0141]

[0142] I z represents the rotational inertia of the vehicle around the z-axis, SKE represents the side slip kinetic energy, and YKE represents the yaw kinetic energy.

[0143] Step three: determination of the region boundary of the extension theory

[0144] A large number of SY energy ratios and SY energy ratio change rates of the vehicle in operation are collected RT energy ratios and RT energy ratio change rates Data, ensuring that the data cover normal, abnormal and other states.

[0145] In order to eliminate the influence of the feature value size, the SY, RT, is normalized, and the data is scaled to between 0 and 1.

[0146]

[0147] wherein SY min and SY max respectively represent the minimum value and the maximum value of the SY energy ratio in all data. respectively represent the minimum value and the maximum value of the SY energy ratio change rate in all data. RT min and RT max respectively represent the minimum value and the maximum value of the RT energy ratio in all data. respectively represent the minimum value and the maximum value of the RT energy ratio change rate in all data.

[0148] The SY and RT are determined as follows: the extension theory boundary SY1 of SY, SY2,

[0149] In the present application, k is 3, that is, three data points containing SY and information are randomly selected as initial centroids, respectively For each data point remaining Calculate its Euclidean distance with all centroids c k :

[0150]

[0151] Where SY ak and represent two feature values of the kth centroid, k = 3.

[0152] According to the calculation results, each data point x i is assigned to the cluster corresponding to the centroid with the smallest distance. After the assignment of data points in each cluster is completed, the average position of all data points in the cluster is calculated, and the centroid of the cluster is updated:

[0153]

[0154] Where N k represents the number of data points in the kth cluster, S k represents all data points in the kth cluster, and x j represents the jth data point coordinate in the kth cluster.

[0155] Then repeat the above steps until the position of the centroid is stable, and stop iteration. Finally, the stable centroid

[0156] The final determined boundary is:

[0157]

[0158] Similarly, the extension theory boundary RT1 of RT and RT2,

[0159] Step four: first, select the RT energy ratio of the vehicle and the change rate of the RT energy ratio as characteristic quantities. These two characteristic quantities constitute a two-dimensional feature space for describing the dynamics and state of the vehicle. Then, based on the K-means clustering method, the RT and ​Different boundary values ​​define three typical state regions of the vehicle: 1. Classical domain: defined by RT1 and... 1. Defined boundary. 2. Extendable domain: composed of RT2 and... 3. Non-domains: Regions beyond the first two types of boundaries. By dividing these regions, a function K(S) associated with the AFS system can be calculated. AFS ,

[0160] like Figure 2 As shown, let the characteristic quantities (RT and...) of the current vehicle be... In a two-dimensional region, the extension distance from point P3 to the classical domain and the extensional domain is ρ(P3,(P4,P1)) and ρ(P3,(P5,P2)), respectively. These distances can be considered piecewise functions, and the calculation results are as follows:

[0161]

[0162] Correlation function K(S) AFS It reflects the stability state throughout the entire control process, and the final numerical value intuitively represents the difference between the current state and the optimal state. The calculation formula is as follows:

[0163]

[0164] in

[0165] D(P3,(P5,P2),(P4,P1))=ρ(P3,(P5,P2))-ρ(P3,(P4,P1)) (46)

[0166] This function reflects the relationship between the current vehicle status and the AFS intervention requirement.

[0167] When K(S) AFS When ≥1, the vehicle is in the classical domain, which is a relatively stable state and does not require intervention from AFS. Therefore, KA = 0 is set, that is, the weight coefficient of AFS is 0, and AFS does not participate.

[0168] When 0 ≤ K(S) AFS When KA < 1, the vehicle is in the extension domain, meaning the vehicle's state begins to deviate from the stable state and requires a certain degree of adjustment. In this case, KA = K(S). AFS This indicates that AFS will partially intervene to help adjust the vehicle's status.

[0169] If K(S) AFS <0, the vehicle enters a non-domain, which means that the vehicle is in an extremely unstable state. At this time, AFS needs to fully intervene to perform full state correction. Therefore, KA=1 is set to indicate that AFS is fully involved to ensure the safety and stability of the vehicle.

[0170] Step five: select the actual SY energy ratio of the vehicle and the change rate of the SY energy ratio as characteristic quantities. These two characteristic quantities constitute a two-dimensional characteristic space, which can effectively describe the dynamic performance of the vehicle in terms of yaw moment and lateral moment. Subsequently, the boundary values SY1, and SY2, of the characteristic quantities SY and are determined by the K-means clustering method, and the state region of the vehicle is divided into the following three regions: 1. Classical region: the region defined by the boundary values SY1 and , which represents the relatively stable state of the vehicle. 2. Extensible region: the region defined by the boundary values SY2 and , which represents a certain transition state of the vehicle, deviating from the stable state and needing adjustment. 3. Non-region: the region outside the boundary, indicating that the state of the vehicle is unstable and needs emergency intervention. Through the above division of the region, an association function K(S) DYC related to the DYC system can be calculated, and the calculation process is consistent with that of the association function K(S) AFS in step four, please refer to Figure 3 . K(S) DYC is used to reflect the relationship between the current state of the vehicle and the intervention demand of the DYC system.

[0171] When K(S) DYC ≥ 1, the state of the vehicle is in the classical region, and the vehicle body is relatively stable, so there is no need for the intervention of the DYC, and therefore KD = 0, i.e. the weight coefficient of the DYC is 0, and the DYC does not participate.

[0172] When 0 ≤ K(S) DYC < 1, the vehicle is in the extensible region, indicating that the state of the vehicle begins to deviate from the stable state and needs appropriate adjustment. At this time, the DYC system partially intervenes to help the vehicle adjust the state, so KD = K(S) DYC , i.e. the DYC intervenes in the adjustment according to the degree of state deviation.

[0173] When K(S) DYC < 0, it indicates that the vehicle enters the non-region and is in an extremely unstable state, and the dynamic performance of the vehicle may be abnormally severe, so the DYC system needs to be fully involved to restore the stability of the vehicle. Therefore, KD = 1, which means that the DYC system fully participates to ensure the safety of the vehicle.

[0174] Step six: obtain Δδ f* * KA and ΔM z* * KD, which are input to the AFS and DYC distribution execution module to complete the stability control of the vehicle body.

[0175] The application also provides a controller, such as a vehicle-mounted control device of an intelligent vehicle, which can execute the control method.

[0176] The above detailed description is only a specific description of the feasible embodiments of the application, and is not intended to limit the protection scope of the application. Any equivalent changes or modifications made without departing from the spirit of the application shall be included in the protection scope of the application.

Claims

1. A method for AFS / DYC collaborative control of an intelligent chassis of an automobile based on the extension theory, characterized in that, Comprise the following: S1: a two-degree-of-freedom dynamics model of the vehicle is established, and a state space equation is established according to the vehicle dynamics model, wherein the state quantity is defined as the lateral velocity v of the vehicle y , the yaw angular velocity ω, the control input quantity is the total front wheel angle δ f and the additional yaw moment ΔM z ; based on the MPC controller, the additional front wheel angle Δδ f* and the additional yaw moment ΔM z* required to maintain the vehicle body stability are calculated according to the constructed cost function; S2: select the characteristic quantity of extension theory, according to the energy method to the vehicle driving state quantitative description, characteristic quantity is RT energy ratio and SY energy ratio; S3: Determine the extension theory region boundary, the selection of the boundary adopts the method of K-means clustering, and the region is divided through cluster analysis, and finally the boundary of the extension partition is obtained, that is, RT1, RT2, SY1, SY2, S4: select the real-time RT energy ratio of the vehicle and the change rate of the RT energy ratio and as a feature quantity, a two-dimensional region is constructed according to the selected two feature quantities, and the determined boundary RT1, RT2, is divided into classical domain, extension domain and non-domain, and the correlation function K(S) of the AFS system is calculated AFS , the degree of intervention control of the AFS system is determined according to the range of the correlation function K(S) AFS ; S5: select the real-time SY energy ratio of the vehicle and the change rate of the SY energy ratio and as a feature, according to the selected two feature quantities, construct a two-dimensional region, and according to the K-means clustering method, the determined boundary SY1, SY2, is divided into classical domain, extension domain and non-domain, and the correlation function K(S) DYC is calculated, and the degree of intervention control of the DYC system is determined according to the range of the correlation function K(S) DYC ; S6: Synthesizes the degree of AFS system intervention in step S4 and the degree of DYC system intervention in step S5 to obtain a final extension coordination control output Δδ f* *KA and ΔM z* *KD.

2. The AFS / DYC collaborative control method based on the extension theory according to claim 1, characterized in that, The specific method of establishing vehicle state space equation of S1 is as follows: The two degree of freedom dynamic model is established as follows: where m represents the mass of the vehicle, v x and v y represent the longitudinal and lateral vehicle speeds, respectively, F yf , F yr are the lateral forces of the front and rear axles, respectively, generated by the side slip angles of the front and rear wheels, I z is the moment of inertia of the vehicle about the center of mass, l f , l r are the distances from the center of mass to the front and rear axles, respectively, and AM z is the additional yaw moment. The lateral force model of front and rear wheels is established: where C f , C r are the cornering stiffness of the front and rear wheels, respectively, and a f and a r are the side slip angles of the front and rear wheels, respectively. The side slip angle of front and rear wheels can be expressed as the state of vehicle as follows: where δ f represents the total front wheel steering angle, here including the driver input front wheel steering angle δ fs and an additional front wheel steering angle Δδ f ; i.e. δ f = δ fs + Δδ f , Δδ f being provided by the AFS system; The state variables are defined as the lateral velocity v of the vehicle y and the yaw rate ω, which measures the stability of the vehicle, i.e. The control input is defined as the total front wheel angle δ f and the additional yaw moment ΔM z ; That is According to the vehicle lateral motion equation, we can get: According to the vehicle yaw motion equation, we can get: The above two equations are written in matrix form as follows: wherein is the state vector; A is the system matrix, describing the dynamic behavior of the state variables, B1 and B2 are the influence matrices of the additional front wheel steering angle and the additional yaw moment on the state changes, respectively, as follows:

3. The AFS / DYC collaborative control method based on the extension theory of claim 2, wherein, The design of MPC controller of S1 is as follows: Set the ideal reference state as follows: where the ideal yaw rate ω ref is represented by: δ fs front wheel steering angle input by the driver, ideal lateral velocity v y,ref set to 0; l is the wheelbase of the vehicle, K u stability factor; The cost function of MPC is used to minimize the difference between the vehicle state and the ideal state, and to control the size of the total front wheel angle and the additional yaw moment, and the cost function J is defined as: Where Q represents the weight matrix of state error, which is used to control the deviation of lateral velocity and yaw angular velocity from the reference state;R1 is the control weight matrix of total front wheel angle;R2 is the control weight matrix of additional yaw moment; After the discretization of the vehicle dynamics model, the MPC controller predicts the vehicle states for N steps in the future at each time step by optimizing the cost function J, and calculates the optimal total front wheel steering angle δ at each time instant f* and the additional yaw moment ΔM z* The optimization problem can be solved in real-time by a quadratic programming solver (QP). According to the total front wheel steering angle δ f* The optimal additional front wheel steering angle control input Δδ f* = δ f* - δ fs ; Δδ f* and ΔM z* as an input quantity of the fuzzy coordination control part.

4. The AFS / DYC collaborative control method based on the extension theory according to claim 1, characterized in that, The RT energy ratio in S2 represents the ratio of turning kinetic energy and forward kinetic energy, which can be expressed as follows: where is the inertia radius, β is the vehicle's mass center side slip angle, which can generally be considered tan β ≈ β, m represents the vehicle's mass, v represents the vehicle speed, and ω represents the vehicle's yaw rate, so it can be written as: The SY energy ratio represents the ratio of side slip kinetic energy and yaw kinetic energy, which can be expressed as follows: I z denotes the moment of inertia of the vehicle about the z-axis.

5. The AFS / DYC collaborative control method based on the extension theory according to claim 1, characterized in that, The implementation of S3 comprises: SY energy ratio when the vehicle is running, rate of change of the SY energy ratio RT energy ratio, rate of change of the RT energy ratio data, ensuring data cover normal, abnormal state; For SY, RT, Normalization is done to scale the data between 0 and 1: wherein SY min , SY max respectively represent the minimum and maximum of the SY energy ratio over all data, respectively represent the minimum and maximum of the SY energy ratio change rate over all data, RT min , RT max respectively represent the minimum and maximum of the RT energy ratio over all data, respectively represent the minimum and maximum of the RT energy ratio change rate over all data; The extension theory boundary SY1 of SY2, SY2, First, randomly select 3 data points containing SY and information data points as initial centroids, respectively For each data point remaining, calculate its Euclidean distance to all centroids c k : where SY ak and respectively represent two eigenvalues of the kth centroid; According to the calculation result, each data point x i is assigned to the cluster corresponding to the centroid with the smallest distance, and after the assignment of data points in each cluster is completed, the average position of all data points in the cluster is calculated, and the centroid of the cluster is updated: where N k represents the number of data points in the kth cluster, S k represents all data points in the kth cluster, x j represents the jth data point coordinate in the kth cluster; Then repeat the above steps until the position of the centroid is stable, stop iteration; finally get the stable centroid The final determined boundary: The same way to determine RT and The extension theory boundary RT1, RT2, 6. The AFS / DYC collaborative control method based on the extension theory according to claim 1, characterized in that, In the S4, the boundary RT1, RT2, is divided into classical field, extension field and non-field as follows: Classical region: bounded by the RT1 and RT2 determinations. Extension field: the field determined by RT2 and the determined boundary; Non-domain: the area beyond the above two boundaries.

7. The AFS / DYC collaborative control method based on the extension theory according to claim 6, characterized in that, The correlation function K(S) in S4 AFS To reflect the relationship between the current vehicle state and the AFS intervention requirement, specifically as follows: Let the characteristic quantity RT of the current vehicle be At the point P3 in the two-dimensional region, the extension distances of P3 to the classical field and the extension field are ρ(P3,(P4, P1)) and ρ(P3,(P5, P2)), respectively. Taking them as a piecewise function, the calculation result is as follows: Correlation function K(S) AFS The stability state in the whole control process is embodied, and the gap between the current state and the optimal state is intuitively shown in the final value. The calculation formula is as follows: Where D(P3,(P5,P2),(P4,P1))=ρ(P3,(P5,P2))-ρ(P3,(P4,P1)) (23) When K(S) AFS When ≥1, the vehicle is in the classic domain, that is, in a relatively stable state, and does not require intervention from AFS. At this time, KA=0 is set, that is, AFS does not participate. When 0 < K(S) AFS <1, the vehicle is in the extension domain, i.e. the state of the vehicle starts to deviate from the stable state, and needs some degree of adjustment, at this time KA= K(S) AFS , indicating that the AFS will partially intervene to help adjust the state of the vehicle; If K(S) AFS <0, the vehicle enters the non-domain, which means that the vehicle is in a very unstable state, at which time the AFS needs to fully intervene and perform a full state correction. At this time, KA = 1 is set, indicating that the AFS fully participates to ensure the safety and stability of the vehicle.

8. The AFS / DYC collaborative control method based on the extension theory according to claim 1, characterized in that, The S5 will determine the boundary SY1, SY2, Classical domain, extension domain and non-domain, specific division as follows: Classical domain: the region defined by the boundary values SY1 and SY2, which represents the situation where the vehicle is in a relatively stable state; Extension field: the region composed of the boundary value SY2 and SY1, which represents that the vehicle is in a certain transition state, deviates from the stable state and needs to be adjusted. Non-domain: the area beyond the above two boundaries.

9. The AFS / DYC collaborative control method based on the extension theory according to claim 8, characterized in that, The correlation function K(S) of S5 DYC The calculation method of the correlation function K(S) of S4 AFS The calculation method is the same as that of the correlation function K(S) of S4, which is used to reflect the relationship between the current state of the vehicle and the intervention demand of the DYC system, and is specifically as follows: When K(S) DYC When K(S) ≥ 1, the state of the vehicle is in the classical domain, the body is relatively stable, and the intervention of the DYC is not needed, so KD = 0 is set, that is, the DYC does not participate. When 0≤K(S) DYC <1, the vehicle is in the extension domain, indicating that the state of the vehicle begins to deviate from the stable state and needs to be properly adjusted. At this time, the DYC system partially intervenes to help the vehicle adjust the state, and therefore KD=K(S) DYC , that is, the DYC intervenes in the adjustment according to the degree of state deviation. When K(S) DYC <0, it means that the vehicle enters the non-domain, and is in an extremely unstable state. The dynamic performance of the vehicle can be abnormally severe, and the full intervention of the DYC system is needed to restore the stability of the vehicle. Therefore, KD=1 is set, which means that the DYC system fully participates to ensure the safety of the vehicle.

10. An intelligent chassis AFS / DYC collaborative controller based on the extension theory, characterized in that, The controller can perform the content of the intelligent chassis AFS / DYC collaborative control method based on extension theory according to claim 1.

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