Vehicle trajectory tracking and stability collaborative control method and device

Abstract

The application provides a vehicle trajectory tracking and stability cooperative control method and device, comprising: acquiring vehicle body parameters and driving state parameters; determining an adaptive cooperative coefficient according to the vehicle body parameters and the driving state parameters; determining a target expected yaw angular velocity and a target active yaw moment based on a trajectory tracking control model and a stability control model according to the adaptive cooperative coefficient; adjusting the trajectory tracking control model according to the target expected yaw angular velocity and adjusting the stability control model according to the target active yaw moment; and determining the front wheel steering angle of the vehicle and the longitudinal force of each wheel according to the adjusted trajectory tracking control model and the stability control model, and controlling the vehicle. The method adjusts the trajectory tracking control model and the stability control model through the adaptive cooperative coefficient, takes into account the trajectory tracking control and the stability control of the vehicle, can guarantee the stability and the trajectory tracking precision of the vehicle under extreme working conditions, and reduces energy consumption.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, system, and vehicle for coordinated control of vehicle trajectory tracking and stability. Background Technology

[0002] Vehicle motion control refers to the control system controlling the vehicle's trajectory by executing commands such as acceleration, deceleration, and steering, thereby enabling the vehicle to follow a reference trajectory (also known as trajectory tracking). Motion control includes longitudinal motion control and lateral motion control. Compared to longitudinal motion control, which maintains a reference speed and distance between vehicles, lateral motion control involves maneuvers such as steering and lane changes. Therefore, the control system needs to perform not only trajectory tracking control but also stability control.

[0003] However, trajectory tracking control and stability control interfere with each other, especially under extreme conditions (such as emergency obstacle avoidance or sudden deterioration of road surface adhesion), making it difficult to achieve good trajectory tracking control while ensuring vehicle stability control. To address this issue, the industry has introduced active yaw moment as a control variable into trajectory tracking control. Under extreme conditions, this can, to some extent, alter the oversteering or understeering behavior of the vehicle, thereby improving the accuracy of vehicle trajectory tracking.

[0004] However, the yaw rate determined by trajectory tracking control using the above method is different from the yaw rate determined by stability control, making it impossible to simultaneously guarantee vehicle stability and trajectory tracking accuracy. Furthermore, using this method for trajectory tracking control under non-extreme conditions can generate unnecessary longitudinal forces on the vehicle, thereby increasing energy consumption. Summary of the Invention

[0005] This application provides a vehicle trajectory tracking and stability coordinated control method, which can balance vehicle trajectory tracking control and stability control, ensuring vehicle driving stability and trajectory tracking accuracy, while reducing energy consumption for vehicle control. This application also provides the corresponding device, system, and vehicle for the above method.

[0006] Firstly, this application provides a method for coordinated control of vehicle trajectory tracking and stability. The method includes:

[0007] Obtain the vehicle's body parameters and driving status parameters;

[0008] The adaptive coordination coefficient is determined based on the vehicle body parameters and the driving state parameters;

[0009] Based on the adaptive coordination coefficient, the target's desired yaw rate is determined using the trajectory tracking control model and the stability control model. Additionally, based on the adaptive coordination coefficient and the stability control model, the target's active yaw torque is determined.

[0010] The trajectory tracking control model is adjusted according to the target's desired yaw rate, and the stability control model is adjusted according to the target's active yaw moment.

[0011] Based on the adjusted trajectory tracking control model and stability control model, the front wheel steering angle and longitudinal force of each wheel of the vehicle are determined in order to control the vehicle.

[0012] In some possible implementations, determining the target desired yaw rate based on the adaptive coordination coefficient, using both the trajectory tracking control model and the stability control model, includes:

[0013] Based on the trajectory tracking control model and the stability control model, the first desired yaw rate of the trajectory tracking control model and the second desired yaw rate of the stability control model are determined.

[0014] The target expected yaw rate is determined based on the first expected yaw rate, the second expected yaw rate, and the adaptive coordination coefficient.

[0015] In some possible implementations, determining the target active yaw moment based on the adaptive coordination coefficient and the stability control model includes:

[0016] Based on the stability control model, the initial active yaw moment is determined;

[0017] The target active yaw moment is determined based on the initial active yaw moment and the adaptive coordination coefficient.

[0018] In some possible implementations, determining the front wheel steering angle and longitudinal force of each wheel based on the adjusted trajectory tracking control model and stability control model includes:

[0019] The front wheel steering angle of the vehicle is determined based on the adjusted trajectory tracking control model.

[0020] Based on the adjusted trajectory tracking control model, the longitudinal force of the vehicle is determined;

[0021] Based on the vehicle body parameters and the adjusted stability control model, the load level of each wheel of the vehicle is determined.

[0022] The longitudinal force of each wheel of the vehicle is determined based on the load level of each wheel and the longitudinal force of the vehicle.

[0023] In some possible implementations, the vehicle body parameters include tire lateral stiffness, and the driving state parameters include actual yaw rate and tire lateral force. The method further includes:

[0024] The correction factor is determined based on the actual yaw rate and the tire lateral force.

[0025] The target tire lateral stiffness is determined based on the correction factor and the tire lateral stiffness.

[0026] The tire side stiffness of the trajectory tracking control model is corrected to the target tire side stiffness to obtain an optimized trajectory tracking control model.

[0027] In some possible implementations, determining the adaptive coordination coefficient based on the vehicle body parameters and the driving state parameters includes:

[0028] The stability index of the vehicle is determined based on the vehicle body parameters and the driving state parameters.

[0029] When the stability index of the vehicle is less than or equal to the stability index threshold, the adaptive coordination coefficient is determined according to the first relationship, whereby the first relationship represents the correspondence between the adaptive coordination coefficient and the stability index and the stability index threshold.

[0030] When the stability index of the vehicle is greater than the stability index threshold, the adaptive coordination coefficient is 1.

[0031] In some possible implementations, the vehicle body parameters include one or more of the following: vehicle mass, moment of inertia, distance from axle to center of mass, and tire lateral stiffness.

[0032] The driving state parameters include one or more of the following: longitudinal speed, lateral speed, actual yaw rate, actual center of gravity sideslip angle, tire lateral force, and tire sideslip angle.

[0033] Secondly, this application provides a vehicle trajectory tracking and stability coordinated control device. The device includes:

[0034] The acquisition module is used to acquire the vehicle's body parameters and driving status parameters;

[0035] The first determining module is used to determine the adaptive coordination coefficient based on the vehicle body parameters and the driving state parameters;

[0036] The second determining module is used to determine the target's desired yaw rate based on the adaptive coordination coefficient, the trajectory tracking control model, and the stability control model, and to determine the target's active yaw torque based on the adaptive coordination coefficient and the stability control model.

[0037] The adjustment module is used to adjust the trajectory tracking control model according to the target's desired yaw rate, and to adjust the stability control model according to the target's active yaw moment.

[0038] The third determining module is used to determine the front wheel steering angle and the longitudinal force of each wheel of the vehicle based on the adjusted trajectory tracking control model and stability control model, so as to control the vehicle.

[0039] In some possible implementations, the second determining module is specifically used for:

[0040] Based on the trajectory tracking control model and the stability control model, the first desired yaw rate of the trajectory tracking control model and the second desired yaw rate of the stability control model are determined.

[0041] The target expected yaw rate is determined based on the first expected yaw rate, the second expected yaw rate, and the adaptive coordination coefficient.

[0042] In some possible implementations, the second determining module is specifically used for:

[0043] Based on the stability control model, the initial active yaw moment is determined;

[0044] The target active yaw moment is determined based on the initial active yaw moment and the adaptive coordination coefficient.

[0045] In some possible implementations, the third determining module is specifically used for:

[0046] The front wheel steering angle of the vehicle is determined based on the adjusted trajectory tracking control model.

[0047] Based on the adjusted trajectory tracking control model, the longitudinal force of the vehicle is determined;

[0048] Based on the vehicle body parameters and the adjusted stability control model, the load level of each wheel of the vehicle is determined.

[0049] The longitudinal force of each wheel of the vehicle is determined based on the load level of each wheel and the longitudinal force of the vehicle.

[0050] In some possible implementations, the vehicle body parameters include tire lateral stiffness, the driving state parameters include actual yaw rate and tire lateral force, and the device further includes:

[0051] The optimization module is used to determine a correction coefficient based on the actual yaw rate and the tire lateral force; determine a target tire lateral stiffness based on the correction coefficient and the tire lateral stiffness; and correct the tire lateral stiffness of the trajectory tracking control model to the target tire lateral stiffness to obtain an optimized trajectory tracking control model.

[0052] In some possible implementations, the first determining module is specifically used for:

[0053] The stability index of the vehicle is determined based on the vehicle body parameters and the driving state parameters.

[0054] When the stability index of the vehicle is less than or equal to the stability index threshold, the adaptive coordination coefficient is determined according to the first relationship, whereby the first relationship represents the correspondence between the adaptive coordination coefficient and the stability index and the stability index threshold.

[0055] When the stability index of the vehicle is greater than the stability index threshold, the adaptive coordination coefficient is 1.

[0056] In some possible implementations, the vehicle body parameters include one or more of the following: vehicle mass, moment of inertia, distance from axle to center of mass, and tire lateral stiffness.

[0057] The driving state parameters include one or more of the following: longitudinal speed, lateral speed, actual yaw rate, actual center of gravity sideslip angle, tire lateral force, and tire sideslip angle.

[0058] Thirdly, this application provides a vehicle trajectory tracking and stability collaborative control system, the vehicle control system including a controller and an actuator, the controller storing instructions, the actuator executing the instructions, causing the vehicle trajectory tracking and stability collaborative control system to perform the method described in the first aspect or any implementation thereof.

[0059] Fourthly, this application provides a vehicle. The vehicle includes a vehicle trajectory tracking and stability co-control system as described in the third aspect of this application.

[0060] Based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods.

[0061] Based on the above description, it can be seen that the technical solution of this application has the following beneficial effects:

[0062] This method first acquires the vehicle's body parameters and driving state parameters, then determines adaptive coordination coefficients based on these parameters. Next, based on these coefficients, it determines the target desired yaw rate using a trajectory tracking control model and a stability control model, and determines the target active yaw moment using the stability control model. This allows for the adjustment of the trajectory tracking control model and the stability control model. Finally, based on the adjusted models, it determines the front wheel steering angle and the longitudinal forces of each wheel to achieve vehicle control. This method adjusts the trajectory tracking control model and the stability control model through adaptive coordination coefficients, thus balancing vehicle trajectory tracking and stability control. Under extreme conditions, it can ensure vehicle stability and trajectory tracking accuracy while reducing energy consumption. Attached Figure Description

[0063] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0064] Figure 1 A flowchart illustrating a vehicle trajectory tracking and stability collaborative control method provided in an embodiment of this application;

[0065] Figure 2 A schematic diagram of a vehicle dynamics model provided in an embodiment of this application;

[0066] Figure 3 A schematic diagram of a vehicle trajectory tracking control provided in an embodiment of this application;

[0067] Figure 4 This is a schematic diagram illustrating the changing trend of the adaptive synergy coefficient with the stability index, provided in an embodiment of this application.

[0068] Figure 5 This is a schematic diagram of a vehicle driving scenario provided in an embodiment of this application;

[0069] Figure 6 A simulation result diagram of a trajectory tracking control model provided in an embodiment of this application;

[0070] Figure 7 This is a schematic diagram of a vehicle driving scenario provided in an embodiment of this application;

[0071] Figure 8 A simulation result diagram of vehicle control provided in an embodiment of this application;

[0072] Figure 9This application provides an example of a controller output diagram for implementing vehicle control.

[0073] Figure 10 This is a schematic diagram of a vehicle driving scenario provided in an embodiment of this application;

[0074] Figure 11 A simulation result diagram of vehicle control provided in an embodiment of this application;

[0075] Figure 12 This application provides an example of a controller output diagram for implementing vehicle control.

[0076] Figure 13 This is a schematic diagram of a vehicle trajectory tracking and stability coordinated control device provided in an embodiment of this application. Detailed Implementation

[0077] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0078] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0079] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0080] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0081] To facilitate understanding of the technical solution of this application, the specific application scenarios of this application are described below.

[0082] As a key technology in the field of intelligent vehicles, vehicle motion control refers to the control of a vehicle's movement along a planned trajectory by its actuators through acceleration, deceleration, steering, and other operations. The performance of vehicle motion control directly affects the safety, stability, and overall satisfaction of the driver and passengers. Vehicle motion control can be divided into longitudinal motion control and lateral motion control. Longitudinal motion control generally refers to tracking the desired speed and distance between vehicles in the vehicle's direction of travel, through coordinated control of drive and braking to meet the vehicle's speed requirements. Lateral motion control includes trajectory tracking control and stability control, which must enable the vehicle to follow the planned trajectory while ensuring that the vehicle does not lose its dynamic stability.

[0083] In vehicle lateral motion control, trajectory tracking control and stability control interfere with each other, especially in extreme situations such as emergency obstacle avoidance or sudden deterioration of road surface adhesion. The enhanced nonlinearity of tire forces in these extreme cases affects the accuracy of trajectory tracking control, making it impossible to achieve good tracking accuracy while maintaining vehicle stability. To address this issue, the industry typically introduces active yaw moment as a control variable into trajectory tracking control, thereby improving trajectory tracking performance in extreme conditions to some extent. However, in this method, the desired yaw rate for trajectory tracking control differs from the desired yaw rate for stability control, potentially increasing vehicle instability. Furthermore, when this method is applied under good driving conditions, the controller outputs active yaw moment whenever there is lateral or yaw deviation, generating unnecessary longitudinal force distribution, increasing energy consumption and actuator wear, and failing to fully utilize the benefits of trajectory tracking control.

[0084] Based on this, embodiments of this application provide a vehicle trajectory tracking and stability coordinated control method. This method first acquires the vehicle's body parameters and driving state parameters, determines adaptive coordination coefficients based on these parameters, and then determines the target desired yaw rate based on the trajectory tracking control model and stability control model, and determines the target active yaw moment based on the stability control model, thereby adjusting the trajectory tracking control model and stability control model. Finally, based on the adjusted trajectory tracking control model and stability control model, the front wheel steering angle and the longitudinal force of each wheel are determined to achieve vehicle control. This method adjusts the trajectory tracking control model and stability control model through adaptive coordination coefficients, thus balancing vehicle trajectory tracking control and stability control. Under extreme operating conditions, it can ensure vehicle stability and trajectory tracking accuracy while reducing energy consumption.

[0085] Next, the vehicle trajectory tracking and stability collaborative control method provided in the embodiments of this application will be described in detail with reference to the accompanying drawings.

[0086] See Figure 1 The diagram shows a flowchart of a vehicle trajectory tracking and stability coordinated control method, which can be executed by the vehicle control system and specifically includes the following steps:

[0087] S101: The vehicle control system acquires the vehicle's body parameters and driving status parameters.

[0088] Specifically, vehicle body parameters can be parameters that characterize the physical properties of the vehicle. For example, vehicle body parameters can include one or more of the following: vehicle mass, moment of inertia, distance from the axle to the center of gravity, and tire lateral stiffness. Driving state parameters can be parameters that characterize the current driving condition of the vehicle. For example, driving state parameters can include one or more of the following: longitudinal velocity, lateral velocity, actual yaw rate, actual center of gravity slip angle, tire lateral force, and tire slip angle.

[0089] In some possible implementations, vehicle body parameters can be pre-stored in the vehicle's controller, while driving status parameters can be collected in real time by sensors during vehicle operation.

[0090] Based on the vehicle's body parameters and driving state parameters, the vehicle control system can construct trajectory tracking control models and stability control models. Next, combining... Figure 2 and Figure 3 The process of constructing the trajectory tracking control model is introduced.

[0091] According to Newton's second law of motion, the dynamic equations of the vehicle can be established:

[0092]

[0093] Where m is the vehicle mass, I z Let l be the moment of inertia of the vehicle body about the z-axis. f and l r v represents the distance from the front and rear axles to the center of gravity, respectively. x γ is the longitudinal velocity, β is the actual yaw rate, and F is the actual sideslip angle of the center of mass. yf and F yr These are the lateral forces of the front and rear tires, respectively.

[0094] Due to F yf and F yr Since the relationship with the tire slip angle is approximately linear, the vehicle's dynamic equations can be further expressed as:

[0095]

[0096] Among them, C f and C r These are the lateral stiffnesses of the front and rear tires, respectively, which are twice the lateral stiffness of a single tire, δ.f This refers to the steering angle of the front wheels.

[0097] When a vehicle is equipped with lane line detection visual perception capabilities, a lateral trajectory tracking control model based on single-point preview can be established according to the relative positional relationship between the vehicle and the reference trajectory.

[0098]

[0099] Among them, v y e represents the lateral velocity. y For lateral velocity deviation, e ψ Let L be the yaw angle deviation, L be the aiming distance, and ψ be the actual yaw angle. ψ d For the desired yaw angle, For the desired yaw rate, κ d The road curvature serves as the reference trajectory.

[0100] Correspondingly, a longitudinal trajectory tracking control model based on single-point preview is established:

[0101]

[0102] Among them, e x For longitudinal velocity deviation, F x This refers to the longitudinal force of the vehicle, which can be used for speed tracking, v xd For the longitudinal reference velocity, a d For reference acceleration.

[0103] Combining equations (2) to (4), the state variable is determined as x = [e x ,e y ,β,e ψ ,γ] T The control variable is determined as u = [F x ,δ f ] T The disturbance variable is determined as w = [―a d ,0,0,―κ d v x ,0] T And select the output variable as y = [e x ,e y ,e ψ ] T At this point, the state-space equation of the trajectory tracking control model can be expressed as:

[0104]

[0105] in,

[0106] Discretizing the state-space equation of the trajectory tracking control model in equation (5) and combining the discrete state variable x(k) with the control variable u(k) yields the expanded-dimensional state-space equation:

[0107]

[0108] in, Δu(k)=u(k+1)―u(k), A d =I+AT,B d =BT,C d =C.

[0109] When the trajectory tracking control model has N in the prediction time domain p The control time domain is N c When executed, the output variable in the prediction time domain can be represented as:

[0110]

[0111] in, Y(k)=[y(k+1),y(k+2),…,y(k+N p )] T , ΔU(k)=[Δu(k),Δu(k+1),…,Δu(k+N c ―1)] T ,

[0112]

[0113]

[0114] In trajectory tracking control models, the selection of the objective function can comprehensively consider both control and output variables to achieve overall optimization of both. In some possible implementations, solving the trajectory tracking control model can be transformed into a quadratic programming problem.

[0115] minJ=[ΔU T ,ε] T H[ΔU T ,ε]+G[ΔU T ,ε]+P (8)

[0116] Where J is the objective function of the trajectory tracking control model, and ε is the relaxation factor. Q is the weight matrix of the output variables.

[0117] Based on the above steps, a trajectory tracking control model for the vehicle can be constructed. By solving the trajectory tracking control model, the front wheel angle and longitudinal force of the vehicle at different times can be determined. The vehicle control system controls the vehicle's driving trajectory based on the front wheel angle and longitudinal force.

[0118] Next, the process of constructing the stability control model will be introduced.

[0119] Taking the Laplace transform of the vehicle dynamics equation shown in equation (2), we can obtain the transfer function:

[0120]

[0121] Among them, G β =(a 12 b2―a 22 b1) / D A G γ =(a 21 b1―a 11 b2) / D A T β =b1 / (a 12 b2―a 22 b1), T γ =b2 / (a 21 b1―a 11 b2), T A =a 11 +a 22 D A =a 11 a 22 ―a 12 a 21 , a 21 =(l r C r ―l f C f ) / I z , b1 = C f / mv x b2 = l f C f / I z .

[0122] According to equations (9) and (10), when the vehicle's driving conditions are good, the expected centroid sideslip angle β in steady state is... s and the expected yaw rate γ s It can be represented as:

[0123] β s =G β δ f (11)

[0124] γ s =G γ δ f (12)

[0125] Since the road adhesion limit may cause tire force saturation, there is a constraint on the steady-state yaw rate: γ sc =sign(γ) s )min{|γ s |,γ0}, where γ0=μg / v x , represents the limit value of the yaw rate, μ is the road adhesion coefficient, and g is the gravitational acceleration.

[0126] When the vehicle is in a steady state, the slip angle of the maximum lateral force generated by the rear axle tires can be expressed as: α r,peak =tan ― 1 (3 mg / μl) f / C αr l), where C αr Let β be the lateral stiffness of the tire, and l be the vehicle wheelbase. Meanwhile, the limiting value of the vehicle's center-of-gravity sideslip angle can be expressed as: β0 = l r γ / v x ―α r,peak Therefore, the centroid sideslip angle under steady state is subject to a constraint: β sc =sign(β) s )min{|β s |,γ0}.

[0127] Based on the constraints of the steady-state yaw rate and the center-of-mass sideslip angle, the desired instantaneous center-of-mass sideslip angle β can be obtained. d and the expected yaw rate γ d :

[0128]

[0129] In some possible implementations, a stability control model can be designed based on a sliding mode control algorithm. When the desired sideslip angle and desired yaw rate are defined as state variables, the sliding surface can be defined as: s = γ - γ d +ξ(β―β d ), where ξ is the normalized weight, for example, ξ can be the ratio of the yaw rate limit to the center of mass sideslip angle limit.

[0130] Based on this, the active yaw moment M z Introduce the vehicle dynamics equations to represent yaw motion:

[0131]

[0132] By solving equation (15), the active yaw moment M of the vehicle's stability control model can be obtained using the following formula. z :

[0133]

[0134] Based on the above steps, a vehicle stability control model can be constructed. By solving the stability control model, the active yaw moment of the vehicle at different times can be determined, thereby controlling the stability of the vehicle's driving.

[0135] It should be noted that the above-described method for constructing the trajectory tracking control model and the stability control model is only one possible implementation. In other embodiments, different methods can be used to construct the trajectory tracking model and the stability control model, and this application does not impose any restrictions on this.

[0136] S102: The vehicle control system determines the adaptive coordination coefficient based on the vehicle body parameters and driving status parameters.

[0137] It should be noted that the adaptive coordination coefficient can be used to adjust the desired yaw rate in the trajectory tracking control model and the active yaw moment in the stability control model. It is understood that the desired yaw rate in the trajectory tracking control model (i.e., the yaw moment in the embodiments of this application) is... ) and the desired yaw rate in the stability control model (i.e., γ in the embodiments of this application) d The two methods are not the same, and it is impossible to achieve good trajectory tracking accuracy while ensuring vehicle stability. Therefore, in the embodiments of this application, the desired yaw rate in the trajectory tracking control model can be adjusted by an adaptive coordination coefficient, while the degree of intervention of the active yaw moment in the stability control model can be adjusted, so as to a certain extent take into account both trajectory tracking control and stability control.

[0138] Specifically, the vehicle control system can determine the vehicle's stability index based on vehicle body parameters and driving state parameters, and then determine the adaptive coordination coefficient based on the stability index. When the stability index is less than or equal to a stability index threshold, the adaptive coordination coefficient can be determined according to a first relational expression, which represents the correspondence between the adaptive coordination coefficient, the stability index, and the stability index threshold. When the stability index is greater than the stability index threshold, the adaptive coordination coefficient can be set to 1.

[0139] In some possible implementations, based on the trajectory tracking control model and stability control model constructed above, the stability index can be determined using the following formula, which can be used to evaluate the degree of vehicle instability:

[0140]

[0141] Where p is the relative importance coefficient, which can be selected according to the actual situation. Considering the vehicle's handling requirements, the weight of yaw rate should be greater than the weight of sideslip angle, for example, p = 1.5. It can be found that the larger the stability index, the more severe the vehicle instability.

[0142] To ensure that the active yaw moment does not intervene excessively when the vehicle is in good driving condition, thereby avoiding excessive energy consumption, the adaptive coordination coefficient can be determined using the following formula:

[0143]

[0144] Wherein, λ0 is the stability index threshold, for example, λ0 = 1.5, and c is a parameter reflecting the adjustment speed, which can be selected according to the actual situation, for example, c = 5.

[0145] Combination Figure 4 The diagram showing the trend of the adaptive synergy coefficient with the stability index reveals that the adaptive synergy coefficient gradually increases with the increase of the stability index. When the stability index is greater than the stability index threshold, the adaptive synergy coefficient is 1.

[0146] The method described above for determining the adaptive coordination coefficient based on the vehicle stability index can adjust the degree of adjustment according to the vehicle's driving conditions. This allows for a balance between trajectory tracking control and stability control under different driving conditions, avoiding problems such as energy consumption or poor control performance caused by excessive or insufficient intervention.

[0147] S103: The vehicle control system determines the target's desired yaw rate based on the adaptive coordination coefficient, the trajectory tracking control model, and the stability control model, and determines the target's active yaw moment based on the adaptive coordination coefficient and the stability control model.

[0148] Specifically, the vehicle control system can determine the first desired yaw rate based on the vehicle's trajectory tracking control model, determine the second desired yaw rate and the initial active yaw moment based on the vehicle's stability control model, then determine the target desired yaw rate of the trajectory tracking control model based on the first desired yaw rate, the second desired yaw rate and the adaptive coordination coefficient, and determine the target active yaw moment of the stability control based on the initial active yaw moment and the adaptive coordination coefficient.

[0149] In the trajectory tracking control model and stability control model constructed in the embodiments of this application, the first desired yaw rate is: In other words, the vehicle control system can determine the corresponding time at different times by solving equation (8). Thus, the first desired yaw rate is determined; the second desired yaw rate is γ. d The initial active yaw moment is M z In other words, the vehicle control system can determine the corresponding γ at different times by solving equations (14) and (16). d and M z This allows us to determine the second desired yaw rate and the initial active yaw moment.

[0150] Furthermore, the vehicle control system can determine the target desired yaw rate γ of the trajectory tracking control model using the following formula. c The target active yaw moment M of the stability control model c :

[0151]

[0152] M c =qM z (20)

[0153] It can be observed that the target desired yaw rate γ c The target active yaw moment M is related to the first desired yaw rate representing trajectory tracking control and the second desired yaw rate representing stability control. c The degree of intervention varies under different adaptive coordination coefficients, thereby achieving coordination between trajectory tracking control and stability control.

[0154] S104: The vehicle control system adjusts the trajectory tracking control model according to the target's desired yaw rate and the stability control model according to the target's active yaw moment.

[0155] Specifically, the vehicle control system can determine the desired yaw rate in the trajectory tracking control model as the target desired yaw rate γ. c By combining stability control with trajectory tracking control, the accuracy of trajectory tracking control can be improved while ensuring stability control.

[0156] Meanwhile, when the vehicle's driving conditions are poor (i.e., the stability index is high), the adaptive coordination coefficient is larger, and the proportion of the second expected yaw rate, which represents stability control, in the target expected yaw rate is larger, thereby increasing the importance of stability control in vehicle motion control and ensuring the stability of the vehicle when the driving conditions are poor.

[0157] Furthermore, the vehicle control system can adjust the initial active yaw moment in the stability control model to a target active yaw moment. When the vehicle's driving conditions are poor, the target active yaw moment is larger, resulting in a higher degree of intervention of the active yaw moment to meet the vehicle's stability control requirements. Conversely, when the vehicle's driving conditions are good, the target active yaw moment is smaller, resulting in a lower degree of intervention of the active yaw moment to avoid energy consumption.

[0158] S105: The vehicle control system determines the front wheel steering angle and the longitudinal force of each wheel based on the adjusted trajectory tracking control model and stability control model in order to control the vehicle.

[0159] Specifically, the vehicle control system can determine the front wheel steering angle of the vehicle based on the adjusted trajectory tracking control model.

[0160] Furthermore, the vehicle control system can determine the longitudinal force of the vehicle based on the adjusted trajectory tracking control model, determine the load level of each wheel of the vehicle based on the vehicle body parameters and the adjusted stability control model, and determine the longitudinal force of each wheel of the vehicle based on the load level of each wheel of the vehicle and the longitudinal force of the vehicle.

[0161] In the trajectory tracking control model and stability control model constructed in the embodiments of this application, the vehicle control system can... Adjust to γ c It takes into account both trajectory tracking control and stability control, and based on the adjusted trajectory tracking control model, the front wheel steering angle δ is determined by calculating formula (8). f Control the vehicle to drive at the adjusted front wheel angle.

[0162] Furthermore, the vehicle control system can determine the load level of each wheel of the vehicle using the following formula:

[0163]

[0164] Among them, F xm,i To generate the longitudinal force for the active yaw moment, i = 1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. s w is the wheelbase of the vehicle. i This represents the load factor for each wheel. Specifically, the load factor for each wheel can be determined by w. i =F zi / F z Determined, of which F z =mg is the vehicle load, F zi The vertical load of each tire can be determined using the following formula:

[0165]

[0166] Next, the vehicle control system determines the longitudinal force F of the vehicle using formula (8). x Then, based on the longitudinal force F of the vehicle x Adjust the longitudinal force on each wheel according to its load level. In some possible implementations, the longitudinal force on each wheel can be F. xi =F xv,i +F xm,i , of which F xv,i For the longitudinal force of each wheel used for speed tracking, we have F xv,i =Fx / 4.

[0167] In some possible implementations, the vehicle control system can determine the correction coefficient based on the vehicle's actual yaw rate and tire lateral force, and determine the target tire lateral stiffness based on the correction coefficient and tire lateral stiffness. The tire lateral stiffness of the trajectory tracking control model is then corrected to the target tire lateral stiffness, thereby obtaining the optimized trajectory tracking control model.

[0168] Specifically, in the trajectory tracking control model constructed in the embodiments of this application, a second-order random walk model can be used to describe the changes in tire lateral force during vehicle movement:

[0169]

[0170] Where F0 is the tire lateral force to be estimated, F1 is the first derivative of F0, and w F It is random noise.

[0171] When the state variable is The measured variable is z = [α] y ,γ] T At that time, combining equations (1) and (5), and using the first-order Euler formula with a sampling time of T to approximately discretize the system, the correction coefficient for the tire lateral stiffness of the vehicle can be obtained:

[0172]

[0173] The tire lateral stiffness is adjusted based on the correction factor, and the target tire lateral stiffness is:

[0174]

[0175] It can be observed that the vehicle control system determines the correction coefficient by the difference between the calculated value of the tire lateral force and the estimated value of the tire lateral force, thereby adjusting the tire lateral stiffness and improving the accuracy of the trajectory tracking control model, so as to achieve more precise vehicle trajectory control.

[0176] This method first acquires the vehicle's body parameters and driving state parameters, then determines adaptive coordination coefficients based on these parameters. Next, based on these coefficients, it determines the target desired yaw rate using a trajectory tracking control model and a stability control model, and determines the target active yaw moment using the stability control model. This allows for the adjustment of the trajectory tracking control model and the stability control model. Finally, based on the adjusted models, it determines the front wheel steering angle and the longitudinal forces of each wheel to achieve vehicle control. This method adjusts the trajectory tracking control model and the stability control model through adaptive coordination coefficients, thus balancing vehicle trajectory tracking and stability control. Under extreme conditions, it can ensure vehicle stability and trajectory tracking accuracy while reducing energy consumption.

[0177] To intuitively demonstrate the control effect of the vehicle trajectory tracking and stability coordinated control method provided in the embodiments of this application, the following will be explained in detail with reference to three specific application examples.

[0178] For the trajectory tracking control model constructed in the embodiments of this application, Application Example 1 compares the performance of the original trajectory tracking control model (i.e., the trajectory tracking control model constructed according to equation (2), hereinafter referred to as the MPC model) and the optimized trajectory tracking control model (i.e., the trajectory tracking control model optimized according to equation (25), hereinafter referred to as the AMPC model). Figure 5 A scenario diagram for Application Example 1 is provided. In this application example, the road surface adhesion coefficient is 0.6, and the speed of the main vehicle (i.e., the vehicle equipped with the vehicle control system provided in this application embodiment) is 70 km / h. Car No. 1 in front suddenly stops at a close distance to the lane where the main vehicle is located. At the same time, there is Car No. 2 at a similar speed to the main vehicle on the left rear. In this scenario, the vehicle control system needs to control the main vehicle to change lanes to the left and then continue driving straight.

[0179] Figure 6 Simulation results for the MPC and AMPC models in the scenario of Application Example 1 are presented. Figure 6 (a) in the diagram shows the changes in the vehicle's trajectory. Figure 6 (b) in the figure shows the change in the vehicle's longitudinal velocity. Figure 6 (c) in the diagram shows the variation of the tire's lateral force. Figure 6 (d) in the figure shows the variation of tire lateral stiffness.

[0180] according to Figure 6 (a) and Figure 6Simulation results in (b) show that, compared to the desired trajectory and speed, the AMPC model can track the vehicle's trajectory faster and more smoothly, outperforming the MPC model. This is because the AMPC model adjusts the tire lateral stiffness, thereby improving the accuracy of trajectory tracking control. Figure 6 The graph showing the variation of tire lateral force (c) reveals that even when the tire lateral force reaches saturation, the AMPC model can still achieve a relatively high accuracy in estimating the tire lateral force. Furthermore, according to... Figure 6 As shown in (d) of the figure, the change in tire lateral stiffness can be seen that when the tire lateral force reaches saturation, the adjustment of tire lateral stiffness is very significant. Thus, by adjusting the tire lateral stiffness, the accuracy of trajectory tracking control is improved.

[0181] Application Example 2 analyzes a scenario where different controllers control vehicles for single lane change. Controller A is a controller that only deploys a trajectory tracking control model (MPC model). Controller B is a controller that deploys both a trajectory tracking control model and a stability control model, with the stability control model always in operation (i.e., active yaw moment is always involved in vehicle control). Controller C is the controller in this embodiment that implements the coordination of trajectory tracking control and stability control (i.e., adjusts the trajectory tracking control model and stability control model in real time according to the adaptive coordination coefficient). Figure 7 A scenario diagram for Application Example 2 is provided. In this example, the road surface adhesion coefficient is 0.95, the speed of the main vehicle is 120 km / h, and vehicle number 1 in front suddenly stops at a close distance to the main vehicle in its lane. At the same time, there is vehicle number 2 to the left rear, traveling at a similar speed to the main vehicle. In this scenario, the vehicle control system needs to control the main vehicle to change lanes to the left and then continue driving straight.

[0182] Figure 8 Simulation results of vehicle control implemented with different controllers in application example 2 are presented. Figure 8 (a) in the diagram shows the changes in the vehicle's trajectory. Figure 8 (b) in the figure shows the change in the vehicle's longitudinal velocity. Figure 8 (c) in the graph shows the variation of the lateral deviation. Figure 8 (d) in the graph shows the change in the yaw angle deviation. Figure 8 (e) in the graph shows the change in yaw rate. Figure 8 (f) in the figure shows the change in the centroid sideslip angle.

[0183] according to Figure 8The simulation results shown reveal that the vehicle with controller A deployed in the single lane change scenario of application example 2 suffers severe instability, making trajectory and speed tracking difficult. This is because controller A only deploys a trajectory tracking control model and cannot perform stability control, causing the output front wheel steering angle to exceed the stability limit, leading to vehicle instability. The vehicle with controllers B and C deployed can achieve trajectory and speed tracking while maintaining vehicle stability. This is because controllers B and C deploy trajectory tracking and stability control models. However, according to... Figure 8 (c) and Figure 8 As can be seen from (d) in the figure, the lateral deviation and yaw angle deviation of the vehicle with controller C deployed are smaller than those of the vehicle with controller B deployed. Meanwhile, according to... Figure 8 (e) and Figure 8 As can be seen from (f) in the diagram, the vehicle with controller C deployed has a smaller yaw rate and a smaller sideslip angle, resulting in faster convergence. Therefore, the vehicle trajectory tracking and stability coordinated control method provided in this application embodiment can take into account both vehicle trajectory tracking control and stability control, improving the vehicle trajectory tracking accuracy while ensuring vehicle stability.

[0184] Figure 9 The output results of different controllers implementing vehicle control in the scenario of Application Example 2 are shown in the figure. Figure 9 (a) in the diagram shows the change in the front wheel steering angle. Figure 9 (b) in the graph shows the variation of the longitudinal force (i.e., wheel torque) of each wheel output by controller B. Figure 9 (c) in the graph shows the variation of the longitudinal force (i.e., wheel torque) of each wheel output by controller C. Figure 9 (d) in the figure represents the variation of the adaptive coordination coefficient in controller C.

[0185] according to Figure 9 The output results shown reveal that, while improving overall vehicle performance, controller C outputs the smallest front wheel steering angle and the fastest convergence. Simultaneously, the longitudinal forces of each wheel output by controller B reach the driving limit of the hub motor within a certain time period and continue to intervene after the vehicle begins lane changing; that is, the active yaw torque is continuously involved in the vehicle control process. In contrast, the longitudinal forces of each wheel output by controller C only appear for a very short period. This indicates that the vehicle trajectory tracking and stability coordinated control method provided in this application embodiment can adjust the degree of intervention of the active yaw torque according to the vehicle's stability status, thereby avoiding excessive energy consumption.

[0186] Application Example 3 analyzes a collision avoidance scenario where different controllers control vehicles to perform double lane change. Controllers A, B, and C are the same as in Application Example 2. Figure 10A scenario diagram for application example 3 is provided. In this example, the road surface adhesion coefficient is 0.8, and the speed of the main vehicle is 110 km / h. Car No. 1 in front suddenly stops at a close distance to the lane where the main vehicle is located. At the same time, Car No. 2 and Car No. 3 are located to the left rear and left front respectively, and their speeds are lower than the speed of the main vehicle. In response to the above scenario, the vehicle control system needs to control the main vehicle to decelerate and change lanes to the left to avoid a collision, and then change lanes again to return to the right lane.

[0187] Figure 11 Simulation results of vehicle control implemented with different controllers in application example 3 are presented. Figure 11 (a) in the diagram shows the changes in the vehicle's trajectory. Figure 11 (b) in the figure shows the change in the vehicle's longitudinal velocity. Figure 11 (c) in the graph shows the variation of the lateral deviation. Figure 11 (d) in the graph shows the change in the yaw angle deviation. Figure 11 (e) in the graph shows the change in yaw rate. Figure 11 (f) in the figure shows the change in the centroid sideslip angle.

[0188] according to Figure 11 The simulation results show that the vehicle with controller A lost its dynamic stability during the second lane change, deviating significantly from the desired trajectory, and its speed control failed. Vehicles with controllers B and C both smoothly completed the two lane changes; however, the lateral deviation, yaw angle deviation, yaw rate, and sideslip angle of vehicle with controller C were all smaller than those with controller B. Therefore, the vehicle trajectory tracking and stability coordinated control method provided in this application can balance trajectory tracking control and stability control under extreme conditions, improving trajectory tracking accuracy while ensuring vehicle stability, and exhibiting good control performance.

[0189] Figure 12 The output results of different controllers implementing vehicle control in application example 3 are shown in the diagram. Figure 12 (a) in the diagram shows the change in the front wheel steering angle. Figure 12 (b) in the graph shows the variation of the longitudinal force (i.e., wheel torque) of each wheel output by controller B. Figure 12 (c) in the graph shows the variation of the longitudinal force (i.e., wheel torque) of each wheel output by controller C. Figure 12 (d) in the figure represents the variation of the adaptive coordination coefficient in controller C.

[0190] according to Figure 12As shown in the output diagram, the energy consumed by controller C to control the vehicle is much less than that consumed by controller B to control the vehicle. This is because the vehicle trajectory tracking and stability coordinated control method provided in this application embodiment can adjust the degree of intervention of active yaw torque in real time according to the vehicle's driving conditions, thereby avoiding excessive energy consumption.

[0191] Based on the vehicle trajectory tracking and stability coordinated control method provided in the embodiments of this application, the embodiments of this application also provide a vehicle trajectory tracking and stability coordinated control device corresponding to the above method. The units / modules described in the embodiments of this application can be implemented in software or hardware. The names of the units / modules do not necessarily constitute a limitation on the unit / module itself.

[0192] See Figure 13 The schematic diagram shown illustrates the structure of a vehicle trajectory tracking and stability coordinated control device 1300, which includes:

[0193] The acquisition module 1301 is used to acquire the vehicle's body parameters and driving status parameters;

[0194] The first determining module 1302 is used to determine the adaptive coordination coefficient based on the vehicle body parameters and driving state parameters;

[0195] The second determining module 1303 is used to determine the target's desired yaw rate based on the adaptive coordination coefficient, the trajectory tracking control model, and the stability control model, and to determine the target's active yaw torque based on the adaptive coordination coefficient and the stability control model.

[0196] The adjustment module 1304 is used to adjust the trajectory tracking control model according to the target's desired yaw rate and to adjust the stability control model according to the target's active yaw moment.

[0197] The third determining module 1305 is used to determine the front wheel steering angle and the longitudinal force of each wheel of the vehicle based on the adjusted trajectory tracking control model and stability control model, so as to control the vehicle.

[0198] In some possible implementations, the second determining module 1303 is specifically used for:

[0199] Based on the trajectory tracking control model and the stability control model, the first desired yaw rate of the trajectory tracking control model and the second desired yaw rate of the stability control model are determined.

[0200] The target expected yaw rate is determined based on the first expected yaw rate, the second expected yaw rate, and the adaptive coordination coefficient.

[0201] In some possible implementations, the second determining module 1303 is specifically used for:

[0202] The initial active yaw moment is determined based on the stability control model;

[0203] The target active yaw moment is determined based on the initial active yaw moment and the adaptive coordination coefficient.

[0204] In some possible implementations, the third determining module 1305 is specifically used for:

[0205] The front wheel steering angle of the vehicle is determined based on the adjusted trajectory tracking control model;

[0206] Based on the adjusted trajectory tracking control model, the longitudinal force of the vehicle is determined;

[0207] Based on the vehicle body parameters and the adjusted stability control model, the load level of each wheel of the vehicle is determined.

[0208] The longitudinal force of each wheel of the vehicle is determined based on the load on each wheel and the longitudinal force of the vehicle.

[0209] In some possible implementations, the vehicle body parameters include tire lateral stiffness, and the driving state parameters include actual yaw rate and tire lateral force. The device also includes:

[0210] The optimization module is used to determine the correction coefficient based on the actual yaw rate and tire lateral force; determine the target tire lateral stiffness based on the correction coefficient and tire lateral stiffness; and correct the tire lateral stiffness of the trajectory tracking control model to the target tire lateral stiffness to obtain the optimized trajectory tracking control model.

[0211] In some possible implementations, the first determining module 1302 is specifically used for:

[0212] The vehicle's stability index is determined based on the vehicle body parameters and driving status parameters;

[0213] When the vehicle's stability index is less than or equal to the stability index threshold, the adaptive coordination coefficient is determined according to the first relational formula, which represents the correspondence between the adaptive coordination coefficient and the stability index and the stability index threshold.

[0214] When the vehicle's stability index is greater than the stability index threshold, the adaptive coordination coefficient is 1.

[0215] In some possible implementations, vehicle body parameters include one or more of the following: vehicle mass, moment of inertia, distance from axle to center of mass, and tire lateral stiffness.

[0216] Driving status parameters include one or more of the following: longitudinal speed, lateral speed, actual yaw rate, actual center of gravity sideslip angle, tire lateral force, and tire sideslip angle.

[0217] The vehicle trajectory tracking and stability coordinated control device 1300 according to the embodiments of this application can correspondingly execute the method described in the embodiments of this application, and the above and other operations and / or functions of each module / unit of the vehicle trajectory tracking and stability coordinated control device 1300 are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0218] This application also provides a vehicle control system, which includes a controller and an actuator. The controller stores instructions, and the actuator executes these instructions, causing the vehicle control system to perform a vehicle trajectory tracking and stability coordinated control method.

[0219] This application also provides a vehicle. The vehicle includes the vehicle control system described above.

[0220] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

[0221] While several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0222] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for coordinated control of vehicle trajectory tracking and stability, characterized in that, The method includes: Obtain the vehicle's body parameters and driving status parameters; The adaptive coordination coefficient is determined based on the vehicle body parameters and the driving state parameters; Based on the adaptive coordination coefficient, the target desired yaw rate is determined using the trajectory tracking control model and the stability control model. Based on the adaptive coordination coefficient and the stability control model, the target active yaw moment is determined. The trajectory tracking control model is adjusted according to the target's desired yaw rate, and the stability control model is adjusted according to the target's active yaw moment. Based on the adjusted trajectory tracking control model and stability control model, the front wheel steering angle and longitudinal force of each wheel of the vehicle are determined in order to control the vehicle. The step of determining the target's desired yaw rate based on the adaptive coordination coefficient, the trajectory tracking control model, and the stability control model includes: Based on the trajectory tracking control model and the stability control model, the first desired yaw rate of the trajectory tracking control model and the second desired yaw rate of the stability control model are determined. The target expected yaw rate is determined based on the first expected yaw rate, the second expected yaw rate, and the adaptive coordination coefficient. The step of determining the target active yaw moment based on the adaptive coordination coefficient and the stability control model includes: Based on the stability control model, the initial active yaw moment is determined; based on the initial active yaw moment and the adaptive coordination coefficient, the target active yaw moment is determined.

2. The method according to claim 1, characterized in that, The step of determining the front wheel steering angle and longitudinal force of each wheel of the vehicle based on the adjusted trajectory tracking control model and stability control model includes: The front wheel steering angle of the vehicle is determined based on the adjusted trajectory tracking control model. Based on the adjusted trajectory tracking control model, the longitudinal force of the vehicle is determined; Based on the vehicle body parameters and the adjusted stability control model, the load level of each wheel of the vehicle is determined. The longitudinal force of each wheel of the vehicle is determined based on the load level of each wheel and the longitudinal force of the vehicle.

3. The method according to claim 1, characterized in that, The vehicle's body parameters include tire lateral stiffness, and the driving state parameters include actual yaw rate and tire lateral force. The method further includes: The correction factor is determined based on the actual yaw rate and the tire lateral force. The target tire lateral stiffness is determined based on the correction factor and the tire lateral stiffness. The tire side stiffness of the trajectory tracking control model is corrected to the target tire side stiffness to obtain an optimized trajectory tracking control model.

4. The method according to claim 1, characterized in that, The step of determining the adaptive coordination coefficient based on the vehicle body parameters and the driving state parameters includes: The stability index of the vehicle is determined based on the vehicle body parameters and the driving state parameters. When the stability index of the vehicle is less than or equal to the stability index threshold, the adaptive coordination coefficient is determined according to the first relationship, whereby the first relationship represents the correspondence between the adaptive coordination coefficient and the stability index and the stability index threshold. When the stability index of the vehicle is greater than the stability index threshold, the adaptive coordination coefficient is 1.

5. The method according to any one of claims 1 to 4, characterized in that, The vehicle body parameters include one or more of the following: vehicle mass, moment of inertia, distance from axle to center of gravity, and tire lateral stiffness. The driving state parameters include one or more of the following: longitudinal speed, lateral speed, actual yaw rate, actual center of gravity sideslip angle, tire lateral force, and tire sideslip angle.

6. A vehicle trajectory tracking and stability coordinated control device, characterized in that, The device includes: The acquisition module is used to acquire the vehicle's body parameters and driving status parameters; The first determining module is used to determine the adaptive coordination coefficient based on the vehicle body parameters and the driving state parameters; The second determining module is used to determine, based on the trajectory tracking control model and the stability control model, a first expected yaw rate of the trajectory tracking control model and a second expected yaw rate of the stability control model; determine a target expected yaw rate based on the first expected yaw rate, the second expected yaw rate, and the adaptive coordination coefficient; determine an initial active yaw moment based on the stability control model; and determine a target active yaw moment based on the initial active yaw moment and the adaptive coordination coefficient. The adjustment module is used to adjust the trajectory tracking control model according to the target's desired yaw rate, and to adjust the stability control model according to the target's active yaw moment. The third determining module is used to determine the front wheel steering angle and the longitudinal force of each wheel of the vehicle based on the adjusted trajectory tracking control model and stability control model, so as to control the vehicle.

7. A vehicle trajectory tracking and stability collaborative control system, characterized in that, The system includes a controller and an actuator, the controller storing instructions, and the actuator executing the instructions to cause the system to perform the method as described in any one of claims 1 to 5.

8. A vehicle, characterized in that, The vehicle includes the vehicle trajectory tracking and stability co-control system as described in claim 7.

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