An unmanned vehicle steering control method based on a barrier function double-layer sliding mode algorithm

Abstract

The present disclosure provides a barrier function-based double-layer sliding mode algorithm for unmanned vehicle steering control method, which adopts double-layer sliding mode function for sliding mode control of unmanned vehicle steering. In the double-layer sliding mode, the first layer sliding mode is composed of a sliding mode variable s β about the tracking error of the center of mass side slip angle and a sliding mode variable s γ about the tracking error of the yaw rate, for limiting the system to converge to the sliding mode surface in a limited time; the second layer sliding mode adds an integral term s I to the first layer sliding mode variable, for limiting the system to be located on the sliding mode surface from the beginning; the present disclosure also designs an adaptive control gain with barrier function for the reaching law of the sliding mode control, to ensure fast response and approach to the target of the system, and to avoid excessive control and high-frequency oscillation, to ensure the smooth transition of the unmanned vehicle to the target trajectory or position, to reduce unnecessary oscillation and energy consumption, and to improve the robustness and precision of the unmanned vehicle control system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned vehicle control, and particularly relates to an unmanned vehicle steering control method based on a barrier function double-layer sliding mode algorithm. BACKGROUND

[0002] Compared with traditional fuel vehicles, unmanned vehicles have been widely used in the world due to their environmental protection, high energy efficiency, low operating cost, small amount of maintenance demand and strong acceleration performance. With the development of electric vehicle technology, as a key technology to improve the controllability and flexibility, the steering system of the vehicle has gradually become an important configuration of the unmanned vehicle. However, in a complex driving environment, especially when turning sharply, the steering system is prone to lose control, thereby affecting the controllability and stability of the unmanned vehicle. The traditional vehicle stability control system often cannot provide sufficient response speed and adaptability under these working conditions, and it is difficult to effectively ensure the safe driving of the vehicle.

[0003] In the stability control of the steering system of the unmanned vehicle, the trajectory tracking and the vehicle stability problem are closely related. In order to simplify the dynamic modeling of the unmanned vehicle, it can be simplified as a 2-degree-of-freedom model. The model concentrates the main motion behavior of the unmanned vehicle on two key degrees of freedom: yaw rate and center of mass side slip angle. The trajectory tracking problem is usually represented by the center of mass side slip angle of the unmanned vehicle, which reflects the deviation between the actual trajectory and the expected trajectory. The center of mass side slip angle is too large, which may cause the unmanned vehicle to deviate from the path and increase the risk of losing control, so accurate control of the center of mass side slip angle is crucial for trajectory tracking. Closely related to this is the stability problem of the unmanned vehicle, especially in terms of yaw rate control. The yaw rate reflects the rotation speed of the unmanned vehicle around the vertical axis, and directly affects the stability when turning. The yaw rate is too high, which may cause excessive steering or loss of control, so the stability control system needs to adjust the steering input in real time to ensure the smooth driving of the unmanned vehicle. Trajectory tracking and stability problems are closely related, and the center of mass side slip angle is too large, which may increase the yaw rate and increase the risk of losing control. Conversely, the yaw rate is too high, which may increase the center of mass side slip angle and 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 stability and accurate control when turning or driving at high speed.

[0004] The stability control of the unmanned vehicle involves the joint regulation of the yaw rate and the center of mass side slip angle, and the goal is to make the actual yaw rate and the center of mass side slip angle of the unmanned vehicle follow the target values respectively. The model comprehensively considers the influence of the dynamic characteristics of the unmanned vehicle and external disturbances. In the control process, the system calculates the error between the actual yaw rate and the ideal yaw rate, and the error between the actual center of mass side slip angle and the ideal center of mass side slip angle, which reflects the dynamic deviation of the unmanned vehicle, and the controller outputs an additional yaw moment based on these errors. The additional yaw moment is used to adjust the steering response of the unmanned vehicle and is input to the torque distribution module, thereby affecting the lateral dynamic behavior of the unmanned vehicle. The control system dynamically adjusts the relationship between the yaw moment and the center of mass side slip angle according to the real-time dynamic state of the unmanned vehicle and the control target, optimizes the cooperative control, and ensures that the unmanned vehicle maintains the best handling and stability in complex driving environments.

[0005] The sliding mode control technology is a nonlinear control method with strong robustness and anti-interference ability. The core idea is to design a suitable control law to make the state variables of the system enter and move along the predetermined sliding mode surface in a limited time, ensuring the stability of the system under external disturbances and parameter uncertainties. Sliding mode control can quickly suppress the influence of model uncertainties and external disturbances by forcing the system state to move along the sliding mode surface, preventing the system state from diverging. In the four-wheel steering system, sliding mode control can effectively regulate the yaw rate and the center of mass side slip angle, making the control system have strong adaptability to environmental changes and disturbances of the driver input, and ensuring the stability and safety of the unmanned vehicle in complex driving conditions. By selecting appropriate sliding mode surfaces and control strategies, the sliding mode control technology can achieve efficient system tracking and accurate dynamic regulation, and still ensure the stability and robustness of the system in extreme conditions. SUMMARY

[0006] Therefore, the present application provides an unmanned vehicle steering control method based on a barrier function double-layer sliding mode algorithm, which can achieve efficient system tracking and accurate dynamic regulation, and ensure the stability and robustness of the system.

[0007] To solve the above technical problems, the present application is implemented as follows.

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

[0009] determining the dynamic model of the unmanned vehicle, wherein the state variables are the center of mass side slip angle β and the yaw rate γ;

[0010] determining a double-layer sliding mode function, including: 1) determining a sliding mode variable s β about the center of mass side slip angle tracking error e β and a sliding mode variable s γ about the yaw rate tracking error eγ According to the sliding mode variable s β and s γ The first-level sliding mode variable s is determined by combining the variables to constrain the system to converge to the sliding surface s within a finite time. β s γ 2) Determine the sliding mode variable s and integral term s containing the first layer of sliding mode. I The second sliding mode variable σ, the integral term is used to restrict the system to initially lie on the sliding surface;

[0011] Based on the aforementioned unmanned vehicle dynamics model and combined with the two-layer sliding mode function, the equivalent control law for the unmanned vehicle's yaw moment control quantity is determined; based on the adaptive control gain with the introduction of a barrier function, the approach law for the unmanned vehicle's yaw moment control quantity is determined.

[0012] The state variables of the unmanned vehicle are collected and substituted into the two-layer sliding mode function to obtain the current sliding mode variable; the adaptive control gain of the reaching law is updated based on the current sliding mode variable; and an additional yaw moment is determined based on the control law and the reaching law to adjust the steering response of the unmanned vehicle in order to control the center of gravity sideslip angle β and yaw rate γ to track the target value.

[0013] Preferably, the first layer sliding mode function is designed as follows:

[0014] Regarding the tracking error e of the centroid side slip angle β sliding mode variable s β for: Θ represents the error exponent;

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

[0016] The method based on sliding mode variable s β and e γ The first sliding mode variable s is defined as: s = s β +ζs γ ;

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

[0018] Preferably, the error exponentiation term Θ is:

[0019]

[0020] Where sign(·) is the sign function; These are the control parameters for the autonomous vehicle's sensitivity to error changes.

[0021] Preferably, the second-layer sliding mode function is designed as follows:

[0022]

[0023] Where θ is the amplification factor of the integral term, and θ>0.

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

[0025]

[0026] Where η is the sensitivity control parameter of s, 1 < η < 2.

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

[0028]

[0029] Among them, e β (0) and e γ (0) represents the initial values ​​of the tracking error of the center of gravity side slip angle and the tracking error of the yaw rate, respectively.

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

[0031]

[0032] Where, ΔM eq The output of the control law; m is the mass of the autonomous vehicle, u is the known forward speed of the autonomous vehicle, and K is the output of the control law. f K r Let be the lateral stiffness of the front and rear wheels, respectively; and let a and b be the distances from the center of gravity to the front axle and the rear axle, respectively. z Let δ be the yaw moment of inertia of the autonomous vehicle. f β is the steering angle of the front wheels of the autonomous vehicle. d The target centroid sideslip angle of the autonomous vehicle; and These are the first and second derivatives of the parameter x, respectively.

[0033] Preferably, the approach law for the yaw moment control of the unmanned vehicle is:

[0034]

[0035] Where, ΔM sw The output of the reaching law; m is the mass of the autonomous vehicle, u is the known forward speed of the autonomous vehicle, and K is the mass of the vehicle. f K rLet be the lateral stiffness of the front and rear wheels, respectively; and let a and b be the distances from the center of gravity to the front axle and the rear axle, respectively. z For the yaw moment of inertia of the driverless car, To introduce an adaptive control gain for the barrier function.

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

[0037]

[0038] in, The adaptive control gain is denoted by t, where t is time, σ(t) is the value of the second-level sliding mode variable at time t, and ε is the boundary value of the preset zero neighborhood. when hour, For the first arrival of the sliding variable σ(t) in the interval The time; otherwise,

[0039] Preferably, the determination of the additional yaw moment based on the control law and the approach law is: the equivalent control quantity ΔM output by the equivalent control law. eq The compensation amount ΔM of the approach law output sw Adding them together yields an additional yaw moment.

[0040] In existing technologies, stability control of autonomous vehicles largely relies on traditional sliding mode control methods. While these methods possess a certain degree of robustness, they often suffer from slow response, excessive oscillations, or insufficient control precision when facing complex nonlinear systems, external disturbances, and system uncertainties. Especially in dynamic systems such as four-wheeled electric vehicles, traditional sliding mode control struggles to effectively balance system stability and precision, potentially leading to poor control performance under extreme conditions. Furthermore, the switching gain adjustment in traditional sliding mode control exhibits instability, which can result in excessive control oscillations or response lag.

[0041] This invention overcomes these shortcomings in the prior art by introducing a barrier function-based bi-layer sliding mode control, with the following specific advantages:

[0042] (1) Improved control accuracy and dynamic response performance: This invention designs a double-layer sliding surface. The design goal of the first layer of sliding variables is to ensure that the system output can quickly converge to the reference signal, and that the system can remain stable even in the presence of uncertainties and disturbances, thereby achieving fast response and robustness. The design goal of the second layer of sliding variables is to ensure that the system is on the sliding surface from the beginning, thereby avoiding the influence of uncertainties in the arrival stage, reducing the chattering phenomenon of the system, and improving the stability and control accuracy of the system. At the same time, the integral term s IThe introduction of this mechanism enables the system to compensate for persistent disturbances, thereby improving its robustness. It is evident that the recursive alternation control mechanism using a two-layer sliding surface ensures rapid convergence within a finite time and accurate tracking of the target state, thus enhancing control accuracy and dynamic response performance.

[0043] (2) Adaptive control gain: This invention introduces a barrier function into the control system and designs it as an adaptive gain, limiting the sliding mode variable σ(t) to the interval (-ε, ε), thereby ensuring the tracking error e of the centroid sideslip angle. β and yaw rate error e γ Within a preset range, this design allows the control gain to be dynamically adjusted according to changes in the sliding mode variable. This ensures a strong control input when the system error is large, while reducing the control input when the error approaches zero, thus avoiding over-control and high-frequency oscillations. Therefore, this design effectively improves the system's response speed and ensures a smooth transition when approaching the target, avoiding the excessive oscillations or response lag problems caused by improper gain settings in traditional sliding mode control.

[0044] (3) Significantly improves system stability: Through the recursive mechanism of dual-layer sliding mode control, even if the system is subjected to external disturbances or parameter changes, the second-layer sliding mode variable can still ensure that the system remains on the sliding surface and maintains stable control performance. Therefore, this 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 disturbances and system uncertainties, thereby significantly improving the robustness and adaptability of the system and ensuring stable and accurate control even under extreme conditions.

[0046] In summary, this 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. Attached Figure Description

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

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

[0049] Figure 3 Barrier function in the embodiments of the present invention The curve representation diagram.

[0050] Figure 4 Figure e shows the tracking error curve of the center of gravity sideslip angle of the unmanned vehicle in an embodiment of the present invention. β .

[0051] Figure 5 Figure e shows the yaw rate error curve of the unmanned vehicle in an embodiment of the present invention. γ .

[0052] Figure 6 This is a flowchart illustrating the control process of the unmanned vehicle in an example of the present invention. Detailed Implementation

[0053] This invention provides a steering control method for unmanned vehicles based on a barrier function-based bi-layer sliding mode algorithm. The core idea is to ensure rapid convergence of the system within a finite time and accurate tracking of the target state through a bi-layer sliding mode recursive alternating control mechanism, thereby improving control accuracy and dynamic response performance. By introducing a barrier function into the control gain of the reaching law, the control gain can adaptively adjust according to changes in the system state, avoiding excessive oscillations or response lag problems caused by improper gain settings in traditional sliding mode control.

[0054] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0055] Step S1: Determine the dynamic model of the unmanned vehicle, where the state variables are the sideslip angle β and the yaw rate γ.

[0056] In this step, through appropriate simplification, the dynamic equations of the two-degree-of-freedom autonomous vehicle were derived using principles of theoretical mechanics. (This is in response to...) Figure 1 The four-wheel electric drive autonomous vehicle shown is based on Figure 2 Force analysis reduces the autonomous vehicle model to a two-degree-of-freedom model, yielding:

[0057]

[0058] In the formula, β is the centroid sideslip angle, and K f K r Let be the lateral stiffness of the front and rear wheels, respectively; m be the mass of the autonomous vehicle; u be the known forward speed of the autonomous vehicle; a and b be the distances from the center of mass to the front axle and the center of mass to the rear axle, respectively; and I be the lateral stiffness of the front and rear wheels, respectively. z Let γ be the yaw moment of inertia of the autonomous vehicle, γ be the yaw angular velocity, and δ be the yaw rotational inertia. f Let d1 and d2 be the front wheel steering angle of the autonomous vehicle, and d1 and d2 be disturbances. ΔM is the yaw moment of the autonomous vehicle.

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

[0060] Table 1

[0061]

[0062] Meanwhile, this embodiment sets the front wheel steering angle of the unmanned vehicle.

[0063] Step S2: Based on the autonomous vehicle dynamics model, construct the direct relationship between the centroid sideslip angle β and the autonomous vehicle yaw moment ΔM.

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

[0065]

[0066] Step S3: Construct a two-layer sliding mode function.

[0067] The idea behind this invention for constructing a two-layer sliding mode is as follows: the first-layer sliding mode variable is mainly used to handle the tracking error e of the centroid sideslip angle of the autonomous vehicle. β The yaw rate error e of the autonomous vehicle γ Specifically, the design goal of the first layer of sliding mode variables is to ensure that the system output converges quickly to the reference signal and remains stable even in the presence of uncertainties and disturbances. By introducing these sliding mode variables, the system can converge to the sliding surface s in a finite time. β s γ And s, thus achieving fast response and robustness. The design goal of the second-level sliding mode variable 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 system chattering, and improving system stability and control accuracy; at the same time, the integral term s I The introduction of this second-level sliding mode variable enables the system to compensate for persistent disturbances, thereby improving the system's robustness. Even when the system is subjected to external disturbances or parameter changes, the second-level sliding mode variable ensures that the system remains on the sliding surface, maintaining stable control performance.

[0068] Two-layer sliding mode interaction: When the second-layer sliding mode variable σ approaches 0, the first-layer sliding mode variable s satisfies the finite-time convergence condition, meaning s will approach 0 within a finite time. When s approaches 0, the centroid sideslip angle tracking error e of the autonomous vehicle... β The yaw rate error e of the autonomous vehicle γ All will approach 0.

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

[0070] ① Introduce the first layer of sliding mode variable s β s γ and s; where s β tracking error e of centroid side slip angle β Related: s γ Tracking error e of yaw rate γ Related; based on sliding mode variable s β and e γ The first-level sliding mode variable s is determined by comprehensive analysis.

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

[0072]

[0073] Among them, s β Let s be the sliding mode variable with respect to the centroid sideslip angle β. γ Let be the sliding mode variable relating to yaw rate γ, s be the sliding mode variable considering both sideslip angle β and yaw rate γ, and ζ be the weighting coefficient balancing the weights of sideslip angle β and yaw rate γ in the sliding mode variable s; e β For the tracking error e of the centroid sideslip angle of the unmanned vehicle β =β-β d ;e γ The yaw rate error of the unmanned vehicle is e γ =γ-γ d ;β d γ is the sideslip angle of the target centroid. d The target yaw rate; For e β The derivative of λ; the control parameter λ>0 needs to be designed.

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

[0075]

[0076] ② Introducing a second-layer sliding mode function:

[0077] σ=s+θs I (4)

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

[0079]

[0080] Where σ is the second-level sliding mode variable, and the control parameters θ>0, 1<η<2 are to be designed. To ensure the system is on the sliding surface from the initial moment, the control design is simplified, the system stability is improved, and chattering during the transient process is reduced. The sliding mode variable σ(0) = 0, i.e., the integral term s... I The initial value is set as follows:

[0081]

[0082] In this invention, the integral term s I Design This is to achieve multiple control objectives, specifically including: eliminating the arrival phase of sliding mode control, reducing chattering, accelerating system convergence, enhancing anti-interference capability, simplifying control design, and significantly improving control accuracy. This is achieved by introducing an integral term s. I The system is situated on the sliding surface from the outset, avoiding the sensitivity of traditional sliding mode control to uncertainties and external disturbances during the arrival phase, thus improving the system's robustness and stability. Simultaneously, the continuity and dynamic adjustment mechanism of the integral term effectively reduce high-frequency chattering of the control input, extending system lifetime and improving control smoothness. Furthermore, the design of the integral term accelerates the system's convergence speed, enabling it to reach a steady state more quickly and exhibiting stronger anti-interference capabilities in the face of persistent or periodic disturbances. Ultimately, this design not only simplifies the complexity of the controller but also significantly improves the system's control accuracy, enabling it to achieve fast, stable, and precise control even in the presence of uncertainties and external disturbances.

[0083] When initializing parameters, let β d =0, For various control parameters in double-layer sliding mode The parameter λ controls the amplification factor of the error exponential term, affecting the system's sensitivity. Increasing λ will increase the response, potentially improving the system's reaction speed, but may also lead to instability. The value is typically taken between (1,2) to control the nonlinear response to the rate of change of error. This makes the system more sensitive to rapidly changing errors, while smaller ones... This will make the system's response to rate of change smoother. Appropriate selection... It can balance the system's fast response and ability to suppress chattering. The parameter θ is used to correct, adjust, or compensate for 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, the preferred values ​​for each control parameter are λ = 0.1. θ=60, η=0.5, ζ=1.

[0085] Step S4: Based on the unmanned vehicle dynamics model and combined with the two-layer sliding mode function, determine the equivalent control law for the yaw moment control of the unmanned vehicle.

[0086] Assume the disturbance in formula (2) d2 = 0 and let the sliding mode derivative By organizing the data, the equivalent control quantity ΔM is obtained. eq ;

[0087]

[0088] Wherein, ΔM eq The output of the control law; m is the mass of the autonomous vehicle, u is the known forward speed of the autonomous vehicle, and K is the output of the control law. f K r Let be the lateral stiffness of the front and rear wheels, respectively; and let a and b be the distances from the center of gravity to the front axle and the rear axle, respectively. z Let δ be the yaw moment of inertia of the autonomous vehicle. f β is the steering angle of the front wheels of the autonomous vehicle. d The target centroid sideslip angle of the autonomous vehicle; and These are the first and second derivatives of the parameter x, respectively.

[0089] Step S5: Based on the adaptive control gain with the introduced barrier function, determine the approach law of the yaw moment control quantity of the unmanned vehicle.

[0090]

[0091] in, To adaptively control the gain, the following is introduced: Figure 3 The barrier function shown:

[0092]

[0093] in, The adaptive control gain is denoted by t, where t is time, σ(t) is the value of the second-level sliding mode variable at time t, and ε is the boundary value of the preset zero neighborhood. when hour, For the first arrival of the sliding variable σ(t) in the interval The time; otherwise,

[0094] In a preferred embodiment, ε is set to 0.1 rad.

[0095] In autonomous vehicle control systems, a barrier function can be used to adaptively adjust the control gain based on 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 approach law ΔM is strong. sw The corresponding increase enables the autonomous vehicle to quickly reduce its deviation from the target trajectory or position, ensuring a rapid system response and approach to the target. As the sliding mode variable σ(t) approaches zero, the gain gradually decreases, and the reaching law ΔM... sw The corresponding reduction in gain avoids over-control and high-frequency oscillations, ensuring a smooth transition of the autonomous vehicle to the target trajectory or position. This adaptive gain adjustment strategy not only improves the system's response speed but also enables smooth trajectory tracking when approaching the target, reducing unnecessary oscillations and energy consumption, thereby enhancing the robustness and accuracy of the autonomous vehicle control system.

[0096] Therefore, the overall control input

[0097] ΔM=ΔM eq +ΔM sw

[0098] centroid sideslip angle tracking error e β and yaw rate error e γ It will converge to the following region within a 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: In actual control, such as Figure 6 As shown, the state variables of the unmanned vehicle are collected, including the sideslip angle β and yaw rate γ; based on the target sideslip angle β... d and the target yaw rate γ d Calculate the tracking error e of the centroid sideslip angle β and yaw rate tracking error e γ The first-layer sliding mode variable s is calculated based on formula (3). β s γ The result is s; then the second-level sliding mode 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-level sliding mode variable σ. Then, the equivalent control quantity ΔM is obtained using the equivalent control law and the approach law. eq and compensation amount ΔM sw The two are added together to obtain the final additional yaw moment ΔM, which is provided to the autonomous vehicle to adjust its steering response and as an input to the torque distribution module, thereby affecting the lateral dynamics of the autonomous vehicle.

[0102] Figures 4-5 Figure e shows the tracking error curve of the center of gravity sideslip angle of the unmanned vehicle in the embodiment of the present invention. β Figure e shows the yaw rate error curve of the unmanned vehicle in this embodiment of the invention. γ As shown in the curve, the tracking error curve of the unmanned vehicle's center of gravity sideslip angle in this embodiment of the invention is shown in curve e. β Figure e shows the yaw rate error curve of the unmanned vehicle in this embodiment of the invention. γ Satisfy | e β |<0.1rad,|e γ |<0.1rad. Therefore, the theory proposed in this invention is completely correct.

[0103] The specific embodiments described above only illustrate the design principles of the present invention. The shapes and names of the components in this description may differ and are not limited. Therefore, those skilled in the art can modify or make equivalent substitutions to the technical solutions described in the foregoing embodiments; and these modifications and substitutions do not depart from the inventive spirit and technical solutions of the present invention, and should all fall within the protection scope of the present invention.

Claims

1. A barrier function-based double-layer sliding mode algorithm for steering control of an unmanned vehicle, characterized in that, The application relates to a control method for a vehicle, and belongs to the field of vehicle control. The application comprises the following steps: Determine the two-layer sliding mode function, including: 1) Determine the tracking error e with respect to the centroid sideslip angle. β sliding mode variable s β And regarding the yaw rate tracking error e γ sliding mode variable s γ According to the sliding mode variable s β and s γ The first-level sliding mode variable s is determined by combining the variables to constrain the system to converge to the sliding surface s within a finite time. β s γ 2) Determine the sliding mode variable s and integral term s containing the first layer of sliding mode. I The second sliding mode variable σ, the integral term is used to restrict the system to initially lie on the sliding surface; determining a vehicle dynamics model, wherein the state quantity is a centroid side slip angle beta and a yaw rate gamma; based on the vehicle dynamics model, combining a double-layer sliding mode function, determining an equivalent control law of a vehicle yaw moment control quantity; based on an adaptive control gain of a barrier function, determining a reaching law of the vehicle yaw moment control quantity; 2. The method of claim 1, wherein, collecting the state quantity of the vehicle, substituting the double-layer sliding mode function, obtaining a current sliding mode variable; updating the adaptive control gain of the reaching law based on the current sliding mode variable; determining an additional yaw moment based on the control law and the reaching law, which is used for adjusting the steering response of the vehicle, so that the centroid side slip angle beta and the yaw rate gamma track target values. The side-slip angle tracking error e β of the center of mass β is: Θ is the error power term; The yaw angular velocity tracking error e γ The sliding mode variable s γ is: γ s γ = e The first layer sliding mode variable s is determined as: s = s β and e γ The first layer sliding mode variable s is determined as: s = s β + ζs γ ; The first-layer sliding mode function is designed as follows:

3. The method of claim 2, wherein, wherein sign(·) is a sign function; zeta is a weight coefficient of a balance centroid side slip angle beta and a yaw rate gamma in a sliding mode variable s weight proportion; lambda is an error power term amplification coefficient; lambda>0. wherein sign( ) is a sign function; is a control parameter of the sensitivity of the unmanned vehicle to error variation, 4. The method of claim 2, wherein, The error power term theta is as follows: σ = s + θs I The second-layer sliding mode function is designed as follows:

5. The method of claim 4, wherein, The integral term s I is: wherein theta is an integral term amplification coefficient, theta>0.

6. The method of claim 4 or 5, wherein, The integral term s I The integral initial value of s is set to: where e β (0) and e γ (0) are the initial values of the centroid side slip angle tracking error and the yaw rate tracking error, respectively; the error power term used in the sliding mode variable calculation sign(·) is the sign function; is the control parameter of the sensitivity of the unmanned vehicle to error changes, 7. The method of claim 4, wherein, wherein eta is a s sensitivity control parameter, 1<eta<2. where ΔM eq is the output of the control law; m is the mass of the vehicle, u is the known forward speed of the vehicle, K f , K r are the cornering stiffness of the front and rear wheels, a, b are the distances from the center of mass to the front and rear axles, I z is the yaw moment of inertia of the vehicle, δ f is the steering angle of the front wheels of the vehicle; β d is the target side slip angle of the vehicle; and are the first and second derivatives of the parameter x, respectively.

8. The method of claim 4, wherein, The equivalent control law of the vehicle yaw moment control quantity is as follows: where ΔM sw is the output of the approaching 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 side slip stiffness of the front and rear wheels respectively, a, b are the distances from the mass center to the front and rear axles respectively, I z is the yaw moment of inertia of the unmanned vehicle, is the adaptive control gain with the barrier function introduced.

9. The method of claim 1 or 8, wherein, The reaching law of the vehicle yaw moment control quantity is as follows: In the reaching law, the adaptive control gain of the barrier function is as follows: wherein, is an 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 , is the time when the sliding variable σ(t) first reaches the interval ; otherwise, 10. The method of claim 1, wherein, The additional yaw moment is determined based on the control law and the approaching law as follows: the equivalent control amount ΔM of the equivalent control law output is added to the compensation amount ΔM of the approaching law output to obtain the additional yaw moment. eq sw ​​

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