Closed-loop drift control method and system for autonomous vehicle in uncertain environment

Abstract

The application relates to the technical field of closed-loop drift control, and particularly discloses a closed-loop drift control method and system for an automatic driving vehicle in an uncertain environment, which comprises the following steps: establishing a vehicle kinematics model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism; establishing a three-degree-of-freedom whole vehicle dynamics model with an additional yaw moment; establishing a UniTire-Ctrl tire dynamics model; based on the whole vehicle dynamics model and the tire dynamics model, combining LQR and integral sliding mode control to design a robust drift controller; based on ideal rear wheel speed and the additional yaw moment output by the robust controller, designing a torque distribution module with the minimum distribution error and tire load utilization rate as the target, and calculating and outputting left and right rear wheel torque instructions at the actuator level. The application can realize closed-loop drift control, improve the control performance and robustness of a drifting vehicle, and provide safety guarantee for limit control of a vehicle on an uncertain road surface.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of closed-loop drift control, in particular to a closed-loop drift control method and system for an autonomous vehicle in an uncertain environment. BACKGROUND

[0002] With the continuous development of automobile intelligence, more and more vehicle controls are gradually pursuing higher maneuverability and stability. The existing related control technology is to control the vehicle stability in the linear region, so that the vehicle is in a safer state. However, in the face of some more extreme working conditions, professional drivers can use vehicle control to achieve more extreme actions. For example, through the drift action to achieve extreme bending at high speed and other working conditions.

[0003] In the face of current drift working conditions, only professional drivers can make the drift action by using the hand brake and the accelerator. At present, there is no corresponding simple and mass-produced algorithm for this working condition for autonomous vehicles. In addition, the drift controller has very high requirements for the accuracy of state parameters. In the face of uncertain environment, the current drift controller lacks robustness in controlling uncertainty. Therefore, it is necessary to design a stable and practical drift controller for the uncertain road environment. SUMMARY

[0004] The purpose of the present application is to provide a closed-loop drift control method and system for an autonomous vehicle in an uncertain environment to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical solutions:

[0006] The closed-loop drift control method for an autonomous vehicle in an uncertain environment comprises:

[0007] A vehicle kinematics model is established based on the Frenet coordinate system, and an MPC path tracking controller with a preview mechanism is constructed based on the vehicle kinematics model;

[0008] A three-degree-of-freedom vehicle dynamics model with additional yaw moment is established;

[0009] A UniTire-Ctrl tire dynamics model is established, and the tire parameters are fitted based on experimental data;

[0010] Based on the vehicle dynamics model and the tire dynamics model, a robust controller is designed by combining LQR and integral sliding mode control, and the control quantities include front wheel steering angle, rear wheel speed and vehicle additional yaw moment;

[0011] Based on the ideal rear wheel speed and additional yaw moment output by the robust controller, a torque distribution module is designed to minimize the distribution error and tire load utilization, and to calculate and output the left and right rear wheel torque instructions at the actuator level.

[0012] As a further scheme of the present application, the pre-look mechanism is realized by defining a pre-look distance d, and the path parameter of the pre-look point L1 is calculated by the following formula:

[0013] ;

[0014] ;

[0015] wherein e L1 is the lateral distance at the pre-look point, s L1 is the distance traveled by the vehicle at the pre-look point of the reference path, is the heading error of the vehicle at the pre-look point, e is the lateral distance of the current vehicle from the reference path, and s is the distance traveled by the vehicle along the reference path, is the heading error of the current vehicle.

[0016] As a further scheme of the present application, the extended kinematic model containing the current state and the pre-look point state is linearized to obtain a state space equation for the design of the MPC controller, with the state vector ζ and the control vector U M defined as:

[0017] ;

[0018] ;

[0019] wherein V xeq , V yeq , and r eq are the drift lateral vehicle speed, the longitudinal vehicle speed, and the yaw rate of the drift equilibrium point, respectively.

[0020] As a further scheme of the present application, the differential equation of the three-degree-of-freedom vehicle dynamics model is as follows:

[0021] ;

[0022] ;

[0023] ;

[0024] wherein m is the self-weight of the vehicle, V x is the longitudinal vehicle speed, β is the center of mass side slip angle of the vehicle, r is the yaw rate of the vehicle, δ is the front wheel steering angle of the vehicle, is the additional yaw moment of the vehicle, and IZ For the whole vehicle moment of inertia, F xr and F yr is the longitudinal force and lateral force of the rear wheel of the vehicle, l f is the distance from the mass center to the front wheel, F yf is the lateral force of the front wheel of the vehicle, l r is the distance from the mass center to the rear wheel.

[0025] As a further scheme of the present application, the robust controller is designed for a system model containing parameter uncertainty and external disturbance:

[0026] ;

[0027] Wherein, ΔA and ΔB represent unknown time-varying function matrices of parameter uncertainty, D is the external disturbance corresponding to random road, A is a state matrix, B is a control input matrix, X is a state space error parameter, and U is a control input.

[0028] As a further scheme of the present application, the integral sliding mode control surface is designed as:

[0029] ;

[0030] ;

[0031] Wherein, S(t) is a drift integral sliding mode surface, H is a constant matrix, and satisfies , K is a feedback matrix of LQR, X(t) is a state space parameter, and U L (t) is a controller input of LQR;

[0032] The integral sliding mode control law is:

[0033] ;

[0034] The final control law of the robust controller is composed of the LQR control input and the integral sliding mode control input.

[0035] As a further scheme of the present application, the torque distribution module distributes the ideal whole vehicle additional yaw moment to the left and right rear wheels according to the load transfer caused by the body roll:

[0036] ;

[0037] ;

[0038] Wherein, is the mass of the roll offset, is the expected additional yaw moment of the whole vehicle, F zr is the load of the rear axle of the vehicle, , are the additional yaw moments assigned to the left and right rear wheels, respectively.

[0039] As a further aspect of the present application, the final torque commands for the left and right rear wheels are calculated by the following formula:

[0040] ;

[0041] ;

[0042] wherein, is a proportional coefficient, is the moment of inertia of the wheel relative to the rotation axis, is the left rear wheel speed of the vehicle, is the right rear wheel speed of the vehicle, is the ideal drive force for the left rear wheel of the vehicle, is the ideal drive force for the right rear wheel of the vehicle, is the ideal rear wheel speed output by the overall drift controller, R e is the effective radius of the wheel, , are the ideal rear wheel speeds for the left and right wheels, respectively.

[0043] The present application also provides a closed-loop drift control system for an autonomous vehicle in an uncertain environment, for implementing the closed-loop drift control method for an autonomous vehicle in an uncertain environment, characterized in that the system comprises:

[0044] a preview mechanism module for establishing a vehicle kinematics model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism based on the vehicle kinematics model;

[0045] a whole vehicle dynamics model construction module for establishing a three-degree-of-freedom whole vehicle dynamics model with an additional yaw moment;

[0046] a tire dynamics model construction module for establishing a UniTire-Ctrl tire dynamics model, and fitting tire parameters based on experimental data;

[0047] a controller design module for designing a robust controller based on the whole vehicle dynamics model and the tire dynamics model, in combination with LQR and integral sliding mode control, the control quantities of which include front wheel steering angle, rear wheel speed and whole vehicle additional yaw moment;

[0048] an output module for designing a torque distribution module based on the ideal rear wheel speed and additional yaw moment output by the robust controller, with the goal of minimizing distribution error and tire load utilization, and calculating and outputting left and right rear wheel torque commands at the actuator level.

[0049] Compared with the prior art, the application has the beneficial effects that: the application adopts a hierarchical control system structure, the upper layer adopts a preview MPC controller to follow the trajectory of the vehicle, the drift balance point is obtained according to path information, and corresponding parameters are provided for the lower layer; the lower layer considers the parameters when the tire drifts and the road surface uncertainty, adopts an LQR algorithm to superimpose integral sliding mode control as a vehicle controller, the hierarchical control structure separates path tracking and drift state control, reduces the complexity of control, and forms closed-loop drift control. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application.

[0051] Figure 1 A flow chart of the closed-loop drift control method of the automatic driving vehicle in an uncertain environment is provided for the embodiments of the present application.

[0052] Figure 2 A whole vehicle model schematic diagram for controller design is provided for the embodiments of the present application.

[0053] Figure 3 An algorithm architecture schematic diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0054] In order to make the technical problems to be solved by the present application, technical solutions and beneficial effects more clearly, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0055] Figure 1 A flow chart of the closed-loop drift control method of the automatic driving vehicle in an uncertain environment is provided for the embodiments of the present application. In the embodiments of the present application, the closed-loop drift control method of the automatic driving vehicle in an uncertain environment, the method comprises:

[0056] A vehicle kinematics model is established based on a Frenet coordinate system, and a model predictive control (MPC) path tracking controller with a preview mechanism is constructed based on the vehicle kinematics model;

[0057] A three-degree-of-freedom whole vehicle dynamics model with additional yaw moment is established;

[0058] A UniTire-Ctrl tire dynamics model is established, and tire parameters are fitted based on experimental data;

[0059] Based on the whole vehicle dynamics model and tire dynamics model, a robust controller is designed by combining a linear quadratic regulator (LQR) and an integral sliding mode control, and the control quantity includes front wheel steering angle, rear wheel speed and whole vehicle additional yaw moment;

[0060] Based on the ideal rear wheel speed and additional yaw moment output by the robust controller, a torque distribution module is designed to minimize distribution error and tire load utilization, and the left and right rear wheel torque instructions at the actuator level are calculated and output.

[0061] In this embodiment, step one, the pre-look MPC path tracking controller is established, and the path tracking model adopted is the vehicle kinematics model in the Frenet coordinate system.

[0062] Step two, a whole vehicle dynamics model suitable for controller design is established. Due to the limitation of controller computing power and the requirement of whole vehicle model accuracy, the application adopts a three-degree-of-freedom vehicle dynamics model with additional yaw moment.

[0063] Step three, a tire dynamics model is established. The tire dynamics model adopts the UniTire-Ctrl tire model, and the corresponding tire parameters are fitted according to the MatLab toolbox.

[0064] Step four, according to the above whole vehicle dynamics model and tire dynamics model, considering parameter uncertainty and road uncertainty, a controller is established by fusing LQR and integral sliding mode, and the control quantity is front wheel steering angle, rear wheel speed and whole vehicle additional yaw moment.

[0065] Step five, the ideal rear wheel speed and additional yaw moment obtained are considered uniformly, a torque distribution execution module is designed to minimize distribution error, minimize tire load utilization and consider actuator constraints, and is applied to the vehicle.

[0066] As a preferred embodiment of the application, the vehicle kinematics model is established based on the Frenet coordinate system, and the MPC path tracking controller with pre-look mechanism is constructed based on the vehicle kinematics model in the following steps:

[0067] The vehicle kinematics model established by the Frenet coordinate system is:

[0068] ;

[0069] ;

[0070] ;

[0071] Where e is the lateral distance of the vehicle from the reference path, s is the distance traveled by the vehicle on the reference path, ​V represents the vehicle's heading error. x V y Let r be the vehicle's longitudinal and lateral velocities, β be the vehicle's yaw rate, β be the vehicle's sideslip angle, and k be the vehicle's curvature.

[0072] By incorporating a pre-aiming mechanism, which is implemented by defining a pre-aiming distance d, L1 = s + d can be approximated as the reference path distance at the pre-aiming point. The vehicle kinematics model at the pre-aiming point is as follows:

[0073] ;

[0074] ;

[0075] ;

[0076] Among them, e L1 s represents the lateral distance at the aiming point. L1 It represents the distance already traveled at the vehicle's reference path aiming point. Let be the heading error at the vehicle's aiming point, e be the lateral distance between the current vehicle and the reference path, and s be the distance the vehicle has traveled along the reference path. V represents the current heading error of the vehicle. x and V y Let k be the longitudinal and lateral velocities of the vehicle. L1 Let be the curvature at the aiming point.

[0077] Differentiating the above equation yields...

[0078] ;

[0079] ;

[0080] Where r is the yaw rate of the vehicle. Linearizing the above kinematic model, its state-space equation is:

[0081] ;

[0082] ;

[0083] ζ represents the vehicle's state-space error, U represents the difference between the actual control input and the path reference input, η represents the control system output, and A represents the path reference input. M and B M These are the system matrix and the input matrix, C. M It is an identity matrix.

[0084] Wherein, system matrix A M and input matrix B M Obtained in the following ways:

[0085] ;

[0086] ;

[0087] ;

[0088] As a preferred embodiment of the present application, the extended kinematic model containing current state and preview point state is linearized to get state space equation for the design of MPC controller, whose state vector ζ and control vector U M is defined as:

[0089] ;

[0090] ;

[0091] where V xeq , V yeq , r eq are the drift lateral velocity, longitudinal velocity, yaw rate of the drift equilibrium point respectively.

[0092] After the system model is proposed, the optimization problem of MPC needs to be constructed, and the optimization problem is solved in the form of solving QP problem, and the cost function of the controller is as follows:

[0093] ;

[0094] where , N p and N c are the prediction step and control step, Q M and R M are the weight matrices of MPC. At each time step, the goal is to optimize the cost function J, so that the cost function is minimized, at this time:

[0095] ;

[0096] ;

[0097] ;

[0098] At this time, the ideal longitudinal velocity V xd , lateral velocity V yd , yaw rate r d are:

[0099] .

[0100] As shown in Figure 2 , as a preferred embodiment of the present application, the differential equation of the three-degree-of-freedom vehicle dynamics model is as follows:

[0101] ;

[0102] ;

[0103] ;

[0104] where m is the self-weight of the vehicle, V x is the longitudinal vehicle speed, β is the vehicle's center of mass side slip angle, r is the vehicle's yaw rate, δ is the vehicle's front wheel steering angle, is the additional yaw moment of the vehicle, I Z is the vehicle's moment of inertia, F xr and F yr are the longitudinal and lateral forces of the vehicle's rear wheels, l f is the distance from the center of mass to the front wheels, F yf is the vehicle's front wheel lateral force, l r is the distance from the center of mass to the rear wheels.

[0105] As a preferred embodiment of the present application, in the step of establishing the UniTire-Ctrl tire dynamics model and fitting tire parameters based on experimental data:

[0106] the longitudinal slip ratio S x and the lateral slip ratio S y are:

[0107] ; ;

[0108] where α is the tire side slip angle, is the wheel speed, R e is the effective radius of the wheel, and V is the vehicle speed. The normalized longitudinal slip ratio , the lateral slip ratio and the combined slip ratio are given by:

[0109] ;

[0110] ;

[0111] ;

[0112] where K x and K y are the longitudinal and cornering stiffness of the tire, respectively, λ is the alignment factor, μ x and μ y are the longitudinal and lateral adhesion coefficients, respectively, and F z is the tire vertical force.

[0113] Normalized dimensionless total cornering force and longitudinal force F x and lateral force F y are respectively:

[0114]

[0115]

[0116] where E is the curvature factor of the total cornering force curve, μ x and μ y are the longitudinal and lateral adhesion coefficients respectively, and Φ is the relative overall slip ratio.

[0117] The vehicle dynamics model is substituted into the forward Euler state equation to obtain the A and B matrices, and the steering wheel angle command, wheel speed command and additional yaw moment command are output by LQR control, and the integral sliding mode control is superimposed to enhance the robustness of the control system.

[0118] Considering parameter uncertainty and road disturbance, the linearized state equation is established as:

[0119]

[0120] where ΔA and ΔB represent unknown time-varying function matrices of parameter uncertainty, D is the external disturbance corresponding to random road, A is the state matrix, B is the control input matrix, X is the state space error parameter, and U is the control input.

[0121] For the LQR controller, the state space equation is assumed to be the nominal space equation:

[0122]

[0123]

[0124]

[0125] where β d are the ideal vehicle mass side slip angles under path tracking control, , is the expected rear wheel speed of the LQR controller, , and are the front wheel angle, rear wheel speed and additional yaw moment of the whole vehicle respectively under drift equilibrium state.

[0126] The LQR drift controller is redesigned, and the quadratic optimization function of the LQR drift controller is: ​​​​​​​

[0127] ;

[0128] wherein Q L and R L are the weight matrix of state variable and control variable respectively; ; ;

[0129] wherein are the weight of corresponding , are the weight of corresponding .

[0130] By solving the equation P, the feedback matrix can be obtained, and the expected control quantity is:

[0131] .

[0132] As a preferred embodiment of the present application, the integral sliding mode control surface is designed as:

[0133] ;

[0134] ;

[0135] wherein S(t) is the drift integral sliding mode surface, H is a constant matrix, and satisfies , K is the feedback matrix of the LQR controller, X(t) is the state space parameter, U L (t) is the LQR controller input;

[0136] Differentiating the above equation, the following equation can be obtained:

[0137] ;

[0138] Let , and the integral sliding mode control law is obtained as:

[0139] ;

[0140] The final control law of the robust controller is composed of the LQR control input and the integral sliding mode control input;

[0141] wherein is the control input of the integral sliding mode controller. The control law of the overall robust controller is obtained as:

[0142] ;

[0143] ;

[0144] wherein, are normal numbers.

[0145] The steering wheel angle instruction directly controls the steering wheel angle of the vehicle, the wheel speed instruction is fused with the current wheel speed of the vehicle to perform wheel speed control, and an additional yaw moment is superimposed, so as to finally output a rear wheel torque instruction to control the rear wheel torque of the vehicle.

[0146] As a preferred embodiment of the present application, the ideal rear wheel speed and the additional yaw moment based on the output of the robust controller are used to design a torque distribution module, which aims to minimize the distribution error and the tire load utilization, and the steps of calculating and outputting the left and right rear wheel torque instructions at the actuator level are as follows:

[0147] During vehicle drifting, the weight side shift of the vehicle body should be considered when calculating the rear wheel torque of the vehicle, and the ideal rear wheel speeds of the left and right wheels are different, specifically as follows: 、

[0148] ;

[0149] ;

[0150] wherein d is the body width of the vehicle, is the ideal rear wheel speed output by the overall drift controller, and the mass that causes the side shift at this time is:

[0151] ;

[0152] ;

[0153] ;

[0154] wherein h is the height of the vehicle center of mass, F zr is the load of the rear axle of the vehicle, is the ideal driving force of the left rear wheel of the vehicle, is the ideal driving force of the right rear wheel of the vehicle, is the driving force of the rear axle when the vehicle drifts to balance. At the same time, the overall expected additional yaw moment output is considered. The additional yaw moment distribution is defined according to the load transfer caused by the body side shift, and the torque distribution module distributes the ideal overall additional yaw moment to the left and right rear wheels according to the load transfer caused by the body side shift:

[0155] ;

[0156] ;

[0157] wherein,​ The mass that causes the roll deviation. To add a yaw moment to the vehicle as desired, F zr This refers to the load on the rear axle of the vehicle. , The additional yaw moment is distributed to the left and right rear wheels, respectively.

[0158] In a preferred embodiment of the present invention, the final torque command for the left and right rear wheels is calculated using the following formula:

[0159] ;

[0160] ;

[0161] in, This is the proportionality coefficient. Let be the moment of inertia of the wheel relative to the axis of rotation. This refers to the wheel speed of the vehicle's left rear wheel. This is the wheel speed of the vehicle's right rear wheel. Ideal driving force for the left rear wheel of the vehicle. Ideal driving force for the right rear wheel of the vehicle. R is the ideal rear wheel speed output by the overall drift controller. e The effective radius of the wheel, , These are the ideal rear wheel speeds for the left and right wheels, respectively.

[0162] Based on the front wheel steering angle command and the rear wheel torque command, the vehicle is controlled to complete closed-loop tracking drift. The overall algorithm architecture diagram of this invention is as follows: Figure 3 As shown.

[0163] This invention employs a hierarchical control system structure. The upper layer uses a pre-aiming MPC controller to follow the vehicle's trajectory, obtain the drift equilibrium point based on path information, and provide corresponding parameters to the lower layer. The lower layer considers tire drift parameters and road surface uncertainties, using an LQR algorithm superimposed with integral sliding mode control as the vehicle controller. Based on the parameters provided by the upper-layer path tracking controller and the current vehicle state, it calculates the front wheel steering angle, rear wheel drive torque, and additional yaw moment as vehicle control variables. The lower layer considers vehicle weight, roll offset, minimizing tire compliance utilization, and actuator constraints to obtain the torque of the left and right wheels. This hierarchical control structure separates path tracking and drift state control, reducing control complexity and forming a closed-loop drift control, providing a technical solution for closed-loop drift control.

[0164] The present invention also provides a closed-loop drift control system for autonomous vehicles in uncertain environments, used to implement the aforementioned closed-loop drift control method for autonomous vehicles in uncertain environments, the system comprising:

[0165] a preview mechanism module for establishing a vehicle kinematic model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism based on the vehicle kinematic model;

[0166] a vehicle dynamics model construction module for establishing a three-degree-of-freedom vehicle dynamics model with an additional yaw moment;

[0167] a tire dynamics model construction module for establishing a UniTire-Ctrl tire dynamics model, and fitting tire parameters based on experimental data;

[0168] a controller design module for designing a robust controller based on the vehicle dynamics model and the tire dynamics model, combining LQR and integral sliding mode control, and the control quantities including front wheel steering angle, rear wheel speed and vehicle additional yaw moment;

[0169] an output module for outputting ideal rear wheel speed and additional yaw moment based on the robust controller, and designing a torque distribution module to minimize distribution error and tire load utilization, and calculating and outputting left and right rear wheel torque instructions at the actuator level.

[0170] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A closed-loop drift control method for an autonomous vehicle in an uncertain environment, characterized in that, The method comprises: a vehicle kinematics model is established based on a Frenet coordinate system, and an MPC path tracking controller with a preview mechanism is constructed based on the vehicle kinematics model; a three-degree-of-freedom vehicle dynamics model with an additional yaw moment is established; a UniTire-Ctrl tire dynamics model is established, and tire parameters are fitted based on experimental data; a robust controller is designed based on the vehicle dynamics model and the tire dynamics model, in combination with LQR and integral sliding mode control, and the control quantities include front wheel steering angle, rear wheel speed and vehicle additional yaw moment; a torque distribution module is designed based on the ideal rear wheel speed and additional yaw moment output by the robust controller, with the aim of minimizing distribution error and tire load utilization, to calculate and output left and right rear wheel torque instructions at the actuator level; the preview mechanism is realized by defining a preview distance d, and the path parameters of the preview point L1 are calculated by the following formula: ； ； where e L1 is the lateral distance at the preview point, s L1 is the distance traveled by the vehicle at the preview point of the reference path, is the heading error at the preview point of the vehicle, e is the lateral distance of the current vehicle from the reference path, s is the distance traveled by the vehicle from the reference path, is the heading error of the current vehicle; Linearizing the extended kinematic model including the current state and preview point state, state space equations are obtained for the design of the MPC controller, whose state vector ζ and control vector U M is defined as: ； ； where V xeq , V yeq , r eq are the drift lateral vehicle speed, longitudinal vehicle speed, yaw rate of the drift equilibrium point, respectively. the differential equation of the three-degree-of-freedom vehicle dynamics model is as follows: ； ； ； wherein m is the self-weight of the vehicle, V x is the longitudinal vehicle speed, β is the vehicle's center of mass side slip angle, r is the vehicle's yaw rate, δ is the vehicle's front wheel steering angle, is the additional yaw moment of the vehicle, I Z is the vehicle's moment of inertia, F xr and F yr are the vehicle's rear wheel longitudinal and lateral forces, l f is the distance from the center of mass to the front wheels, F yf is the vehicle's front wheel lateral force, l r is the distance from the center of mass to the rear wheels; the robust controller is designed for a system model containing parameter uncertainty and external disturbance: ； where ∆A and ∆B represent unknown time-varying function matrices of parameter uncertainty, D is an external disturbance corresponding to a random road, A is a state matrix, B is a control input matrix, X is a state space error parameter, and U is a control input; the integral sliding mode control surface is designed as: ； ； Wherein, S(t) is a drift integral sliding mode surface, H is a constant matrix, and satisfies , K is the feedback matrix of LQR, X(t) is the state space parameter, U L (t) is the controller input of LQR; the integral sliding mode control law is: ； the final control law of the robust controller is composed of LQR control input and integral sliding mode control input; the ideal vehicle additional yaw moment is distributed to the left and right rear wheels according to the load transfer caused by vehicle body roll by the torque distribution module: ； ； wherein, is the mass that is subject to roll displacement, is the total desired additional yaw moment of the vehicle, F zr is the load of the rear axle of the vehicle, , are the additional yaw moments assigned to the left and right rear wheels, respectively; the final torque instructions of the left and right rear wheels are calculated by the following formula: ； ； wherein, is a proportionality factor, is the moment of inertia of the wheel with respect to the rotation axis, is the left rear wheel speed of the vehicle, is the right rear wheel speed of the vehicle, is the ideal drive force of the left rear wheel of the vehicle, is the ideal drive force of the right rear wheel of the vehicle, is the ideal rear wheel speed output by the overall drift controller, R e is the effective radius of the wheel, , are the ideal rear wheel speeds of the left and right wheels, respectively.

2. A closed-loop drift control system for an autonomous vehicle in an uncertain environment for implementing the closed-loop drift control method for an autonomous vehicle in an uncertain environment as claimed in claim 1, characterized in that, The system comprises: a preview mechanism module for establishing a vehicle kinematics model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism based on the vehicle kinematics model; a vehicle dynamics model construction module for establishing a three-degree-of-freedom vehicle dynamics model with an additional yaw moment; a tire dynamics model construction module for establishing a UniTire-Ctrl tire dynamics model, and fitting tire parameters based on experimental data; a controller design module for designing a robust controller based on the vehicle dynamics model and the tire dynamics model, in combination with LQR and integral sliding mode control, and the control quantities include front wheel steering angle, rear wheel speed and vehicle additional yaw moment; an output module for designing a torque distribution module based on the ideal rear wheel speed and additional yaw moment output by the robust controller, with the aim of minimizing distribution error and tire load utilization, to calculate and output left and right rear wheel torque instructions at the actuator level.

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