A cross-wind stability cooperative control method based on intelligent chassis multi-actuator fusion

Abstract

This invention discloses a crosswind stability collaborative control method based on intelligent chassis and multi-actuator fusion. It acquires and preprocesses vehicle driving state parameters in real time using onboard sensors to obtain crosswind control input; it estimates the crosswind moment by combining vehicle operating parameters and a dynamic model; it determines the timing of crosswind control intervention based on the trend of lateral wind force changes and the fluctuation characteristics of the vehicle's heading angle; it introduces feedback control to dynamically correct the anti-crosswind compensation yaw moment; and it further designs a multi-actuator collaborative torque distribution strategy, allocating compensation torque based on the optimal working range of each actuator to achieve stable vehicle operation under sudden crosswind conditions. This crosswind stability collaborative control method based on intelligent chassis and multi-actuator fusion has the advantages of strong vehicle stability and controllability under crosswind conditions.

Description

Technical Field

[0001] This invention relates to the field of vehicle stability control, and in particular to a crosswind stability collaborative control method based on the fusion of multiple actuators in an intelligent chassis, which provides strong vehicle stability and controllability. Background Technology

[0002] With the improvement of highway networks and the increase in vehicle speeds, crosswind interference has become a significant factor affecting highway driving safety. When a vehicle encounters a sudden crosswind, the lateral airflow exerts a significant lateral force and yaw moment on the vehicle body, causing lateral drift and yaw oscillation. In severe cases, it can exceed the tire's traction limit, inducing skidding, lane departure, or even rollover accidents. Vehicle stability control is one of the core technologies of vehicle chassis control. Current crosswind stability control technologies mostly use the braking or drive system to generate a yaw moment opposite to the crosswind moment. However, when the crosswind moment is large, it can easily cause the tires to exceed the road friction limit, still failing to maintain normal vehicle operation.

[0003] Therefore, it is necessary to propose an improvement to overcome the shortcomings of the existing technology. Summary of the Invention

[0004] The purpose of this invention is to solve the problems in the prior art and provide a crosswind stability cooperative control method based on the fusion of multiple actuators in an intelligent chassis, which has strong vehicle stability and controllability.

[0005] The technical solution of this invention is: A crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion includes the following steps: S1, Vehicle driving state information acquisition and processing: Vehicle motion state signals during driving are acquired through onboard sensors, and the acquired raw sensor signals are preprocessed. The vehicle motion state signals include yaw rate; S2, Crosswind moment estimation based on vehicle dynamics: Based on the vehicle dynamics model, the crosswind moment is predicted and estimated according to the vehicle driving state information and road adhesion coefficient to obtain the feedforward anti-wind yaw moment; S3, Crosswind stability control intervention timing judgment: The crosswind force is predicted and determined according to the magnitude and change of the crosswind force. The trend is combined with the vehicle's heading angle to determine the timing of crosswind stabilization control intervention. When it is determined that crosswind stabilization control intervention is required, the current yaw rate is latched. S4, Crosswind Compensation Yaw Moment Calculation: The reference yaw rate latched at the moment of crosswind control intervention is used as the steady-state control target. After crosswind stabilization control is activated, a feedback correction yaw moment is dynamically generated based on the deviation between the vehicle's real-time yaw rate and the latched reference value. The feedforward anti-wind yaw moment and the feedback correction yaw moment are superimposed to obtain the crosswind compensation yaw moment. S5, Intelligent Chassis Multi-Actuator Yaw Moment Allocation: The crosswind compensation yaw moment is allocated according to the optimal working range of each actuator.

[0006] As a preferred technical solution, the vehicle motion state signal in step S1 also includes lateral acceleration, longitudinal acceleration, four-wheel wheel speed, vehicle longitudinal speed, steering wheel angle, and front wheel angle.

[0007] As a preferred technical solution, the preprocessing of the acquired raw sensor signal in step S1 specifically includes: digital filtering, signal limiting, and outlier removal of the raw sensor signal.

[0008] As a preferred technical solution, in step S1, the preprocessing of the acquired raw sensing signal specifically involves performing a differential operation on the raw yaw rate signal to solve for the yaw rate acceleration, and then performing smoothing filtering on the differentiated signal.

[0009] As a further preferred technical solution, step S2, "based on the vehicle dynamics model, predicting and estimating the crosswind moment according to the vehicle driving state information and road adhesion coefficient," specifically means: based on the vehicle dynamics model, calculating the real-time net external torque of the vehicle through yaw acceleration, mass, and moment of inertia.

[0010] As a preferred technical solution, step S2 further includes: decoupling and eliminating tire deformation coupling error; taking two typical non-transient wind disturbance conditions, namely straight-line constant speed driving and steady-state circular steering, as a benchmark, using state parameters such as wheelbase, longitudinal vehicle speed, and measured yaw rate to back-calculate the theoretical front wheel steering angle; relying on the benchmark yaw moment component corresponding to the theoretical steering input, separating the torque estimation deviation introduced by tire elastic deformation; and compensating the feedforward anti-wind yaw moment according to the torque estimation deviation.

[0011] As a preferred technical solution, the "prediction of crosswind force" in step S3 specifically involves: converting the yaw moment predicted and estimated in step S2 into a lateral force acting on the vehicle body through vehicle geometry, and predicting the change in crosswind force by filtering and differentiation to calculate the trend of lateral force change.

[0012] As a preferred technical solution, the vehicle heading angle in step S3 is obtained by integrating the yaw rate. Step S3 also includes: when it is initially determined that crosswind stabilization control intervention is required, the current yaw rate is latched, and the difference between the latched yaw rate and the real-time yaw rate is integrated from the current moment to obtain the local vehicle heading angle change. By comparing the local heading angle change value with a calibrable threshold, the timing of crosswind stabilization control intervention is further confirmed after the threshold is exceeded.

[0013] As a preferred technical solution, the multi-actuator in step S5 includes a torque vector control system and a rear wheel steering system.

[0014] As a further preferred technical solution, step S5 specifically involves: when the anti-crosswind yaw moment is small, it is preferentially allocated to the rear wheel steering system, and the yaw moment is converted into a rear turn angle using a tire model and a bisection method; when the anti-crosswind yaw moment is large, the capacity margin of the rear wheel steering system is first determined, and the portion exceeding the capacity limit is allocated to the torque vector control system; the capacity margin of the rear wheel steering system is calculated by estimating the rear axle center of gravity sideslip angle, the rear turn actuator rate, and the maximum allowable rear turn angle; when the anti-crosswind yaw moment is a transient large yaw moment, it is directly allocated to the torque vector control system; the yaw moment allocated to the torque vector control system is distributed to each actuator torque by the torque vector control system in conjunction with the capacity margin; the capacity margin of the torque vector control system is calculated in real time based on the motor capacity and road surface adhesion parameters.

[0015] This invention discloses a crosswind stability collaborative control method based on intelligent chassis and multi-actuator fusion. It acquires and preprocesses vehicle driving state parameters in real time using onboard sensors to obtain crosswind control input; it estimates the crosswind moment by combining vehicle operating parameters and a dynamic model; it determines the timing of crosswind control intervention based on the trend of lateral wind force changes and the fluctuation characteristics of the vehicle's heading angle; it introduces feedback control to dynamically correct the anti-crosswind compensation yaw moment; and it further designs a multi-actuator collaborative torque distribution strategy, allocating compensation torque based on the optimal working range of each actuator to achieve stable vehicle operation under sudden crosswind conditions. This crosswind stability collaborative control method based on intelligent chassis and multi-actuator fusion has the advantages of strong vehicle stability and controllability under crosswind conditions. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a specific implementation method of the crosswind stability collaborative control method based on the fusion of multiple actuators in an intelligent chassis according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0018] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. “Multiple” generally includes at least two, but does not exclude the inclusion of at least one.

[0019] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0020] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0021] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0022] like Figure 1 The diagram illustrates a specific implementation of a crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to the present invention. This embodiment of the crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion includes the following steps: S1. Vehicle driving status information collection and processing: The vehicle motion status signal during driving is collected by on-board sensors, and the collected raw sensor signals are preprocessed. The vehicle motion status signal includes yaw rate. S2. Estimation of crosswind moment based on vehicle dynamics: Based on the vehicle dynamics model, the crosswind moment is predicted and estimated according to the vehicle driving state information and the road surface adhesion coefficient to obtain the feedforward anti-wind yaw moment. S3, Crosswind Stabilization Control Intervention Timing Judgment: Estimate the crosswind force, and judge the timing of crosswind stabilization control intervention based on the magnitude and trend of the crosswind force combined with the vehicle heading angle. When it is determined that crosswind stabilization control intervention is required, lock the current yaw rate. S4. Calculation of anti-crosswind yaw moment: The reference yaw rate latched at the moment of crosswind control intervention is used as the steady-state control target. After the crosswind stabilization control is activated, the feedback correction yaw moment is dynamically generated based on the deviation between the real-time yaw rate of the vehicle and the latched reference value. The feedforward anti-wind yaw moment and the feedback correction yaw moment are superimposed to obtain the anti-crosswind compensation yaw moment. S5, Intelligent Chassis Multi-Actuator Yaw Torque Distribution: Distributes anti-crosswind compensation yaw torque according to the optimal working range of each actuator.

[0023] In this embodiment, the vehicle motion state signal mentioned in step S1 also includes lateral acceleration, longitudinal acceleration, four-wheel wheel speed, vehicle longitudinal speed, steering wheel angle, and front wheel angle. The preprocessing of the collected raw sensor signals in step S1 specifically involves digital filtering, signal limiting, and outlier removal. That is, the vehicle motion state parameters during driving are collected in real time using the vehicle's standard onboard sensors. The collected signals include raw measurement signals such as yaw rate, lateral acceleration, longitudinal acceleration, four-wheel wheel speed, vehicle longitudinal speed, steering wheel angle, and front wheel angle. All raw sensor signals undergo unified preprocessing operations, including digital filtering, signal limiting, and outlier removal, to eliminate measurement noise and signal jumps, improve the signal-to-noise ratio, and adapt to the computational input requirements of the crosswind stability control algorithm. In this embodiment, the preprocessing of the acquired raw sensor signal in step S1 specifically involves performing a differential operation on the raw yaw rate signal to solve for the yaw acceleration, and then performing a smoothing filter on the differentiated signal. Performing a differential operation on the raw yaw rate signal to solve for the yaw acceleration, and then performing a smoothing filter on the differentiated signal, can suppress noise interference in the differential term. It should be understood that in practical applications, the signal processing method is not limited to the examples above, and any possible signal processing method that facilitates subsequent calculations is included within the protection scope of claim 1 of this application.

[0024] In this embodiment, step S2, "predicting and estimating the crosswind moment based on the vehicle dynamics model, vehicle driving state information, and road surface adhesion coefficient," specifically involves: calculating the real-time net external torque of the vehicle using yaw acceleration, mass, and moment of inertia, based on the vehicle dynamics model. The crosswind moment is then predicted and estimated based on vehicle driving state information and road surface adhesion coefficient. The real-time net external torque of the vehicle is calculated using yaw acceleration, mass, and moment of inertia, based on the vehicle dynamics model, to determine the vehicle's motion trend during driving. Subsequently, road surface information is used to infer the direction of the crosswind and the position of the lateral center relative to the vehicle's center of mass. In this embodiment, to eliminate the interference of the additional yaw moment generated by tire elastic deformation on the accuracy of crosswind moment identification, tire deformation coupling errors are decoupled and eliminated. Step S2 further includes: decoupling and eliminating tire deformation coupling errors. Using two typical non-transient wind disturbance conditions—straight-line constant speed driving and steady-state circular steering—as benchmarks, the theoretical front wheel angle is inferred using state parameters such as wheelbase, longitudinal vehicle speed, and measured yaw rate. Based on the benchmark yaw moment component corresponding to the theoretical steering input, the torque estimation deviation introduced by tire elastic deformation is separated, and the feedforward anti-wind yaw moment is compensated according to the torque estimation deviation. The two typical non-transient wind disturbance conditions—straight-line constant speed driving and steady-state circular steering—are used as benchmarks because the vehicle motion under these conditions strictly satisfies linear kinematic relationships. The theoretical front wheel angle can be inferred using state parameters such as wheelbase, longitudinal vehicle speed, and measured yaw rate. Based on the benchmark yaw moment component corresponding to the theoretical steering input, the torque estimation deviation introduced by tire elastic deformation is separated and compensated, improving the accuracy of crosswind yaw moment prediction.

[0025] In this embodiment, step S3, "predicting crosswind force," specifically involves: converting the predicted yaw moment from step S2 into a lateral force acting on the vehicle body using vehicle geometry; calculating the trend of lateral force change through filtering and differentiation to predict crosswind magnitude changes. The vehicle heading angle in step S3 is obtained by integrating the yaw rate. Step S3 also includes: when it is initially determined that crosswind stabilization control intervention is needed, latching the current yaw rate; integrating the difference between the latched yaw rate and the real-time yaw rate from the current moment to obtain the local vehicle heading angle change; comparing the local heading angle change value with a calibrable threshold; and further confirming the timing of crosswind stabilization control intervention if the threshold is exceeded. In this embodiment, after estimating the crosswind yaw moment, to further accurately determine whether the vehicle is under crosswind interference and achieve more precise control, the crosswind yaw moment is converted into a lateral force acting on the vehicle body through the vehicle's geometry. The changing trend of the lateral force is calculated using methods such as filtering and differentiation to predict changes in crosswind magnitude, correcting the threshold for entering crosswind stabilization control, and reducing frequent triggering of crosswind stabilization control and system oscillations. Based solely on the reported changes in crosswind force magnitude, the timing for crosswind stabilization control intervention cannot be fully determined. Therefore, a local heading angle change is designed as a judgment condition to further determine the timing for crosswind stabilization intervention. Yaw rate is an important parameter characterizing the lateral movement trend of the vehicle. The vehicle heading angle can be obtained by integrating the yaw rate, but the heading angle obtained by integration during vehicle movement is prone to saturation due to errors and other factors, leading to a decrease in heading angle accuracy. Therefore, when it is initially determined that crosswind stabilization control needs to be intervened based on changes in lateral wind force, the current yaw rate is latched, and the difference between the latched yaw rate and the real-time yaw rate is integrated from the current moment to obtain the local vehicle heading angle change. By comparing the local heading angle change value with a calibrable threshold, the timing of crosswind stabilization control intervention is further confirmed after the threshold is exceeded.

[0026] In this implementation, step S4 is the calculation of the anti-crosswind compensation yaw moment. When a vehicle encounters a sudden crosswind disturbance, it is affected by complex environmental factors such as road surface adhesion fluctuations, changes in vehicle load, and airflow turbulence. The crosswind moment identified solely by the dynamic model is prone to positive or negative estimation errors. If this identified moment is directly used as the correction feedforward, it will cause over-correction or under-correction of vehicle attitude, making it difficult to achieve precise and stable control. Therefore, this application introduces a PID feedback correction stage: the reference yaw rate latched at the moment of crosswind control intervention is used as the steady-state control target. After the crosswind stabilization control is activated, closed-loop feedback adjustment is implemented based on the deviation between the vehicle's real-time yaw rate and the latched reference value to dynamically generate a feedback correction yaw moment to compensate for the crosswind moment estimation error caused by the dynamic model, so that the vehicle attitude converges quickly under crosswind disturbance, while eliminating steady-state residual deviations. The feedback correction yaw moment output by the PID closed loop is superimposed with the feedforward anti-wind yaw moment solved in real time by the dynamic model to finally obtain the total target anti-crosswind correction yaw moment required by the whole vehicle.

[0027] In this embodiment, the multi-actuator in step S5 includes a torque vector control system and a rear-wheel steering system. Step S5 specifically involves: when the anti-crosswind yaw moment is small, it is preferentially allocated to the rear-wheel steering system, and the yaw moment is converted into a rear-wheel steering angle using a tire model and a bisection method; when the anti-crosswind yaw moment is large, the capacity margin of the rear-wheel steering system is first determined, and the portion exceeding the capacity limit is allocated to the torque vector control system; the capacity margin of the rear-wheel steering system is calculated using the estimated rear axle center of gravity sideslip angle, the rear-wheel steering actuator rate, and the maximum permissible rear-wheel steering angle; when the anti-crosswind yaw moment is a transient large yaw moment, it is directly allocated to the torque vector control system; the yaw moment allocated to the torque vector control system is distributed to each actuator by the torque vector control system in conjunction with the capacity margin; the capacity margin of the torque vector control system is calculated in real-time based on the motor capacity and road surface adhesion parameters. In practical applications, vehicle torque vector control systems, rear-wheel steering systems, active suspension systems, and braking systems are all important components of intelligent chassis handling and stability control. Multi-actuator fusion maximizes the efficiency of each actuator's optimal operating range. Taking torque vectoring and rear-wheel steering systems as examples, the rear-wheel steering system has the advantage of changing the direction of the rear axle lateral force and suppressing small-amplitude yaw disturbances, making the rear axle less prone to exceeding its saturation boundary. The torque vector control system, due to its faster response, is more often used in transient, large-amplitude yaw disturbance conditions. When the total demand for crosswind resistance yaw moment is small, it is preferentially allocated to the rear-wheel steering system, converting the yaw moment into a rear-wheel steering angle using a tire model and a bisection method. Furthermore, when the yaw moment demand is large, the rear-wheel steering capacity margin is assessed, and the portion exceeding the capacity limit is allocated to the torque vectoring system. Specifically, the rear-wheel steering capacity margin needs to be calculated using the estimated rear axle center-of-gravity sideslip angle, the rear-wheel steering actuator rate, and the maximum permissible rear-wheel steering angle. When faced with transient large yaw torque demands, the torque is directly allocated to the torque vectoring system. Similarly, the torque vectoring system also requires torque capability margin limitations, which are calculated in real time based on parameters such as motor capability and road surface adhesion. This allocation method ensures that the vehicle has a higher safety margin and controllability when encountering sudden crosswinds.

[0028] This embodiment provides an intelligent chassis multi-actuator fusion crosswind stability cooperative control method for coping with crosswinds. This method acquires and preprocesses vehicle driving state parameters in real time using onboard sensors to obtain crosswind control input; it combines vehicle operating parameters with dynamics and kinematic models to predict the wind force amplitude and location of the lateral wind load; it determines the timing of crosswind control intervention based on the trend of lateral wind force changes and the fluctuation characteristics of the vehicle's heading angle; it introduces feedback control to dynamically correct the anti-crosswind compensation yaw moment to address deviations in the dynamic model's wind force prediction caused by environmental and load disturbances; and it further designs a multi-actuator cooperative torque distribution strategy, allocating compensation torque based on the optimal working range of each actuator to achieve stable driving under sudden crosswind conditions.

[0029] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the invention. All equivalent changes and modifications made in accordance with the scope of the claims of this invention should fall within the technical scope of this invention.

Claims

1. A crosswind stability collaborative control method based on the fusion of multiple actuators in an intelligent chassis, characterized in that: Includes the following steps: S1. Vehicle driving status information collection and processing: The vehicle motion status signal during driving is collected by on-board sensors, and the collected raw sensor signals are preprocessed. The vehicle motion status signal includes yaw rate. S2. Estimation of crosswind moment based on vehicle dynamics: Based on the vehicle dynamics model, the crosswind moment is predicted and estimated according to the vehicle driving state information and the road surface adhesion coefficient to obtain the feedforward anti-wind yaw moment. S3, Crosswind Stabilization Control Intervention Timing Judgment: Estimate the crosswind force, and judge the timing of crosswind stabilization control intervention based on the magnitude and trend of the crosswind force combined with the vehicle heading angle. When it is determined that crosswind stabilization control intervention is required, lock the current yaw rate. S4. Calculation of anti-crosswind yaw moment: The reference yaw rate latched at the moment of crosswind control intervention is used as the steady-state control target. After the crosswind stabilization control is activated, the feedback correction yaw moment is dynamically generated based on the deviation between the real-time yaw rate of the vehicle and the latched reference value. The feedforward anti-wind yaw moment and the feedback correction yaw moment are superimposed to obtain the anti-crosswind compensation yaw moment. S5, Intelligent Chassis Multi-Actuator Yaw Torque Distribution: Distributes anti-crosswind compensation yaw torque according to the optimal working range of each actuator.

2. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: The vehicle motion state signals mentioned in step S1 also include lateral acceleration, longitudinal acceleration, four-wheel wheel speed, vehicle longitudinal speed, steering wheel angle, and front wheel angle.

3. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: The preprocessing of the acquired raw sensor signal in step S1 specifically includes: digital filtering, signal limiting, and outlier removal of the raw sensor signal.

4. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: In step S1, the preprocessing of the acquired raw sensor signal specifically involves performing a differential operation on the raw yaw rate signal to solve for the yaw acceleration, and then performing a smoothing filter on the differentiated signal.

5. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 4, characterized in that: In step S2, "based on the vehicle dynamics model, predict and estimate the crosswind moment according to the vehicle driving state information and the road surface adhesion coefficient" specifically means: based on the vehicle dynamics model, calculate the real-time net external torque of the vehicle through yaw acceleration, mass and moment of inertia.

6. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: Step S2 further includes: decoupling and eliminating tire deformation coupling errors; taking two typical non-transient wind disturbance conditions, namely straight-line constant speed driving and steady-state circular steering, as a benchmark, using state parameters such as wheelbase, longitudinal vehicle speed, and measured yaw rate to back-calculate the theoretical front wheel steering angle; relying on the benchmark yaw moment component corresponding to the theoretical steering input, separating the torque estimation deviation introduced by tire elastic deformation; and compensating the feedforward anti-wind yaw moment according to the torque estimation deviation.

7. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: In step S3, "predicting the crosswind force" specifically involves converting the yaw moment predicted in step S2 into a lateral force acting on the vehicle body through vehicle geometry, and then calculating the trend of the lateral force through filtering and differentiation to predict the change in crosswind magnitude.

8. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: The vehicle heading angle mentioned in step S3 is obtained by integrating the yaw rate. Step S3 also includes: when it is initially determined that crosswind stabilization control intervention is required, the current yaw rate is latched, and the difference between the latched yaw rate and the real-time yaw rate is integrated from the current moment to obtain the local vehicle heading angle change. By comparing the local heading angle change value with the calibrable threshold, the timing of crosswind stabilization control intervention is further confirmed after the threshold is exceeded.

9. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 1, characterized in that: The multi-actuator mentioned in step S5 includes a torque vector control system and a rear-wheel steering system.

10. The crosswind stability collaborative control method based on intelligent chassis multi-actuator fusion according to claim 9, characterized in that: Step S5 is as follows: When the anti-crosswind compensation yaw moment is small, it is preferentially allocated to the rear wheel steering system, and the yaw moment is converted into the rear steering angle through the tire model and the dichotomy method. When the anti-crosswind compensation yaw moment is large, the capacity margin of the rear wheel steering system is first determined, and the part exceeding the capacity limit is allocated to the torque vector control system; the capacity margin of the rear wheel steering system is calculated by estimating the rear axle center of gravity sideslip angle, the rear steering actuator rate, and the maximum allowable rear steering angle; When the anti-crosswind compensation yaw moment is a transient large yaw moment, it is directly allocated to the torque vector control system; the yaw moment allocated to the torque vector control system is distributed to each actuator by the torque vector control system in combination with the capability margin; the capability margin of the torque vector control system is calculated in real time based on the motor capability and road surface adhesion parameters.

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