Unmanned vehicle steering control method based on barrier function double-layer sliding mode algorithm

Abstract

The invention provides an unmanned vehicle steering control method based on a barrier function double-layer sliding mode algorithm. According to the method, a double-layer sliding mode function is adopted to perform sliding mode control on steering of an unmanned vehicle. The first-layer sliding mode in the double-layer sliding mode is composed of a sliding mode variable s [beta] related to a side slip angle tracking error and a sliding mode variable s [gamma] related to a yaw velocity tracking error, and is used for limiting the system to converge to a sliding mode surface within finite time; an integral item sI is added to the second-layer sliding mode on the basis of the variable of the first-layer sliding mode and used for limiting the system to be located on the sliding mode surface from the beginning; the self-adaptive control gain introducing the barrier function is further designed for the reaching law of sliding mode control, it is ensured that the system quickly responds and approaches the target, excessive control and high-frequency oscillation are avoided, it is ensured that the unmanned vehicle smoothly transits to the target track or position, unnecessary oscillation and energy consumption are reduced, and the system is suitable for large-scale popularization and application. Therefore, the robustness and precision of the unmanned vehicle control system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vehicle control, and in particular to an unmanned vehicle steering control method based on a barrier function double-layer sliding mode algorithm. Background Art

[0002] Compared with traditional fuel vehicles, driverless vehicles have been widely used around the world due to their environmental friendliness, high energy efficiency, low operating costs, low maintenance requirements and strong acceleration performance. With the development of electric vehicle technology, the vehicle's steering system has gradually become an important configuration for driverless vehicles as a key technology to improve controllability and flexibility. However, in complex driving environments, especially when making sharp turns, the steering system is prone to the risk of losing control, which in turn affects the controllability and stability of the driverless vehicle. Traditional vehicle stability control systems often fail to provide sufficient response speed and adaptability under these conditions, making it difficult to effectively ensure the safe driving of the vehicle.

[0003] In the stability control of the steering system of an unmanned vehicle, trajectory tracking is closely related to the vehicle stability problem. In order to simplify the dynamic modeling of the unmanned vehicle, it can usually be simplified into a 2-DOF model. This model concentrates the main motion behaviors of the unmanned vehicle on two key degrees of freedom: yaw rate and center of mass slip angle. The trajectory tracking problem is usually represented by the center of mass slip angle of the unmanned vehicle, which reflects the deviation between the actual trajectory and the expected trajectory. A large center of mass slip angle may cause the unmanned vehicle to deviate from the path and increase the risk of loss of control, so accurate control of the center of mass slip angle is crucial for trajectory tracking. Closely related to this is the stability problem of the unmanned vehicle, especially in the control of yaw rate. The yaw rate reflects the speed at which the unmanned vehicle rotates around the vertical axis and directly affects the stability when turning. Excessive yaw rate may cause oversteering or loss of control, so the stability control system needs to adjust the steering input in real time to ensure that the unmanned vehicle drives smoothly. Trajectory tracking is closely related to the stability problem. A large center of mass slip angle will increase the yaw rate, thereby increasing the risk of loss of control. On the contrary, too high yaw rate will increase the sideslip angle of the center of mass, which will affect the stability of the unmanned vehicle. An effective steering control system needs to balance trajectory tracking and stability to ensure that the unmanned vehicle can maintain stable and precise control when turning or driving at high speed.

[0004] The stability control of unmanned vehicles involves the joint regulation of yaw rate and center of mass slip angle, with the goal of making the actual yaw rate and center of mass slip angle of the unmanned vehicle follow the target values ​​respectively. The model comprehensively considers the dynamic characteristics of the unmanned vehicle and the influence of external disturbances. During the control process, the system calculates the error between the actual yaw rate and the ideal yaw rate, as well as the error between the actual center of mass slip angle and the ideal center of mass slip angle. These errors reflect the dynamic deviation of the unmanned vehicle, and the controller outputs additional yaw torque based on these errors. The additional yaw torque is used to adjust the steering response of the unmanned vehicle and serves as the input of the torque distribution module, thereby affecting the lateral dynamic behavior of the unmanned vehicle. The control system dynamically adjusts the relationship between the yaw torque and the center of mass slip angle according to the real-time dynamic state and control objectives of the unmanned vehicle, optimizes the coordinated control, and ensures that the unmanned vehicle maintains optimal handling and stability in complex driving environments.

[0005] Sliding mode control technology is a nonlinear control method with strong robustness and anti-interference ability. Its core idea is to design a suitable control law so that the state variables of the system enter and move along the predetermined sliding surface within a limited time, ensuring that the system remains stable under external disturbances and parameter uncertainties. Sliding mode control prevents the divergence of the system state by forcing the system state to move along the sliding surface, and can quickly suppress the influence of model uncertainty and external disturbances. In the four-wheel steering system, sliding mode control can effectively adjust the yaw rate and the sideslip angle of the center of mass, so that the control system has strong adaptability, copes with environmental changes and disturbances input by the driver, and ensures the stability and safety of the unmanned vehicle under complex driving conditions. By selecting a suitable sliding surface and control strategy, sliding mode control technology can achieve efficient system tracking and precise dynamic adjustment, and can still ensure the stability and robustness of the system in the face of extreme working conditions. Summary of the invention

[0006] In view of this, the present invention provides an unmanned vehicle steering control method based on a barrier function double-layer sliding mode algorithm, which can achieve efficient system tracking and precise dynamic adjustment, and ensure the stability and robustness of the system.

[0007] In order to solve the above technical problems, the present invention is implemented as follows.

[0008] A steering control method for an unmanned vehicle based on a barrier function double-layer sliding mode algorithm, comprising:

[0009] Determine the unmanned vehicle dynamics model, where the state variables are the center of mass sideslip angle β and the yaw rate γ;

[0010] Determine the double-layer sliding film function, including: 1) determine the tracking error e about the center of mass sideslip angle β The sliding mode variable s β and the yaw rate tracking error e γ The sliding mode variable sγ , according to the sliding mode variable s β and γ Comprehensively determine the first layer of sliding film variables s, which is used to limit the system to converge to the sliding surface s within a limited time β 、s γ and s; 2) determine the first layer of synovial variables s and integral term s I The second-layer sliding mode variable σ, the integral term is used to limit the system to initially be located on the sliding surface;

[0011] Based on the unmanned vehicle dynamics model and in combination with the double-layer sliding film function, the equivalent control law of the yaw moment control quantity of the unmanned vehicle is determined; based on the adaptive control gain of the barrier function, the approach law of the yaw moment control quantity of the unmanned vehicle is determined;

[0012] The state quantity of the unmanned vehicle is collected and substituted into the double-layer synovial function to obtain the current synovial variable; the adaptive control gain of the reaching law is updated based on the current synovial variable; and the additional yaw torque is determined based on the control law and the reaching law to adjust the steering response of the unmanned vehicle to control the sideslip angle β of the center of mass and the yaw angular velocity γ to track the target value.

[0013] Preferably, the first layer synovial function is designed as:

[0014] The center of mass sideslip angle tracking error e β The sliding mode variable s β for: Θ is the error power term;

[0015] The yaw rate tracking error e γ The sliding mode variable s γ For: γ =e γ ;

[0016] According to the sliding mode variable s β and e γ Determine the first layer synovial variable s as: s = s β +ζs γ ;

[0017] Where sign(·) is the sign function; ζ is the weight coefficient of the sideslip angle β and yaw rate γ in the sliding mode variable s; λ is the amplification coefficient of the error power term; λ>0.

[0018] Preferably, the error power term θ is:

[0019]

[0020] Among them, sign(·) is the sign function; is the control parameter of the unmanned vehicle’s sensitivity to error changes,

[0021] Preferably, the second layer synovial function is designed as:

[0022] σ=s+θs I

[0023] Among them, θ is the amplification factor of the integral term, θ>0.

[0024] Preferably, the integral term s I for:

[0025]

[0026] Among them, η is the sensitivity control parameter, 1<η<2.

[0027] Preferably, the integral term s I The initial value of the integral is set to:

[0028]

[0029] Among them, e β (0) and e γ (0) are the initial values ​​of the center of mass sideslip angle tracking error and the yaw rate tracking error, respectively.

[0030] Preferably, the equivalent control law of the yaw moment control quantity of the unmanned vehicle is:

[0031]

[0032] Among them, ΔM eq is the output of the control law; m is the mass of the unmanned vehicle, u is the known forward speed of the unmanned vehicle, K f , K r are the cornering stiffness of the front and rear wheels, a and b are the distances from the center of mass to the front axle and the center of mass to the rear axle, respectively. z is the yaw moment of inertia of the unmanned vehicle, δ f is the front wheel turning angle of the unmanned vehicle; β d is the target center of mass sideslip angle of the unmanned vehicle; and are the first and second derivatives of the parameter x, respectively.

[0033] Preferably, the reaching law of the yaw moment control amount of the unmanned vehicle is:

[0034]

[0035] Among them, ΔM sw is the output of the reaching law; m is the mass of the unmanned vehicle, u is the known forward speed of the unmanned vehicle, K f , K rare the cornering stiffness of the front and rear wheels, a and b are the distances from the center of mass to the front axle and the center of mass to the rear axle, respectively. z is the yaw moment of inertia of the unmanned vehicle, is the adaptive control gain of the barrier function.

[0036] Preferably, in the reaching law, the adaptive control gain of the barrier function is introduced as follows:

[0037]

[0038] in, is the adaptive control gain; t is time, σ(t) is the second-layer sliding mode variable value at time t; ε is the boundary value of the preset zero neighborhood; when hour, The sliding variable σ(t) first reaches the interval time; otherwise,

[0039] Preferably, the additional yaw moment is determined based on the control law and the reaching law as follows: the equivalent control amount ΔM output by the equivalent control law is eq The compensation amount ΔM output by the reaching law sw Add together to obtain the additional yaw moment.

[0040] In the existing technology, the stability control of unmanned vehicles mostly relies on traditional sliding mode control methods. Although these methods have certain robustness, they often have problems such as slow response, excessive oscillation or insufficient control accuracy when facing complex nonlinear systems, external disturbances and system uncertainties. Especially in dynamic systems such as four-wheel electric drive vehicles, traditional sliding mode control is difficult to effectively balance the stability and accuracy of the system, resulting in the system may not be able to maintain good control performance under extreme conditions. In addition, the switching gain adjustment of traditional sliding mode control is unstable, which may cause excessive control oscillation or response lag.

[0041] The present invention overcomes these defects in the prior art by introducing a double-layer sliding mode control based on a barrier function. The specific advantages are as follows:

[0042] (1) Improving control accuracy and dynamic response performance: The present invention designs a double-layer sliding surface. The design goal of the first layer of sliding mode variables is to ensure that the output of the system can quickly converge to the reference signal, and the system can remain stable in the presence of uncertainty and interference, thereby achieving fast response and robustness; the design goal of the second layer of sliding mode variables is to ensure that the system is on the sliding surface from the beginning, thereby avoiding the influence of uncertainty in the arrival stage, reducing the system's chattering phenomenon, and improving the system's stability and control accuracy; at the same time, the integral term s IThe introduction enables the system to compensate for continuous disturbances, thereby improving the robustness of the system. It can be seen that the control mechanism of the recursive alternation of the double-layer sliding surface ensures that the system can converge quickly within a limited time and accurately track the target state, thereby improving the control accuracy and dynamic response performance.

[0043] (2) Adaptive control gain: The present invention introduces a barrier function into the control system and designs it as an adaptive gain, which limits the sliding mode variable σ(t) to the interval (-ε, ε), thereby ensuring that the center of mass sideslip angle tracking error e β and the yaw rate error e γ Within the preset range. This design enables the control gain to be dynamically adjusted according to the change of the sliding mode variable, ensuring a stronger control input when the system error is large, and reducing the control input when the error is close to zero, thereby avoiding over-control and high-frequency oscillation. Therefore, this design effectively improves the response speed of the system and ensures a smooth transition when approaching the target, avoiding excessive oscillation or response lag caused by improper gain setting in traditional sliding mode control.

[0044] (3) Significantly improve system stability: Through the recursive mechanism of double-layer sliding mode control, even if the system is subject to external disturbances or parameter changes, the second-layer sliding mode variable can still ensure that the system remains on the sliding mode surface and maintains stable control performance. Therefore, the present invention can effectively balance the stability and accuracy of the system and ensure good control performance in unmanned vehicles.

[0045] (4) Enhanced system robustness and adaptability: The present invention can better cope with external interference and system uncertainty, thereby significantly improving the robustness and adaptability of the system and ensuring stable and precise control even under extreme conditions.

[0046] In summary, the present invention effectively overcomes the shortcomings of traditional sliding mode control methods and significantly improves the stability, response speed and control accuracy of complex systems such as unmanned vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a physical picture of the unmanned vehicle in an embodiment of the present invention.

[0048] Figure 2 This is a two-degree-of-freedom force analysis diagram of the unmanned vehicle in an embodiment of the present invention.

[0049] Figure 3 is the barrier function in the embodiment of the present invention Curve expression diagram of .

[0050] Figure 4 The following is a graph showing the tracking error curve of the center of mass sideslip angle of the unmanned vehicle in the embodiment of the present invention. β .

[0051] Figure 5 The yaw rate error curve of the unmanned vehicle in the embodiment of the present invention is shown in FIG. γ .

[0052] Figure 6 This is a control flow chart of the unmanned vehicle in the example of the present invention. DETAILED DESCRIPTION

[0053] The present invention provides a steering control method for an unmanned vehicle based on a barrier function double-layer sliding mode algorithm. The core idea is to ensure that the system can converge quickly within a limited time and accurately track the target state through a control mechanism of a double-layer sliding mode surface recursively alternating, thereby improving the control accuracy and dynamic response performance. The barrier function is introduced into the control gain of the reaching law, and the control gain can be adaptively adjusted according to the change of the system state, avoiding the problem of excessive oscillation or response lag caused by improper gain setting in traditional sliding mode control.

[0054] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0055] Step S1: Determine the unmanned vehicle dynamics model, where the state variables are the center of mass sideslip angle β and the yaw angular velocity γ.

[0056] In this step, the dynamic equations of the two-degree-of-freedom unmanned vehicle are derived by using the principles of theoretical mechanics through appropriate simplification. Figure 1 The four-wheel electric drive unmanned vehicle shown is based on Figure 2 The force analysis reduces the unmanned vehicle model to a two-degree-of-freedom model, and we get:

[0057]

[0058] Where β is the sideslip angle of the center of mass, K f , K r are the cornering stiffness of the front and rear wheels respectively, m is the mass of the unmanned vehicle, u is the known forward speed of the unmanned vehicle, a and b are the distances from the center of mass to the front axle and from the center of mass to the rear axle respectively, I z is the yaw moment of inertia of the unmanned vehicle, γ is the yaw angular velocity, δ f is the front wheel turning angle of the unmanned vehicle, d 1 d 2 are disturbances. ΔM is the yaw moment of the unmanned vehicle.

[0059] For a specific unmanned vehicle example, the known parameters in the above formula (1) are shown in Table 1.

[0060] Table 1

[0061]

[0062] At the same time, this embodiment sets the front wheel turning angle of the unmanned vehicle

[0063] Step S2: Based on the unmanned vehicle dynamics model, construct a direct relationship between the center of mass sideslip angle β and the unmanned vehicle yaw moment △M.

[0064] Deriving formula (1-1) with respect to time t and substituting formula (1-2) into it, we obtain:

[0065]

[0066] Step S3: construct a double-layer synovial function.

[0067] The idea of ​​constructing a double-layer sliding mode in the present invention is that the first-layer sliding mode variable is mainly used to process the center of mass side slip angle tracking error e of the unmanned vehicle. β and the yaw rate error e of the unmanned vehicle γ Specifically, the design goal of the first layer of sliding mode variables is to ensure that the output of the system can quickly converge to the reference signal, and the system can remain stable in the presence of uncertainty and interference. By introducing these sliding mode variables, the system can converge to the sliding surface s in a finite time. β 、s γ and s, thereby achieving fast response and robustness. The design goal of the second-layer sliding mode variable is to ensure that the system is on the sliding mode surface from the beginning, thereby avoiding the uncertainty influence of the arrival stage, reducing the system's chattering phenomenon, and improving the system's stability and control accuracy; at the same time, the integral term s I The introduction enables the system to compensate for continuous disturbances, thereby improving the robustness of the system. Even if the system is subject to external disturbances or parameter changes, the second-layer sliding mode variable can ensure that the system remains on the sliding surface and maintains stable control performance.

[0068] Double-layer sliding film interaction: When the second-layer sliding mode variable σ approaches 0, the first-layer sliding mode variable s satisfies the finite time convergence condition, that is, s will approach 0 in a finite time. When s approaches 0, the center of mass sideslip angle tracking error e of the unmanned vehicle β and the yaw rate error e of the unmanned vehicle γ will approach 0.

[0069] The specific design of the synovial surface in this embodiment is as follows:

[0070] ①Introduce the first layer sliding mode variable s β 、s γ and s; where s β The tracking error of the sideslip angle with the center of mass is β Related: γ and the yaw rate tracking error e γ Related; According to the sliding mode variable s β and eγ The first layer of synovial variables s are determined comprehensively.

[0071] In a preferred embodiment, the first layer sliding mode variables are designed as:

[0072]

[0073] Among them, s β is the sliding mode variable about the sideslip angle β, s γ is the sliding mode variable about the yaw rate γ, s is the sliding mode variable that comprehensively considers the sideslip angle β and the yaw rate γ, ζ is the weight coefficient that balances the weight of the sideslip angle β and the yaw rate γ in the sliding mode variable s; β is the tracking error of the center of mass of the unmanned vehicle e β =β-β d ;e γ is the yaw rate error e of the unmanned vehicle γ =γ-γ d β d is the target center of mass sideslip angle, γ d is the target yaw rate; for e β The derivative of ; the control parameter λ>0 is to be designed.

[0074] In the above formula, is the sliding mode variable s β The error power term, Control parameters To be designed, sign(·) is the sign function:

[0075]

[0076] ② The second layer sliding mode function is designed as:

[0077] σ=s+θs I (4)

[0078] In the formula, the integral term s I Designed to:

[0079]

[0080] Among them, σ is the second-layer sliding mode variable, and the control parameters θ>0, 1<η<2 are to be designed. Ensure that the system is on the sliding mode surface from the initial moment, thereby simplifying the control design, improving the stability of the system, and reducing the chattering phenomenon during the transition process. Set the sliding mode variable σ(0)=0, that is, the integral term s I The initial value of is set to:

[0081]

[0082] The integral term s in the present invention I Design and sig(s) η =|s| η sign(s) is to achieve multiple control objectives, including: eliminating the arrival phase of sliding mode control, reducing chattering, accelerating system convergence, enhancing anti-interference ability, simplifying control design, and significantly improving control accuracy. By introducing the integral term s I , the system is on the sliding surface from the beginning, avoiding the sensitivity of traditional sliding mode control to uncertainty and external disturbances in the arrival stage, thereby improving the robustness and stability of the system. At the same time, the continuity and dynamic adjustment mechanism of the integral term can effectively reduce the high-frequency jitter of the control input, extend the life of the system and improve the smoothness of the control. In addition, the design of the integral term also speeds up the convergence of the system, allowing the system to reach a stable state faster and show stronger anti-interference ability when facing continuous or periodic disturbances. Ultimately, this design not only simplifies the complexity of the controller, but also significantly improves the control accuracy of the system, so that it can still achieve fast, stable and precise control effects in the presence of uncertainty and external disturbances.

[0083] When initializing parameters, let β d =0, For various control parameters in the double-layer synovial film The parameter λ controls the amplification factor of the error power term and affects the sensitivity of the system. Increasing λ will increase the response and may increase the reaction speed of the system, but it may also cause instability. Usually the value is between (1,2), controlling the nonlinear response to the error change rate. Makes the system more sensitive to rapidly changing errors, while smaller This will make the system's response to the rate of change smoother. It can balance the system's fast response and ability to suppress chattering. The parameter θ is used to correct, adjust or compensate s, and can be used to stabilize the system, reduce errors, and adjust the response speed. The parameter η adjusts the sensitivity of s.

[0084] In a preferred embodiment, each control parameter preferably takes a value of λ=0.1, θ=60, η=0.5, ζ=1.

[0085] Step S4: Based on the unmanned vehicle dynamics model and in combination with the double-layer sliding film function, an equivalent control law of the unmanned vehicle yaw moment control quantity is determined.

[0086] Assume that the perturbation in formula (2) d 2 = 0 and let the synovial derivative By sorting out, the equivalent control quantity △M is obtained eq ;

[0087]

[0088] Among them, ΔM eq is the output of the control law; m is the mass of the unmanned vehicle, u is the known forward speed of the unmanned vehicle, K f , K r are the cornering stiffness of the front and rear wheels, a and b are the distances from the center of mass to the front axle and the center of mass to the rear axle, respectively. z is the yaw moment of inertia of the unmanned vehicle, δ f is the front wheel turning angle of the unmanned vehicle; β d is the target center of mass sideslip angle of the unmanned vehicle; and are the first and second derivatives of the parameter x, respectively.

[0089] Step S5: Determine the reaching law of the yaw moment control quantity of the unmanned vehicle based on the adaptive control gain of the barrier function.

[0090]

[0091] in, For adaptive control gain, introduce Figure 3 The barrier function shown:

[0092]

[0093] in, is the adaptive control gain; t is time, σ(t) is the second-layer sliding mode variable value at time t; ε is the boundary value of the preset zero neighborhood; when hour, The sliding variable σ(t) first reaches the interval time; otherwise,

[0094] In a preferred embodiment, ε=0.1 rad.

[0095] In the unmanned vehicle control system, the barrier function can be used to adaptively adjust the control gain according to the value of the sliding mode variable σ(t). When the sliding mode variable σ(t) is far from the target value, the control gain is large and the reaching law △M sw The gain increases accordingly, so that the unmanned vehicle can quickly reduce the deviation from the target trajectory or position, ensuring that the system responds quickly and approaches the target. As the sliding mode variable σ(t) approaches zero, the gain gradually decreases, and the approach law △M swThe gain is reduced accordingly to avoid over-control and high-frequency oscillation, ensuring that the unmanned vehicle smoothly transitions to the target trajectory or position. This adaptive gain adjustment strategy not only improves the response speed of the system, but also enables smooth trajectory tracking when approaching the target, reducing unnecessary oscillations and energy consumption, thereby improving the robustness and accuracy of the unmanned vehicle control system.

[0096] So the overall control input

[0097] ΔM=ΔM eq +ΔM sw

[0098] Center of mass sideslip angle tracking error e β and the yaw rate error e γ Will converge to the following region in finite time:

[0099] |e β |<ε,|e γ |<ε

[0100] That is, the tracking error of the present invention |e β |<0.1rad,|e γ |<0.1rad.

[0101] Step S6: During actual control, Figure 6 As shown, the state of the unmanned vehicle is collected, including the center of mass sideslip angle β and the yaw angular velocity γ; according to the target center of mass sideslip angle β d and the target yaw rate γ d Calculate the center of mass sideslip angle tracking error e β and the yaw rate tracking error e γ ; Calculate the first layer synovial variable s based on formula (3) β 、s γ , and then s is obtained by integration; then the second-layer synovial variable σ is calculated based on formula (4). Based on formula (7), the adaptive control gain of the reaching law is updated using the current second-layer synovial variable σ Then, the equivalent control amount ΔM is obtained by using the equivalent control rate and reaching law. eq and compensation ΔM sw The final additional yaw moment ΔM is added together and provided to the unmanned vehicle to adjust the steering response of the unmanned vehicle and serve as the input of the torque distribution module, thereby affecting the lateral dynamic behavior of the unmanned vehicle.

[0102] Figure 4-5 They are respectively the center of mass sideslip angle tracking error curves of the unmanned vehicle in the embodiment of the present invention. β , Yaw angular velocity error curve of unmanned vehicle in the embodiment of the present invention γ It can be seen from the curve graph that: the unmanned vehicle center of mass sideslip angle tracking error curve graph e in the embodiment of the present inventionβ , Yaw angular velocity error curve of unmanned vehicle in the embodiment of the present invention γ Satisfy|e β |<0.1rad,|e γ |<0.1rad. Therefore, the theory proposed by the present invention is completely correct.

[0103] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A steering control method for an unmanned vehicle based on a barrier function double-layer sliding mode algorithm, characterized in that: include: Determine the unmanned vehicle dynamics model, where the state variables are the center of mass sideslip angle β and the yaw rate γ; Determine the double-layer sliding film function, including: 1) determine the tracking error e about the center of mass sideslip angle β The sliding mode variable s β and the yaw rate tracking error e γ The sliding mode variable s γ , according to the sliding mode variable s β and γ Comprehensively determine the first layer of sliding film variables s, which is used to limit the system to converge to the sliding surface s within a limited time β 、s γ and s; 2) determine the first layer of synovial variables s and integral term s I The second-layer sliding mode variable σ, the integral term is used to limit the system to initially be located on the sliding surface; Based on the unmanned vehicle dynamics model and combined with the double-layer sliding film function, an equivalent control law of the yaw moment control quantity of the unmanned vehicle is determined; Based on the adaptive control gain of the barrier function, the reaching law of the yaw moment control quantity of the unmanned vehicle is determined; The state quantity of the unmanned vehicle is collected and substituted into the double-layer synovial function to obtain the current synovial variable; the adaptive control gain of the reaching law is updated based on the current synovial variable; and the additional yaw torque is determined based on the control law and the reaching law to adjust the steering response of the unmanned vehicle to control the sideslip angle β of the center of mass and the yaw angular velocity γ to track the target value.

2. The method according to claim 1, characterized in that The first layer of synovial function is designed as: The center of mass sideslip angle tracking error e β The sliding mode variable s β for: Θ is the error power term; The yaw rate tracking error e γ The sliding mode variable s γ For: γ =e γ ; According to the sliding mode variable s β and e γ Determine the first layer synovial variable s as: s = s β +ζs γ ; Wherein, sign(·) is the sign function; ζ is the weight coefficient of the sideslip angle β and yaw rate γ in the sliding mode variable s; λ is the amplification coefficient of the error power term; λ>0.

3. The method according to claim 2, characterized in that The error power term θ is: Among them, sign(·) is the sign function; is the control parameter of the unmanned vehicle’s sensitivity to error changes, 4. The method according to claim 2, characterized in that The second layer synovial function is designed as: σ=s+θs I Among them, θ is the amplification factor of the integral term, θ>0.

5. The method according to claim 4, characterized in that The integral term s I for: Among them, η is the sensitivity control parameter, 1<η<2.

6. The method according to claim 4 or 5, characterized in that The integral term s I The initial value of the integral is set to: Among them, e β (0) and e γ (0) are the initial values ​​of the center of mass sideslip angle tracking error and the yaw rate tracking error; the error power term used in the sliding mode variable calculation is sign(·) is the sign function; is the control parameter of the unmanned vehicle’s sensitivity to error changes, 7. The method according to claim 4, characterized in that The equivalent control law of the yaw moment control quantity of the unmanned vehicle is: Among them, ΔM eq is the output of the control law; m is the mass of the unmanned vehicle, u is the known forward speed of the unmanned vehicle, K f , K r are the cornering stiffness of the front and rear wheels, a and b are the distances from the center of mass to the front axle and the center of mass to the rear axle, respectively. z is the yaw moment of inertia of the unmanned vehicle, δ f is the front wheel turning angle of the unmanned vehicle; β d is the target center of mass sideslip angle of the unmanned vehicle; and are the first and second derivatives of the parameter x, respectively.

8. The method according to claim 4, characterized in that The reaching law of the yaw moment control quantity of the unmanned vehicle is: Among them, ΔM sw is the output of the reaching law; m is the mass of the unmanned vehicle, u is the known forward speed of the unmanned vehicle, K f , K r are the cornering stiffness of the front and rear wheels, a and b are the distances from the center of mass to the front axle and the center of mass to the rear axle, respectively. z is the yaw moment of inertia of the unmanned vehicle, is the adaptive control gain of the barrier function.

9. The method according to claim 1 or 8, characterized in that In the reaching law, the adaptive control gain of the barrier function is introduced as: in, is the adaptive control gain; t is time, σ(t) is the second-layer sliding mode variable value at time t; ε is the boundary value of the preset zero neighborhood; when hour, The sliding variable σ(t) first reaches the interval time; otherwise, 10. The method according to claim 1, characterized in that The additional yaw moment is determined based on the control law and the reaching law as follows: the equivalent control amount ΔM output by the equivalent control law is eq The compensation amount ΔM output by the reaching law sw Add together to obtain the additional yaw moment.

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