An adaptive robust anti-chattering sliding mode control method for unmanned aerial vehicles

Abstract

This invention belongs to the field of unmanned aerial vehicle (UAV) control system design. To address the problem that existing sliding mode control cannot adapt to the strong disturbance environment of complex nonlinear systems, it provides an adaptive robust anti-chattering sliding mode control method for UAVs. This invention establishes the system state equation, designs an adaptive mechanism to estimate and compensate for system modeling deviations online, combines robust terms to suppress external disturbances, and uses a second-order superspiral sliding mode reaching law to replace the traditional reaching law, effectively eliminating chattering. Its technical effect is to achieve rapid and stable tracking of maneuver overload commands, significantly improving the control accuracy and maneuver stability of the UAV in complex environments.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle control system design, specifically relating to an adaptive robust anti-chattering sliding mode control method for unmanned aerial vehicles. Background Technology

[0002] Currently, the structural design of unmanned aerial vehicles (UAVs) is gradually evolving from conventional axisymmetric shapes to symmetrical shapes, such as waverider shapes, large-wingspan canard shapes, and tail-shaped shapes. These shapes possess strong maneuverability and range extension characteristics, but they also have inherent drawbacks, namely, complex fuselage shapes, high static instability, and strong nonlinear characteristics. This poses a significant challenge to the design of control systems on UAVs, attracting the attention of many researchers.

[0003] Traditional PID controllers are mostly suitable for linear or near-linear systems. However, for complex time-varying nonlinear systems like unmanned aerial vehicles (UAVs), their control performance is poor under conditions of significant disturbances. Furthermore, the controller design is highly dependent on the accuracy of the system model and the engineer's experience, resulting in poor portability. Sliding mode controllers, based on the switching characteristics of the sliding surface switching function, can achieve stable control while ensuring the system reaches the sliding surface. They possess strong robustness and fast response tracking capabilities, and have received widespread attention and application in engineering.

[0004] When sliding mode controllers are applied to the design of unmanned aerial vehicle (UAV) control systems, there are issues with design matching and chattering on the sliding surface switching function. Currently, the design of sliding mode controllers for UAVs mainly includes the following categories:

[0005] One type is based on attitude angle loop to carry out sliding mode controller design, using attitude angle as feedback and actuator action as input to build control system design, so as to achieve precise control of the flight attitude of unmanned aerial vehicle. However, the feedback observation of unmanned aerial vehicles currently all use maneuver overload information. The above-mentioned attitude angle control design scheme requires a calculation to obtain maneuver overload information, which greatly affects the response speed of the overload control system.

[0006] The second category addresses the chattering phenomenon in sliding mode controllers. Different experts and scholars have proposed different approach law design methods, such as exponential approach law and power approach law. These methods have alleviated the chattering phenomenon in sliding mode controllers to some extent, but they have affected the controller's response capability and steady-state accuracy.

[0007] Currently, the application of sliding mode controllers in unmanned aerial vehicles (UAVs) is mainly based on traditional sliding mode controllers, which do not simultaneously consider the system modeling deviations of UAVs and the situation of strong external interference, thus limiting the application of sliding mode controllers in UAVs.

[0008] Therefore, the existing sliding mode controllers used in unmanned aerial vehicles cannot adapt to the strong interference environment of complex nonlinear systems. Summary of the Invention

[0009] In order to solve at least one of the above-mentioned technical problems in the prior art, the present invention provides an adaptive robust anti-jitter sliding mode control method for unmanned aerial vehicles.

[0010] This invention is achieved using the following technical solution: an adaptive robust anti-bounce sliding mode control method for unmanned aerial vehicles, comprising the following steps:

[0011] S1: Based on the dynamic, aerodynamic and structural parameters of the unmanned aerial vehicle, determine the functional relationship between pitch deflection and maneuver overload, and establish the kinematic equation of the unmanned aerial vehicle based on Newton's second law, and then construct the system state equation with pitch deflection as the control input and maneuver overload as the feedback.

[0012] S2: Introduce system modeling errors and external disturbances into the system state equations to establish enhanced system state equations that meet uncertainty conditions;

[0013] S3: Determine the sliding mode switching function based on the principle of the sliding mode controller;

[0014] S4: Based on the enhanced system state equation and the sliding mode switching function, an adaptive law is designed to estimate the system modeling error online, and the adaptive sliding mode control law is derived based on Lyapunov stability theory;

[0015] S5: Based on the adaptive sliding mode control law and the external disturbance, construct an adaptive robust sliding mode controller, and replace the constant velocity reaching law at the output of the adaptive robust sliding mode controller with a second-order super-helical sliding mode reaching law to obtain the final adaptive robust anti-jerk sliding mode control command, and output the adaptive robust anti-jerk sliding mode control command to the actuator of the unmanned aerial vehicle.

[0016] Preferably, the functional relationship between the pitch deflection angle and the maneuver overload in step S1 is as follows:

[0017]

[0018] In the formula, For engine thrust, For the angle of attack of the unmanned aerial vehicle, This is the aerodynamic normal force coefficient. Mach number, The pitch deflection angle of the unmanned aerial vehicle. For the dynamic pressure of unmanned aerial vehicles, For reference area, For the quality of unmanned aerial vehicles, It is the acceleration due to gravity. The ballistic pitch angle of the unmanned aerial vehicle. This indicates motor overload.

[0019] Preferably, the system state equation in step S1, with pitch deflection as the control input and maneuver overload as the feedback, is as follows:

[0020]

[0021] In the formula, The first derivative of the position state quantity; Represents velocity state variables; This indicates a motor overload; , represents the coefficient of the constant term. The ideal control coefficients are represented by the control coefficients and the constant term coefficients, which are calculated from the dynamic parameters, aerodynamic parameters and structural parameters of the unmanned aerial vehicle. , which represents the pitch deflection angle of the unmanned aerial vehicle.

[0022] Preferably, the enhanced system state equation in step S2 is:

[0023]

[0024] In the formula, This represents the control coefficient considering system modeling errors. This indicates the system modeling error. External disturbances that change in real time.

[0025] Preferably, the sliding mode switching function is:

[0026]

[0027] In the formula, This represents the control gain of the adaptive robust sliding mode controller; Indicates system state error. , Indicates the position status quantity. Indicates the control input of the controller; The first derivative represents the system state error.

[0028] Preferably, step S4 specifically includes:

[0029] The enhanced system state equation is updated based on the error boundary of the control coefficients under the system modeling error;

[0030] Based on the updated enhanced system state equation and sliding mode switching function, a Lyapunov function is defined, and Lyapunov stability is determined.

[0031] Based on the criterion that the Lyapunov stability judgment result is less than 0, and combined with the constant velocity approach law, the adaptive sliding mode control law is derived.

[0032] Preferably, the control input of the adaptive sliding mode control law is:

[0033]

[0034] In the formula, express The estimated value, ; Indicates the design parameters of the sliding mode controller's reaching law; Represents a symbolic function; This indicates the pitch deflection angle of an unmanned aerial vehicle (UAV).

[0035] Preferably, in step S5, an adaptive robust sliding mode controller is constructed based on the adaptive sliding mode control law and the external disturbance, including:

[0036] Based on the determination criteria of the Lyapunov stability theory, the disturbance control quantity corresponding to the external disturbance and the control quantity coefficient considering the system modeling error are analyzed, and an adaptive robust sliding mode controller is constructed based on the analysis results.

[0037] Preferably, the second-order superspiral sliding mode reaching law in step S5 is:

[0038]

[0039] In the formula, The first design parameter is the second-order superspiral sliding mode reaching law. All of these are the second design parameters of the second-order superspiral sliding mode reaching law. This refers to the reaching law control variable portion of the control variable.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This application provides an adaptive robust anti-bluster sliding mode control method for unmanned aerial vehicles (UAVs). By constructing a system state equation based on kinematic and dynamic equations, an adaptive mechanism is introduced to estimate and compensate for system modeling deviations in real time. Combined with a robust control strategy to cope with strong external disturbances, and a second-order super-spiral sliding mode reaching law is used to replace the traditional reaching law, effectively suppressing the chattering phenomenon. Thus, while ensuring the rapid response of the control system, the maneuverability and trajectory tracking accuracy of the UAV in complex nonlinear and strong disturbance environments are significantly improved. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic flowchart of an adaptive robust anti-bounce sliding mode control method for an unmanned aerial vehicle provided in an embodiment of the present invention;

[0044] Figure 2 This is an example of how the system follows instructions under conditions of modeling deviation and external interference, as provided by this invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should fall within the scope of the technical content disclosed in the present invention. It should be noted that in this specification, relational terms such as "first" and "second" are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0047] This invention provides an embodiment:

[0048] like Figure 1 As shown in the figure, this embodiment of the invention provides a flowchart of an adaptive robust anti-bounce sliding mode control method for an unmanned aerial vehicle, which includes the following steps:

[0049] S1: Based on the dynamic, aerodynamic and structural parameters of the unmanned aerial vehicle (UAV), determine the functional relationship between pitch deflection and maneuver overload, and establish the kinematic equations of the UAV based on Newton's second law. Then, construct the system state equations with pitch deflection as the control input and maneuver overload as the feedback.

[0050] In this embodiment, the equation relationship between pitch deflection and maneuver overload information of the UAV under different ballistic characteristic points is first determined based on the UAV's dynamic parameters, aerodynamic parameters and structural parameters; then, the kinematic equation of the UAV is established according to Newton's second law; finally, the system state equation is established based on the kinematic equation, with the actuator action as input and the maneuver overload information as feedback.

[0051] In this embodiment, based on the dynamic parameters, aerodynamic parameters, and structural parameters of the unmanned aerial vehicle, the equation relationship between the pitch deflection angle and maneuver overload under different ballistic characteristics is determined as follows:

[0052]

[0053] In the formula, For engine thrust, For the angle of attack of the unmanned aerial vehicle, This is the aerodynamic normal force coefficient. Mach number, The pitch deflection angle of the unmanned aerial vehicle. For the dynamic pressure of unmanned aerial vehicles, For reference area, For the quality of unmanned aerial vehicles, It is the acceleration due to gravity. The ballistic pitch angle of the unmanned aerial vehicle.

[0054] Taking into full account the short duration of the unmanned aerial vehicle's dynamic process, the coefficient freezing method can be used to calculate the above relationship expression, thereby determining the functional relationship between maneuver overload and pitch deflection angle in the actuator as follows:

[0055]

[0056] In the formula, For engine thrust, For the angle of attack of the unmanned aerial vehicle, This is the aerodynamic normal force coefficient. Mach number, The pitch deflection angle of the unmanned aerial vehicle. For the dynamic pressure of unmanned aerial vehicles, For reference area, For the quality of unmanned aerial vehicles, It is the acceleration due to gravity. The ballistic pitch angle of the unmanned aerial vehicle. This indicates motor overload.

[0057] In this embodiment, after determining the relationship between the maneuver overload and the pitch deflection angle of the actuator, the system state equation of the sliding mode controller is established. The system state equation is as follows:

[0058]

[0059] In the formula, The first derivative of the position state quantity; Represents velocity state variables; , representing the first derivative of the velocity state quantity; This represents the ideal control coefficient. ; , representing the constant term coefficient, the control quantity coefficient and the constant term coefficient are calculated from the dynamic parameters, aerodynamic parameters and structural parameters of the unmanned aerial vehicle; , which represents the pitch deflection angle of the unmanned aerial vehicle.

[0060] S2: Introduce system modeling errors and external disturbances into the system state equations to establish enhanced system state equations that meet uncertainty conditions.

[0061] In this embodiment, when establishing the kinematic model of the unmanned aerial vehicle, the deviation of aerodynamic parameters is the main influencing factor, which will lead to the inaccuracy of the system state equation modeling. At the same time, the unmanned aerial vehicle has large external disturbances during flight. Due to the existence of external disturbances, the establishment of the system state equation must take into account the impact of external disturbances.

[0062] In this embodiment, the enhanced system state equation considering the modeling error of the unmanned aerial vehicle system and external disturbances is as follows:

[0063]

[0064] In the formula, This represents the control coefficient considering system modeling errors. This indicates the system modeling error. External disturbances that change in real time.

[0065] S3: Determine the sliding mode switching function based on the principle of the sliding mode controller.

[0066] In this embodiment, based on the principle of sliding mode controller, a sliding surface switching function is defined, and the sliding mode switching function is determined in combination with the enhanced system state equation.

[0067] Sliding mode switching function This establishes the design approach for the subsequent design of the sliding mode controller, and its expression is:

[0068]

[0069] In the formula, This represents the control gain of the adaptive robust sliding mode controller; Indicates system state error. , Indicates the position status quantity. Indicates the control input of the controller; The first derivative represents the system state error.

[0070] S4: Based on the enhanced system state equation and the sliding mode switching function, an adaptive law is designed to estimate the system modeling error online, and the adaptive sliding mode control law is derived based on Lyapunov stability theory.

[0071] In this embodiment, the deviation between the aerodynamic parameter design and the actual situation is fully considered, and such deviation is generally bounded. Therefore, the adaptive control law of the adaptive robust sliding mode controller is calculated based on the boundedness condition. After designing a suitable reaching law, Lyapunov stability is used for judgment, and finally the design of the adaptive sliding mode control law is completed.

[0072] Optionally, the specific steps include: updating the enhanced system state equation based on the boundary of the gain coefficient under the system modeling error; defining a Lyapunov function based on the updated enhanced system state equation and the sliding mode switching function, and performing a Lyapunov stability judgment; deriving an adaptive sliding mode control law based on the judgment criterion that the Lyapunov stability judgment result is less than 0, and in combination with the constant velocity approach law.

[0073] In this embodiment, considering system modeling errors, the equivalent control law is calculated in the following way:

[0074] 1) Determine the control coefficients considering system modeling errors. Boundary:

[0075]

[0076] In the formula, This is the lower limit of the system error parameter. This represents the upper limit of the system error parameter.

[0077] 2) The state equation of the enhanced system is updated as follows:

[0078]

[0079] in, They represent the control coefficients respectively. The first simplified parameter set is based on the control coefficient. The second simplified parameter is set along with the constant term coefficients and the real-time varying external disturbances, where, , ,use The subsequent calculation process is simplified to facilitate subsequent analysis.

[0080] 3) Based on the sliding mode switching function Determine the system state error , Represents the control system command, while also in the control gain of the adaptive robust sliding mode controller. Under the given conditions, the following deformation is performed:

[0081]

[0082] In the formula, It represents the first derivative of the external disturbance in the system.

[0083] 4) Settings For the first simplified parameter The estimated value is defined by the Lyapunov function. for:

[0084]

[0085] In the formula, The cross-term coefficients of the constructed Lyapunov function are used to determine the effective region of stability; express The deviation between the estimated value and the true value, ;

[0086] 5) Perform Lyapunov stability assessment The expression is:

[0087]

[0088] In the formula, Represents the sliding mode switching function The first derivative; express The first derivative of the deviation between the estimated value and the true value; This represents the second derivative of the controller input command.

[0089] Furthermore, based on the Lyapunov stability criterion expression, it can be simplified to:

[0090]

[0091] 6) Adopt the constant-rate approach law Design methodology, combined with Lyapunov stability assessment The criterion takes into account the second derivative of the controller input command. It can be equivalent to zero, and ignores real-time changes in external disturbances. In this case, the control input is:

[0092]

[0093] In the formula, This represents the design parameters of the constant-rate approaching law.

[0094] 7) Substitute the calculated control input results into the Lyapunov stability test. From the criterion, we obtain the following formula:

[0095]

[0096] In the formula, Indicates parameters The first derivative.

[0097] 8) Based on the above analysis results, the Lyapunov function It is bounded, the first simplified parameter The estimated value It is bounded, but it cannot be shown that... It will approach the true value. According to the LaSalle invariance principle, when the constant-rate approach law... When approaching infinity, the constant velocity approaching law in It will tend to zero, but there is no guarantee that the estimated value will be zero. The value tends to zero, therefore, to prevent the estimated value from being zero, When the condition of exceeding the limit occurs, adaptive rate design is needed to make the estimated value... Changes in Within the specified range, an adaptive correction method is defined as follows:

[0098]

[0099] 9) In summary, the calculation results of the adaptive sliding mode control law under the constant velocity approach law are summarized as follows:

[0100]

[0101] In the formula, This represents the discretization step size of a continuous system.

[0102] In summary, the design of an adaptive sliding mode control law under system modeling bias was achieved.

[0103] S5: Based on the adaptive sliding mode control law and the external disturbance, construct an adaptive robust sliding mode controller, and replace the constant velocity reaching law at the output of the adaptive robust sliding mode controller with a second-order super-helical sliding mode reaching law to obtain the final adaptive robust anti-jerk sliding mode control command, and output the adaptive robust anti-jerk sliding mode control command to the actuator of the unmanned aerial vehicle.

[0104] Optionally, in step S5, an adaptive robust sliding mode controller is constructed based on the adaptive sliding mode control law and the external disturbance, including: analyzing the disturbance control quantity corresponding to the external disturbance and the control quantity coefficient considering the system modeling error based on the determination criteria of the Lyapunov stability theory, and constructing an adaptive robust sliding mode controller based on the analysis results.

[0105] In this embodiment, a disturbance boundary is set to address the strong external interference present during the ballistic flight of the unmanned aerial vehicle. Based on the set disturbance boundary, an adaptive robust sliding mode controller is designed.

[0106] According to the updated enhanced system state equations, the external disturbance exists in the second simplified parameter. Therefore, in the Lyapunov stability judgment expression... In the middle, if external disturbances lead to This will lead to instability in the adaptive robust sliding mode controller. Therefore, when designing an adaptive robust sliding mode controller, it is necessary to perform system robustness design. The design process of an adaptive robust sliding mode controller considering external disturbances is as follows:

[0107] 1) The criteria for determining the stability of the Lyapunov stability theory of the adaptive robust sliding mode controller under external disturbances have been updated as follows:

[0108]

[0109] The updated expression for the Lyapunov stability criterion can be derived. The conditions are ,when The sign is determined only in relation to real-time changing external disturbances. Related.

[0110] 2) Real-time changing external disturbances This is an unknown factor, but considering the ballistic flight characteristics of general unmanned aerial vehicles, the external interference factor is bounded. ,in, This represents a lower bound for known external disturbances; This represents the upper bound of a known perturbation.

[0111] 3) Assume that the control coefficients under system modeling error are considered. Under the condition of setting Current Improved Sliding Mode Controller As shown below:

[0112] when At that time, in order to make To ensure the stability of the adaptive robust sliding mode controller, let ;

[0113] when At that time, in order to make To ensure the stability of the adaptive robust sliding mode controller, ;

[0114] Based on the above logical relationship, the improved sliding mode controller can be derived. The design results are as follows:

[0115]

[0116] 4) Based on the above calculation process for the improved sliding mode controller, the total disturbance control quantity of the adaptive robust sliding mode controller is calculated as follows:

[0117]

[0118] In this embodiment, taking into full account the chattering phenomenon of the adaptive robust sliding mode controller caused by the constant velocity approach law design, the approach method adopts a second-order superspiral sliding mode approach law design based on the adaptive robust sliding mode control law.

[0119] In this embodiment, the superspiral algorithm is used to enable the system state to not only quickly approach the sliding surface, but also to have better stability and anti-interference ability during the approach process. By introducing a combination of integral and nonlinear terms, the system state can be quickly adjusted and accurately tracked, while effectively suppressing the chattering problem caused by high-frequency switching.

[0120] In this embodiment, the constant velocity reaching law is used at the output of the adaptive robust sliding mode controller. The law is changed to a second-order superspiral sliding mode reaching law composed of integral and nonlinear terms, and its reaching method is shown in the following equation:

[0121]

[0122] In the formula, The first design parameter is the second-order superspiral sliding mode reaching law. All are the second design parameters of the second-order superspiral sliding mode approach law; the first term accelerates the approach speed of the controller to the sliding surface, and the second term is used to avoid the chattering effect of the adaptive robust sliding mode controller. By reasonably configuring the first and second design parameters, the chattering effect of the controller can be suppressed without affecting the performance of the controller. This refers to the reaching law control variable portion of the control variable.

[0123] The second-order superspiral sliding mode approach law and , Substituting these values ​​into the total disturbance control formula, we can obtain the adaptive robust anti-chattering sliding mode control command for the adaptive robust sliding mode controller applied to unmanned aerial vehicles as follows:

[0124]

[0125] In summary, an adaptive robust anti-jitter sliding mode controller for unmanned aerial vehicles was designed.

[0126] Figure 2 The figure shows a performance comparison between the proposed adaptive robust anti-bounce sliding mode control method for unmanned aerial vehicles (UAVs) and the classic PID control scheme in related technologies under system modeling bias and external disturbances. It can be seen that the proposed method has significant advantages in terms of speed and stability. In the figure, the input command is the maneuver overload command for the tracking control of the UAV, used to test the control system's fast response capability, tracking accuracy, and stability.

[0127] Using the above method, the kinematic equations of the UAV are calculated based on its aerodynamic, dynamic, and structural parameters. The system state equations of the sliding mode controller are then established based on the kinematic equations. Taking into full account the modeling deviations of the UAV system, external disturbance deviations, and chattering effects, an adaptive robust anti-chattering sliding mode controller is designed, which improves the speed and stability of the UAV's maneuvering process.

[0128] This invention utilizes the kinematic and dynamic equations of unmanned aerial vehicles (UAVs) to design a sliding mode controller in the time domain. By fully considering the system modeling deviations of the UAV system and the limits of strong external disturbances, it can calculate effective control commands in real time based on the current flight information of the UAV, thereby achieving stable flight and rapid tracking of maneuver overload commands. At the same time, it enables the UAV to fly along a predetermined ballistic trajectory, improving the stability of the UAV during maneuvers.

[0129] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An adaptive robust anti-chattering sliding mode control method for unmanned aerial vehicles, characterized in that, Includes the following steps: S1: Based on the dynamic, aerodynamic and structural parameters of the unmanned aerial vehicle, determine the functional relationship between pitch deflection and maneuver overload, and establish the kinematic equation of the unmanned aerial vehicle based on Newton's second law, and then construct the system state equation with pitch deflection as the control input and maneuver overload as the feedback. The functional relationship between pitch deflection and maneuver overload is as follows: In the formula, For engine thrust, For the angle of attack of the unmanned aerial vehicle, This is the aerodynamic normal force coefficient. Mach number, The pitch deflection angle of the unmanned aerial vehicle. For the dynamic pressure of unmanned aerial vehicles, For reference area, For the quality of unmanned aerial vehicles, It is the acceleration due to gravity. The ballistic pitch angle of the unmanned aerial vehicle. Indicates motor overload; The system state equation, with pitch deflection as the control input and maneuver overload as the feedback, is shown below: In the formula, The first derivative of the position state quantity; Represents velocity state variables; This indicates a motor overload; , represents the coefficient of the constant term. The ideal control coefficient is represented by the ideal control coefficient and the constant term coefficient, which are calculated from the dynamic parameters, aerodynamic parameters and structural parameters of the unmanned aerial vehicle. , indicating the pitch deflection angle of the unmanned aerial vehicle; S2: Introduce system modeling errors and external disturbances into the system state equations to establish enhanced system state equations that meet uncertainty conditions; The state equation of the enhanced system is: In the formula, This represents the control coefficient considering system modeling errors. This indicates the system modeling error. For external disturbances that change in real time; S3: Determine the sliding mode switching function based on the principle of the sliding mode controller; The sliding mode switching function is: In the formula, This represents the control gain of the adaptive robust sliding mode controller; Indicates system state error. , Indicates the position status quantity. Indicates the control input of the controller; The first derivative represents the system state error. This is the sliding mode switching function; S4: Based on the enhanced system state equation and the sliding mode switching function, an adaptive law is designed to estimate the system modeling error online, and the adaptive sliding mode control law is derived based on Lyapunov stability theory; The control input for the adaptive sliding mode control law is: In the formula, express The estimated value, , Indicates based on control quantity coefficient The first simplified parameter is set; Indicates the design parameters of the sliding mode controller's reaching law; Represents a symbolic function; Indicates the pitch deflection angle of an unmanned aerial vehicle; S5: Based on the adaptive sliding mode control law and the external disturbance, an adaptive robust sliding mode controller is constructed. At the same time, the constant velocity approach law at the output of the adaptive robust sliding mode controller is replaced with a second-order super-helical sliding mode approach law to obtain the final adaptive robust anti-jerk sliding mode control command. The adaptive robust anti-jerk sliding mode control command is then output to the actuator of the unmanned aerial vehicle.

2. The adaptive robust anti-bounce sliding mode control method for an unmanned aerial vehicle according to claim 1, characterized in that, Step S4 specifically includes: The enhanced system state equation is updated based on the error boundary of the control coefficients under the system modeling error; Based on the updated enhanced system state equation and sliding mode switching function, a Lyapunov function is defined, and Lyapunov stability is determined. Based on the criterion that the Lyapunov stability judgment result is less than 0, and combined with the constant velocity approach law, the adaptive sliding mode control law is derived.

3. The adaptive robust anti-bounce sliding mode control method for an unmanned aerial vehicle according to claim 1, characterized in that, Step S5 involves constructing an adaptive robust sliding mode controller based on the adaptive sliding mode control law and the external disturbance, including: Based on the determination criteria of the Lyapunov stability theory, the disturbance control quantity corresponding to the external disturbance and the control quantity coefficient considering the system modeling error are analyzed, and an adaptive robust sliding mode controller is constructed based on the analysis results.

4. The adaptive robust anti-chattering sliding mode control method for an unmanned aerial vehicle according to claim 1, characterized in that, The second-order superspiral sliding mode reaching law mentioned in step S5 is: In the formula, The first design parameter is the second-order superspiral sliding mode reaching law. All of these are the second design parameters of the second-order superspiral sliding mode reaching law. This refers to the reaching law control variable portion of the control variable.

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