An adaptive anti-disturbance sliding mode steering stability control method for unmanned vehicle

Abstract

This disclosure provides an adaptive anti-disturbance sliding mode steering stability control method for unmanned vehicles. Based on the unmanned vehicle's dynamics model, the method assumes zero system disturbance and, combined with a sliding mode function, determines the control law for the unmanned vehicle's yaw moment control quantity. The system disturbance is modeled as a disturbance function containing three disturbance terms: time-invariant disturbance, variable disturbance, and rate-of-change disturbance. Using the disturbance function with adaptive parameters, a reaching law for the unmanned vehicle's yaw moment control quantity is determined. The unmanned vehicle's state variables are collected and substituted into the sliding mode function related to the sideslip angle and yaw rate to obtain sliding mode variables. Based on the sliding mode variables and state variables, the equivalent control quantity is obtained using the control law. Based on the sliding mode variables and state variables, the anti-disturbance compensation quantity is obtained using the reaching law. The total control input is determined based on the equivalent control quantity and the anti-disturbance compensation quantity. This invention overcomes the limitations of fixed gain in traditional sliding mode control and also compensates for complex disturbances in real time through an adaptive mechanism.

Description

Technical Field

[0001] This invention relates to the field of unmanned vehicle control technology, specifically to an adaptive anti-disturbance sliding mode steering stability control method for unmanned vehicles. Background Technology

[0002] With the rapid development of intelligent transportation technology and autonomous driving technology, driverless cars are gradually moving from the laboratory to practical applications, becoming an important part of the future transportation system. Compared to traditional internal combustion engine vehicles, driverless cars have significant advantages in terms of innovative drive systems. By equipping the wheels with electric motor drive systems, driverless cars improve the precision of power distribution and the flexibility of steering control. This technological breakthrough enhances handling precision, strengthens safety and stability, and demonstrates strong adaptability, especially in complex road conditions and adverse weather.

[0003] While autonomous vehicles possess many advantages, their dynamic control still faces significant challenges in high-speed driving, sharp turns, external disturbances, and complex environments. The stability and safety of autonomous vehicles may be threatened, and they could even experience loss of control. The highly nonlinear and strongly coupled characteristics of the autonomous vehicle's power system pose major challenges to traditional control methods in practical applications. Finding a balance between high-precision control and stability in complex environments has become a key research problem in the field.

[0004] Currently, the control methods for autonomous vehicles mainly rely on traditional control strategies (such as PID control and fuzzy control) and modern control methods (such as sliding mode control and model predictive control). However, these control strategies all have significant limitations in practical applications. PID control is simple in structure and easy to implement, but its linear characteristics make it difficult to cope with the nonlinear and strongly coupled characteristics of autonomous vehicles, especially under external disturbances, where control accuracy and robustness decrease significantly. While backstepping control can handle the stability problems of nonlinear systems to some extent and can adjust for the dynamic characteristics of the system, its design process is relatively complex. For highly dynamic systems like autonomous vehicles, backstepping control may exhibit response lag or accuracy degradation when facing strong disturbances and complex environments, limiting its effectiveness in practical applications. Although linear control methods can ensure system stability, their control performance is insufficient for practical application requirements of typical nonlinear systems like autonomous vehicles.

[0005] Sliding mode control, as a robust control method, has been widely used in the control of complex systems such as autonomous vehicles in recent years due to its strong ability to suppress system uncertainties and external disturbances. However, traditional sliding mode control methods have a significant drawback: their switching gain is usually fixed, making it difficult to adapt to dynamic changes in the system and the real-time impact of external disturbances. Inappropriate gain selection can lead to excessive oscillations or response lag in the system, thereby reducing the control effect. Furthermore, in the steering control of autonomous vehicles, due to the nonlinear characteristics of the system and the more significant impact of external disturbances, traditional sliding mode control methods struggle to simultaneously meet the requirements of high precision and strong robustness.

[0006] As a robust control method, sliding mode control has been widely used in the control of complex systems such as autonomous vehicles in recent years due to its strong suppression capabilities. However, traditional sliding mode control methods have significant problems: their switching gain is fixed and cannot be adjusted in real time according to the dynamic changes of the system and external disturbances. Inappropriate gain selection may lead to excessive system oscillation or response lag, thereby reducing control performance. In the steering control of a two-degree-of-freedom model of an autonomous vehicle, due to the significant nonlinearity of the system and the influence of external disturbances, traditional sliding mode control methods struggle to provide sufficient robustness while ensuring high accuracy. Since traditional methods are unable to cope with the real-time effects of system changes and disturbances, more adaptive control strategies are needed to improve system performance. Summary of the Invention

[0007] In view of this, the present invention proposes a steering stability control method for unmanned vehicles based on adaptive disturbance rejection sliding mode control. The present invention introduces adaptive gain adjustment into sliding mode control. With its disturbance rejection compensation mechanism, the system estimates external disturbances in real time and dynamically adjusts the gain, significantly improving system robustness and control accuracy. This invention not only overcomes the limitations of fixed gain in traditional sliding mode control but also compensates for complex disturbances in real time through an adaptive mechanism, ensuring that the autonomous vehicle maintains excellent stability and handling performance under turning conditions.

[0008] To solve the above-mentioned technical problems, the present invention is implemented as follows.

[0009] An adaptive disturbance-resistant sliding mode steering stability control method for unmanned vehicles includes:

[0010] Determine the dynamic model of the unmanned vehicle, where the state variables are the sideslip angle β and the yaw rate γ.

[0011] Based on the aforementioned unmanned vehicle dynamics model, the system disturbance D is set to zero, and the control law for the yaw moment control quantity of the unmanned vehicle is determined by combining the sliding mode function.

[0012] The system disturbance D is modeled as a disturbance function containing three disturbance terms: a time-invariant disturbance, a variable disturbance, and a rate-of-change disturbance with respect to β. The coefficients of the three disturbance terms are adaptive parameters related to the sliding mode variable s. Using the disturbance function containing the adaptive parameters, the approach law of the yaw moment control quantity of the unmanned vehicle is determined.

[0013] The state variables of the autonomous vehicle are collected and substituted into a sliding mode function related to the sideslip angle β and yaw rate γ to obtain the sliding mode variable s. Based on the sliding mode variable s and the state variables, the equivalent control variable ΔM is obtained using the control law. eq Based on the sliding mode variable s and the state variables, the disturbance compensation amount ΔM is obtained using the reaching law. sw Based on the equivalent control quantity ΔM eq and disturbance rejection compensation amount △M sw Determine the total control input ΔM;

[0014] The total control input △M controls the unmanned vehicle's center of gravity sideslip angle β and yaw rate γ to track the target values.

[0015] Preferably, the sliding mode function related to the sideslip angle β and the yaw rate γ is:

[0016]

[0017] Where s is the sliding mode variable; e β The sideslip angle β of the acquired centroid and the sideslip angle β of the target centroid are... d The error between; e γ The yaw rate γ collected is the same as the target yaw rate γ. d The error between; For e β The derivative of λ; α and λ are the control parameters of the sliding mode function; sign(·) is the sign function.

[0018] Preferably, based on the autonomous vehicle dynamics model, the system disturbance D is set to zero, and the control law for determining the yaw moment control quantity of the autonomous vehicle, combined with the sliding mode function, is as follows:

[0019] Setting the system disturbance D of the autonomous vehicle dynamics model to zero, and the derivative of the sliding mode function to zero, the control law for the yaw moment control of the autonomous vehicle is determined by combining the autonomous vehicle dynamics model and the derivative of the sliding mode function:

[0020]

[0021] 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 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 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.

[0022] Preferably, the system disturbance D is modeled as follows:

[0023]

[0024] Where b0, b1, and b2 are unknown but bounded positive numbers, representing the coefficients of time-invariant, variable, and rate-of-change disturbances with respect to β, respectively; during the control process, the estimated values ​​of b0, b1, and b2 are calculated based on the sliding mode variable s. It is the derivative of β.

[0025] Preferably, during the control process, the estimated values ​​of b0, b1, and b2 are calculated based on the sliding mode variable s:

[0026]

[0027] in, for The first derivative, η0, η1, and η2 are estimated values; η0, η1, and η1 are adjustment coefficients for b0, b1, and b2, respectively.

[0028] Preferably, the approach law for determining the yaw moment control quantity of the unmanned vehicle using the disturbance function containing adaptive parameters is as follows:

[0029]

[0030] 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 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 K is the yaw moment of inertia of the unmanned vehicle; k1 is the approach speed control coefficient; k2 is the nonlinear influence control coefficient in the approach law, increasing k2 improves robustness to disturbances; μ is the nonlinear term strength control coefficient, increasing μ improves convergence speed; k1, k2>0, 0<μ<1; The estimated values ​​of coefficients b0, b1, and b2 for time-invariant disturbance, changing disturbance, and rate-of-change disturbance, respectively, are used as adaptive parameters and are determined as follows:

[0031]

[0032] in, for The first derivative; η0, η1, and η2 are the adjustment coefficients of b0, b1, and b2, respectively; e β The sideslip angle β of the acquired centroid and the sideslip angle β of the target centroid are... d The error between; For e β The derivative of; sign(·) is the sign function; It is the derivative of β.

[0033] The present invention also provides an adaptive anti-disturbance sliding mode steering stability control system for unmanned vehicles, including a data acquisition module, a sliding mode control module, a control module, a compensation module, and a comprehensive module;

[0034] The data acquisition module is used to collect state parameters from the controlled unmanned vehicle, including the sideslip angle β and the yaw rate γ.

[0035] The sliding mode control module is used to obtain the sliding mode variable s by substituting the state variables into the sliding mode function related to the centroid sideslip angle β and yaw rate γ, and then sending it to the control module and the compensation module.

[0036] The control module is used to obtain the equivalent control quantity ΔM of the yaw moment of the unmanned vehicle using a control law based on the sliding mode variable s and the state variables. eq The control law is determined as follows: based on the dynamics model of the unmanned vehicle, the system disturbance D is set to zero, and the control law is determined by combining the sliding mode function.

[0037] The compensation module is used to update the adaptive parameters of the reaching law based on the sliding mode variable s and the state variables, and then use the updated reaching law to obtain the disturbance rejection compensation amount ΔM of the yaw moment of the unmanned vehicle. sw The approach law is determined as follows: the system disturbance D is modeled as a disturbance function with three disturbance terms, namely, a time-invariant disturbance, a variable disturbance, and a rate-of-change disturbance with respect to β; the coefficients of the three disturbance terms are adaptive parameters related to the sliding mode variable s; the approach law is determined using the disturbance function containing the adaptive parameters.

[0038] The integrated module is used to base the equivalent control quantity ΔM on... eq and disturbance rejection compensation amount △M sw Determine the total control input △M; control the unmanned vehicle's center of gravity sideslip angle β and yaw rate γ to track the target values ​​through the total control input △M.

[0039] Preferably, the sliding mode control module uses the following sliding mode function:

[0040]

[0041] Where s is the sliding mode variable; e β The sideslip angle β of the acquired centroid and the sideslip angle β of the target centroid are... d The error between; e γ The yaw rate γ collected is the same as the target yaw rate γ. d The error between; For e β The derivative of λ; α and λ are the control parameters of the sliding mode function; sign(·) is the sign function.

[0042] Preferably, the compensation module uses the following approach law:

[0043]

[0044] 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 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 K is the yaw moment of inertia of the unmanned vehicle; k1 is the approach speed control coefficient; k2 is the nonlinear influence control coefficient in the approach law, increasing k2 improves robustness to disturbances; μ is the nonlinear term strength control coefficient, increasing μ improves convergence speed; k1, k2>0, 0<μ<1; The estimated values ​​of coefficients b0, b1, and b2 for time-invariant disturbance, changing disturbance, and rate-of-change disturbance, respectively, are used as adaptive parameters and are determined as follows:

[0045]

[0046] in, for The first derivative; η0, η1, and η2 are the adjustment coefficients of b0, b1, and b2, respectively; e β The sideslip angle β of the acquired centroid and the sideslip angle β of the target centroid are... d The error between; For e β The derivative of; sign(·) is the sign function; It is the derivative of β.

[0047] Preferably, the control law used by the control module is:

[0048]

[0049] 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.

[0050] Beneficial effects:

[0051] (1) Strong anti-disturbance capability: Adaptive anti-disturbance sliding mode control can automatically adjust control parameters to suppress disturbances and ensure stable system operation when there are external disturbances, model uncertainties and changes in the external environment. This control method can effectively eliminate or reduce the impact of external disturbances on the steering system of unmanned vehicles through the characteristics of sliding mode control.

[0052] (2) High robustness: Sliding mode control has strong robustness and can cope with various uncertainties and nonlinear characteristics. By introducing an adaptive mechanism to adjust b0, b1, and b2, the control parameters are adjusted in real time to cope with complex changes and disturbances that may occur during the driving of the unmanned vehicle (such as changes in road friction, changes in vehicle speed, etc.) and ensure that the system always remains stable.

[0053] (3) Real-time adjustment of control parameters: To cope with these disturbances, this invention has designed a detailed modeling form for external disturbances, specifically including three disturbance terms: constant disturbance with respect to β, changing disturbance, and rate of change disturbance. The adaptive mechanism enables the control system to estimate and adjust system parameters in real time to cope with environmental changes and unknown external disturbances, thereby ensuring that the steering stability of the unmanned vehicle can maintain good stability and adaptability in complex environments, and thus improving the unmanned vehicle's ability to cope with complex changes. Attached Figure Description

[0054] Figure 1 This is a physical image of the unmanned vehicle in an embodiment of the present invention.

[0055] Figure 2 This is a force analysis diagram of the unmanned vehicle with two degrees of freedom in an embodiment of the present invention.

[0056] Figure 3 In this embodiment of the invention, the sideslip angle β of the unmanned vehicle's center of gravity and the sideslip angle β of the target's center of gravity are... d The tracking trajectory diagram.

[0057] Figure 4 In this embodiment of the invention, the yaw rate γ of the motor-driven unmanned vehicle and the target yaw rate γ are... d The tracking trajectory diagram.

[0058] Figure 5 This is a flowchart of the present invention. Detailed Implementation

[0059] This invention provides an adaptive anti-disturbance sliding mode steering stability control method for unmanned vehicles. Its core idea is to introduce adaptive gain adjustment and anti-disturbance compensation mechanisms into sliding mode control, estimate external disturbances in real time and dynamically adjust the gain, thereby significantly improving the robustness and control accuracy of the system.

[0060] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0061] Step S1: Determine the dynamic model of the unmanned vehicle, where the state variables are the sideslip angle β and the yaw rate γ.

[0062] In this step, through appropriate simplification, the dynamic equations of the two-degree-of-freedom unmanned vehicle were derived using principles of theoretical mechanics. (This is in response to...) Figure 1 The four-wheel electric drive autonomous vehicle shown is reduced to a two-degree-of-freedom model, resulting in:

[0063]

[0064] 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; and 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. 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.

[0065] For a specific example of an unmanned vehicle, the known parameters in the above formula (1) are shown in Table 1.

[0066] Table 1 Basic Parameters of Autonomous Vehicles

[0067]

[0068] Meanwhile, this embodiment sets the front wheel steering angle of the unmanned vehicle.

[0069] 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.

[0070] Differentiating formula (1-1) with respect to time t and substituting formula (1-2) into it, we get:

[0071]

[0072] in,

[0073] Step S3: System disturbance D modeling.

[0074] The disturbance D represents the system uncertainty. This invention assumes that it is limited and is related to three disturbance terms: time-invariant disturbance, changing disturbance, and rate-of-change disturbance.

[0075]

[0076] In the formula, b0, b1, and b2 are unknown but bounded positive numbers, representing the coefficients of the invariant, variable, and rate-of-change perturbations with respect to β, respectively. This assumption has practical significance: a time-invariant perturbation b0 (such as changes in temperature or humidity) may continuously affect the system's performance, but its changes are slow and predictable; the perturbation related to the autonomous vehicle's sideslip angle b1|β| reflects the dynamic changes caused by steering or road friction during the autonomous vehicle's operation, and usually increases with changes in the autonomous vehicle's state; while the perturbation related to the rate of change of the sideslip angle... Related disturbances typically occur when the autonomous vehicle accelerates, decelerates, or turns, and can become more severe, especially when the system's dynamic response is fast. Effectively suppressing these disturbances is crucial for ensuring the stability and precise control of the autonomous vehicle in complex, dynamic environments. By designing robust control algorithms, the system can adjust and respond to these disturbances in real time, ensuring that the autonomous vehicle can still achieve smooth yaw rate and center-of-gravity sideslip angle tracking under various disturbances.

[0077] Step S4: Construct the sliding mode function, control law, and reaching law.

[0078] Based on the dynamic equations derived in step S2, an adaptive anti-disturbance sliding mode steering stability control algorithm for unmanned vehicles was developed. This algorithm ensures that when facing uncertainties and external disturbances (such as wind force, changes in road friction, etc.), the unmanned vehicle's yaw moment ΔM is controlled to ensure that the vehicle's actual yaw rate γ and center-of-gravity sideslip angle β track the target yaw rate γ within a finite time. d And the target centroid sideslip angle β d .

[0079] The specific method for step S4 is as follows:

[0080] Step S401: Introduce sliding mode variable s:

[0081]

[0082] In the formula, s is the sliding mode variable that comprehensively considers the sideslip angle β and the yaw rate γ, and β d e is the sideslip angle of the target centroid. βTo address the tracking error of the autonomous vehicle's center of gravity sideslip angle, control parameters λ>0, 1<α<2 are to be designed, and γ... d Let e ​​be the target yaw rate. γ This refers to the yaw rate error of the unmanned vehicle.

[0083] The parameter λ controls the amplification factor of the correction term. λ affects the system's sensitivity; increasing λ increases the response, potentially improving the system's reaction speed, but may also lead to instability. The parameter α typically takes values ​​between (1,2) and controls the nonlinear response to the rate of change of error. A larger α makes the system more sensitive to rapidly changing errors, while a smaller α makes the system's response to the rate of change smoother. Appropriately selecting α can balance the system's fast response and chatter suppression capabilities. Therefore, in this invention, λ = 0.3 and α = 1.6 are preferred.

[0084] Initialize the parameters, let β d =0,

[0085] Step S402: Based on the unmanned vehicle dynamics model, set the system disturbance D to zero, and combine the sliding mode function to determine the control law for the yaw moment control quantity of the unmanned vehicle.

[0086] In this step, assume that D = 0 in formula (2) and let The equivalent control input △M is obtained. eq :

[0087]

[0088] This equivalent control input △M eq It is obtained by balancing all terms in the dynamics of the autonomous vehicle except for the disturbance D.

[0089] Step S403: Utilize parameters including adaptive parameters Given the perturbation function (Equation 4), design an adaptive parameter... The approach law △M sw .

[0090]

[0091] Where, k1s+k2|s| μThe sliding mode control gain is used to adjust the control input on the sliding surface. k1 is the gain proportional to the sliding mode variable s, and k2 is the gain proportional to the μ power of the absolute value of the sliding mode variable s. They control the system's velocity and stability on the sliding surface, and the choice of gain affects the system's response speed and steady-state error. The sign function sign(s) is a key switching mechanism in sliding mode control. It defines the direction of the control input. When the system state deviates from the sliding surface, the sign function adjusts the direction of the control input, causing the system to return to the sliding surface. It refers to the invariant disturbance, the varying disturbance β, and the rate of change disturbance of the sideslip angle of the autonomous vehicle's center of gravity. Compensation terms are introduced. By incorporating these terms, the controller can better adapt to external disturbances or uncertainties and remain stable in the face of complex changes.

[0092] Simultaneous parameters Satisfy the following adaptive rate:

[0093]

[0094] The purpose of these three adaptive rate equations is to improve the system's adaptability to external disturbances and dynamic changes by dynamically estimating disturbances and errors in the system and adjusting the control gain in real time. The first adaptive rate equation... The perturbation estimate is updated based on the sliding mode variable s. This allows the system to adjust the control gain more quickly when faced with large errors, thereby compensating for disturbances and improving robustness. The second equation... This combines the error term β in the system with the absolute value of the sliding mode variable |s|, and dynamically adjusts... This enhances the system's responsiveness to dynamic changes or external disturbances. The third equation... |This takes into account the rate of change of the disturbance. The absolute value of the sliding mode variable, |s|, enables the controller to respond more sensitively to changes in disturbances, especially to make rapid adjustments when disturbances change rapidly. Through these three adaptive rate equations, the control system can dynamically adjust the gain according to the actual error and disturbance conditions, ensuring the stability and robustness of the system in complex and uncertain environments.

[0095] In the formula, k1 and k2 > 0, and 0 < μ < 1. k1 mainly controls the approach speed; a larger value helps to achieve rapid convergence, but may lead to system oscillations. k2 controls the nonlinear effects in the approach law; increasing k2 improves robustness to disturbances, but may make the system behavior too aggressive. μ controls the strength of the nonlinear term; a larger value increases the nonlinear effects of the system, which helps to accelerate convergence, but may also bring instability.

[0096] In an optimal solution, k1 and k2 are set to 20, and μ is set to 0.85.

[0097] Step S404: Obtain the overall control input:

[0098] ΔM=ΔM eq +ΔM sw (10)

[0099] Step 5: In actual control, such as Figure 5 As shown, the state variables of the unmanned vehicle are collected, and the sliding mode function related to the sideslip angle β and yaw rate γ is substituted to obtain the sliding mode variable s. Based on the sliding mode variable s and the state variables, the equivalent control variable ΔM is obtained by substituting it into the control law. eq Based on the sliding mode variable s and the state variables, adjust the adaptive parameters in the reaching law. Then, substituting the sliding mode variable s and the state variable into the adjusted reaching law, we obtain the disturbance rejection compensation amount ΔM. sw .

[0100] Step 6: Equivalent control quantity ΔM eq and disturbance rejection compensation amount △M sw The total control input ΔM is obtained by summing the values. The unmanned vehicle is controlled using the total control input ΔM, so that the unmanned vehicle's sideslip angle β and yaw rate γ track the target's sideslip angle and yaw rate.

[0101] Figures 3-4 These are the sideslip angle β of the unmanned vehicle's center of gravity and the sideslip angle β of the target's center of gravity, respectively, in this embodiment of the invention. d The tracking trajectory diagram, the yaw rate γ of the motor-driven unmanned vehicle and the target yaw rate γ in the embodiment of the present invention. d The tracking trajectory diagram.

[0102] As shown in the graph, in this embodiment of the invention, the unmanned vehicle's center of gravity sideslip angle β can track the target's center of gravity sideslip angle β. d In this embodiment of the invention, the yaw rate γ of the motor-driven unmanned vehicle can track the yaw rate γ of the target. d Therefore, the theory proposed in this invention is entirely correct.

[0103] Based on the above method, the present invention also provides an adaptive anti-disturbance sliding mode steering stability control system for unmanned vehicles, such as... Figure 5 As shown, the system includes an acquisition module, a sliding mode control module, a control module, a compensation module, and a comprehensive module.

[0104] The data acquisition module is used to collect state parameters from the controlled unmanned vehicle, including the sideslip angle β and the yaw rate γ.

[0105] The sliding mode control module is used to obtain the sliding mode variable s by substituting the state variables into the sliding mode function related to the centroid sideslip angle β and yaw rate γ, and then sending it to the control module and the compensation module.

[0106] The control module is used to obtain the equivalent control quantity ΔM of the yaw moment of the unmanned vehicle using a control law based on the sliding mode variable s and the state variables. eq The control law is determined as follows: based on the dynamics model of the unmanned vehicle, the system disturbance D is set to zero, and the control law is determined by combining the sliding mode function.

[0107] The compensation module is used to update the adaptive parameters of the reaching law based on the sliding mode variable s and the state variables, and then use the updated reaching law to obtain the disturbance rejection compensation amount ΔM of the yaw moment of the unmanned vehicle. sw The approach law is determined as follows: the system disturbance D is modeled as a disturbance function with respect to β, which includes three disturbance terms: time-invariant disturbance, variable disturbance, and rate-of-change disturbance; the coefficients of the three disturbance terms are adaptive parameters related to the sliding mode variable s; the approach law is determined using the disturbance function containing the adaptive parameters.

[0108] The integrated module is used to base the equivalent control quantity ΔM on... eq and disturbance rejection compensation amount △M sw Determine the total control input △M; control the unmanned vehicle's center of gravity sideslip angle β and yaw rate γ to track the target values ​​through the total control input △M.

[0109] The sliding mode control module uses sliding mode functions defined by formulas (5) and (6). The control law used by the control module is given in formula (7); the approach law used by the compensation module is given in formulas (8) and (9); the synthesis module is based on the equivalent control quantity ΔM. eq and disturbance rejection compensation amount △M sw Determine the total control input ΔM using formula (10).

[0110] 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 method for adaptive disturbance-resistant sliding mode steering stability control of an unmanned vehicle, characterized in that, include: Determine the dynamic model of the autonomous vehicle, where the state variable is the centroid sideslip angle. β and yaw rate γ ; Based on the aforementioned autonomous vehicle dynamics model, let the system be disturbed. D If the value is zero, the control law for the yaw moment control quantity of the unmanned vehicle is determined by combining the sliding mode function; Disturb the system D The model is a perturbation function containing three perturbation terms, which are respectively about... β The time-invariant disturbance, the variable disturbance, and the rate of change disturbance; the coefficients of the three disturbance terms are related to the sliding mode variable. s The relevant adaptive parameters; using the disturbance function containing the adaptive parameters, the approach law of the yaw moment control quantity of the unmanned vehicle is determined; Collect the state variables of the unmanned vehicle and substitute them with the sideslip angle of the center of gravity. β and yaw rate γ The relevant sliding mode function obtains the sliding mode variable. s Based on the sliding mode variable s And the state quantity, using the control law to obtain the equivalent control quantity Δ M eq Based on the sliding mode variable s And the state quantity, using the reaching law to obtain the disturbance compensation quantity Δ M sw Based on the equivalent control quantity Δ M eq And disturbance compensation amount △ M sw Determine the total control input △ M ; Through the total control input △ M Control the sideslip angle of the autonomous vehicle's center of gravity β and yaw rate γ Tracking the target value; The angle with the center of mass side deflection β and yaw rate γ The relevant sliding mode function is: in, s For sliding mode variables; The centroid side slip angle for acquisition β Side slip angle with the target centroid The error between; The yaw rate collected yaw rate of the target The error between; for The derivative; and These are the control parameters for the sliding mode function; It is a symbolic function.

2. The method as described in claim 1, characterized in that, Based on the autonomous vehicle dynamics model, the system is disturbed. D Given that the value is zero, and combining this with the sliding mode function, the control law for the yaw moment control of the unmanned vehicle is determined as follows: Let the system perturbation of the autonomous vehicle dynamics model D The sliding mode function is set to zero after differentiation. Combining the autonomous vehicle dynamics model and the derivative of the sliding mode function, the control law for the yaw moment control of the autonomous vehicle is determined as follows: in, For the output of the control law; m For the quality of driverless cars, u Given the known forward speed of the driverless car, K f , K r These are the lateral stiffness of the front and rear wheels, respectively. a , b These are the distances from the center of gravity to the front axle and the distances from the center of gravity to the rear axle, respectively. I z For the yaw moment of inertia of the driverless car, δ f For the front wheel angle of the driverless car; The target centroid sideslip angle of the autonomous vehicle; and Parameters x The first and second derivatives.

3. The method as described in claim 1, characterized in that, The system disturbance D The model is as follows: in, b 0、 b 1 and b 2 is an unknown but bounded positive number, representing the following about... β The coefficients of time-invariant disturbances, varying disturbances, and rate-of-change disturbances; during the control process, based on the sliding mode variables... s calculate b 0、 b 1 and b The estimated value of 2, for The derivative of .

4. The method as described in claim 3, characterized in that, During the control process, based on the sliding mode variable s calculate b 0、 b 1 and b The estimated value of 2 is: in, , , for , , The first derivative, , , for b 0、 b 1 and b The estimated value of 2; , , They are respectively b 0、 b 1 and b The adjustment coefficient is 2.

5. The method as described in claim 1, characterized in that, The approach law for determining the yaw moment control quantity of the unmanned vehicle using the disturbance function containing adaptive parameters is as follows: in, The output of the reaching law; m For the quality of driverless cars, u Given the known forward speed of the driverless car, K f , K r These are the lateral stiffness of the front and rear wheels, respectively. a , b These are the distances from the center of gravity to the front axle and the distances from the center of gravity to the rear axle, respectively. I z The moment of inertia of the autonomous vehicle's yaw motion; k 1 represents the approach speed control coefficient; k 2 represents the nonlinear influence control coefficient in the reaching law; increasing it... k 2. Improve robustness to disturbances; μ This is the control coefficient for the strength of the nonlinear term; increasing it... μ Improve convergence speed; k 1. k 2>0, 0< μ <1; , , The coefficients are respectively for time-invariant disturbances, variable disturbances, and rate-of-change disturbances. , , The estimated value, which serves as an adaptive parameter, is determined as follows: in, , , for , , The first derivative; , , They are respectively b 0、 b 1 and b The adjustment coefficient is 2.

6. An adaptive anti-disturbance sliding mode steering stability control system for unmanned vehicles, characterized in that, It includes a data acquisition module, a sliding mode control module, a control module, a compensation module, and a comprehensive module; The data acquisition module is used to collect state parameters from the controlled unmanned vehicle, including the centroid sideslip angle. β and yaw rate γ ; The sliding mode control module is used to substitute the state variables with the centroid sideslip angle. β and yaw rate γ The relevant sliding mode function obtains the sliding mode variable. s This information is sent to the control module and the compensation module. Control module, used for based on the sliding mode variable s And the state variables, using the control law to obtain the equivalent control variable Δ of the yaw moment of the unmanned vehicle. M eq The control law is determined as follows: based on the autonomous vehicle dynamics model, the system disturbance is... D If the value is zero, the control law is determined by combining the sliding mode function; The compensation module is used to compensate for the sliding mode variable. s The adaptive parameters of the reaching law are updated using the state variables, and then the updated reaching law is used to obtain the disturbance rejection compensation amount Δ of the yaw moment of the autonomous vehicle. M sw The method for determining the reaching law is as follows: The system disturbance is... D Modeled as a perturbation function with three perturbation terms, the perturbation terms being about... β The time-invariant disturbance, the variable disturbance, and the rate of change disturbance; the coefficients of the three disturbance terms are related to the sliding mode variable. s The relevant adaptive parameters; using the perturbation function containing the adaptive parameters, the reaching law is determined; The integrated module is used to base the equivalent control quantity Δ M eq And disturbance compensation amount △ M sw Determine the total control input △ M ; through the total control input △ M Control the sideslip angle of the autonomous vehicle's center of gravity β and yaw rate γ Tracking the target value; The sliding mode control module uses the following sliding mode function: in, s For sliding mode variables; The centroid side slip angle for acquisition β Side slip angle with the target centroid The error between; The yaw rate collected yaw rate of the target The error between; for The derivative; and These are the control parameters for the sliding mode function; It is a symbolic function.

7. The control system as described in claim 6, characterized in that, The compensation module uses the following approach law: in, The output of the reaching law; m For the quality of driverless cars, u Given the known forward speed of the driverless car, K f , K r These are the lateral stiffness of the front and rear wheels, respectively. a , b These are the distances from the center of gravity to the front axle and the distances from the center of gravity to the rear axle, respectively. I z The moment of inertia of the autonomous vehicle's yaw motion; k 1 represents the approach speed control coefficient; k 2 represents the nonlinear influence control coefficient in the reaching law; increasing it... k 2. Improve robustness to disturbances; μ This is the control coefficient for the strength of the nonlinear term; increasing it... μ Improve convergence speed; k 1. k 2>0, 0< μ <1; , , The coefficients are respectively for time-invariant disturbances, variable disturbances, and rate-of-change disturbances. , , The estimated value, which serves as an adaptive parameter, is determined as follows: in, , , for , , The first derivative; , , They are respectively b 0、 b 1 and b The adjustment coefficient is 2.

8. The control system as described in claim 6, characterized in that, The control law used by the control module is: in, For the output of the control law; m For the quality of driverless cars, u Given the known forward speed of the driverless car, K f , K r These are the lateral stiffness of the front and rear wheels, respectively. a , b These are the distances from the center of gravity to the front axle and the distances from the center of gravity to the rear axle, respectively. I z For the yaw moment of inertia of the driverless car, δ f For the front wheel angle of the driverless car; The target centroid sideslip angle of the autonomous vehicle; and Parameters x The first and second derivatives.

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