Distributed automobile cooperative control method, distributed automobile and product

Abstract

The invention specifically discloses a cooperative control method of a distributed automobile, the distributed automobile and a product, and relates to the technical field of intelligent driving control. According to the method, the stability of the vehicle is subjected to multi-state grading by adopting Gaussian mixture model clustering, and the real-time stability state of the vehicle is determined according to the minimum Euclidean distance criterion, so that intervention opportunities and intervention weights of model prediction control and yaw stability control are reasonably distributed according to intervention rules to realize cooperative control; it is ensured that the vehicle has enough yaw stability while maintaining good path tracking precision under the high-speed working condition, and extra energy consumption caused by frequent intervention of invalid stability control under the low-speed working condition of the vehicle can be greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving control technology, specifically to: 1. a cooperative control method for a distributed vehicle; 2. a distributed vehicle using the cooperative control method; and 3. a computer program product. Background Technology

[0002] Distributed vehicles are vehicles that use distributed drive systems. Typically, the drive motor is installed directly inside or near the drive wheels, thus eliminating the need for transmission components such as clutches, gearboxes, drive shafts, differentials, and half-shafts.

[0003] For distributed vehicles, path tracking and yaw stability are two important control requirements for vehicle operation. The common approach for existing distributed vehicles is to control the torque of each motor based on a PID controller, thereby adjusting the vehicle's driving posture in an effort to achieve the control objectives of path tracking and yaw stability. However, this approach is prone to oscillations and poor stability when faced with sudden or complex changes in operating conditions. Moreover, it is prone to frequent intervention of ineffective stability control at low speeds, resulting in significant additional energy consumption. Summary of the Invention

[0004] Therefore, it is necessary to address the problems of oscillation, poor stability, and high additional energy consumption that arise with existing PID controllers, and to provide a cooperative control method for distributed vehicles, as well as a distributed vehicle and related products.

[0005] This invention is achieved using the following technical solution: In a first aspect, the present invention discloses a cooperative control method for distributed vehicles, comprising: Step 1: Based on the target vehicle's current driving status parameters [ β 0, r 0, a y0 According to the minimum Euclidean distance criterion, the current stability state of the target car is determined; β 0、 r 0、 a y0 These represent the vehicle's current sideslip angle, yaw rate, and lateral acceleration, respectively.

[0006] The division of stability states is based on Gaussian mixture model clustering.

[0007] Step two: Based on the current stability state of the target vehicle, coordinate and control the vehicle in accordance with the intervention rules.

[0008] The intervention rules are as follows: If the target vehicle is currently in a stable state, only the front wheel steering is controlled; If the target vehicle is currently in a transitional state, control the front wheel steering and rear wheel steering; If the target vehicle is currently in an unstable state, control the steering of the front and rear wheels, and apply only half of the control torque to each wheel; If the target vehicle is currently in an unstable state, control the steering of the front wheels and the steering of the rear wheels, and apply complete control torque to each wheel.

[0009] The front wheel steering angle and rear wheel steering angle are calculated through path tracking control; the control torque of each wheel is calculated through yaw stability control.

[0010] The implementation of this distributed vehicle cooperative control method is based on the method or process of embodiments of this disclosure.

[0011] Secondly, the present invention provides a distributed vehicle that employs the cooperative control method for distributed vehicles as disclosed in the first aspect.

[0012] Thirdly, the present invention discloses a computer program product, comprising a computer program. When executed by a processor, the computer program implements the steps of the cooperative control method for distributed vehicles disclosed in the first aspect.

[0013] Compared with the prior art, the present invention has the following beneficial effects: This invention employs Gaussian mixture model clustering to classify vehicle stability into multiple state levels, and determines the real-time stability state of the vehicle based on the minimum Euclidean distance criterion. Then, it rationally allocates the intervention timing and intervention weight of model predictive control and yaw stability control according to intervention rules to achieve coordinated control. This ensures that the vehicle maintains good path tracking accuracy at high speeds while also possessing sufficient yaw stability, and can also greatly reduce the additional energy consumption caused by frequent intervention of ineffective stability control at low speeds. Attached Figure Description

[0014] To more clearly illustrate the solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0015] Figure 1 A flowchart of the cooperative control method for distributed vehicles provided in Embodiment 1 of the present invention; Figure 2 A flowchart for the stability state division provided in Embodiment 1 of the present invention; Figure 3A schematic diagram of the 7-DOF dynamic model provided in Embodiment 1 of the present invention; Figure 4 The flowchart of path tracking control provided in Embodiment 1 of the present invention; Figure 5 This is a flowchart of yaw stability control provided in Embodiment 1 of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0018] 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 invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] Example 1 This embodiment 1 provides a cooperative control method for distributed vehicles, which is used for coordinated control of path tracking and yaw stability of a target vehicle. The target vehicle is, of course, a distributed vehicle.

[0020] See Figure 1 It illustrates a flowchart of a cooperative control method for distributed vehicles, including: Step 1: Based on the target vehicle's current driving status parameters [ β 0, r 0, a y0 Determine its stability state; in, β 0、 r 0、 a y0 These represent the vehicle's current sideslip angle, yaw rate, and lateral acceleration, respectively.

[0021] It should be noted that the division of stability states is based on Gaussian mixture model clustering—see [link / reference] Figure 2 The specific method is as follows: S101, Collect driving status parameters of the target vehicle under different road conditions. β , r , a y ], to constitute the sample dataset D .in, β Indicates the centroid sideslip angle; r Indicates yaw rate; a y This indicates lateral acceleration.

[0022] It is important to note that, in order to facilitate cluster analysis and prevent the data from being too discrete, it is recommended to take the absolute value of all collected data.

[0023] S102, for D Data preprocessing is performed to eliminate outliers and standardize the dataset, resulting in a corrected dataset. D' .

[0024] ① To ensure data validity, D Errors caused by sensor measurement failures and instantaneous peak values ​​under extreme conditions are eliminated to remove outliers.

[0025] ② To eliminate dimensional differences, each feature value should be standardized to prevent any single feature from dominating the clustering results. Generally, Z-score standardization is recommended for processing driving state parameters.

[0026] S103, for D' Gaussian mixture model clustering was performed to obtain four clustering results corresponding to stable state, transitional state, tendency to unstable state, and unstable state.

[0027] Gaussian mixture model clustering is a probability distribution-based clustering method. Its basic idea is to use multiple Gaussian distribution functions to fit a probability distribution of arbitrary shape. Simply put, each cluster can be described by a Gaussian distribution, and the dataset can be considered as a mixture of these Gaussian distributions. The goal of Gaussian mixture model clustering is to find the parameters (mean, covariance, and mixing coefficients) of the Gaussian distribution that best represent the data.

[0028] It is important to note the number of clusters in Gaussian mixture model clustering. k Set it to 4. Then, D' Clustering using a Gaussian mixture model will yield four clustering results, and the cluster means of each clustering result will differ: The clustering result with the smallest cluster mean is the stable state—its cluster mean is [ β 1, r 1, a y1 ]; The clustering result with the second smallest cluster mean is a transitional state—its cluster mean is [ β 2, r 2, a y2 ]; The clustering result with the second largest cluster mean tends to be in an unstable state—its cluster mean is [ β 3, r 3, a y3 ]; The clustering result with the largest cluster mean is an unstable state—its cluster mean is [ β 4, r 4, a y4 ].

[0029] Then, according to the minimum Euclidean distance criterion, the current stability state of the target car is determined. That is, the stability state of the target car is calculated separately. β 0, r 0, a y0 The Euclidean distance to the cluster mean of the four clustering results is used to determine the stability state of the target car, and the stability state corresponding to the smallest Euclidean distance is taken as the current stability state of the target car.

[0030] For ease of understanding, let [ β 0, r 0, a y0 ]arrive[ β i , r i , a yi The Euclidean distance is m i If i = 1, 2, 3, 4, then we have: .

[0031] Step two: Based on the current stability state of the target vehicle, coordinate and control the vehicle in accordance with the intervention rules.

[0032] The intervention rules are as follows: 1. If the target vehicle is currently in a stable state, only partial path tracking control will be implemented. Specifically, only the front wheel steering will be controlled.

[0033] 2. If the target vehicle is currently in a transitional state, intervene in all path tracking controls. Specifically, control the front-wheel steering and rear-wheel steering.

[0034] 3. If the target vehicle is currently in a state approaching instability, intervene in all path tracking controls and simultaneously intervene in half of the yaw stability control. Specifically, control the front-wheel steering and rear-wheel steering, and apply only half of the control torque to each wheel.

[0035] 4. If the target vehicle is currently in an unstable state, intervene in all path tracking controls and all yaw stability controls. Specifically, control the front-wheel steering and rear-wheel steering, and apply a complete control torque to each wheel.

[0036] The above intervention rules can also be shown in Table 1: Table 1 Intervention Rules Stability state Front wheel steering Rear wheel steering Control torque steady state <![CDATA[ δ f ]]> 0 0 Transition state <![CDATA[ δ f ]]> <![CDATA[ δ r ]]> 0 Tend to an unstable state <![CDATA[ δ f ]]> <![CDATA[ δ r ]]> <![CDATA[ T ij / 2]]> Unstable state <![CDATA[ δ f ]]> <![CDATA[ δ r ]]> <![CDATA[ T ij ]]> In Table 1, δ f and δ r represent the front-wheel steering angle and rear-wheel steering angle respectively; T ij represents the control torque of the corresponding wheel; the subscript i represents front and rear (can take f or r ): i = f When it is, it represents the front wheel; i = r When it is, it represents the rear wheel; the subscript j represents left and right (can take l or r ): j = l When it is, it represents the left wheel; j = r When it is, it represents the right wheel.

[0037] That is to say, the above intervention rules will reasonably allocate the intervention timing and intervention weight of the two controls according to the current stability state of the target vehicle, which can not only reduce energy consumption but also improve the efficiency and accuracy of control.

[0038] As recorded above, Step 2 involves two aspects of control: ①. Path tracking control - which is used to calculate the front-wheel steering angle and rear-wheel steering angle; ②. Yaw stability control - which is used to calculate the control torque of each wheel.

[0039] Before specifically introducing ① and ②, it is necessary to perform dynamic modeling on the target vehicle - see Figure 3Considering the vehicle's longitudinal, lateral, and yaw motions, as well as the rotation of its four wheels, a 7-DOF dynamic model is constructed, with the specific mathematical expression as follows: ; In the formula, β Indicates the centroid sideslip angle; r Indicates yaw rate; v x , v y These represent longitudinal vehicle speed and lateral vehicle speed, respectively. m Indicates the overall vehicle weight; δ f , δ r These represent the front wheel steering angle and the rear wheel steering angle, respectively. F xfl , F yfl These represent the longitudinal force and lateral force of the front left wheel, respectively. F xfr , F yfr These represent the longitudinal force and lateral force of the front right wheel, respectively. F xrl , F yrl These represent the longitudinal force and lateral force of the rear left wheel, respectively. F xrr , F yrr These represent the longitudinal force and lateral force of the rear right wheel, respectively. I z Indicates the whole vehicle is around Z Moment of inertia of the shaft; Δ M z Indicates the additional yaw moment; d f , d r These represent the front axle track width and the rear axle track width, respectively. a , b These represent the distances from the front axle to the center of mass and the distances from the rear axle to the center of mass, respectively.

[0040] ① The path tracking control is designed based on the MPC model predictive control algorithm. This is because: as the vehicle speed increases during driving, its dynamic characteristics impose multiple constraints on the control, and the model predictive control algorithm can incorporate various constraints during the control process, making it very suitable for path tracking control in the above scenario.

[0041] So, see Figure 4 The specific process of path tracking control is as follows: S201, the front wheel steering angle, rear wheel steering angle, and four-wheel drive torque are selected as control variables, and the Euler method is used to discretize the 7-DOF dynamic model to establish a discrete-time model.

[0042] The mathematical expression for the discrete-time model is: In the formula, k Indicates the current moment; k +1 indicates the next moment; T s Indicates the interval between adjacent moments; u ( k () indicates time k The control amplitude; ; T dfl , T dfr , T drl , T drr These represent the front left wheel drive torque, front right wheel drive torque, rear left wheel drive torque, and rear right wheel drive torque, respectively. x ( k () indicates time k The state vectors consist of: the seven state vectors of the seven-DOF dynamic model and three additional extended vectors; ; ω fl , ω fr , ω rl , ω rr These represent the angular velocities of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively. x , y These represent the longitudinal and lateral positions of the centroid, respectively. θ Indicates the vehicle's heading angle; y ( k () indicates time k The output vector, which is used to characterize time. k The state range; C k Indicates the output matrix; .

[0043] S202, Construct the objective function for model predictive control.

[0044] The objective function must guarantee that the vehicle can quickly and stably track the target path. For ease of understanding, let the objective function be... J ( k ),butJ ( k The mathematical expression for ) is: ; In the formula, κ Indicates the weighting coefficient; γ Indicates the relaxation factor; η ( k + i () indicates time k + i The actual state vector; η ref ( k + i () indicates time k + i The reference state vector; η ( k + i )- η ref ( k + i ) represents the state tracking error vector; Q , R Represents the weight matrix; N c This represents the number of sampling steps in the control time domain, used to optimize future... N c Control quantity for each sampling step; N p This represents the number of sampling steps in the prediction time domain, used to predict the future. N p Control quantity for each sampling step.

[0045] in, Q Corresponding to the state tracking error term: Q The larger the value, the higher the tracking accuracy; R Corresponding to the control increment term: R The larger the value, the smoother the output control.

[0046] S203 sets constraints on the objective function to match the vehicle dynamics characteristics and converts it into a quadratic form to facilitate the solution of the front wheel steering angle and the rear wheel steering angle.

[0047] Specifically, constraints are imposed on the control amplitude, control increment, and state range, respectively: ; In the formula, u ( k + i () indicates time k + i The control amplitude; u minIndicates the lower limit of the control amplitude; u max Indicates the upper limit of the control amplitude; Δ u ( k + i () indicates time k + i Control increment; Δ u min Indicates the lower limit of the control increment; Δ u max This indicates the upper limit of the control increment; y ( k + i () indicates time k + i The output vector; y min Indicates the lower limit of the state range; y max Indicates the upper limit of the state range; γ 0 indicates the allowable error.

[0048] So, J ( k This can be converted into the following quadratic form: ; In the formula, H t Represents the weight matrix of the quadratic terms; G t This represents a vector of linear terms.

[0049] It is important to note that the intervention rules for coordinated control should be consulted: When the target vehicle is currently in a stable state, there is no need to control the rear wheel steering angle. δ r It remains at 0. Therefore, the control variable in S201 can be... δ r Set it to 0 to reduce computational complexity and avoid redundant calculations.

[0050] When the target vehicle is in any of the other three states, the rear wheel steering angle needs to be controlled, and a complete calculation is then performed.

[0051] ② The yaw stability control is designed based on the fuzzy sliding mode algorithm—the basic idea is: First, calculate the longitudinal and lateral tire forces, then calculate the additional yaw moment, and finally distribute the additional yaw moment to the control torque of each tire. The ultimate goal is to stabilize the vehicle's yaw rate and center of gravity sideslip angle by adjusting the control torque of each tire.

[0052] So, see Figure 5The specific process of yaw stability control is as follows: S301, Establish a combined slip Dugoff tire model to calculate longitudinal and lateral tire forces.

[0053] Because there is a strong coupling between the lateral and longitudinal motion of the vehicle, and the tire mechanical properties are in a highly nonlinear state, the combined slip Dugoff tire model is more accurate in calculating the longitudinal and lateral tire forces.

[0054] The mathematical expression for the slip-dugged Dugoff tire model is: ; In the formula, C sij , C αij These represent the longitudinal stiffness coefficient and lateral stiffness coefficient of the corresponding wheel, respectively. S ij This indicates the longitudinal slip ratio of the corresponding wheel; α ij This indicates the tire slip angle of the corresponding wheel; G sij , G αij This indicates the load gain term output for the corresponding wheel; f ( λ ij ) represents the road surface adhesion coefficient correction function for the corresponding wheel; λ ij This indicates the tire adhesion utilization factor of the corresponding wheel.

[0055] in, G sij , G αij The expression is: ; In the formula, μ max This represents the peak adhesion coefficient of the road surface.

[0056] f ( λ ij The expression for ) is: ; In the formula, F zij This indicates the vertical load on the corresponding wheel.

[0057] S302 uses a fuzzy sliding mode algorithm combined with a 7-DOF dynamic model to calculate the additional yaw moment.

[0058] S302 actually calculates the additional yaw moment required by the vehicle based on the deviation between the vehicle's current state variables and the desired state variables.

[0059] Therefore, S302 includes: S3021, Determine the deviation between the actual state variables and the expected state variables of the vehicle. e And design the sliding surface s .

[0060] in, e The expression is: ; In the formula, r real , β real These represent the actual yaw rate and the actual sideslip angle of the center of mass, respectively. r d , β d These represent the ideal yaw rate and the ideal center-of-mass sideslip angle, respectively.

[0061] s The expression is: ; In the formula, λ 1. λ Both 2 are weighting coefficients greater than 0.

[0062] S3022, for s Differentiate along time and and e Substitute the values ​​to determine the exponential reaching law.

[0063] Specifically, s Differentiating along the time axis yields: ; Will e After substituting, we get: ; The law of convergence of the selection exponent yields: ; ; In the formula, ε 1. ε 2 are both weighting coefficients that are greater than 0; sat ( s ) represents a saturation function; ξ Indicates the boundary layer thickness.

[0064] S3023 combines the exponential approach law with a 7-DOF dynamic model to calculate the additional yaw moment.

[0065] Then, Δ M z The expression is: ; In the formula, S The term represents a merged item, and its expression is: .

[0066] S303 distributes the additional yaw moment to each wheel as a control torque through a torque distribution method.

[0067] Since the front axle track and the rear axle track are generally equal, that is: ; d This represents the wheelbase value. Therefore, the relationship between the additional yaw moment and the torque of each wheel is as follows: ; In the formula, T fl , T fr , T rl , T rr These represent the control torque of the front left wheel, the control torque of the front right wheel, the control torque of the rear left wheel, and the control torque of the rear right wheel, respectively. T d Indicates the target torque; R Indicates the effective radius of the tire.

[0068] It should be noted that, T d It is the torque required to track the desired vehicle speed, calculated using PID control: the deviation between the desired vehicle speed and the actual vehicle speed is defined. The formula for calculating the target torque is: ; In the formula, K p , K i , K d These are the proportional coefficient, integral coefficient, and differential coefficient, respectively.

[0069] Based on the 7-DOF dynamic model, the relationship between the front and rear axle loads and the torque of each wheel can be obtained as follows: ; In the formula, F zfl , F zfr , F zrl ,F zrr These represent the vertical loads of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively. The vertical loads of each wheel are calculated using the following formula: ; In the formula, g Represents gravitational acceleration; L Indicates the distance between the front and rear axles; L = a + b ; h g Indicates the height of the center of mass.

[0070] Substituting the relationship between the front and rear axle loads and the torque of each wheel into the relationship between the additional yaw moment and the torque of each wheel, we can obtain the braking torque (which could be either the driving torque or the braking torque) required for each wheel: .

[0071] In summary, the above-mentioned collaborative control method will reasonably adjust the intervention of the two types of control based on the vehicle stability analysis results and the intervention rules of collaborative control.

[0072] Simulation experiments verified that the aforementioned cooperative control method, by adjusting the controller's intervention time and weights, effectively improves the vehicle's handling stability while balancing path tracking accuracy and stability. Simultaneously, reduced controller intervention significantly lowers the additional energy consumption caused by frequent, ineffective stability control interventions at low speeds.

[0073] Example 2 This embodiment 2 discloses a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the distributed vehicle cooperative control method disclosed in embodiment 1.

[0074] The computer equipment can be either a mobile terminal or a fixed terminal. Examples of the former include mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (such as in-vehicle navigation terminals); examples of the latter include digital TVs and desktop computers.

[0075] This embodiment 2 also discloses a readable storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the cooperative control method for distributed vehicles disclosed in embodiment 1 are performed.

[0076] The readable storage medium may include, but is not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0077] This embodiment 2 also discloses a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the cooperative control method for distributed vehicles disclosed in embodiment 1.

[0078] It should be noted that the computer program used to execute the above can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar languages. The computer program can be executed entirely on the user's computer, partially on the user's computer, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer through any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN).

[0079] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A cooperative control method for distributed vehicles, characterized in that, It includes: Step 1: Based on the target vehicle's current driving status parameters [ β 0, r 0, a y0 According to the minimum Euclidean distance criterion, the current stability state of the target car is determined; β 0、 r 0、 a y0 These represent the vehicle's current sideslip angle, yaw rate, and lateral acceleration, respectively. The division of stability states is based on Gaussian mixture model clustering. Step two: Based on the current stability state of the target vehicle, coordinate control is performed according to the intervention rules; the intervention rules are as follows: If the target vehicle is currently in a stable state, only the front wheel steering is controlled; If the target vehicle is currently in a transitional state, control the front wheel steering and rear wheel steering; If the target vehicle is currently in an unstable state, control the steering of the front and rear wheels, and apply only half of the control torque to each wheel; If the target vehicle is currently in an unstable state, control the steering of the front wheels and the steering of the rear wheels, and apply complete control torque to each wheel; The front wheel steering angle and rear wheel steering angle are calculated through path tracking control; the control torque of each wheel is calculated through yaw stability control.

2. The cooperative control method for distributed vehicles according to claim 1, characterized in that, In step one, the methods for dividing stability states include: S101, Collect driving status parameters of the target vehicle under different road conditions. β , r , a y ], to constitute the sample dataset D ;in, β Indicates the centroid sideslip angle; r Indicates yaw rate; a y Indicates lateral acceleration; S102, for D Data preprocessing is performed to eliminate outliers and standardize the dataset, resulting in a corrected dataset. D' ; S103, for D' Gaussian mixture model clustering was performed to obtain four clustering results corresponding to the stable state, transitional state, tendency to be unstable state, and unstable state. Among them, the cluster result with the smallest cluster mean is a stable state; the cluster result with the second smallest cluster mean is a transitional state; the cluster result with the second largest cluster mean is a state that tends to be unstable; and the cluster result with the largest cluster mean is an unstable state.

3. The cooperative control method for distributed vehicles according to claim 2, characterized in that, In step one, calculate [ β 0, r 0, a y0 The Euclidean distance to the cluster mean of the four clustering results is used to determine the stability state of the target car, and the stability state corresponding to the smallest Euclidean distance is taken as the current stability state of the target car.

4. The cooperative control method for distributed vehicles according to claim 1, characterized in that, Step two, the path tracking control process includes: S201, front wheel steering angle, rear wheel steering angle, and four-wheel drive torque are selected as control variables, and the Euler method is used to discretize the 7-DOF dynamic model to establish a discrete-time model. S202, Construct the objective function for model predictive control; S203 sets constraints on the objective function to match the vehicle dynamics characteristics and converts it into a quadratic form to facilitate the solution of the front wheel steering angle and the rear wheel steering angle.

5. The cooperative control method for distributed vehicles according to claim 4, characterized in that, When the target vehicle is currently in a stable state, set the rear wheel steering angle in the control variables to 0.

6. The cooperative control method for distributed vehicles according to claim 1, characterized in that, Step two, the process of yaw stability control includes: S301, Establish a combined slip Dugoff tire model to calculate longitudinal and lateral tire forces; S302 uses a fuzzy sliding mode algorithm combined with a 7-DOF dynamic model to calculate the additional yaw moment; S303 distributes the additional yaw moment to each wheel as a control torque through a torque distribution method.

7. The cooperative control method for distributed vehicles according to claim 6, characterized in that, S302 includes: S3021, Determine the deviation between the actual state variables and the expected state variables of the vehicle. e And design the sliding surface s ; S3022, for s Differentiate along time and and e Substitute the values ​​to determine the exponential reaching law; S3023 combines the exponential approach law with a 7-DOF dynamic model to calculate the additional yaw moment.

8. The cooperative control method for distributed vehicles according to claim 4 or 6, characterized in that, The mathematical expression for the 7-DOF dynamic model is: ； In the formula, β Indicates the centroid sideslip angle; r Indicates yaw rate; v x , v y These represent longitudinal vehicle speed and lateral vehicle speed, respectively. m Indicates the overall vehicle weight; δ f , δ r These represent the front wheel steering angle and the rear wheel steering angle, respectively. F xfl , F yfl These represent the longitudinal force and lateral force of the front left wheel, respectively. F xfr , F yfr These represent the longitudinal force and lateral force of the front right wheel, respectively. F xrl , F yrl These represent the longitudinal force and lateral force of the rear left wheel, respectively. F xrr , F yrr These represent the longitudinal force and lateral force of the rear right wheel, respectively. I z Indicates the whole vehicle is around Z Moment of inertia of the shaft; Δ M z Indicates the additional yaw moment; d f , d r These represent the front axle track width and the rear axle track width, respectively. a , b These represent the distances from the front axle to the center of mass and the rear axle to the center of mass, respectively.

9. A distributed vehicle, characterized in that, It employs the cooperative control method for distributed vehicles as described in any one of claims 1-8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the cooperative control method for a distributed vehicle as described in any one of claims 1-8.

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