Full-state control method for dynamic drifting of a distributed drive vehicle

Abstract

The present invention is applicable to the technical field of autonomous vehicle control, and provides a full-state control method for dynamic drifting of a distributed drive vehicle, including the following steps: performing reference path tracking to obtain desired state variables; establishing a full-state dynamic error to obtain the desired state derivatives of speed, sideslip angle of the center of mass, and yaw rate; calculating the rear tire force weight to implement the rear tire force constraint of the distributed drive vehicle; inverting the vehicle dynamics model to obtain the desired longitudinal and lateral forces of each wheel; implementing tire model inversion to obtain the desired front wheel sideslip angle and the wheel speeds of each wheel; adjusting the actuator according to the desired front wheel sideslip angle and the wheel speeds of each wheel to generate control commands for the steering wheel angle and the torque of each wheel. The present invention can accurately control the vehicle state, making it have good maneuverability during conventional turning and extreme drifting, and being able to flexibly cope with complex working conditions; enhancing the stability and safety of the vehicle, and realizing full-state dynamic drifting control of the distributed drive vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous vehicle control, and particularly relates to a full-state control method for dynamic drifting of a distributed drive vehicle. Background Technique

[0002] Since the development of autonomous driving technology, great progress has been made. To achieve the goal of improving traffic safety, the key lies in that the autonomous driving system can utilize the maximum tire force as much as possible in emergency situations to control the vehicle to reach the handling limit. In terms of making full use of the road tire friction limit, professional drivers are a benchmark that is difficult to surpass. They are good at pushing the vehicle to the dynamic limit, especially in some challenging special road conditions, such as sharp turning lanes (hairpin turns) and loose roads with poor road surface adhesion conditions. By performing drifting maneuvers, the cornering time can be shortened, and these operations often exceed the stability limit. Therefore, endowing autonomous vehicles with the drifting ability similar to professional drivers is of great significance. This not only enables autonomous vehicles to reach the dynamic limit during normal turning driving, but also allows them to perform drifting driving outside the stability limit, thereby giving full play to the vehicle's maneuverability and improving the stability and safety of autonomous vehicles.

[0003] Rear-wheel drive is the basis for autonomous vehicles to achieve continuous and stable drifting. Distributed drive vehicles add the control of front-wheel driving force and yaw moment on the basis of rear-wheel drive. Therefore, they have stronger ability to stabilize the vehicle near the drifting balance point. However, the addition of front-wheel longitudinal force and yaw moment makes it exhibit the characteristics of an over-driven system, increasing the difficulty of controller design. In addition, existing research has not yet achieved the integrated control of vehicle normal turning and drifting under a unified controller, that is, dynamic drifting has not been realized. In view of this, the present invention proposes a full-state control method for dynamic drifting of a distributed drive vehicle. Dynamic drifting refers to the integration of the conventional turning maneuver during normal vehicle driving and the drifting maneuver during extreme driving. Full-state control means that all vehicle states (including speed, center-of-mass sideslip angle, and yaw angular velocity) are independently controlled during the dynamic drifting maneuver. Summary of the Invention

[0004] The purpose of the present invention is to provide a full-state control method for dynamic drifting of a distributed drive vehicle, aiming to solve the problems proposed in the above background technique.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A full-state control method for dynamic drifting of a distributed drive vehicle includes the following steps:

[0007] Step S1: Perform reference path tracking to obtain desired state variables, including speed, center-of-mass sideslip angle, and yaw angular velocity;

[0008] Step S2: Establish a full-state dynamic error to obtain the desired state derivatives of speed, sideslip angle of the center of mass, and yaw rate.

[0009] Step S3: Calculate the rear tire force weight and implement the rear tire force constraint for the distributed drive vehicle.

[0010] Step S4: Invert the vehicle dynamics model to obtain the desired longitudinal and lateral forces of each wheel.

[0011] Step S5: Implement tire model inversion, including front tire model inversion and thrust angle model inversion of each wheel, to obtain the desired front wheel sideslip angle and wheel speeds of each wheel.

[0012] Step S6: Adjust the actuator according to the desired front wheel sideslip angle and wheel speeds of each wheel to generate control commands for the steering wheel angle and torque of each wheel.

[0013] Furthermore, in the said Step S1, the vehicle kinematic model is used to associate the position and state of the vehicle with the reference path information. The vehicle kinematic model is expressed by the following formula:

[0014] ;

[0015] ;

[0016] In the formula, is the rate of change of the vehicle's position in the global coordinate system direction; is the rate of change of the vehicle's position in the global coordinate system direction; is the rate of change of the vehicle's yaw angle; is the speed of the vehicle; is the sideslip angle of the vehicle's center of mass; the vehicle's yaw rate;

[0017] Taking the vehicle kinematic model as the prediction model, select as the state variable, as the control variable, where is the position of the vehicle in the global coordinate system, is the yaw angle of the vehicle; The interaction between the vehicle and the path is realized through the model predictive control algorithm. The optimal control problem composed of the cost function and constraints of the model predictive control is as follows:

[0018] ;

[0019] In the formula, is the optimization variable in the model predictive control algorithm; is the cost function of the model predictive control algorithm; is the state quantity of the prediction model; is the state quantity of the prediction model at time is the state quantity of the prediction model at time is the control quantity of the prediction model at time is the output quantity of the prediction model; are the state matrix, the input matrix, and the unit output matrix respectively; represents each time step; is the current time; and are the prediction time domain and the control time domain respectively; is the linear maximum stable recovery envelope constraint;

[0020] Finally, through optimal solution, the desired vehicle state is generated, including the desired speed , the desired centroid side slip angle and the desired yaw rate , and the calculation formula is as follows:

[0021] ;

[0022] In the formula, is the deviation between the actual control quantity and the reference control quantity at the current time obtained by optimal solution; are the reference values of speed, centroid side slip angle, and yaw rate respectively.

[0023] Furthermore, in step S2, the vehicle state is stabilized by establishing the error relationship between the desired vehicle state and the actual state, and at the same time, the desired state derivatives of the vehicle full state, namely the centroid side slip angle, yaw rate, and speed, are calculated , and the calculation formula is as follows:

[0024] ;

[0025] In the formula, is the differential of the desired centroid side slip angle ; is the differential of the centroid side slip angle ; is the differential of the desired yaw rate ; and are adjustable control parameters.

[0026] Further, in step S3, the total expected acceleration of the rear wheels is obtained by combining the expected state and state derivative of the vehicle, without considering load transfer, and the weight of the total expected acceleration of the rear wheels accounting for the acceleration limit of the rear wheels, i.e., the expected rear tire force weight, is calculated. The calculation formula is as follows:

[0027] ;

[0028] In the formula, is between 0 and 1 and serves as the rear tire force constraint imposed in the inverse dynamics model; is the expected longitudinal acceleration; is the expected lateral acceleration; is the road surface adhesion coefficient; is the gravitational acceleration; is the distance from the front axle to the center of mass; is the wheelbase of the vehicle; is the expected state derivative of the speed.

[0029] Further, in step S4, a cost function is constructed based on the sum of squares of the errors between the expected state derivative and the state derivative feedback of the vehicle, and an optimal control problem is formed under the constraints of the distributed drive vehicle dynamics model and the rear tire force weight. Then, the nonlinear programming method is used to solve it to obtain the expected longitudinal force and lateral force of each wheel of the distributed drive vehicle.

[0030] Further, in the inversion of the front tire model in step S5, a neural network tire model is established. First, the state data during the vehicle driving process under high-adhesion road surfaces and low-adhesion road surfaces are collected, including the vehicle speed, center of mass side slip angle, yaw rate, steering wheel angle, and wheel speed. Then, the side slip angle, longitudinal slip, and lateral force of the front tires are estimated. The vertical force of the front tires is calculated by the formula Calculate, is the vehicle mass, is the distance from the rear axle to the center of mass; The data of high-adhesion road surfaces and low-adhesion road surfaces are mixed to form a data set and input into the neural network for training. The neural network includes three hidden layers with 64 neurons each. Finally, the neural network tire model of the front wheels is inverted through the following formula to obtain the expected front wheel side slip angle :

[0031] ;

[0032] In the formula, is the lateral force of the front axle calculated by the neural network tire model; are the expected lateral forces of the front left wheel and front right wheel respectively; is the neural network tire model; is the longitudinal slip; Lateral deviation angle limit value of the tire;

[0033] In the inversion of the wheel thrust angle model for each wheel, based on the expected longitudinal force and lateral force of each wheel obtained in step S4, the expected wheel speed of each wheel is inversely calculated by using the wheel thrust angle model. , and the specific formula is as follows:

[0034] ;

[0035] In the formula, are the expected wheel speeds of the front left wheel, front right wheel, rear left wheel, and rear right wheel respectively; is the front wheel steering angle of the vehicle; is the wheel radius; is the wheel track of the left and right wheels of the vehicle; are the expected longitudinal forces of the front left wheel, front right wheel, rear left wheel, and rear right wheel respectively; are the expected lateral forces of the front left wheel, front right wheel, rear left wheel, and rear right wheel respectively.

[0036] Furthermore, in the actuator adjustment link of step S6, it includes wheel speed control and steering system control;

[0037] In wheel speed control, based on the wheel speed dynamics model, a closed-loop control method of feedforward plus feedback is adopted to generate the control command of the torque of each wheel, so as to realize the control of the expected wheel speed of each wheel in step S5. The formula is as follows:

[0038] ;

[0039] In the formula, is the expected wheel torque; is the adjustable control gain; is the actual wheel speed of the wheel; is the expected wheel speed of each wheel; is the moment of inertia of the wheel; is the expected longitudinal force; where represents the front axle and rear axle, represents the left wheel and right wheel;

[0040] In steering system control, its goal is to indirectly control the lateral deviation angle of the front wheels of the vehicle by controlling the steering wheel. The steering wheel angle is calculated based on the expected lateral deviation angle of the front wheels inversely output by the neural network tire model of the front wheels in step S5. The specific formula is as follows:

[0041] ;

[0042] In the formula, is the expected steering wheel angle; is the transmission ratio of the steering system; is the adjustable control gain; is the desired front wheel slip angle.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] Improve vehicle maneuverability: The present invention endows the autonomous vehicle with a drifting ability similar to that of a professional driver, enabling the vehicle to not only reach the dynamic limit during conventional turning, but also perform drifting outside the stability limit, giving full play to the maneuverability of the vehicle. Through precise control of the vehicle state in each step, such as controlling the wheel force and steering according to the desired state quantity and state derivative, the vehicle can be flexibly controlled under complex working conditions.

[0045] Enhance vehicle stability and safety: By establishing a full-state dynamic error, stabilizing the vehicle state, and implementing rear tire force constraints and precise wheel force control, during the vehicle movement, the vehicle speed, the sideslip angle of the center of mass, and the yaw rate are adjusted in real time to ensure that the vehicle can maintain stability in various driving states and avoid losing control, thereby improving the stability and safety of the autonomous vehicle.

[0046] Optimize control accuracy: The present invention uses a model predictive control algorithm for reference path tracking, combined with techniques such as vehicle dynamics model inversion and tire model inversion, to accurately calculate the desired quantities of each vehicle state, as well as the desired forces and wheel speeds of each wheel, etc. By adjusting the actuator to generate precise control commands, precise control of the vehicle is achieved, improving the control accuracy compared with traditional control methods. Brief Description of the Drawings

[0047] Figure 1 is the flowchart of the method of the present invention.

[0048] Figure 2 is the framework diagram of the method of the present invention.

[0049] Figure 3 is the test scenario.

[0050] Figure 4 is the vehicle state. Detailed Embodiments

[0051] For a clearer understanding of the technical features, objectives, and beneficial effects of the present invention, the technical solution of the present invention will be described in detail below, but it should not be construed as a limitation on the implementable scope of the present invention.

[0052] The present invention provides a full-state control method for dynamic drifting of a distributed drive vehicle. The flowchart and framework diagram of this method are respectively as Figure 1 and Figure 2 shown, and specifically includes the following steps:

[0053] Step S1: Perform reference path tracking to obtain desired state variables, including speed, centroidal side slip angle, and yaw rate.

[0054] In step S1, the vehicle kinematic model is used to relate the position and state of the vehicle to the reference path information. The vehicle kinematic model is represented by the following equations:

[0055] ;

[0056] ;

[0057] where is the rate of change of the vehicle's position in the global coordinate system direction; is the rate of change of the vehicle's position in the global coordinate system direction; is the rate of change of the vehicle's yaw angle; is the vehicle's speed; is the centroidal side slip angle of the vehicle; is the yaw rate of the vehicle.

[0058] Taking the vehicle kinematic model as the prediction model, select as the state variables, as the control variables, where is the vehicle's position in the global coordinate system, is the yaw angle of the vehicle; The interaction between the vehicle and the path is realized through the model predictive control algorithm, and then the reference path is tracked. The optimal control problem composed of the cost function and constraints of the model predictive control is as follows:

[0059] ;

[0060] where is the optimization variable in the model predictive control algorithm; is the cost function of the model predictive control algorithm; is the state variable of the prediction model; is the state variable of the prediction model at time is the state variable of the prediction model at time is the control variable of the prediction model at time is the output variable of the prediction model; are the state matrix, input matrix, and unit output matrix respectively; represents each time step; is the current time; and are the prediction horizon and control horizon respectively; It is the linear maximum stable recovery envelope constraint.

[0061] Finally, through optimal solution, the desired vehicle state is generated, including the desired speed , the desired centroid side slip angle and the desired yaw rate , and the calculation formulas are as follows:

[0062] ;

[0063] In the formula, is the deviation between the actual control quantity and the reference control quantity at the current moment obtained by optimal solution; are the reference values of speed, centroid side slip angle and yaw rate respectively.

[0064] Step S2: Establish the dynamic error of the full state to obtain the desired state derivatives of speed, centroid side slip angle and yaw rate.

[0065] In step S2, the vehicle state is stabilized by establishing the error relationship between the desired state and the actual state of the vehicle, and at the same time, the desired state derivatives of the full state of the vehicle, namely the centroid side slip angle, yaw rate and speed, are calculated , and the calculation formulas are as follows:

[0066] ;

[0067] In the formula, is the desired centroid side slip angle; is the centroid side slip angle of the vehicle; is the differential of the desired centroid side slip angle ; is the differential of the centroid side slip angle ; is the desired yaw rate; is the yaw rate of the vehicle; is the differential of the desired yaw rate ; is the desired speed; is the speed of the vehicle; and are adjustable control parameters.

[0068] Step S3: Calculate the rear tire force weight and implement the rear tire force constraint of the distributed drive vehicle.

[0069] In step S3, the total desired acceleration of the rear wheels is obtained by combining the desired state and the state derivative of the vehicle, without considering load transfer, and the weight of the total desired acceleration of the rear wheels accounting for the acceleration limit of the rear wheels is calculated. This weight is the desired rear tire force weight, which is used to implement the rear tire force constraint of the distributed drive vehicle. The calculation formula is as follows:

[0070] ;

[0071] In the formula, is between 0 and 1 and is the rear tire force constraint imposed in the inverse dynamics model; is the desired longitudinal acceleration; is the desired lateral acceleration; is the road surface adhesion coefficient; is the gravitational acceleration; is the distance from the front axle to the center of mass; is the wheelbase of the vehicle; is the desired state derivative of the speed; is the desired center of mass side slip angle; is the desired yaw rate; is the desired speed.

[0072] Step S4: Invert the vehicle dynamics model to obtain the desired longitudinal and lateral forces of each wheel.

[0073] Due to the characteristic of four-wheel independent drive of the distributed drive vehicle, compared with the traditional rear-wheel drive vehicle, it can generate additional front-wheel driving force and yaw moment. This enables the vehicle to achieve state decoupling during the drift maneuver, that is, the speed, center of mass side slip angle, and yaw rate of the vehicle can be independently controlled, and then the full-state control of the vehicle during the integrated control of conventional turning and drift maneuver, namely the dynamic drift control process, can be realized.

[0074] During the inversion process of the distributed drive vehicle dynamics model, the complete state control of the speed, center of mass side slip angle, and yaw rate is synchronously achieved. The specific implementation method is: construct a cost function based on the sum of squares of the error between the desired state derivative and the state derivative feedback of the vehicle, and form an optimal control problem under the constraints of the distributed drive vehicle dynamics model and the rear tire force weight, and then use the nonlinear programming method to solve it to obtain the desired longitudinal and lateral forces of each wheel of the distributed drive vehicle. The relevant formulas are as follows:

[0075] ;

[0076] In the formula, is the desired longitudinal force of each wheel, is the desired lateral force of each wheel, where represents the front axle and the rear axle, Denote the left wheel and the right wheel; is the differential of speed; is the derivative of the expected state of speed; is the differential of the sideslip angle at the center of mass; is the yaw rate of the vehicle; is the derivative of the expected state of the sideslip angle at the center of mass; is the expected yaw rate; is the differential of the yaw rate; is the derivative of the expected state of the yaw rate; are the longitudinal force of the front wheels, the lateral force of the front wheels, the longitudinal force of the rear wheels, and the lateral force of the rear wheels respectively; are the longitudinal force of the front left wheel, the longitudinal force of the front right wheel, the longitudinal force of the rear left wheel, and the longitudinal force of the rear right wheel respectively; are the lateral force of the front left wheel, the lateral force of the front right wheel, the lateral force of the rear left wheel, and the lateral force of the rear right wheel respectively; is the steering angle of the front wheels of the vehicle; is the sideslip angle at the center of mass of the vehicle; is the total mass of the vehicle; is the speed of the vehicle; is the yaw rate of the vehicle; are the distances from the front axle and the rear axle to the center of mass respectively; is the moment of inertia of the vehicle; is the yaw moment; is the track width of the left and right wheels of the vehicle; are the expected longitudinal forces of the front left wheel, the front right wheel, the rear left wheel, and the rear right wheel respectively; are the expected lateral forces of the front left wheel, the front right wheel, the rear left wheel, and the rear right wheel respectively; are the vertical forces of the front and rear tires respectively; are the load transfers of the front and rear axles. During vehicle driving, the load transfer is calculated as follows:

[0077] ;

[0078] ;

[0079] In the formula, is the acceleration due to gravity; is the wheelbase of the vehicle; is the height of the center of mass of the vehicle; are the lateral load transfer coefficients of the front axle and the rear axle respectively.

[0080] Step S5: Implement the tire model inversion, including the front tire model inversion and the thrust angle model inversion of each wheel, to obtain the expected front wheel side slip angle and the wheel speeds of each wheel.

[0081] In the inversion of the front tire model, a neural network tire model is established. First, the state data during vehicle driving on high-friction and low-friction road surfaces are collected, including vehicle speed, center-of-mass sideslip angle, yaw rate, steering wheel angle, and wheel speed. Then, the sideslip angle, longitudinal slip, and lateral force of the front tire are estimated. The vertical force of the front tire can be calculated by the formula Calculate, is the total vehicle mass, is the acceleration due to gravity, is the distance from the rear axle to the center of mass, is the wheelbase of the vehicle; the data of the high-friction and low-friction road surfaces are mixed to form a data set and input into the neural network for training. The neural network contains three hidden layers with 64 neurons. Finally, the neural network tire model of the front wheel is inverted through the following formula to obtain the desired front-wheel sideslip angle :

[0082] ;

[0083] In the formula, is the lateral force of the front axle calculated by the neural network tire model; are the desired lateral forces of the left front wheel and the right front wheel respectively; is the neural network tire model; is the longitudinal slip; is the road surface adhesion coefficient; is the vertical force of the front tire; is the limit value of the sideslip angle of the tire. Once this limit is exceeded, the lateral force of the tire will reach saturation.

[0084] In the inversion of the thrust angle model of each wheel, based on the desired longitudinal and lateral forces of each wheel obtained in step S4, the desired wheel speed of each wheel is inversely calculated using the wheel thrust angle model , and the specific formula is as follows:

[0085] ;

[0086] In the formula, are the desired wheel speeds of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively; is the front-wheel steering angle of the vehicle; is the vehicle speed; is the center-of-mass sideslip angle of the vehicle; is the wheel radius; is the yaw rate of the vehicle; and are the distances from the front axle and the rear axle to the center of mass respectively; is the wheel track of the left and right wheels of the vehicle; are the desired longitudinal forces of the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively; are the expected lateral forces on the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively.

[0087] Step S6: The actuator is adjusted according to the desired front wheel slip angle and the wheel speed of each wheel to generate control instructions for the steering wheel angle and the torque of each wheel.

[0088] The adjustment actuator link in step S6 includes wheel speed control and steering system control.

[0089] Wheel speed control: Based on the wheel speed dynamics model, a feedforward plus feedback closed-loop control method is used to generate control instructions for the torque of each wheel to achieve the control of the desired wheel speed of each wheel in step S5. The formula is as follows:

[0090] ;

[0091] Where, is the desired wheel torque; is the adjustable control gain; is the actual wheel speed; is the expected wheel speed of each wheel; is the wheel moment of inertia; is the wheel radius; is the desired longitudinal force; where Indicates the front axle and rear axle, Indicates left wheel and right wheel.

[0092] Steering system control: Its goal is to indirectly control the vehicle's front wheel slip angle by controlling the steering wheel. The steering wheel angle is calculated based on the desired front wheel slip angle output by the neural network tire model inversion in step S5. The specific formula is as follows:

[0093] ;

[0094] Where, is the desired steering wheel angle; is the gear ratio of the steering system; is an adjustable control gain; is the speed of the vehicle; is the vehicle's center of mass sideslip angle; is the distance from the front axle to the center of mass; is the vehicle's yaw rate; is the front wheel turning angle of the vehicle; is the desired front wheel slip angle.

[0095] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0096] Example 1: To verify the feasibility of the method of the present invention, a dynamic drift control test was carried out. The test scenario is as Figure 3 shown, and the purpose is to enable the vehicle to track the reference trajectory in the form of an integration of conventional turning maneuvers and drift maneuvers. At the same time, to highlight the full-state control advantage of the distributed drive vehicle during the dynamic drift process, its dynamic drift control performance was compared with that of the rear-wheel drive vehicle under the same test scenario. The comparison results are as Figure 4 shown:

[0097] From the perspective of speed control performance, the rear-wheel drive vehicle had the largest speed error. When it traveled near 210 m, the error value reached 1.45 m / s; while the speed control of the distributed drive vehicle was more stable, and the speed error was only 0.48 m / s. This indicates that the addition of front-wheel drive significantly improved the speed control stability of the distributed drive electric vehicle.

[0098] In terms of the control of the sideslip angle of the center of mass, when the vehicle was in the steady-state drift stage (such as the interval of 400 m - 450 m), the sideslip angle of the center of mass of the distributed drive vehicle was closest to the reference value. Further comparing the root mean square value of the error, it was found that the root mean square value of the error of the sideslip angle of the center of mass of the distributed drive vehicle was the smallest, which was 10.6°, while the value of this index of the rear-wheel drive vehicle was the largest. This shows that in the integrated control of drift and conventional turning, the distributed drive electric vehicle has obvious advantages over the rear-wheel drive vehicle.

[0099] When the vehicle transitions between the two maneuver modes of drift and conventional turning, the sideslip angle of the center of mass changes violently. At this time, the vehicle needs to satisfy the yaw rate required to track the path while also requiring an additional yaw rate to satisfy the tracking of the sideslip angle of the center of mass. From the yaw rate curve, it can be seen that the yaw rates of both the distributed drive vehicle and the rear-wheel drive vehicle can be effectively controlled during the dynamic drift process. Based on the above results, it can be known that during the dynamic drift process, the distributed drive vehicle can better control the speed and the sideslip angle of the center of mass on the basis of ensuring the stable control of the yaw rate; while the control effect of the rear-wheel drive vehicle on the speed and the sideslip angle of the center of mass is significantly weaker. The results fully reflect the characteristics of the distributed drive vehicle in the full-state control of dynamic drift and also strongly prove the significant advantages of the method of the present invention.

[0100] The above is only the preferred embodiment of the present invention. It should be noted that for those skilled in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent.

Claims

1. A full-state control method for dynamic drifting of a distributed drive vehicle, characterized in that Including the following steps: Step S1: Perform reference path tracking to obtain desired state variables, including speed, center of gravity sideslip angle, and yaw rate; Step S2: Establish a full-state dynamic error to obtain the desired state derivatives of speed, center of gravity sideslip angle, and yaw rate; Step S3: Calculate the rear tire force weight and implement the rear tire force constraint of the distributed drive vehicle; Step S4: Invert the vehicle dynamics model to obtain the desired longitudinal and lateral forces of each wheel; Step S5: Implement tire model inversion, including front tire model inversion and thrust angle model inversion of each wheel, to obtain the desired front wheel sideslip angle and wheel speeds of each wheel; Step S6: Adjust the actuator according to the desired front wheel sideslip angle and wheel speeds of each wheel to generate control commands for the steering wheel angle and torque of each wheel; In the said Step S1, the vehicle kinematic model is used to associate the position and state of the vehicle with the reference path information, and the vehicle kinematic model is expressed by the following formula: ； ； In the formula, is the rate of change of the vehicle's position in the global coordinate system direction; is the rate of change of the vehicle's position in the global coordinate system direction; is the rate of change of the vehicle's yaw angle; is the vehicle's speed; is the sideslip angle of the vehicle's center of mass; is the yaw angular velocity of the vehicle; Taking the vehicle kinematic model as the prediction model, select as the state variables, as the control variables, where is the position of the vehicle in the global coordinate system, is the yaw angle of the vehicle; the interaction between the vehicle and the path is realized through the model predictive control algorithm, and the optimal control problem composed of the cost function and constraints of the model predictive control is as follows: ； In the formula, is the optimization variable in the model predictive control algorithm; is the cost function of the model predictive control algorithm; is the state variable of the prediction model; is the state variable of the prediction model at time is the state variable of the prediction model at time is the control variable of the prediction model at time is the output variable of the prediction model; are the state matrix, input matrix, and unit output matrix respectively; represents each time step; [[ID=!]] is the current time; and are the prediction horizon and control horizon respectively; is the linear maximum stable recovery envelope constraint; Finally, through optimal solution, the desired vehicle state is generated, including the desired speed , the desired sideslip angle of the center of mass and the desired yaw rate , and the calculation formula is as follows: ； wherein, is the deviation between the actual control quantity and the reference control quantity at the current moment obtained by optimal solution; are the reference values of speed, sideslip angle of the center of mass and yaw rate respectively; In the step S2, the vehicle state is stabilized by establishing the error relationship between the desired state and the actual state of the vehicle, and meanwhile, the desired state derivatives of the full state of the vehicle, namely the sideslip angle of the center of mass, the yaw rate and the speed, are calculated. , and the calculation formula is as follows: ； wherein, is the differential of the desired centroid sideslip angle ; is the differential of the centroid sideslip angle ; is the differential of the desired yaw rate ; and are adjustable control parameters; In the said Step S3, combining the desired state and state derivative of the vehicle to obtain the total desired acceleration of the rear wheels, without considering load transfer, and calculating the weight of the total desired acceleration of the rear wheels accounting for the acceleration limit of the rear wheels, that is, the desired rear tire force weight, and the calculation formula is as follows: ； wherein, is between 0 and 1 and serves as the rear tire force constraint imposed in the inverse dynamics model; is the desired longitudinal acceleration; is the desired lateral acceleration; is the road surface adhesion coefficient; is the gravitational acceleration; is the distance from the front axle to the center of mass; is the vehicle wheelbase; is the desired state derivative of the speed.

2. The full-state control method for dynamic drifting of a distributed drive vehicle according to claim 1, wherein In the said Step S4, a cost function is constructed based on the sum of squares of the error between the desired state derivative and the state derivative feedback of the vehicle, and an optimal control problem is formed under the constraints of the distributed drive vehicle dynamics model and the rear tire force weight, and then solved by using the nonlinear programming method to obtain the desired longitudinal and lateral forces of each wheel of the distributed drive vehicle.

3. The full-state control method for dynamic drifting of a distributed drive vehicle according to claim 2, wherein, In the inversion of the front tire model in step S5, a neural network tire model is established. First, the state data during vehicle driving on high-adhesion roads and low-adhesion roads are collected, including vehicle speed, centroid side slip angle, yaw rate, steering wheel angle, and wheel speed. Then, the side slip angle, longitudinal slip, and lateral force of the front tire are estimated. The vertical force of the front tire is calculated by the formula Calculated, is the vehicle mass, is the distance from the rear axle to the centroid; Mix the data of high-friction coefficient road surfaces and low-friction coefficient road surfaces to form a data set, and input it into a neural network for training. The neural network includes three hidden layers with 64 neurons; finally, invert the neural network tire model of the front wheel through the following formula to obtain the desired front wheel sideslip angle : ； In the formula, is the lateral force of the front axle calculated by the neural network tire model; are the expected lateral forces of the left front wheel and the right front wheel respectively; is the neural network tire model; is the longitudinal slip; is the limit value of the side slip angle of the tire; In the inversion of each wheel thrust angle model, based on the expected longitudinal and lateral forces of each wheel obtained in step S4, the expected wheel speed of each wheel is inversely calculated using the wheel thrust angle model , and the specific formula is as follows: ； wherein, are the expected wheel speeds of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively; is the front wheel steering angle of the vehicle; is the wheel radius; is the track width between the left and right wheels of the vehicle; are the expected longitudinal forces of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively; are the expected lateral forces of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively.

4. The full-state control method for dynamic drifting of a distributed drive vehicle according to claim 3, wherein In the link of adjusting the actuator in the said Step S6, it includes wheel speed control and steering system control; In wheel speed control, based on the wheel speed dynamics model, a feedforward plus feedback closed-loop control method is adopted to generate control commands for the torque of each wheel to achieve the control of the desired wheel speeds of each wheel in Step S5, and the formula is as follows: ； Wherein, is the desired wheel torque; is an adjustable control gain; is the actual wheel speed; is the desired wheel speed of each wheel; is the moment of inertia of the wheel; is the desired longitudinal force; wherein represents the front axle and the rear axle, represents the left wheel and the right wheel; In steering system control, its goal is to indirectly control the front wheel sideslip angle of the vehicle by controlling the steering wheel, and the steering wheel angle is calculated according to the desired front wheel sideslip angle output by the neural network tire model inversion of the front wheels in Step S5, and the specific formula is as follows: ； wherein, is the desired steering wheel angle; is the transmission ratio of the steering system; is the adjustable control gain; is the desired front wheel slip angle.

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