Vehicle active rear wheel steering control method and system based on driving conditions and vehicle

Abstract

The present application relates to the technical field of vehicle steering control, more particularly to a vehicle active rear wheel steering control method and system based on driving conditions. The present application first acquires the current driving state of a target vehicle; determines the driving condition of the target vehicle according to the current driving state; and then controls the rear wheels of the target vehicle according to the corresponding control strategy switched according to the driving condition. Unlike the traditional rear wheel follow-up steering technology, the present application can control the rear wheel steering by using different control methods according to different driving conditions of the target vehicle to achieve optimal control and improve the steering stability and flexibility of the target vehicle, thereby solving the problem of low adaptability of the existing rear wheel follow-up steering to different driving conditions and general operation stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle steering control, and more particularly, to a vehicle active rear wheel steering control method and system based on driving conditions and a vehicle. BACKGROUND

[0002] Rear wheel steering refers to the rear wheels generating a certain degree of steering angle in addition to the front wheels providing steering force and steering direction when the vehicle is turning.

[0003] Traditional vehicles mostly use rear wheel follow-up steering. The rear wheel follow-up steering is not a complete steering mechanism arranged on the rear wheels, but only some rubber cushions are arranged between the rear wheels, suspensions and the vehicle body, and the suspensions and the vehicle body are flexibly connected through the rubber. Since the rubber has a certain elasticity, the rubber cushions at the rear suspension connection points can be elastically deformed to a certain extent under the action of the lateral force when the vehicle is turning, thereby driving the wheels to change by a certain angle.

[0004] However, the rear wheel follow-up steering is actually passive control, and the adaptation to different driving conditions is not high, and the operation stability is general. This is because the material and structure parameters are fixed, and there are certain limitations, and only the vehicle handling stability can be improved within a certain speed range. When the related parameters change or the driving conditions are complex, not only the expected optimization effect cannot be achieved, but also the steering characteristics may be deteriorated, resulting in loss of stability of the vehicle. SUMMARY

[0005] Therefore, it is necessary to provide a vehicle active rear wheel steering control method and system based on driving conditions and a vehicle to solve the problem that the adaptation to different driving conditions is not high and the operation stability is general for the existing rear wheel follow-up steering.

[0006] The present application adopts the following technical solutions:

[0007] In a first aspect, the present application discloses a vehicle active rear wheel steering control method based on driving conditions, comprising the following steps:

[0008] Step 1: obtaining the current driving state of the target vehicle;

[0009] The current driving state includes the current driving speed and the current driving direction.

[0010] Step 2: determining the driving condition of the target vehicle according to the current driving state;

[0011] If the current driving speed is not more than a preset threshold and the current driving direction is forward, the driving condition is a low-speed forward condition.

[0012] If the current driving speed exceeds the preset threshold and the current driving direction is forward, the driving condition is a medium-high speed forward driving condition.

[0013] If the current driving direction is backward, the driving condition is a reverse driving condition.

[0014] Step three, according to the driving condition, switch the corresponding control strategy to control the rear wheels of the target vehicle.

[0015] If the driving condition is a low speed forward driving condition, the rotation amplitude and rotation rate of the steering wheel of the target vehicle are obtained, and a fuzzy rule is formulated to calculate the rear wheel turning angle, and the rear wheel turning angle is opposite to the front wheel turning angle.

[0016] If the driving condition is a medium-high speed forward driving condition, the real-time state parameters of the target vehicle are obtained, and the running state of the target vehicle is analyzed by referring to the offline clustering mean Means(k, i); k = 1, 2, 3; i = 1, 2, 3, 4.

[0017] If the target vehicle is in a stable state, the rear wheels are not turned; if the target vehicle tends to be unstable, the rear wheel turning angle is calculated by using a sliding mode control algorithm, the rear wheel turning angle is the same as the front wheel turning angle, and the gain coefficient of the sliding mode control algorithm is adaptively adjusted according to the instability degree; if the target vehicle is in an unstable state, the rear wheel turning angle is calculated by using a sliding mode control algorithm, the rear wheel turning angle is the same as the front wheel turning angle, and the gain coefficient of the sliding mode control algorithm is a constant value.

[0018] If the driving condition is a reverse driving condition, the front wheel turning angle of the target vehicle is obtained, and the relationship between the rear wheel turning angle and the front wheel turning angle is calculated according to the two-degree-of-freedom dynamic model of the vehicle, and the rear wheel turning angle is calculated, and the rear wheel turning angle is opposite to the front wheel turning angle.

[0019] The vehicle active rear wheel steering control method based on the driving condition realizes the method or process according to the embodiments of the present disclosure.

[0020] In a second aspect, the present disclosure discloses a vehicle active rear wheel steering control system based on driving conditions, which uses the vehicle active rear wheel steering control method based on driving conditions disclosed in the first aspect.

[0021] The vehicle active rear wheel steering control system based on driving conditions comprises a driving state acquisition module, a driving condition judgment module, and an active rear wheel control module.

[0022] The driving state acquisition module is used to acquire the current driving state of the target vehicle. The driving condition judgment module is used to determine the driving condition of the target vehicle according to the current driving state. The active rear wheel control module is used to switch the corresponding control strategy to control the rear wheels of the target vehicle according to the driving condition.

[0023] This vehicle active rear-wheel steering control system based on driving conditions implements the method or process according to embodiments of this disclosure.

[0024] Thirdly, the present invention discloses a vehicle that uses the vehicle active rear-wheel steering control method based on driving conditions disclosed in the first aspect.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. Unlike traditional rear-wheel steering technology, this invention can use different control methods to control the rear wheel steering according to different driving conditions of the target vehicle to achieve optimal control and improve the handling stability and flexibility of the target vehicle.

[0027] 2. The control method of the present invention, under low-speed forward driving conditions, uses the steering wheel rotation amplitude and rotation rate to determine the rear wheel steering angle according to fuzzy rules. Fuzzy rules provide a natural framework for describing human behavior and decision analysis, analyzing the required control values ​​in a human-like way. This allows for adaptation to changes in vehicle dynamics, environmental characteristics, and driving conditions, and better suits the vehicle's handling capabilities.

[0028] 3. The control method of the present invention, under medium and high speed forward conditions, determines the intervention and withdrawal timing of rear wheel steering control by analyzing the operating state of the target vehicle, which can reduce unnecessary energy loss and make the control more precise and effective; and the operating state analysis is based on the Gaussian mixture model clustering method, which comprehensively considers the state parameters related to vehicle stability and performs hierarchical quantification of vehicle driving stability, which facilitates accurate intervention for control. Attached Figure Description

[0029] Figure 1 This is a simplified flowchart of the vehicle active rear wheel steering control method based on driving conditions in Embodiment 1 of the present invention;

[0030] Figure 2 This is a detailed flowchart of the vehicle active rear wheel steering control method based on driving conditions in Embodiment 1 of the present invention;

[0031] Figure 3 for Figure 2 A schematic diagram of a two-degree-of-freedom yaw dynamics model of a vehicle. Detailed Implementation

[0032] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0033] It should be noted that when a component is referred to as being "mounted on" another component, it can be directly on the other component or there can be a middle component. When a component is referred to as being "disposed on" another component, it can be directly disposed on the other component or there can be a middle component. When a component is referred to as being "fixed on" another component, it can be directly fixed on the other component or there can be a middle component.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "including" and "having" are intended to be inclusive and mean that there can be additional

[0035] Embodiment 1

[0036] Referring to Figure 1 , a schematic diagram of a vehicle active rear wheel steering control method based on driving conditions disclosed in Embodiment 1 of the present application. In general, the control method is to control the rear wheel steering according to different driving conditions of the vehicle by using different control methods.

[0037] Referring to Figure 2 , a detailed flow chart of the vehicle active rear wheel steering control method based on driving conditions in Embodiment 1 of the present application. As shown in Figure 2 , the control method includes the following steps:

[0038] Step 1, obtaining the current driving state of the target vehicle.

[0039] The current driving state includes the current driving speed and the current driving direction. These parameters can be collected by the sensors (speed sensor, direction sensor) provided in the target vehicle.

[0040] Step 2, judging the driving condition of the target vehicle according to the current driving state.

[0041] The method divides the driving conditions that the target vehicle may involve into three categories: 1, low-speed forward driving condition, 2, medium-high speed forward driving condition, and 3, reverse driving condition.

[0042] The basis for the corresponding judgment is:

[0043] If the current driving speed does not exceed the preset threshold and the current driving direction is forward, the driving condition is a low-speed forward driving condition.

[0044] If the current driving speed exceeds the preset threshold and the current driving direction is forward, the driving condition is a medium-high-speed forward driving condition.

[0045] If the current driving direction is backward, the driving condition is a reverse driving condition.

[0046] The preset threshold is a standard for measuring the real-time speed of the target vehicle, and exceeding the preset threshold can be regarded as medium-high speed. Generally, the preset threshold is 30 km / h to 35 km / h. In this embodiment 1, the preset threshold is 30 km / h.

[0047] Step three, according to the driving condition, switch the corresponding control strategy to control the rear wheels of the target vehicle.

[0048] This step is the core of the entire control method, which adopts different control strategies according to different driving conditions.

[0049] The control range of the rear wheel angle is [-δ max , δ max ]. δ max represents the maximum value of the rear wheel angle. δ max The values in different conditions can be different.

[0050] The above three conditions are described one by one:

[0051] (1) If the driving condition is a low-speed forward driving condition, the rotation amplitude and rotation rate of the steering wheel of the target vehicle are obtained, and a fuzzy rule is formulated to calculate the rear wheel angle.

[0052] In the low-speed forward driving condition, a principle is followed that the rear wheel angle is opposite to the front wheel angle to increase the over-steering.

[0053] The fuzzy rule is to establish a mapping relationship between the rotation amplitude, rotation rate and rear wheel angle.

[0054] The domain of the steering wheel rotation amplitude is set as [0, U max ]; wherein U max represents the maximum angle of the steering wheel rotation. It should be noted that the steering wheel rotation amplitude is actually from -U max to U max , but since the direction of the rear wheel angle is determined according to the direction of the front wheel angle; therefore, the absolute value of the steering wheel rotation amplitude is taken, and the domain of the steering wheel rotation amplitude is set as [0, U max ].

[0055] The argument of the steering wheel rotation rate is set as [0, w max ]; wherein w max represents the maximum angular velocity of the steering wheel rotation. max , w max can be adjusted according to different vehicle models and different users.

[0056] The argument of the rear wheel rotation angle is set as [0, δ max ]. The similar reason as the steering wheel rotation amplitude: although the control range of the rear wheel rotation angle is from -δ max to δ max , since the direction of the rear wheel rotation angle is determined according to the direction of the front wheel rotation angle, the absolute value of the rear wheel rotation angle is taken, and the argument of the rear wheel rotation angle is set as [0, δ max ].

[0057] In the fuzzy rules, the fuzzy subsets of the steering wheel rotation amplitude are defined as {NB1, NM1, NS1, ZO1, PS1, PM1, PB1}. The fuzzy subsets of the steering wheel rotation rate are defined as {NB2, NM2, NS2, ZO2, PS2, PM2, PB2}. The fuzzy subsets of the rear wheel rotation angle are defined as {NB3, NM3, NS3, ZO3, PS3, PM3, PB3}.

[0058] NB j represents negative big, NM j represents negative medium, NS j represents negative small, ZO j represents zero, PS j represents positive small j , PM j represents positive medium, and PB j represents positive big; j = 1, 2, 3.

[0059] In this embodiment 1, the division of the above fuzzy subsets can adopt the equal division method. That is, [0, U max ] is equally divided into 7 subintervals, corresponding to {NB1, NM1, NS1, ZO1, PS1, PM1, PB1}; [0, w max ] is equally divided into 7 subintervals, corresponding to {NB2, NM2, NS2, ZO2, PS2, PM2, PB2}; and [0, δ max ] is equally divided into 7 subintervals, corresponding to {NB3, NM3, NS3, ZO3, PS3, PM3, PB3}. Of course, the division of the fuzzy subsets can also adopt a non-equal division method, which is adjusted according to the control effect.

[0060] Based on the above fuzzy subsets, the fuzzy rules are set as:

[0061] When the steering wheel is rotated by NB1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to NB3, NB3, NM3, NS3, ZO3, PS3, PM3 in turn. When the steering wheel is rotated by NM1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to NB3, NM3, NS3, ZO3, PS3, PM3, PM3 in turn. When the steering wheel is rotated by NS1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to NM3, NS3, NS3, ZO3, PS3, PM3, PM3 in turn. When the steering wheel is rotated by ZO1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to NS3, NS3, ZO3, PS3, PS3, PM3, PB3 in turn. When the steering wheel is rotated by PS1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to NS3, ZO3, ZO3, PS3, PM3, PM3, PB3 in turn. When the steering wheel is rotated by PM1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to ZO3, ZO3, PS3, PS3, PM3, PB3, PB3 in turn. When the steering wheel is rotated by PB1, the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to PS3, PS3, PM3, PM3, PB3, PB3, PB3 in turn.

[0062] The above fuzzy rules can also be represented in a table, which is shown in Table 1.

[0063] Table 1 Fuzzy rules

[0064]

[0065] It should be noted that the rear wheel rotation angle obtained according to the fuzzy rules falls into one of NB3, NM3, NS3, ZO3, PS3, PM3, PB3, and then needs to be de-fuzzied according to the membership degree, so as to obtain a specific angle value. The calculation of de-fuzzification is a conventional method of fuzzy rule calculation, which is not described herein.

[0066] In general, the driving condition is low-speed forward driving condition, the rear wheel rotation angle is opposite to the front wheel rotation angle, and the control range of the rear wheel rotation angle is -8°-8°.

[0067] (2) When the driving condition is a medium-high speed forward driving condition, a real-time state parameter of the target vehicle is obtained, and a running state of the target vehicle is analyzed by referring to the offline clustering mean Means(k, i); k = 1, 2, 3; i = 1, 2, 3, 4.

[0068] The state in which the target vehicle is located includes three types: in a stable state, tends to an unstable state, and in an unstable state. In this condition, the running state of the target vehicle is determined, and the control of the rear wheel steering angle is carried out accordingly.

[0069] First, the offline clustering mean Means(k, i) is obtained by testing the target vehicle.

[0070] Specifically, the method for obtaining the offline clustering mean Means(k, i) includes:

[0071] The historical state parameters of the target vehicle under a test condition are obtained; wherein the test condition is a double shift line test and a fishhook condition test on a road surface with an adhesion coefficient of 0.8 and 0.4, with a step of 10 km / h from 30 km / h to 120 km / h. The types of state parameters include: yaw rate, center side slip angle, lateral acceleration, and vertical load of the vehicle heading angle side wheel.

[0072] The historical state parameters are processed by using a Gaussian mixture model clustering algorithm to obtain the offline clustering mean Means(k, i). The offline clustering mean Means(k, i) obtained in this way includes 12 clustering means:

[0073] For Means(k, i); k represents the running state of the target vehicle: k = 1 corresponds to a stable state; k = 2 corresponds to a tendency to an unstable state; k = 3 corresponds to an unstable state. i represents a state parameter: i = 1 corresponds to a yaw rate; i = 2 corresponds to a center side slip angle; i = 3 corresponds to a lateral acceleration; i = 4 corresponds to a vertical load of the vehicle heading angle side wheel.

[0074] For example, Means(1, 1) represents the offline clustering mean of the yaw rate in the stable state; the meanings of other conditions are analogized according to Means(1, 1), which will not be described here.

[0075] Then, the method for analyzing the running state of the target vehicle includes:

[0076] S301, according to the real-time state parameter, the offline clustering mean Means(k, i) is updated by using the mean method to obtain the real-time clustering mean NewMeans(k, i).

[0077] The real-time clustering mean NewMeans(k, i) is also 12: 4 of which correspond to the stable state, the real-time clustering mean NewMeans(1, i) of the target vehicle in the stable state; 4 correspond to the unstable state, the real-time clustering mean NewMeans(2, i) in the unstable state; 4 correspond to the unstable state, the real-time clustering mean NewMeans(3, i) in the unstable state.

[0078] For example, NewMeans(1, 1) represents the real-time clustering mean of the yaw rate in the stable state; the meanings of other cases are similar to NewMeans(1, 1), which will not be repeated here.

[0079] Specifically, the calculation method of NewMeans(k, i) is as follows:

[0080]

[0081] Wherein, n(k) represents the number of parameters in the kth running state: n(1) corresponds to the stable state; n(2) corresponds to the unstable state; n(3) corresponds to the unstable state.

[0082] x i x1 represents the real-time yaw rate; x2 represents the real-time center side slip angle; x3 represents the real-time lateral acceleration; x4 represents the real-time vertical load.

[0083] For example, if the clustering mean of the yaw rate in the first running state (in the stable state) obtained under the test working condition is 4, that is, Means(1, 1) = 4, and the number of sample parameters of the yaw rate classified into this category at this time is 100, that is, n(1) = 100; the real-time vehicle running state yaw rate is 6, that is, x1 = 6, then the updated real-time clustering mean NewMeans(1, 1) is The clustering mean updating principle of other cases is referred to this example and will not be repeated.

[0084] S302, calculate the Euclidean distance d(k) from the real-time state parameter to the real-time clustering mean NewMeans(k, i).

[0085] Specifically,

[0086] Wherein, d(1) represents the Euclidean distance of the real-time state parameter to the real-time cluster mean NewMeans(1, i), d(2) represents the Euclidean distance of the real-time state parameter to the real-time cluster mean NewMeans(2, i), and d(3) represents the Euclidean distance of the real-time state parameter to the real-time cluster mean NewMeans(3, i).

[0087] If d(h) is the smallest, the target vehicle is in the operating state corresponding to the real-time cluster mean NewMeans(h, i), h ∈ [1, 3].

[0088] It should be noted that the Euclidean distance d'(k) of the real-time state parameter to the offline cluster mean Means(k, i) can also be directly calculated. If d'(h) is the smallest, the target vehicle is in the operating state corresponding to the offline cluster mean Means(h, i), h ∈ [1, 3]. However, compared with the method of calculating the real-time cluster mean, the accuracy is slightly reduced when directly using the offline cluster mean.

[0089] Next, control is performed according to the operating state of the target vehicle:

[0090] a) If the target vehicle is in a stable state, the rear wheels are not steered.

[0091] b) If the target vehicle tends to be in an unstable state, a sliding mode control algorithm is used to calculate the rear wheel steering angle, the rear wheel steering angle is in the same direction as the front wheel steering angle, and the gain coefficient of the sliding mode control algorithm is adaptively adjusted according to the instability degree.

[0092] Specifically, a two-degree-of-freedom vehicle dynamics model is first established:

[0093] Referring to Figure 3 , a ground coordinate system XOY and a vehicle body coordinate system xoy are constructed. CoG represents the center of gravity of the target vehicle. x v is the longitudinal vehicle speed; y v is the lateral speed of the vehicle. f is the side slip angle of the front wheel; and r is the side slip angle of the rear wheel. is the yaw angle. The front wheel has tire longitudinal force F xf and lateral force F yf ; the rear wheel has tire longitudinal force F xr and lateral force F yr .

[0094] For the real-time center of gravity side slip angle β and the real-time yaw rate r, there are:

[0095]

[0096] In the formula, m is the mass of the vehicle; vx is the vehicle longitudinal speed; C f is the target vehicle front cornering stiffness; C r is the target vehicle rear cornering stiffness; δ f is the target vehicle front wheel steering angle; δ r is the target vehicle rear wheel steering angle; a is the distance from the target vehicle mass center to the front axle; b is the distance from the target vehicle mass center to the rear axle; β is the real-time mass center cornering angle; r is the real-time yaw rate; I z is the yaw moment of inertia; represents the first-order derivative of β d , represents the first-order derivative of r d .

[0097] An ideal yaw rate r d , ideal mass center cornering angle β d is introduced as:

[0098]

[0099] where L is the wheelbase; K is the stability factor; μ is the road adhesion coefficient; and g is the gravitational acceleration. Then, the deviation e between the real-time state parameters and the theoretical state parameters is:

[0100] e = ε1(r - r d ) + ε2(β - β d );

[0101] where ε1 and ε2 are positive real numbers.

[0102] A sliding surface s is introduced as:

[0103]

[0104] where λ1 and λ2 are positive real numbers, and t represents time.

[0105] The approach rate is:

[0106]

[0107] where k1 and k2 are preset gain coefficients.

[0108] sat(.) represents a saturation function. θ is the boundary layer thickness.

[0109] The above formulas are integrated and transformed to obtain:

[0110]

[0111] where A1 and A2 are transition parameters.

[0112] Since the degree of the unstable tendency of the target vehicle is different at this time, if k1 and k2 are always constant, the output will produce chattering, which will make the control effect worse, so it is necessary to adjust k1 and k2 in real time to obtain

[0113] wherein,

[0114] wherein, is the adjusted gain coefficient;

[0115] d max is the maximum value in d(k), that is, d max = max{d(1), d(2), d(3)}.

[0116] Further, we obtain:

[0117]

[0118] In general, if the target vehicle tends to be unstable,

[0119] The calculation formula of the rear wheel angle is:

[0120]

[0121] c) If the target vehicle is in an unstable state, the rear wheel angle is calculated by using the sliding mode control algorithm, the rear wheel angle is the same as the front wheel angle in direction, and the gain coefficient of the sliding mode control algorithm at this time is a constant.

[0122] The specific derivation steps are referred to b), except that k1 and k2 do not need to be further adjusted, which is not repeated here.

[0123] In general, if the target vehicle tends to be unstable,

[0124] The calculation formula of the rear wheel angle is:

[0125]

[0126] In addition, if the rear wheel angle calculated in the medium-high speed forward working condition is less than -δ max , the rear wheel angle is controlled according to -δ max ; if the rear wheel angle calculated in the medium-high speed forward working condition is greater than δ max , the rear wheel angle is controlled according to δ max . In this embodiment 1, in the medium-high speed forward working condition, δ max is set to 5°.

[0127] (3) If the driving condition is the reverse driving condition, the front wheel steering angle of the target vehicle is obtained, and the relationship between the rear wheel steering angle and the front wheel steering angle is calculated according to the two-degree-of-freedom dynamics model of the vehicle, so as to calculate the rear wheel steering angle, and the rear wheel steering angle is opposite to the front wheel steering angle.

[0128] Specifically, it is assumed that the rear wheel steering angle and the front wheel steering angle have a certain proportional relationship:

[0129] δ r = τδ f ;

[0130] Wherein, τ represents a proportional coefficient.

[0131] The two-degree-of-freedom dynamics model of the vehicle is established:

[0132]

[0133] The δ r = τδ f is brought into the two-degree-of-freedom dynamics model, and Laplace transform is performed to obtain the response relationship of the center of mass side slip angle to the front wheel steering angle:

[0134]

[0135] The center of mass side slip angle is set to 0, and then the following is obtained:

[0136]

[0137] That is, the relationship between the front wheel steering angle and the rear wheel steering angle is obtained:

[0138]

[0139] Then, the rear wheel steering angle is converted according to the front wheel steering angle. However, it should be noted that the rear wheel steering angle is opposite to the front wheel steering angle.

[0140] In addition, if the rear wheel steering angle calculated in the reverse driving condition is less than -δ max , the rear wheel steering angle is controlled according to -δ max ; if the rear wheel steering angle calculated in the reverse driving condition is greater than δ max , the rear wheel steering angle is controlled according to δ max . In this embodiment 1, δ max is set to 8° in the reverse driving condition.

[0141] Through the above method, different control methods are used to control the rear wheel steering according to different driving conditions of the target vehicle, so as to improve the steering stability of the target vehicle.

[0142] Embodiment 2

[0143] The embodiment 2 discloses a vehicle active rear wheel steering control system based on driving conditions, which uses the vehicle active rear wheel steering control method based on driving conditions of the embodiment 1.

[0144] The vehicle active rear wheel steering control system based on driving conditions comprises a driving state acquisition module, a driving condition judgment module and an active rear wheel control module.

[0145] The driving state acquisition module is used to acquire the current driving state of the target vehicle. The driving condition judgment module is used to judge the driving condition of the target vehicle according to the current driving state. The active rear wheel control module is used to control the rear wheel of the target vehicle according to the corresponding control strategy switched according to the driving condition.

[0146] The embodiment 2 also discloses a vehicle, which uses the vehicle active rear wheel steering control method based on driving conditions of the embodiment 1.

[0147] Embodiment 3

[0148] The embodiment 3 discloses a readable storage medium, which stores computer program instructions. When the computer program instructions are read and run by a processor, the steps of the vehicle active rear wheel steering control method based on driving conditions of the embodiment 1 are executed.

[0149] The method of the embodiment 1 can be applied in the form of software, such as a program designed to be independently run on a computer readable storage medium, which can be a U disk or a U shield, or a program designed to start the whole method through external triggering.

[0150] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present disclosure.

[0151] The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method of active rear wheel steering control for a vehicle based on driving conditions, characterized by, The method comprises the following steps: Step one, obtaining the current driving state of the target vehicle; Wherein, the current driving state includes the current driving speed and the current driving direction; Step two, judging the driving condition of the target vehicle according to the current driving state; Wherein, if the current driving speed is not more than the preset threshold value and the current driving direction is forward, the driving condition is low-speed forward driving condition; If the current driving speed is more than the preset threshold value and the current driving direction is forward, the driving condition is medium-high-speed forward driving condition; If the current driving direction is backward, the driving condition is reverse driving condition; Step three, switching the corresponding control strategy to control the rear wheels of the target vehicle according to the driving condition; If the driving condition is low-speed forward driving condition, the rotation amplitude and rotation rate of the steering wheel of the target vehicle are obtained, and a fuzzy rule is formulated to calculate the rear wheel rotation angle, and the direction of the rear wheel rotation angle is opposite to that of the front wheel rotation angle; If the driving condition is a medium-high speed forward condition, real-time state parameters of the target vehicle are acquired, and reference is made to offline clustering means Means ( k,i ) to analyze the running state of the target vehicle; k =1,2,3; i =1,2,3,4; For Means ( k,i ) : k denotes the operating state in which the target vehicle is located: k = 1 corresponds to a stable state; k = 2 corresponds to a state tending to instability; k = 3 corresponds to an unstable state; i denotes the state parameter: i = 1 corresponds to the yaw rate; i = 2 corresponds to the center of mass side slip angle; i = 3 corresponds to the lateral acceleration; i = 4 corresponds to the vehicle heading angle - the vertical load on the side wheel; Wherein, if the target vehicle is in a stable state, the rear wheels have no steering; if the target vehicle tends to be in an unstable state, a sliding mode control algorithm is used to calculate the rear wheel rotation angle, the direction of the rear wheel rotation angle is the same as that of the front wheel rotation angle, and the gain coefficient of the sliding mode control algorithm is adaptively adjusted according to the instability degree; if the target vehicle is in an unstable state, a sliding mode control algorithm is used to calculate the rear wheel rotation angle, the direction of the rear wheel rotation angle is the same as that of the front wheel rotation angle, and the gain coefficient of the sliding mode control algorithm is a constant value; In step three, if the driving condition is medium-high-speed forward driving condition, the method for analyzing the state of the target vehicle comprises: S301, updating the offline clustering mean value by using the mean method according to the real-time state parameter, to obtain a real-time clustering mean value Means ( k,i ); the method for obtaining the offline clustering mean value NewMeans ( k,i ) comprises the following steps: Means ( k,i ) comprises the following steps: Obtaining the historical state parameters of the target vehicle under the test condition; The historical state parameters are processed by using a Gaussian mixture model clustering algorithm to obtain offline clustering means Means ( k, i ) S302, calculate the Euclidean distance between the real-time state parameter and the real-time clustering mean NewMeans ( k,i ) of the real-time state parameter d ( k ) of the real-time clustering mean If d ( h ) is minimum, the target vehicle is in the running state NewMeans ( h,i ) corresponding to the real-time clustering mean h ∈[1,3] Wherein, if the target vehicle tends to be in an unstable state, the formula for adaptively adjusting the gain coefficient according to the instability degree is: ； In the formula, , is the adjusted gain coefficient; k 1, k 2 is a preset gain coefficient, d max is the maximum value in d ( k ); d (3) represents the Euclidean distance from the real-time state parameter to the real-time clustering mean NewMeans (3 ,i ); If the target vehicle is in an unstable state, k 1, k 2 is a constant value; If the driving condition is reverse driving condition, the front wheel rotation angle of the target vehicle is obtained, and the relationship between the rear wheel rotation angle and the front wheel rotation angle is calculated according to the two-degree-of-freedom dynamics model of the vehicle, and the rear wheel rotation angle is calculated, and the direction of the rear wheel rotation angle is opposite to that of the front wheel rotation angle.

2. The vehicle active rear wheel steering control method based on driving conditions according to claim 1, characterized by, In step three, The fuzzy subsets of the rotation amplitude of the steering wheel are defined as {NB1, NM1, NS1, ZO1, PS1, PM1, PB1}; The fuzzy subsets of the rotation rate of the steering wheel are defined as {NB2, NM2, NS2, ZO2, PS2, PM2, PB2}; The fuzzy subsets of the rear wheel rotation angle are defined as {NB3, NM3, NS3, ZO3, PS3, PM3, PB3}; where NB j represents negative big, NM j represents negative medium, NS j represents negative small, ZO j represents zero, PS j represents positive small j , PM j represents positive medium, PB j represents positive big; j = 1, 2, 3; The fuzzy rule includes: When the rotation amplitude of the steering wheel is NB1, and the rotation rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, PB2 in turn, the rear wheel rotation angle corresponds to NB3, NB3, NM3, NS3, ZO3, PS3, PM3 in turn. When the steering wheel is rotated by an angle of NM1, the steering rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, and PB2 in sequence, and the rear wheel rotation angle is NB3, NM3, NS3, ZO3, PS3, PM3, and PM3 in sequence. When the steering wheel is rotated by an angle of NS1, the steering rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, and PB2 in sequence, and the rear wheel rotation angle is NM3, NS3, NS3, ZO3, PS3, PM3, and PM3 in sequence. When the steering wheel is rotated by an angle of ZO1, the steering rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, and PB2 in sequence, and the rear wheel rotation angle is NS3, NS3, ZO3, PS3, PS3, PM3, and PB3 in sequence. When the steering wheel is rotated by an angle of PS1, the steering rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, and PB2 in sequence, and the rear wheel rotation angle is NS3, ZO3, ZO3, PS3, PM3, PM3, and PB3 in sequence. When the steering wheel is rotated by an angle of PM1, the steering rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, and PB2 in sequence, and the rear wheel rotation angle is ZO3, ZO3, PS3, PS3, PM3, PB3, and PB3 in sequence. When the steering wheel is rotated by an angle of PB1, the steering rate of the steering wheel is NB2, NM2, NS2, ZO2, PS2, PM2, and PB2 in sequence, and the rear wheel rotation angle is PS3, PS3, PM3, PM3, PB3, PB3, and PB3 in sequence.

3. The vehicle active rear wheel steering control method based on driving conditions according to claim 1, characterized by, In step three, if the target vehicle tends to be unstable, The calculation formula of the rear wheel rotation angle is: ； wherein δ r denotes a rear wheel steering angle, δ f denotes a front wheel steering angle, m is a mass of the target vehicle, v x is a longitudinal vehicle speed of the target vehicle, C f is a front wheel cornering stiffness, C r is a rear wheel cornering stiffness, b is a distance from a center of mass of the target vehicle to a rear axle, a is a distance from the center of mass of the target vehicle to a front axle, β is a real-time center of mass cornering angle, r is a real-time yaw rate, I z is a yaw moment of inertia, λ 1, λ 2 is a positive real number, ε 1 , ε 2 is a positive real number, β d is an ideal center of mass cornering angle, denotes a derivative of β d derivative, denotes a derivative of r d derivative, r d is an ideal yaw rate; s is a sliding mode surface, , is an adjusted gain coefficient, sat (.) denotes a saturation function, A 1, A 2 is a transition parameter formula, k 1, k 2 is a preset gain coefficient, d max is d ( k ) is a maximum value.

4. The vehicle active rear wheel steering control method based on driving conditions according to claim 1, characterized by, In step three, if the target vehicle tends to be unstable, The calculation formula of the rear wheel rotation angle is: wherein δ r denotes the rear wheel steering angle, δ f denotes the front wheel steering angle, m is the mass of the target vehicle, v x is the longitudinal vehicle speed of the target vehicle, C f is the front wheel cornering stiffness; C r is the rear wheel cornering stiffness, b is the distance of the center of mass of the target vehicle to the rear axle, a is the distance of the center of mass of the target vehicle to the front axle, β is the real-time center of mass cornering angle, r is the real-time yaw rate, I z is the yaw moment of inertia, λ 1, λ 2 is a positive real number, ε 1 , ε 2 is a positive real number, β d is the ideal center of mass cornering angle, denotes the derivative of β d derivative of denotes the derivative of r d derivative of r d is the ideal yaw rate; s is the sliding mode surface, sat (.) is a saturation function, A 1, A 2 is a transition parameter formula, k 1, k 2 is a preset gain coefficient.

5. The vehicle active rear wheel steering control method based on driving conditions according to claim 1, in step three, if the target vehicle is unstable, The calculation formula of the rear wheel rotation angle is: ； wherein δ r represents a rear wheel steering angle, δ f represents a front wheel steering angle, m is a mass of the target vehicle, v x is a longitudinal vehicle speed of the target vehicle, C r is a rear wheel cornering stiffness, C f is a front wheel cornering stiffness, b is a distance from a center of mass of the target vehicle to a rear axle, a is a distance from a center of mass of the target vehicle to a front axle.

6. The vehicle active rear wheel steering control method based on driving conditions according to claim 1, characterized by, In step three, if the driving condition is a low-speed forward driving condition, the control range of the rear wheel rotation angle is -8°~8°; If the driving condition is a medium-high-speed forward driving condition, the control range of the rear wheel rotation angle is -5°~5°; if the calculated rear wheel rotation angle of the medium-high-speed forward driving condition is less than -5°, the rear wheel rotation angle is controlled at -5°; if the calculated rear wheel rotation angle of the medium-high-speed forward driving condition is greater than 5°, the rear wheel rotation angle is controlled at 5°; If the driving condition is a reverse driving condition, the control range of the rear wheel rotation angle is -8°~8°; if the calculated rear wheel rotation angle of the reverse driving condition is less than -8°, the rear wheel rotation angle is controlled at -8°; if the calculated rear wheel rotation angle of the reverse driving condition is greater than 8°, the rear wheel rotation angle is controlled at 8°.

7. A vehicle active rear wheel steering control system based on driving conditions, characterized by It uses the vehicle active rear wheel steering control method based on driving conditions according to any one of claims 1-6; The vehicle active rear wheel steering control system based on driving conditions comprises: a driving state acquisition module configured to acquire a current driving state of the target vehicle; a driving condition judgment module configured to judge a driving condition of the target vehicle according to the current driving state; and a front-wheel control module configured to control the front wheels of the target vehicle according to the corresponding control strategy. The vehicle active front-wheel steering control method based on driving conditions according to any one of claims 1-6 is used.

8. A vehicle characterized by comprising: ​

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