A vehicle stability control method and system of distributed drive and rear wheel steering cooperation

Abstract

The application discloses a kind of distributed drive and rear wheel steering collaborative vehicle stability control method and system, by constructing the adaptive control system including three model predictive controllers and a controller selector, and by controller selector based on the phase plane analysis result of yaw rate and centroid side angle, the optimal model predictive controller is dynamically selected, can be adaptively matched the most suitable control strategy (by selecting specific model predictive controller is realized) according to the real-time state of vehicle (by phase plane analysis is reflected), so as to dynamically optimize the performance balance of yaw rate and centroid side angle under different driving conditions, and the precision of yaw rate control is improved;At the same time, since the redundant control of single coupling controller in non-optimal condition is avoided, the comprehensive energy consumption of distributed drive and rear wheel steering actuator is effectively reduced, and the collaborative optimization of control performance and energy consumption is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of vehicle control, and particularly relates to a vehicle stability control method and system based on distributed driving and rear wheel steering coordination. BACKGROUND

[0002] In the field of vehicle stability control, distributed driving and rear wheel steering technology provides an effective way to improve the handling stability and driving safety of vehicles. In the prior art, a single coupling controller is often used to coordinate the control of distributed driving and rear wheel steering actuators. This single controller usually needs to consider multiple targets such as yaw rate tracking, center of mass side slip angle suppression, and actuator energy consumption, and its control parameters and strategies are fixed or globally optimized.

[0003] However, this single coupling control scheme has a significant technical problem: it is difficult to simultaneously consider accurate control of yaw rate, effective suppression of center of mass side slip angle, and efficient use of actuator energy under complex and variable vehicle driving conditions, resulting in a dynamic and optimal balance between control performance and energy consumption. SUMMARY

[0004] The present application aims to provide a vehicle stability control method and system based on distributed driving and rear wheel steering coordination, which builds an adaptive control system containing three model predictive controllers and a controller selector, and dynamically selects the optimal model predictive controller based on the phase plane analysis results of yaw rate and center of mass side slip angle by the controller selector to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a vehicle stability control method based on distributed driving and rear wheel steering coordination, comprising the following steps:

[0006] An adaptive control system is constructed, which contains three model predictive controllers and a controller selector;

[0007] The controller selector acquires real-time yaw rate and center of mass side slip angle state information of the vehicle;

[0008] The controller selector performs phase plane analysis based on the acquired yaw rate and center of mass side slip angle state information to determine the control mode of the current vehicle;

[0009] The controller selector selects a target model predictive controller from the three model predictive controllers according to the determined control mode;

[0010] The target model predictive controller generates vehicle stability control instructions for distributed driving and rear wheel steering coordination.

[0011] Preferably, the three model predictive controllers comprise:

[0012] a first model predictive controller corresponding to the rear wheel steering system individual control mode;

[0013] a second model predictive controller corresponding to the direct yaw moment control individual control mode;

[0014] a third model predictive controller corresponding to the rear wheel steering and direct yaw moment collaborative control mode.

[0015] Preferably, the real-time acquisition of the vehicle's yaw rate and center of mass side slip angle state information comprises:

[0016] acquiring a real-time yaw rate signal through a vehicle yaw rate sensor;

[0017] estimating a real-time center of mass side slip angle based on a steering wheel angle, a vehicle speed, and a lateral acceleration signal through a vehicle state estimation algorithm.

[0018] Preferably, the phase plane analysis comprises:

[0019] establishing a two-dimensional phase plane coordinate system with the yaw rate as the horizontal axis and the center of mass side slip angle as the vertical axis;

[0020] dividing a plurality of preset phase plane regions in the phase plane coordinate system;

[0021] mapping the current yaw rate and center of mass side slip angle values into the phase plane coordinate system to determine the phase plane region in which they are located, dividing the stable region, the transition region, and the unstable region according to the vehicle dynamics stability boundary, and corresponding the three determined phase plane regions to the three control modes.

[0022] Preferably, the selection of a target model predictive controller from the three model predictive controllers comprises:

[0023] establishing a mapping relationship table between the control modes and the model predictive controllers;

[0024] looking up the corresponding target model predictive controller in the mapping relationship table according to the determined control mode, and sending an activation instruction to the found target model predictive controller.

[0025] Preferably, the generation of the vehicle stability control instruction for distributed driving and rear wheel steering collaboration comprises:

[0026] establishing a prediction model by the target model predictive controller according to the current vehicle state and the expected reference trajectory;

[0027] Solving an optimization problem within a prediction horizon of the target model predictive controller to minimize yaw rate tracking error and vehicle body side slip angle tracking error;

[0028] Outputting a solution of the optimization problem as a vehicle stability control instruction for the distributed drive and rear wheel steering cooperative vehicle.

[0029] In another aspect, the present application provides a vehicle stability control system for a distributed drive and rear wheel steering cooperative vehicle, comprising:

[0030] Three model predictive controllers for generating vehicle stability control instructions;

[0031] A controller selector communicatively connected to the three model predictive controllers.

[0032] Preferably, the controller selector is configured to perform phase plane analysis based on the acquired yaw rate and vehicle body side slip angle state information to determine a control mode of the current vehicle.

[0033] Preferably, the controller selector is configured to select a target model predictive controller from the three model predictive controllers according to the determined control mode, and trigger the target model predictive controller to generate a vehicle stability control instruction for the distributed drive and rear wheel steering cooperative vehicle.

[0034] Technical effects and advantages of the present application: The vehicle stability control method and system for a distributed drive and rear wheel steering cooperative vehicle proposed by the present application has the following advantages compared with the prior art:

[0035] The present application constructs an adaptive control system comprising three model predictive controllers and a controller selector, and dynamically selects the optimal model predictive controller based on the phase plane analysis results of the yaw rate and vehicle body side slip angle by the controller selector, which can adaptively match the most suitable control strategy (achieved by selecting a specific model predictive controller) according to the real-time state of the vehicle (reflected by the phase plane analysis), thereby dynamically optimizing the performance balance of the yaw rate and vehicle body side slip angle and enhancing the control accuracy of the yaw rate under different driving conditions. At the same time, since the single coupled controller avoids inefficient or redundant control under non-optimal conditions, the overall energy consumption of the distributed drive and rear wheel steering actuators is effectively reduced, achieving a cooperative optimization of control performance and energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 A flowchart of the vehicle stability control method for a distributed drive and rear wheel steering cooperative vehicle of the present application;

[0037] Figure 2 A structure block diagram of the adaptive cooperative control method for a distributed drive four-wheel steering vehicle of the present application;

[0038] Figure 3 For the invention Phase plane diagram;

[0039] Figure 4 For the invention of the medium speed working condition yaw rate response curve;

[0040] Figure 5 For the invention of the medium speed working condition centroid side slip angle response curve;

[0041] Figure 6 For the invention of the medium speed working condition rear wheel steering angle response curve;

[0042] Figure 7 For the invention of the medium speed working condition additional torque response curve;

[0043] Figure 8 For the invention of the medium speed working condition actuator consumption analysis diagram;

[0044] Figure 9 For the invention of the high speed working condition yaw rate response curve;

[0045] Figure 10 For the invention of the high speed working condition centroid side slip angle response curve;

[0046] Figure 11 For the invention of the high speed working condition rear wheel steering angle response curve;

[0047] Figure 12 For the invention of the high speed working condition additional torque response curve;

[0048] Figure 13 For the invention of the high speed working condition actuator consumption analysis diagram. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0050] The present application provides a vehicle stability control method for distributed driving and rear wheel steering cooperation as shown in Figure 1 The present application provides a vehicle stability control method for distributed driving and rear wheel steering cooperation as shown in

[0051] In this embodiment, the vehicle stability control method that coordinates distributed drive and rear-wheel steering includes the following steps:

[0052] An adaptive control system is constructed, comprising three model predictive controllers and one controller selector. Specifically, the three model predictive controllers include: a first model predictive controller, corresponding to the rear-wheel steering system in a separate control mode; a second model predictive controller, corresponding to the direct yaw moment control in a separate control mode; and a third model predictive controller, corresponding to the rear-wheel steering and direct yaw moment combined control mode.

[0053] For different typical vehicle driving conditions (such as dynamic response speed, stability requirements, and performance priorities), dedicated control strategies are pre-designed and optimized, enabling the model predictive controller activated under the corresponding conditions to provide more accurate and efficient control performance. Specifically:

[0054] When driving at high speed in a straight line, the first model predictive controller is activated to prioritize driving stability and comfort and suppress minor disturbances.

[0055] When driving on medium-speed curves, the second model predictive controller is activated, which can accurately track the driver's intentions, optimize the yaw rate response, and effectively control the center of gravity sideslip angle.

[0056] When avoiding obstacles at low speeds or during extreme cornering, activating the third model predictive controller maximizes the use of the vehicle's extreme capabilities and ensures rapid and reliable stability recovery.

[0057] This condition-specific controller design, combined with the dynamic switching of the controller selector, ensures that the system always adopts the most suitable control strategy across the entire operating range, thereby significantly improving the overall performance, robustness, and adaptability of vehicle stability control.

[0058] The controller selector acquires real-time vehicle yaw rate and sideslip angle status information; specifically, this includes: acquiring real-time yaw rate signals through a vehicle yaw rate sensor; and estimating the real-time sideslip angle based on steering wheel angle, vehicle speed, and lateral acceleration signals using a vehicle state estimation algorithm. The sideslip angle, which is difficult to measure directly, is estimated by fusing multiple easily obtainable sensor signals (steering wheel angle, vehicle speed, and lateral acceleration) and applying a state estimation algorithm.

[0059] The controller selector determines the control mode of the current vehicle based on the obtained yaw rate and the state information of the center of mass side slip angle through phase plane analysis, and the phase plane analysis specifically includes: establishing a two-dimensional phase plane coordinate system with the yaw rate as the horizontal axis and the center of mass side slip angle as the vertical axis; in the phase plane coordinate system, a plurality of preset phase plane regions are divided; the current yaw rate and the center of mass side slip angle value are mapped into the phase plane coordinate system to determine the phase plane region where they are located, and according to the vehicle dynamics stability boundary, the stable region and the unstable region are divided, and the three determined phase plane regions are corresponded to three control modes.

[0060] The controller selector selects a target model predictive controller from the three model predictive controllers according to the determined control mode, and specifically includes: establishing a mapping relationship table between the control mode and the model predictive controller; according to the determined control mode, the corresponding target model predictive controller is found in the mapping relationship table, and an activation instruction is sent to the found target model predictive controller.

[0061] By sending an explicit activation instruction to the target model predictive controller, clear and immediate handover of control rights is realized, and the system can be quickly and smoothly switched to the special control strategy that best matches the current vehicle state.

[0062] The target model predictive controller selected generates a vehicle stability control instruction for distributed driving and rear wheel steering cooperation; specifically includes: the target model predictive controller establishes a prediction model according to the current vehicle state and the expected reference trajectory; the target model predictive controller solves an optimization problem in its prediction time domain to minimize the yaw rate tracking error and the center of mass side slip angle tracking error; and the solution of the optimization problem is output as the vehicle stability control instruction for distributed driving and rear wheel steering cooperation.

[0063] Solving the optimization problem in the prediction time domain can comprehensively consider the system constraints (such as actuator capability, stability boundary) and control objectives in a period of time in the future, and the generated control instruction is a globally or locally optimal sequence, effectively coordinating the cooperative action of distributed driving (such as inter-wheel torque distribution) and rear wheel steering. Minimizing the yaw rate tracking error and the center of mass side slip angle tracking error as the core optimization target directly controls the two key indicators of vehicle stability, thereby significantly improving the control accuracy of the yaw rate and effectively suppressing the center of mass side slip angle, achieving fine regulation and control of the vehicle's lateral and longitudinal motion, and finally outputting the optimal cooperative control instruction.

[0064] On the other hand, the application proposes a vehicle stability control system for distributed driving and rear wheel steering cooperation, comprising:

[0065] Three model predictive controllers for generating vehicle stability control instructions;

[0066] a controller selector, communicatively connected to the three model predictive controllers; wherein the controller selector is configured to perform phase plane analysis based on the obtained yaw rate and the state information of the vehicle's center of mass side slip angle, and determine a control mode of the current vehicle; the controller selector is configured to select a target model predictive controller from the three model predictive controllers according to the determined control mode, and trigger the target model predictive controller to generate a vehicle stability control instruction for the distributed drive and rear wheel steering cooperation.

[0067] a sensor module, configured to collect the front wheel steering angle, the rear wheel steering angle and the longitudinal vehicle speed in real time;

[0068] a reference model module, configured to calculate the ideal yaw rate and the center of mass side slip angle;

[0069] a phase plane analysis module, configured to divide a stability region and output a control mode selection signal;

[0070] a MPC controller module, comprising three independent controllers corresponding to the RWS, DYC and DRC modes;

[0071] an actuator distribution module, configured to drive the rear wheel steering device and distribute the in-wheel motor torque.

[0072] The above components are further configured to implement other steps of the above-mentioned vehicle stability control method when executed, as follows:

[0073] The implementation process of the present application is generally divided into two main steps. First, the vehicle state parameters are collected by the sensor; then, based on the stability domain determined by the phase plane analysis of the nonlinear tire model, the appropriate actuator is selected. Finally, the target state provided by the reference vehicle model is tracked by means of the three MPC controllers. The structure diagram is shown in detail in Figure 2 .

[0074] Specifically, the sensor module is responsible for collecting the driving state data of the distributed drive four-wheel steering vehicle, including the front wheel steering angle, the rear wheel steering angle and the longitudinal vehicle speed, and transmitting these data to the reference model module. The reference model module inputs the above data into the two-degree-of-freedom reference model, calculates the ideal yaw rate and the center of mass side slip angle, and then transmits these calculation results to the phase plane analysis module.

[0075] The phase plane analysis module analyzes the received data, determines the stability region of the vehicle, and selects the optimal control mode, and then feeds back the mode selection result to the phase plane analysis module. The phase plane analysis module selects the corresponding MPC controller according to the mode selection result according to the established MPC controller corresponding to the three control modes, outputs the actuator control instruction, and delivers it to the actuator distribution module. Finally, the actuator distribution module executes the control instruction of the above-mentioned chassis actuator.

[0076] Sensor module: obtain the state parameter data of the distributed drive four-wheel steering vehicle during driving, and the output value is: (front wheel steering angle), (rear wheel steering angle), (longitudinal vehicle speed).

[0077] Reference model module: calculate the ideal yaw rate and center side slip angle. The input value is , , , and the output value is (ideal yaw rate), (ideal center side slip angle).

[0078] Step 1: Establish a single-track vehicle dynamics model with rear wheel steering angle additional yaw moment as input. This model is used as the reference vehicle model for controller design, providing ideal vehicle state values. The vehicle dynamics model formula is:

[0079]

[0080] wherein, is the total vehicle mass, is the longitudinal vehicle speed, is the derivative of the yaw rate, is the center side slip angle, is the derivative of the center side slip angle, is the yaw moment of inertia, is the distance from the front axle to the center of mass, is the distance from the rear axle to the center of mass, is the additional yaw moment, is the front wheel lateral tire force, is the rear wheel lateral tire force.

[0081] Step 2: Under ideal assumption conditions, the tire side slip characteristic is in the linear region, and the relationship between the lateral tire force and the tire side slip stiffness and is given as:

[0082]

[0083] Step 3: The vehicle single-track dynamics reference model is established by combining the formulas in Step 1 and Step 2:

[0084]

[0085] where the state vector is defined as , the control vector is defined as , and the coefficient matrix is defined as , , .

[0086] Phase plane analysis module: According to the ideal yaw rate and the vehicle's center of mass side slip angle, the phase plane analysis is performed. The vehicle's center of mass side slip angle and its rate of change are used to construct the phase plane, as shown in Figure 3 .

[0087] Figure 3 In the above formula, the yellow small circles represent the current state points of the vehicle during the simulation process, the blue dashed line indicates the boundary of the stability transition region, and the red dashed line marks the boundary of the unstable region.

[0088] According to the double line method, the phase plane is divided into three regions (stable region, transition region, and unstable region):

[0089]

[0090] where , , is the road adhesion coefficient.

[0091] According to the above phase plane analysis results, the stability region to which the vehicle belongs in actual driving can be determined, and the appropriate control mode can be adaptively selected. The three stability regions correspond to three control modes, namely rear wheel steering (RWS), direct yaw moment control (DYC), and rear wheel steering and direct yaw moment control collaborative control (DRC). The coordination method proposed in this invention is represented as:

[0092]

[0093] where represents the transition boundary, represents the unstable boundary.

[0094] Phase plane analysis module: Based on the three control modes in the phase plane analysis module, three MPC controllers are designed. Considering the differences in the effective range of each state value and control instruction, all state variables and control inputs are standardized to the same dimension. The standardized control system of the distributed drive four-wheel steering vehicle can be represented as:

[0095]

[0096] where the state vector is , and the input vector is .

[0097] The discretization of the above state-space equation using the forward Euler method is expressed as equations:

[0098] ;

[0099] ;

[0100] where denotes the sampling time.

[0101] By normalization, the basic constraint conditions of state variables and control inputs are expressed as equations:

[0102]

[0103] Combining the consumption of the rear-wheel steering actuator system and the energy consumption of the distributed drive module, the complete cost function at time step k+1 is expressed as:

[0104]

[0105] where , , represent the adjustable weights, , ,

[0106] , .

[0107] The optimization problem of the side and longitudinal stability control of the distributed drive four-wheel steering vehicle is expressed as:

[0108]

[0109] The following designs three kinds of controllers respectively:

[0110] (1) RWS controller

[0111] Only the rear wheels steering is involved in the control, the control variable is selected as , the state variable is , and the objective function is:

[0112]

[0113] The cost function is:

[0114]

[0115] (2) DYC controller

[0116] Only DYC is involved in the control, the control variable is chosen as , the state variable is , and the objective function is:

[0117]

[0118] The cost function is:

[0119]

[0120] (3) DRC controller

[0121] Both rear-wheel active steering and DYC are involved in the control, the control variable is chosen as , the state variable is , and the objective function is:

[0122]

[0123] The cost function is:

[0124]

[0125] Actuator distribution module: the rear-wheel steering angle and the additional yaw moment sought by the adaptive coordinated controller are sent to the chassis actuators for action. The rear-wheel steering angle is achieved by the rear-wheel steering actuator, and the additional yaw moment is achieved by distributing the additional wheel torque to the four in-wheel motors.

[0126] Assuming that the steering angles of the two rear wheels are the same, the additional torques applied to the four wheels must satisfy the following conditions:

[0127]

[0128] where denotes the equivalent rolling radius of each wheel, denotes the wheel track, , , , denote the additional torques distributed to the front-left, front-right, rear-left, and rear-right wheels, respectively.

[0129] The total additional yaw moment component is evenly distributed to the four wheels:

[0130] . ​

[0131] To verify the effectiveness of the method of the application, a CarSim-Matlab / Simulink joint simulation platform is used for experimental verification. The actuator consumption of the rear wheel steering and distributed drive system is an important aspect, which reflects the energy efficiency under various driving conditions and steering configurations, and the definition equation is as follows:

[0132]

[0133] Medium-speed working condition: set to double lane shift working condition with longitudinal vehicle speed of 60km / h, the yaw rate and the response curve of the mass center side slip angle are as shown in Figure 4 and Figure 5 , the rear wheel turning angle and the additional torque response curve are as shown in Figure 6 and Figure 7 , and the actuator consumption analysis is as shown in Figure 8 .

[0134] Working condition two: set to double lane shift working condition with longitudinal vehicle speed of 110km / h, the yaw rate and the response curve of the mass center side slip angle are as shown in Figure 9 and Figure 10 , the rear wheel turning angle and the additional torque response curve are as shown in Figure 11 and Figure 12 , and the actuator consumption analysis is as shown in Figure 13 .

[0135] The vehicle stability control effect and actuator energy consumption under the above two working conditions are shown in Table 1:

[0136]

[0137] As can be seen from Table 1, when RWS and DYC are used alone, each has its own advantages in suppressing side slip angle and improving handling effect, and RDC and ACS achieve the balance of the two. The yaw rate and mass center side slip angle indexes of the RDC and ACS control methods are almost the same, indicating that the steering performance of the two systems is similar. However, in terms of actuator energy consumption, the ACS strategy performs significantly better than the RDC, with a total energy consumption reduction of 16.22% in the medium-speed working condition and a total energy consumption reduction of 2.21% in the high-speed working condition.

[0138] The application is an adaptive coordinated control strategy (ACS) of a distributed drive four-wheel steering vehicle. (A collaborative control system composed of three model predictive controllers (MPC) and a controller selector dynamically adjusts the working mode of ARS (active rear wheel steering) and ADYC (active yaw moment control) according to the real-time state of the vehicle.) Through phase plane analysis of the nonlinear tire model, the vehicle stability region (stable domain, critical domain, unstable domain) is divided. According to the current state of the vehicle (such as yaw rate, mass center side slip angle), the optimal control mode is automatically selected.

[0139] Three independent MPC controllers are designed for three control modes, respectively, to achieve multi-objective optimization of yaw rate tracking, center of mass side slip angle optimization, and actuator energy efficiency optimization. The controller selector is used to reduce unnecessary actuator intervention (such as avoiding enabling ADYC in the stable domain), reduce energy consumption, and introduce an energy consumption term in the MPC objective function to optimize drive / brake torque distribution.

[0140] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the foregoing embodiments of the present application have been described in detail, those skilled in the art can modify the technical solutions described in the foregoing embodiments or make equivalent replacements to some technical features, as long as they are within the spirit and principles of the present application. Any modification, equivalent replacement, improvement, etc. made within the scope of the present application should be included in the protection scope of the present application.

Claims

1. A vehicle stability control method with distributed drive and rear-wheel steering coordination, characterized in that, Includes the following steps: Construct an adaptive control system comprising three model predictive controllers and one controller selector; The controller selector acquires the vehicle's yaw rate and center of gravity sideslip angle status information in real time. The controller selector performs phase plane analysis based on the acquired yaw rate and center of gravity sideslip angle state information to determine the current vehicle control mode. The controller selector selects a target model prediction controller from three model prediction controllers based on the determined control mode. The selected target model predictive controller generates vehicle stability control commands for distributed drive and rear-wheel steering coordination, specifically including: the target model predictive controller establishing a predictive model based on the current vehicle state and the desired reference trajectory; solving an optimization problem in its prediction time domain to minimize yaw rate tracking error and center of gravity sideslip angle tracking error; and outputting the solution of the optimization problem as the vehicle stability control command for distributed drive and rear-wheel steering coordination. The three model prediction controllers include: The first model predictive controller corresponds to the independent control mode of the rear wheel steering system; The second model predictive controller corresponds to the direct yaw moment control standalone control mode; The third model predictive controller corresponds to the coordinated control mode of rear wheel steering and direct yaw moment; The phase plane analysis includes: Establish a two-dimensional phase plane coordinate system with yaw rate as the horizontal axis and centroid sideslip angle as the vertical axis; Within the phase plane coordinate system, multiple preset phase plane regions are defined; The current yaw rate and center of gravity sideslip angle are mapped to the phase plane coordinate system to determine the phase plane region in which they are located. Based on the vehicle dynamics stability boundary, the stable region, transition region and unstable region are divided, and the three determined phase plane regions correspond to three control modes.

2. The vehicle stability control method with distributed drive and rear-wheel steering coordination according to claim 1, characterized in that, The real-time acquisition of the vehicle's yaw rate and sideslip angle includes: Real-time yaw rate signals are obtained through vehicle yaw rate sensors; The vehicle state estimation algorithm estimates the real-time centroid sideslip angle based on steering wheel angle, vehicle speed, and lateral acceleration signals.

3. The vehicle stability control method with distributed drive and rear-wheel steering coordination according to claim 1, characterized in that, The step of selecting a target model prediction controller from three model prediction controllers includes: Establish a mapping table between control modes and model predictive controllers; Based on the determined control mode, the corresponding target model prediction controller is found in the mapping table, and an activation command is sent to the found target model prediction controller.

4. A vehicle stability control system for implementing the distributed drive and rear-wheel steering coordination method as described in any one of claims 1-3, characterized in that, include: Three model predictive controllers are used to generate vehicle stability control commands; The three model prediction controllers include: The first model predictive controller corresponds to the independent control mode of the rear wheel steering system; The second model predictive controller corresponds to the direct yaw moment control standalone control mode; The third model predictive controller corresponds to the coordinated control mode of rear wheel steering and direct yaw moment; The phase plane analysis includes: Establish a two-dimensional phase plane coordinate system with yaw rate as the horizontal axis and centroid sideslip angle as the vertical axis; Within the phase plane coordinate system, multiple preset phase plane regions are defined; The current yaw rate and center of gravity sideslip angle are mapped to the phase plane coordinate system to determine the phase plane region in which they are located. Based on the vehicle dynamics stability boundary, the stable region, transition region and unstable region are divided, and the three determined phase plane regions are corresponding to three control modes. A controller selector, communicatively connected to the three model prediction controllers; The controller selector is used to select a target model predictive controller from three model predictive controllers according to a determined control mode, and trigger the target model predictive controller to generate vehicle stability control instructions for distributed drive and rear-wheel steering coordination. Specifically, the target model predictive controller establishes a predictive model based on the current vehicle state and the desired reference trajectory; solves an optimization problem in its prediction time domain to minimize the yaw rate tracking error and the center of gravity sideslip angle tracking error; and outputs the solution of the optimization problem as the vehicle stability control instructions for distributed drive and rear-wheel steering coordination.

5. The vehicle stability control system with distributed drive and rear-wheel steering coordination according to claim 4, characterized in that, The controller selector is used to perform phase plane analysis based on the acquired yaw rate and center of gravity sideslip angle state information to determine the current vehicle control mode.

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