A Fuzzy-Sliding Mode Composite Control System of Four-Wheel Drive AFS / DYC Integrated Control System

Abstract

The present invention claims a fuzzy-sliding mode composite control method for a four-wheel drive AFS (Active Front Steering, active front wheel steering) / DYC (Direct Yaw-moment Control, direct yaw moment control) integrated control system, which is composed of fuzzy control based on slip rate as a control target and AFS (active front wheel angle) and DYC (direct yaw moment) controllers based on sliding film variable structure control to obtain additional values. The method is used to design an AFS / DYC integrated controller based on a four-wheel drive electric vehicle model, and then the weights of each controller are allocated to achieve switching between the controllers, and finally the driving torque of each wheel is allocated according to the dynamic load. Compared with the non-integrated control method under the same working conditions, the yaw stability performance of the four-wheel drive vehicle under high-speed driving conditions can be effectively improved. When the vehicle is unstable, the vehicle can be restored to a stable state by adding the front wheel angle and yaw moment, so that the actual yaw angular velocity can better track its ideal value, which can effectively improve the lateral stability of the vehicle under extreme working conditions.

Description

Technical Field

[0001] The invention belongs to a torque distribution method for a four-wheel drive electric vehicle, in particular to a composite control method combining a fuzzy control algorithm with a sliding mode variable structure algorithm for a four-wheel drive AFS / DYC integrated control system. Background Art

[0002] Four-wheel drive-distributed electric vehicles are different from single-motor centralized drive electric vehicles. Distributed drive electric vehicles are equipped with a hub motor in each wheel hub, which can independently drive each wheel and have a more flexible control method. When the vehicle is in an unstable state, traditional vehicles can only provide proportional braking force through two drive wheels to correct the vehicle's posture. Under extreme working conditions, instability is prone to occur. Distributed drive electric vehicles can be powered by four independently driven motors. By designing a reasonable control strategy, different driving and braking torque distribution can be achieved for each drive wheel at the same time.

[0003] The AFS system can provide additional front wheel steering angle according to the driving conditions of the vehicle to improve the handling stability of the vehicle, but the control effect of the system on low-adhesion roads is limited. The DYC system generates additional yaw torque by adjusting the longitudinal force of the tire to correct the driver's oversteering or understeering, but direct yaw torque control has a greater intervention on the longitudinal speed of the vehicle. In order to give full play to the advantages of the AFS and DYC systems, it is necessary to coordinate the control of AFS and DYC to solve their coupling problems. To this end, a fuzzy PID control and sliding mode variable structure composite control method based on slip rate observation is proposed. This method can allocate the weights of each controller and realize the switching between controllers. Compared with the non-integrated control method, this method can improve the real-time performance of torque distribution and effectively improve the yaw stability performance of four-wheel drive vehicles under high-speed driving conditions.

[0004] Therefore, designing a fuzzy-sliding mode composite control system of the four-wheel drive AFS / DYC integrated control system can effectively improve the lateral stability of the vehicle under extreme working conditions.

[0005] CN104443022B, a stability control method and system for a four-wheel independent drive electric vehicle, solves the technical problem that the two safety systems ARS and DYC of electric vehicles work at the same time in the prior art, resulting in coupling and reducing the performance of the whole vehicle. The method includes: when the vehicle turns, obtaining the steering wheel angle and vehicle speed; based on the mathematical model of vehicle speed and vehicle speed transmission ratio, obtaining the variable transmission ratio between the steering wheel and the rear wheel angle; obtaining the front wheel angle based on the steering wheel angle; obtaining the ideal state of the vehicle based on the ideal model of the variable transmission ratio vehicle, the vehicle speed and the front wheel angle; obtaining the actual state of the vehicle based on the nonlinear eight-degree-of-freedom model of the electric vehicle, the vehicle speed and the front wheel angle; obtaining the vehicle state error of the actual state of the vehicle relative to the ideal state of the vehicle; for the vehicle working in the nonlinear region or the linear region, respectively through ARS+DYC or ARS, control to eliminate or reduce the vehicle state error, so that the vehicle can run stably. Because the four-wheel drive vehicle has to make torque distribution in the face of real-time changing environment, the computing power requirements for the algorithm and controller used are very high, and the algorithm must be guaranteed to achieve the real-time performance of vehicle torque distribution. The real-time performance is not reflected in this patent. In the present invention, real-time observation is performed through the slip rate module, the slip rate is used as the control target, fuzzy control is used to obtain the integrated control factor, the weight of each controller is allocated, and the switching between the controllers is realized. The influence of the vertical axle load transfer on the wheel adhesion ability is also taken into consideration in the present invention. The driving torque allocated to the wheel with large adhesion ability should be larger, and the driving torque allocated to the wheel with small adhesion ability should be smaller. This can effectively prevent the wheel from slipping, so the dynamic load method is adopted to distribute the torque. Summary of the invention

[0006] The present invention aims to solve the above problems of the prior art. A fuzzy-sliding mode composite control system of a four-wheel drive AFS / DYC integrated control system is proposed. The technical solution of the present invention is as follows:

[0007] A fuzzy-sliding mode composite control system of a four-wheel drive AFS / DYC integrated control system is firstly established, and the composite control system comprises: a driver model, a seven-degree-of-freedom vehicle model, a tire model, a drive system model, a two-degree-of-freedom ideal model, a slip rate module, a torque distribution module, an AFS (active front wheel steering) controller, a DYC (direct yaw torque) controller and an AFS / DYC coordinated integrated controller; wherein the driver model is used for vehicle speed following and outputting the driving torque at the current speed, and the seven-degree-of-freedom vehicle model is used for analyzing the longitudinal, The tire model is used to calculate the sideslip angle and wheel slip rate of the wheel, the drive system model is used to output the motor characteristic curve, the two-degree-of-freedom ideal model is used to obtain the yaw angular velocity and the sideslip angle of the center of mass under the ideal state, the slip rate module is used to obtain the slip rate of each wheel under the driving state, the torque distribution module is used to distribute the driving force and the additional yaw moment to each wheel, the AFS controller is used to obtain a stable additional front wheel steering angle, the DYC controller is used to obtain a stable additional yaw moment, and the AFS / DYC coordinated integrated controller is used to allocate the weights of each controller.

[0008] The ideal yaw rate is obtained through the two-degree-of-freedom model. The difference and derivative of the yaw rate output by the whole vehicle model and the ideal yaw rate are used as the input of the sliding mode variable structure control. The AFS controller outputs the initial additional front wheel angle, and the DYC controller outputs the initial additional yaw moment. The AFS / DYC coordinated integrated controller gives priority to the longitudinal force of the wheel for stability control, and then considers the lateral force control. Through real-time observation by the slip rate module, the slip rate is used as the control target, and the fuzzy control is used to obtain the integrated control factor, thereby controlling the usage ratio of AFS and DYC, and then obtaining the final additional yaw moment and additional front wheel angle. The torque distribution module takes this as input, outputs the torque of the four tires in the form of dynamic load, and inputs it into the seven-degree-of-freedom whole vehicle model to form a closed loop. Under the joint action of the additional front wheel angle, the purpose of vehicle stability control is achieved.

[0009] Furthermore, the seven-degree-of-freedom vehicle model of the four-wheel drive is:

[0010]

[0011]

[0012]

[0013] Wherein, M is the vehicle mass; are the longitudinal acceleration and lateral acceleration of the vehicle respectively; ω z is the yaw rate of the vehicle; I z is the moment of inertia of the vehicle around the z axis; L f , Lr are the distances from the vehicle's center of mass to the front and rear axles respectively; A f , A r are the wheelbases of the front and rear wheels respectively; α1, α2, α3, α4 are the side slip angles of the four tires respectively; δ f is the front wheel turning angle; γ is the vehicle yaw rate; v x is the longitudinal velocity of the vehicle; v y is the lateral speed of the vehicle; F x_i , F y_i are the longitudinal force and lateral force on the tire respectively, and i=1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively.

[0014] Furthermore, the two-degree-of-freedom ideal model is:

[0015]

[0016]

[0017] Among them, k f , k r are the sum of the cornering stiffness of the front and rear wheels respectively; M is the vehicle mass; ω z_ide is the ideal yaw rate; β z_ide is the ideal center of mass sideslip angle; v x is the longitudinal velocity of the vehicle; δ f is the front wheel turning angle; L is the distance between the front and rear axles; K is the stability factor and

[0018] When the vehicle turns, the tires will have a limit value due to the limit of ground adhesion, and the vehicle's yaw rate will be limited. Therefore, the desired yaw rate must satisfy the following formula:

[0019] |ω max |=a(μg / v x )

[0020] Among them, a is the safety factor; μ is the road adhesion coefficient; ω max is the maximum yaw angular velocity; g is the gravitational acceleration;

[0021] Finally, the corrected ideal yaw rate can be obtained:

[0022]

[0023] Where L is the distance between the front and rear axles; v x is the longitudinal velocity of the vehicle; δ f is the front wheel turning angle; K is the stability factor.

[0024] Furthermore, the AFS controller is designed as follows:

[0025] Use the isokinetic approach law;

[0026]

[0027] in, is the sliding surface derivative; s is the sliding surface; ε is the reaching law constant, which indicates the rate at which the state point of the system approaches the sliding surface;

[0028] The yaw rate tracking error and its derivative are defined as:

[0029]

[0030] e ωz is the yaw rate tracking error; ω z is the actual yaw angular velocity; ω z_ide is the ideal yaw rate; the sliding mode switching surface function is defined as:

[0031]

[0032] Among them, s ωz is the sliding surface; c ωz is the relative weight coefficient between error and error change rate, and c ωz >0;

[0033] Simplifying, we get:

[0034]

[0035] In order to ensure that the system has a good state when moving to the sliding surface, the additional front wheel steering angle calculation formula is determined as:

[0036]

[0037] Among them, K ω To control the approach speed to the sliding surface, it is a positive constant;

[0038] In order to reduce the chattering phenomenon in sliding mode control, the saturation function sat(s ωz ) replaces the symbolic function sgn(s ωz ), then the additional front wheel turning angle Δδ can be obtained as follows:

[0039]

[0040] Furthermore, the DYC controller is designed as follows:

[0041] The yaw rate tracking error and its derivative are defined as:

[0042]

[0043] The sliding mode switching surface function is defined as:

[0044]

[0045] Among them, c ωz is the relative weight coefficient between error and error change rate, and c ωz >0;

[0046] Simplifying, we can get

[0047]

[0048] In order to ensure that the system has a good state when moving to the sliding surface, the additional yaw moment calculation formula is determined as:

[0049]

[0050] Furthermore, the AFS / DYC coordinated integrated controller is designed as follows:

[0051] The controller uses the slip rate module, takes the slip rate as the control target, and uses the fuzzy PID controller to obtain the integrated control factor, thereby controlling the usage ratio of AFS and DYC, and switching between the AFS and DYC controllers in real time. The mathematical model of the slip rate module is:

[0052]

[0053] Among them, λ i Indicates the slip rate of each wheel; v wx_i Speed ​​of each wheel x w Axis component; ω i Represents the rotational angular velocity of each wheel; R w represents the wheel rotation radius; where λ i The value range is [-1,1]. i ≥0, the tire slips; when λ i When <0, the tire slips.

[0054] Furthermore, the fuzzy PID controller based on slip ratio is designed as follows:

[0055] The fuzzy controller takes the slip ratio deviation and the rate of change of the deviation as input and corrects the parameter Δk p , Δk i , Δk d is the output, then the parameter K of the PID controller is p , K i , K d Then we have:

[0056]

[0057] where k p ', k i ', k d ' is the initial pre-set value;

[0058] The slip rate deviation defines 7 fuzzy subsets [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)] on its fuzzy domain [-1, -0.6, -0.3, 0, 0.3, 0.6, 1]; the slip rate deviation change rate defines 7 fuzzy subsets [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)] on its fuzzy domain [-6, -4, -2, 0, 2, 4, 6]; the output Δk p , Δk i , Δk d The fuzzy domain is [-6, -4, -2, 0, 2, 4, 6], and the 7 fuzzy subsets defined in its domain are [negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), positive large (PB)]. The fuzzy control uses the Mamdani type fuzzy reasoning method for fuzzy logic operations and the area centroid method for fuzzification. The final output u is expressed as:

[0059]

[0060] Where u(k) is the output of the controller; w i is the activation degree of the i-th fuzzy rule; y i is the geometric center.

[0061] Furthermore, the torque distribution model is designed as follows:

[0062]

[0063] Where: T is the torque obtained during speed stability control; r is the rolling radius of the vehicle tire; B is the distance between the tires; ΔM is the additional yaw torque; τ i (i=1, 2, 3, 4) represents the allocation ratio of the left front, right front, left rear and right rear wheels respectively.

[0064] Furthermore, the driver model is designed as follows:

[0065]

[0066]

[0067] Among them, I wis the moment of inertia; k is the inverse of the tire radius; R is the tire radius; v is the vehicle speed; is the acceleration; M is the vehicle mass; T e is the driving torque; T req is the desired torque of the motor; k1, k2, k3 are constants; τ is the time constant; g is the acceleration due to gravity; is the driving torque derivative.

[0068] Furthermore, the tire model is designed as follows:

[0069]

[0070] Among them, α i (i=1,2,3,4) represents the side slip angle of each wheel; δ i (i=1,2) indicates the left and right turning angle of the front wheel; v x_i , v y_i (i=1,2,3,4) represent the longitudinal velocity and lateral velocity of the tire in the vehicle coordinate system respectively.

[0071] Furthermore, the drive system model is designed as follows:

[0072]

[0073] Where: p is the rated power; n is the output speed of the motor; n1 is the maximum speed of the motor when working at constant torque; Te is the rated torque of the motor at constant torque, T i Output torque for the motor.

[0074] The advantages and beneficial effects of the present invention are as follows:

[0075] The innovation of the present invention is mainly the design of the AFS / DYC coordinated integrated controller in claim 6 and the combination of the slip rate module and the fuzzy controller in claim 7. The controller described in claim 6 is observed in real time through the slip rate module, with the wheel slip rate as the control target, and the fuzzy PID controller is used to obtain the integration factor of the controller, thereby controlling the use ratio of AFS and DYC, realizing real-time switching between the two controllers, and ensuring the real-time performance between the controllers. The seven-degree-of-freedom vehicle model described in claim 1 includes the vertical load forces of the four tires. The vertical axle load transfer has a great influence on the wheel adhesion. The driving torque allocated to the wheel with large adhesion should be large, and the driving torque allocated to the wheel with small adhesion should be small. Therefore, the dynamic load method is used in claim 8 to distribute torque, which can effectively prevent the wheel from slipping and improve the stability of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a seven-degree-of-freedom model diagram of a whole vehicle according to a preferred embodiment of the present invention;

[0077] Figure 2 It is a detailed principle block diagram of the control system of the present invention;

[0078] Figure 3 It is the structural diagram of the parameter self-tuning fuzzy PID system of the present invention;

[0079] Figure 4 is a diagram showing the relationship between the input and output of the fuzzy controller of the present invention;

[0080] Figure 5 It is a comparison diagram of yaw rate curves under integrated control and non-integrated control;

[0081] Figure 6 It is a comparison chart of lateral displacement curves under integrated control and non-integrated control;

[0082] Figure 7 It is a comparison diagram of longitudinal vehicle speed curves under integrated control and non-integrated control;

[0083] Figure 8 It is a comparison chart of torque distribution under integrated control and non-integrated control;

[0084] Fig. 9 It is a comparison chart of slip ratio under integrated control and non-integrated control. DETAILED DESCRIPTION

[0085] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0086] The technical solution of the present invention to solve the above technical problems is:

[0087] like Figure 1As shown, a fuzzy-sliding mode composite control system of a four-wheel drive AFS / DYC integrated control system includes: a driver model, a seven-degree-of-freedom vehicle model, a tire model, a drive system model, a two-degree-of-freedom ideal model, a slip rate module, a torque distribution module, an AFS controller, a DYC controller, and an AFS / DYC coordinated integrated controller. First, a seven-degree-of-freedom vehicle model, a tire model, a drive system model, and a driver model are established. The ideal yaw rate is obtained through the two-degree-of-freedom model. The difference and derivative of the yaw rate output by the vehicle model and the ideal yaw rate are used as the input of the sliding mode variable structure control. The AFS controller outputs the initial additional front wheel angle, and the DYC controller outputs the initial additional yaw torque. The AFS / DYC coordinated integrated controller gives priority to the longitudinal force of the wheel for stability control, and secondly considers the lateral force control. Through real-time observation by the slip rate module, the slip rate is taken as the control target, and the integrated control factor is obtained by fuzzy control, thereby controlling the use ratio of AFS and DYC, and then obtaining the final additional yaw torque and additional front wheel angle. The torque distribution module takes this as input and outputs the torque of the four tires in the form of dynamic load, which is input into the seven-degree-of-freedom vehicle model to form a closed loop. With the joint effect of the additional front wheel steering angle, the purpose of vehicle stability control is achieved.

[0088] Furthermore, the four-wheel drive seven-degree-of-freedom vehicle model is:

[0089]

[0090]

[0091]

[0092] Where M is the vehicle mass; ω z is the yaw rate of the vehicle; I z is the moment of inertia of the vehicle around the z axis; L f , L r are the distances from the vehicle's center of mass to the front and rear axles respectively; A f , A r are the wheelbases of the front and rear wheels respectively; α1, α2, α3, α4 are the side slip angles of the four tires respectively; δ f is the front wheel turning angle; γ is the vehicle yaw rate; v x is the longitudinal velocity of the vehicle; v y is the lateral speed of the vehicle; F x_i , F y_i are the longitudinal force and lateral force on the tire respectively, and i=1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively.

[0093] Furthermore, the two-degree-of-freedom ideal model is:

[0094]

[0095]

[0096] Among them, k f , k r are the sum of the cornering stiffness of the front and rear wheels respectively; M is the vehicle mass; ω z_ide is the ideal yaw rate; β z_ide is the ideal center of mass sideslip angle; v x is the longitudinal velocity of the vehicle; δ f is the front wheel turning angle; L is the distance between the front and rear axles; K is the stability factor and

[0097] When the vehicle turns, the tires will produce a limit value due to the limitation of the ground adhesion limit, and the vehicle's yaw rate will be limited. Therefore, the desired yaw rate must satisfy the following formula.

[0098] |ω max |=a(μg / v x )

[0099] Among them, a is the safety factor; μ is the road adhesion coefficient; ω max is the maximum yaw angular velocity; g is the acceleration due to gravity.

[0100] Finally, the corrected ideal yaw rate can be obtained:

[0101]

[0102] Where L is the distance between the front and rear axles; v x is the longitudinal velocity of the vehicle; δ f is the front wheel turning angle; K is the stability factor.

[0103] Furthermore, the AFS controller is designed as follows:

[0104] The uniform velocity reaching law with advantages of good robustness, strong real-time performance and small amount of calculation is selected.

[0105]

[0106] Among them, ε is the reaching law constant, which indicates the rate at which the state point of the system approaches the sliding surface.

[0107] The yaw rate tracking error and its derivative are defined as:

[0108]

[0109] The sliding mode switching surface function is defined as:

[0110]

[0111] Among them, c ωz is the relative weight coefficient between error and error change rate, and c ωz >0.

[0112] Simplifying, we get:

[0113]

[0114] In order to ensure that the system has a good state when moving to the sliding surface, the additional front wheel steering angle calculation formula is determined as:

[0115]

[0116] Among them, K ω To control the approach speed to the sliding surface, it is a positive constant.

[0117] In order to reduce the chattering phenomenon in sliding mode control, the saturation function sat(s ωz ) replaces the symbolic function sgn(s ωz ), then the additional front wheel turning angle Δδ can be obtained as follows:

[0118]

[0119] Furthermore, the DYC controller is designed as follows:

[0120] The yaw rate tracking error and its derivative are defined as:

[0121]

[0122] The sliding mode switching surface function is defined as:

[0123]

[0124] Among them, c ωz is the relative weight coefficient between error and error change rate, and c ωz >0.

[0125] Simplifying, we can get

[0126]

[0127] In order to ensure that the system has a good state when moving to the sliding surface, the additional yaw moment calculation formula is determined as:

[0128]

[0129] Furthermore, the AFS / DYC coordinated integrated controller is designed as follows:

[0130] The controller uses the slip rate observation module, takes the slip rate as the control target, and uses the fuzzy PID controller to obtain the integrated control factor, thereby controlling the usage ratio of AFS and DYC, and switching between AFS and DYC controllers in real time. Among them, the mathematical model of the slip rate module is:

[0131]

[0132] Among them, λ i Indicates the slip rate of each wheel; v wx_i Speed ​​of each wheel x w Axis component; ω i Represents the rotational angular velocity of each wheel; R w represents the wheel rotation radius; where λ i The value range is [-1,1]. i ≥0, the tire slips; when λ i When <0, the tire slips.

[0133] Furthermore, the fuzzy PID controller based on slip ratio is designed as follows:

[0134] The fuzzy controller takes the slip ratio deviation and the rate of change of the deviation as input and corrects the parameter Δk p , Δk i , Δk d is the output. Then the parameter K of the PID controller is p , K i , K d Then we have:

[0135]

[0136] where k p ', k i ', k d ' is the initial preset value.

[0137] The slip ratio deviation defines 7 fuzzy subsets [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)] on its fuzzy domain [-1, -0.6, -0.3, 0, 0.3, 0.6, 1]. The slip ratio deviation change rate defines 7 fuzzy subsets [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)] on its fuzzy domain [-6, -4, -2, 0, 2, 4, 6]. Output Δk p , Δk i , Δk dThe fuzzy domain is [-6, -4, -2, 0, 2, 4, 6], and the 7 fuzzy subsets defined in its domain are [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)]. Fuzzy control uses Mamdani type fuzzy reasoning method for fuzzy logic operation and area centroid method for fuzzification. The final output u is expressed as:

[0138]

[0139] Where u(k) is the output of the controller; w i is the activation degree of the i-th fuzzy rule; y i is the geometric center. Further, the torque distribution model is designed as follows:

[0140]

[0141] Where: T is the torque obtained during speed stability control; r is the rolling radius of the vehicle tire; B is the distance between the tires; ΔM is the additional yaw torque; τ i (i=1, 2, 3, 4) represents the allocation ratio of the left front, right front, left rear and right rear wheels respectively.

[0142] The four-wheel drive seven-degree-of-freedom vehicle model of the present invention is:

[0143]

[0144]

[0145]

[0146] Where M is the vehicle mass; ω z is the yaw rate of the vehicle; I z is the moment of inertia of the vehicle around the z axis; L f , L r are the distances from the vehicle's center of mass to the front and rear axles respectively; A f , A r are the wheelbases of the front and rear wheels respectively; α1, α2, α3, α4 are the side slip angles of the four tires respectively; δ f is the front wheel turning angle; γ is the vehicle yaw rate; v x is the longitudinal velocity of the vehicle; v y is the lateral speed of the vehicle; F x_i , F y_i are the longitudinal force and lateral force on the tire respectively, and i=1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively.

[0147] Attached Figure 1This is a seven-degree-of-freedom model diagram of the whole vehicle of the present invention, Figure 2 It is a detailed principle block diagram of the control system of the present invention. According to these two figures, it is easy to know that

[0148] This implementation includes the following steps:

[0149] 1) Establish a seven-degree-of-freedom steering dynamics model;

[0150] 2) Obtain the ideal yaw rate through the two-degree-of-freedom model;

[0151] 3) The difference between the yaw rate output by the vehicle model and the ideal yaw rate and its derivative are used as the input of the sliding mode variable structure control;

[0152] 4) The AFS controller takes the difference in yaw rate in step 3 as input, and the output is the initial additional front wheel steering angle; the DYC controller takes the difference in yaw rate in step 3 as input, and the output is the initial additional yaw moment;

[0153] 5) Through the slip rate observation module, with the slip rate as the control target, fuzzy control is used to obtain the integrated control factor, control the usage ratio of the AFS controller and the DYC controller, and then obtain the final additional yaw moment and additional front wheel steering angle;

[0154] 6) The torque distribution module takes step 5 as input, outputs the torque of the four tires in the form of dynamic load, and inputs it into the seven-degree-of-freedom vehicle model to form a closed loop.

[0155] Attached Figure 3 This is the structure diagram of the parameter self-tuning fuzzy PID system of the present invention, Figure 4 It is a relationship diagram between the input and output of the fuzzy controller of the present invention. The fuzzy PID controller based on slip rate of the present invention is designed as follows:

[0156] The fuzzy controller takes the slip ratio deviation and the rate of change of the deviation as input and corrects the parameter Δk p , Δk i , Δk d is the output. Then the parameter K of the PID controller is p , K i , K d Then we have:

[0157]

[0158] where k p ', k i ', k d ' is the initial preset value.

[0159] Fuzzy control uses Mamdani type fuzzy reasoning method to perform fuzzy logic operation and area centroid method to perform fuzzification. The expression of the final output u is:

[0160]

[0161] Where u(k) is the output of the controller; w i is the activation degree of the i-th fuzzy rule; y i is the geometric center.

[0162] Attached Figure 5 is a comparison of the yaw rate curves under integrated control and non-integrated control. Figure 6 is a comparison chart of the lateral displacement curves under integrated control and non-integrated control. Figure 7 It is a comparison diagram of the longitudinal vehicle speed curves under integrated control and non-integrated control. The present invention (a fuzzy-sliding mode composite control method for a four-wheel drive AFS / DYC integrated control system) is compared with a non-integrated control system. It can be seen that the control method of the present invention is improved in terms of yaw angular velocity, longitudinal vehicle speed and lateral displacement under sliding mode control and fuzzy control compared with non-integrated control.

[0163] Attached Figure 8 This is a comparison chart of torque distribution curves under integrated control and non-integrated control. Fig. 9 It is a comparison diagram of slip rate curves under integrated control and non-integrated control. The present invention (a fuzzy-sliding mode composite control method of a four-wheel drive AFS / DYC integrated control system) is compared with a non-integrated control system. It can be seen that compared with the non-integrated control, the control method of the present invention, the slip rate and torque distribution control system actively distributes the driving force of the front and rear axle wheels in real time according to the steering trend of the vehicle, meets the requirements of vehicle stability driving, and improves the lateral stability of the vehicle under high-speed driving.

[0164] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0165] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0166] The above embodiments should be understood to be only used to illustrate the present invention and not to limit the protection scope of the present invention. After reading the contents of the present invention, technicians can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A fuzzy-sliding mode composite control system of a four-wheel drive AFS / DYC integrated control system, characterized in that: include: A driver model, a seven-degree-of-freedom vehicle model, a tire model, a drive system model, a two-degree-of-freedom ideal model, a slip rate module, a torque distribution module, an AFS active front wheel angle controller, a DYC direct yaw torque controller, and an AFS / DYC coordinated integrated controller; wherein the driver model is used to follow the vehicle speed and output the driving torque at the current speed, the seven-degree-of-freedom vehicle model is used to analyze the longitudinal, lateral and yaw motions of the vehicle, the tire model is used to calculate the sideslip angle and wheel slip rate of the wheel, the drive system model is used to output the motor characteristic curve, the two-degree-of-freedom ideal model is used to obtain the yaw angular velocity and the sideslip angle of the center of mass under the ideal state, the slip rate module is used to obtain the slip rate of each wheel under the driving state, the torque distribution module is used to distribute the driving force and the additional yaw torque to each wheel, the AFS controller is used to obtain a stable additional front wheel angle, the DYC controller is used to obtain a stable additional yaw torque, and the AFS / DYC coordinated integrated controller is used to distribute the weights of each controller; The ideal yaw rate is obtained through the two-degree-of-freedom model. The difference and derivative of the yaw rate output by the vehicle model and the ideal yaw rate are used as the input of the sliding mode variable structure control. The AFS controller outputs the initial additional front wheel angle, and the DYC controller outputs the initial additional yaw moment. The AFS / DYC coordinated integrated controller gives priority to the longitudinal force of the wheel for stability control, and then considers the lateral force control. Through the real-time observation of the slip rate module, the slip rate is used as the control target, and the integrated control factor is obtained by fuzzy control, so as to control the use ratio of AFS and DYC, and then obtain the final additional yaw moment and additional front wheel angle. The torque distribution module takes this as input, outputs the torque of the four tires in the form of dynamic load, and inputs it into the seven-degree-of-freedom vehicle model to form a closed loop. Under the joint action of the additional front wheel angle, the purpose of vehicle stability control is achieved. The AFS / DYC coordinated integrated controller is designed as follows: The controller uses the slip rate module, takes the slip rate as the control target, and uses the fuzzy PID controller to obtain the integrated control factor, thereby controlling the usage ratio of AFS and DYC, and switching between the AFS and DYC controllers in real time. The mathematical model of the slip rate module is: Among them, λ i Indicates the slip rate of each wheel; v wx_i Wheel speed x w Axis component; ω i Represents the rotational angular velocity of each wheel; R w represents the wheel rotation radius; where λ i The value range is [-1,1]. i ≥0, the tire slips; when λ i When <0, the tire slips; The fuzzy PID controller based on slip ratio is designed as follows: The fuzzy controller takes the slip rate deviation and the rate of change of the deviation as input and corrects the parameter Δk p , Δk i , Δk d is the output, then the parameter K of the PID controller is p , K i , K d Then we have: where k p ', k i ', k d ' is the initial preset value; The slip rate deviation defines 7 fuzzy subsets [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)] on its fuzzy domain [-1, -0.6, -0.3, 0, 0.3, 0.6, 1]; the slip rate deviation change rate defines 7 fuzzy subsets [Negative Large (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Large (PB)] on its fuzzy domain [-6, -4, -2, 0, 2, 4, 6]; the output Δk p , Δk i , Δk d The fuzzy domain is [-6, -4, -2, 0, 2, 4, 6], and the 7 fuzzy subsets defined in its domain are [negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), positive large (PB)]. The fuzzy control uses the Mamdani type fuzzy reasoning method for fuzzy logic operations and the area centroid method for fuzzification. The final output u is expressed as: Where u(k) is the output of the controller; w i is the activation degree of the i-th fuzzy rule; y i is the geometric center; the torque distribution model is designed as follows: Where: T is the torque obtained during speed stability control; r is the rolling radius of the vehicle tire; B is the distance between the tires; ΔM is the additional yaw torque; τ i (i=1, 2, 3, 4) represents the allocation ratio of the left front, right front, left rear and right rear wheels respectively.

2. A fuzzy-sliding mode composite control system of a four-wheel drive AFS / DYC integrated control system according to claim 1, characterized in that: The seven-degree-of-freedom vehicle model of four-wheel drive is: Wherein, M is the vehicle mass; are the longitudinal acceleration and lateral acceleration of the vehicle respectively; ω z is the yaw rate of the vehicle; I z is the moment of inertia of the vehicle around the z axis; L f , L r are the distances from the vehicle's center of mass to the front and rear axles respectively; A f , A r are the wheelbases of the front and rear wheels respectively; α1, α2, α3, α4 are the side slip angles of the four tires respectively; δ f is the front wheel turning angle; γ is the vehicle yaw rate; v x is the longitudinal velocity of the vehicle; v y is the lateral speed of the vehicle; F x_i , F y_i are the longitudinal force and lateral force on the tire respectively, and i=1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively.

3. The fuzzy-sliding mode composite control system of the four-wheel drive AFS / DYC integrated control system according to claim 2 is characterized in that: The two-degree-of-freedom ideal model is: Among them, k f , k r are the sum of the cornering stiffness of the front and rear wheels respectively; M is the vehicle mass; ω z_ide is the ideal yaw rate; β z_ide is the ideal center of mass sideslip angle; v x is the longitudinal velocity of the vehicle; δ f is the front wheel turning angle; L is the distance between the front and rear axles; K is the stability factor and When the vehicle turns, the tires will have a limit value due to the limit of ground adhesion, and the vehicle's yaw rate will be limited. Therefore, the desired yaw rate must satisfy the following formula: |oh max |=a(μg / v x ) Among them, a is the safety factor; μ is the road adhesion coefficient; ω max is the maximum yaw angular velocity; g is the gravitational acceleration; Finally, the corrected ideal yaw rate can be obtained: Where L is the distance between the front and rear axles; v x is the longitudinal velocity of the vehicle; δ f is the front wheel turning angle; K is the stability factor.

4. A fuzzy-sliding mode composite control system of a four-wheel drive AFS / DYC integrated control system according to claim 3, characterized in that: The AFS controller is designed as follows: Use the isokinetic approach law; in, is the sliding surface derivative; s is the sliding surface; ε is the reaching law constant, which indicates the rate at which the state point of the system approaches the sliding surface; The yaw rate tracking error and its derivative are defined as: e ωz is the yaw rate tracking error; ω z is the actual yaw angular velocity; ω z_ide is the ideal yaw rate; The sliding mode switching surface function is defined as: Among them, s ωz is the sliding surface; c ωz is the relative weight coefficient between error and error change rate, and c ωz >0; Simplifying, we get: In order to ensure that the system has a good state when moving to the sliding surface, the additional front wheel steering angle calculation formula is determined as: Among them, K ω To control the approach speed to the sliding surface, it is a positive constant; In order to reduce the chattering phenomenon in sliding mode control, the saturation function sat(s ωz ) replaces the symbolic function sgn(s ωz ), then the additional front wheel turning angle Δδ can be obtained as follows:

5. The fuzzy-sliding mode composite control system of the four-wheel drive AFS / DYC integrated control system according to claim 4 is characterized in that: The DYC controller is designed as follows: The yaw rate tracking error and its derivative are defined as: The sliding mode switching surface function is defined as: Among them, c ωz is the relative weight coefficient between error and error change rate, and c ωz >0; Simplifying, we can get In order to ensure that the system has a good state when moving to the sliding surface, the additional yaw moment calculation formula is determined as:

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