Wind turbine blade trajectory tracking four-rotor unmanned aerial vehicle active disturbance rejection control method

Abstract

The present application relates to a kind of wind turbine blade trajectory tracking four-rotor unmanned aerial vehicle self-disturbance control method, method includes: the mathematical model of four-rotor unmanned aerial vehicle and wind turbine blade including interference function is established;Virtual variable design input parameter is introduced, constructs four-rotor unmanned aerial vehicle flight control framework;The coupling relationship between four-rotor unmanned aerial vehicle and wind turbine blade is established, and the double closed loop self-disturbance control of four-rotor unmanned aerial vehicle is carried out;Wherein, double closed loop self-disturbance control algorithm adopts tracking differentiator to suppress environmental condition disturbance, adopts extended state observer to suppress uncertainty disturbance, the final four-rotor unmanned aerial vehicle control input is calculated by linear feedback law, realize the control to four-rotor unmanned aerial vehicle.Compared with prior art, the present application makes four-rotor unmanned aerial vehicle still be able to realize reasonable and effective tracking control in the process of tracking wind turbine blade trajectory under the condition of being disturbed, improves the fault tolerance and robustness of four-rotor unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quadrotor unmanned aerial vehicle system control, and particularly relates to a quadrotor unmanned aerial vehicle active disturbance rejection control method for tracking wind turbine blade trajectories. BACKGROUND

[0002] In recent years, wind turbines have received increasing attention and been widely used as a sustainable, clean, and renewable source of energy. Compared with traditional fossil fuel power generation equipment, wind turbines not only significantly reduce greenhouse gas emissions but also effectively reduce noise pollution, contributing to global ecological environmental protection. This makes wind energy gradually become an indispensable part of the modern energy structure. Ensuring the safety and efficient operation of wind turbines is crucial to guarantee power generation efficiency. To achieve this goal, wind turbines need to be regularly maintained and inspected to ensure their optimal operation. Quadrotor unmanned aerial vehicles, with their flexibility, easy control, and ease of maintenance, have become a common tool for detecting wind turbine blade parts. During the detection process, stable flight of the quadrotor unmanned aerial vehicle is a key factor.

[0003] Currently, there have been a large number of studies on tracking control strategies using quadrotor unmanned aerial vehicles. However, existing methods, such as sliding mode control, often rely too much on the mathematical model of the controlled object, and in the case of strong model uncertainty, the control effect often cannot achieve an ideal state. When the dynamics model of the quadrotor unmanned aerial vehicle or the external environment changes, traditional control algorithms may fail to accurately reflect these changes, resulting in increased tracking errors. In addition, current quadrotor unmanned aerial vehicle control strategies have a large impact on external disturbances when dealing with uncertain environmental conditions, such as unpredictable wind speed changes and weather conditions. Conventional control strategies such as PID control often fail to compensate for external disturbance quantities, which makes the control effect of the quadrotor unmanned aerial vehicle often unsatisfactory, limiting the reliability and effectiveness of the quadrotor unmanned aerial vehicle in practical applications.

[0004] Chinese patent application CN121050436A discloses a quadrotor unmanned aerial vehicle trajectory tracking control method, a quadrotor unmanned aerial vehicle and a storage medium, and relates to the technical field of unmanned aerial vehicle control. The invention proposes a fast terminal sliding mode disturbance observer for unknown disturbances of the system to achieve accurate estimation of disturbances within a predetermined time. Then, a predetermined time fast terminal sliding mode controller is designed based on disturbance estimation information, and further utilizes a saturation function to weaken the chattering phenomenon of the system. Finally, the system can achieve global predetermined time stability based on the Lyapunov function method. The invention can achieve predefined time convergence of the quadrotor unmanned aerial vehicle trajectory tracking error under unknown disturbances, and the upper bound of the stability time can be arbitrarily preset within the physical limit range to ensure the rapidity of the system and eliminate chattering, thereby improving the stability of the system. However, in the invention, only the unknown disturbances such as external wind, electromagnetic interference and model uncertainty that the quadrotor unmanned aerial vehicle itself may encounter during flight are considered in the control, and the disturbances that the tracking target may encounter during tracking and the disturbances generated by the tracking target to the unmanned aerial vehicle are also considered in the control. When facing a tracking target such as a wind turbine blade that may have a wind field and may be deformed and displaced due to the influence of wind, the tracking error is large.

[0005] Therefore, in order to realize stable flight of the quadrotor unmanned aerial vehicle during tracking of the motion trajectory of the wind turbine blade, a quadrotor unmanned aerial vehicle trajectory tracking control method that considers both unpredictable environmental factors faced by the quadrotor unmanned aerial vehicle and disturbances that the wind turbine blade may encounter is needed. SUMMARY

[0006] The purpose of the present application is to overcome the defects of the prior art and provide a quadrotor unmanned aerial vehicle active disturbance rejection control method for tracking the trajectory of a wind turbine blade. The tracking error generated by the wind turbine blade is also included in the error estimation of the quadrotor unmanned aerial vehicle, and high-precision tracking of the trajectory of the wind turbine blade is achieved.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] A quadrotor unmanned aerial vehicle active disturbance rejection control method for tracking the trajectory of a wind turbine blade, the method comprising:

[0009] Based on actual quadrotor unmanned aerial vehicle and wind turbine data, a mathematical model of the quadrotor unmanned aerial vehicle and the wind turbine blade containing disturbance functions is established for the uncertain disturbances encountered by the quadrotor unmanned aerial vehicle during actual external flight tracking and the environmental condition disturbances encountered by the wind turbine blade;

[0010] Based on the mathematical model and the under-actuated nature of the quadrotor unmanned aerial vehicle, a virtual variable is introduced to design input parameters, and a quadrotor unmanned aerial vehicle flight control framework is constructed;

[0011] establish a coupling relationship between the quadrotor unmanned aerial vehicle and the wind turbine blade, based on a double closed-loop active disturbance rejection control algorithm, utilize a quadrotor unmanned aerial vehicle flight control framework and mathematical models of the quadrotor unmanned aerial vehicle and the wind turbine blade to perform double closed-loop active disturbance rejection control of the quadrotor unmanned aerial vehicle; wherein,

[0012] The uncertainty disturbance includes environmental airflow disturbance, measurement system disturbance, and communication and equipment disturbance, the environmental condition disturbance includes airflow disturbance, structure and vibration disturbance, and additional disturbance, the double closed-loop active disturbance rejection control algorithm adopts a tracking differentiator to suppress the environmental condition disturbance, adopts an extended state observer to suppress the uncertainty disturbance, and calculates a final quadrotor unmanned aerial vehicle control input through a linear feedback law to realize control of the quadrotor unmanned aerial vehicle.

[0013] Further, the environmental airflow disturbance includes random gusts, blade wake turbulence, sustained wind field wind speed, and slow wind direction drift.

[0014] The measurement system disturbance includes GPS signal shielding, multipath effect, IMU zero drift, temperature drift, and visual sensor noise.

[0015] The communication and equipment disturbance includes electromagnetic interference, motor output fluctuation, and battery voltage attenuation.

[0016] The airflow disturbance includes lateral / longitudinal gust impact and atmospheric turbulence action.

[0017] The structure and vibration disturbance includes blade vibration and tower body sway.

[0018] The additional disturbance includes blade surface attachments and blade fatigue damage.

[0019] Further, the disturbance function includes a quadrotor unmanned aerial vehicle disturbance function and a wind turbine blade disturbance function; wherein,

[0020] The quadrotor unmanned aerial vehicle disturbance function is constructed based on the influence of uncertainty disturbance on quadrotor unmanned aerial vehicle y-axis and z-axis position variables and yaw angle, and the wind turbine blade disturbance function is constructed based on the influence of environmental condition disturbance on wind turbine blade y-axis and z-axis directions.

[0021] The expression of the quadrotor unmanned aerial vehicle disturbance function is:

[0022] ;

[0023] wherein, is the disturbance on the z-axis position variable of the quadrotor unmanned aerial vehicle, is the disturbance on the y-axis position variable of the quadrotor unmanned aerial vehicle, an intensity amplitude calibration value of an interference received by a yaw angle of the quadrotor UAV, and an intensity amplitude calibration value of an interference received by a z-axis position variable of the UAV, and an intensity amplitude calibration value of an interference received by a y-axis position variable of the UAV, an intensity amplitude calibration value of an environmental airflow interference received by a yaw angle of the UAV, a fluctuation frequency of the environmental airflow interference, an intensity amplitude calibration value of a measurement system interference or a communication and device interference received by a yaw angle of the UAV, t a current time;

[0024] an expression of the wind turbine blade interference function is:

[0025] ;

[0026] wherein, an interference received by the wind turbine blade in a y-axis direction of the earth coordinate system, an interference received by the wind turbine blade in a z-axis direction of the earth coordinate system, a maximum deviation amplitude of the wind turbine blade in the y-axis direction under the interference, a maximum deviation amplitude of the wind turbine blade in the z-axis direction under the interference.

[0027] Further, in the quadrotor UAV interference function, the intensity amplitude calibration values of the interferences received by the z-axis and y-axis position variables, the intensity amplitude calibration value of the environmental airflow interference received by the yaw angle, and the intensity amplitude calibration value of the measurement system interference or the communication and device interference are obtained based on measured interference data of the UAV in a target wind farm and combined with device parameters of the UAV; in the wind turbine blade interference function, the maximum deviation amplitudes of the wind turbine blade in the y-axis direction and the z-axis direction under the interference are obtained by collecting the maximum deviation amplitudes in the y-axis direction and the z-axis direction when the blade is stably rotating and fitting.

[0028] Further, the mathematical model of the wind turbine blade is established by using a mathematical geometric analysis method based on a horizontal direction component and a vertical direction component of a rotation track of the wind turbine blade, a length of the wind turbine blade, an angular velocity of rotation of the wind turbine blade, and a height of a tower of the wind turbine.

[0029] The mathematical model of the quadrotor UAV is established by using Newton-Euler equation based on position variables, Euler angles, force components of total lift on the position variables, lift generated by a motor, moment of inertia, air resistance coefficients, and torque coefficients of the quadrotor UAV.

[0030] Further, the process of establishing the coupling relationship between the quadrotor unmanned aerial vehicle and the wind turbine blade comprises:

[0031] binding the variable of the wind turbine blade in the vertical direction on the earth coordinate system with the variable of the quadrotor unmanned aerial vehicle in the vertical direction on the earth coordinate system, and binding the variable of the wind turbine blade in the horizontal direction on the earth coordinate system with the variable of the quadrotor unmanned aerial vehicle in the horizontal direction on the earth coordinate system;

[0032] keeping the quadrotor unmanned aerial vehicle variable unchanged, considering the uncertain disturbance encountered by the quadrotor unmanned aerial vehicle in the actual external flight tracking process and the environmental condition disturbance suffered by the wind turbine blade, making the quadrotor unmanned aerial vehicle maintain a stable attitude during flight and track the trajectory of the wind turbine blade in real time, and establishing the coupling relationship between the quadrotor unmanned aerial vehicle and the wind turbine blade.

[0033] Further, the quadrotor unmanned aerial vehicle flight control framework realizes the control of the under-actuated system of the quadrotor unmanned aerial vehicle by introducing six virtual variables and establishing the control relationship between the virtual variables and the quadrotor unmanned aerial vehicle model, and utilizing the conversion relationship between the variables.

[0034] Further, the specific process of the double-loop active disturbance rejection control of the quadrotor unmanned aerial vehicle through the double-loop active disturbance rejection control algorithm comprises:

[0035] collecting the interference trajectory of the wind turbine blade and the real-time state of the unmanned aerial vehicle;

[0036] suppressing the environmental condition disturbance in the interference trajectory through the tracking differentiator, and extracting the smooth wind turbine blade expected trajectory;

[0037] regarding the uncertain disturbance as a new state variable by using the extended state observer, extending the observation of the real-time state of the unmanned aerial vehicle, and observing the uncertain disturbance, the position of the unmanned aerial vehicle and the speed of the unmanned aerial vehicle at the same time, and establishing the ESO observation equation;

[0038] combining the wind turbine blade expected trajectory and the ESO observation equation, generating a virtual input through a linear feedback law, and converting the virtual input into the actual lift of the four motors of the quadrotor unmanned aerial vehicle through the quadrotor unmanned aerial vehicle flight control framework and the mathematical model of the quadrotor unmanned aerial vehicle and the wind turbine blade, to realize under-actuated control.

[0039] Further, the method further comprises: constructing a system state error equation, and proving that the double-loop active disturbance rejection control algorithm can realize stable tracking under disturbance by solving the sufficient condition for the asymptotic stability of the error system.

[0040] Still further, the system state error equation comprises an extended state observer observation error and a tracking differentiator trajectory tracking error.

[0041] The process of constructing the system state error equation and solving the sufficient condition of the asymptotic stability of the error system comprises:

[0042] The extended state observer observation error and the tracking differentiator trajectory tracking error are defined respectively, the system state error equation is established according to the double closed loop active disturbance rejection control algorithm, the system state error equation is expanded through a linear feedback law and solved to obtain a tracking error inequality;

[0043] Based on the tracking error inequality, it is judged whether the tracking error tends to a bounded value when the pole configuration parameters of the extended state observer and the tracking differentiator are increased, and if the tracking error tends to a bounded value, it is proved that the double closed loop active disturbance rejection control algorithm can realize stable tracking under disturbance.

[0044] Compared with the prior art, the beneficial effects of the present application include:

[0045] 1. The present application proposes a four-rotor unmanned aerial vehicle active disturbance rejection control method specially for wind turbine blade trajectory tracking, so that the four-rotor unmanned aerial vehicle can still realize reasonable and effective tracking control under the condition of disturbance in the process of tracking the wind turbine blade trajectory, and the fault tolerance and robustness of the four-rotor unmanned aerial vehicle are improved; the control method in the present application can cope with multiple types of interference at the same time, including environmental airflow interference of the four-rotor unmanned aerial vehicle itself, measurement system interference, communication and equipment interference, and airflow interference, structural and vibration interference and additional interference of the wind turbine blade, the disturbance is estimated in real time through the tracking differentiator and the extended state observer (ESO), and the influence can be quickly compensated through feedback after the disturbance occurs, the tracking error convergence time is shortened compared with the traditional PID control, even under complex random disturbance, the system tracking error can still be kept within a bounded range, the maximum error amplitude is reduced compared with the PID control, and the disturbance suppression effect is stable; the present application provides reliable technical support for wind turbine blade detection, the unmanned aerial vehicle can stably track the rotating blade, and the detection efficiency and safety are improved.

[0046] 2. The present application designs a targeted interference function model, accurately depicts the interference types of the unmanned aerial vehicle and the blade, i.e. position interference, attitude interference and trajectory interference, realizes quantitative description of the interference, the tracking differentiator purifies the effective signal from the blade trajectory containing interference, the extended state observer estimates the interference in real time as an independent state variable, and the combination of the two realizes the closed-loop processing of interference prediction-signal purification-real-time compensation, which is more comprehensive than the traditional single feedback disturbance rejection, and the interference compensation method in the present application is more direct, the total disturbance value estimated by the extended state observer is directly included in the control input to offset the influence of the interference on the system, instead of indirect adjustment after error accumulation, and the timeliness of disturbance rejection is greatly improved.

[0047] 3. The application establishes a linear mapping relationship between virtual input and motor lift by introducing virtual control variables, realizes full-dimensional accurate control of 6 output variables without decoupling error, in addition, the control framework has flexibility and universality, the design of virtual variables does not depend on specific unmanned aerial vehicle parameters, different specifications of equipment can be adapted by adjusting the mapping matrix, the problem of poor adaptability of traditional control algorithm is solved, the linear mapping relationship between virtual variables and motor lift simplifies control instruction calculation, reduces algorithm power consumption, and improves real-time control performance.

[0048] 4. The application gives a clear stability sufficient condition, proves the boundedness of observation error and tracking error by constructing error dynamic equation, provides a theoretical basis for algorithm parameter setting, solves the problem that traditional ADRC parameter debugging depends on experience.

[0049] 5. In the application, the wind turbine blade model is based on geometric analysis to accurately describe the rotating trajectory, and after introducing the disturbance function, it is more suitable for actual working conditions, and the practicability of the ideal trajectory model is significantly improved, the quadrotor unmanned aerial vehicle model is derived by using Newton-Euler equation, and factors such as gravity, air resistance, motor lift and rotational inertia coupling are fully considered, so that the description accuracy of the simplified dynamic model is higher, and a reliable model basis is provided for control algorithm design. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The method flowchart of the application is shown in the figure;

[0051] Figure 2 The wind turbine blade model of the application is shown in the figure;

[0052] Figure 3 The simplified schematic diagram of the structure of the quadrotor unmanned aerial vehicle of the application is shown in the figure;

[0053] Figure 4 The trajectory tracking comparison curve of the x-axis of the position loop of the quadrotor unmanned aerial vehicle in the experiment of an embodiment of the application is shown in the figure;

[0054] Figure 5 The trajectory tracking comparison curve of the y-axis of the position loop of the quadrotor unmanned aerial vehicle in the experiment of an embodiment of the application is shown in the figure;

[0055] Figure 6 The trajectory tracking comparison curve of the z-axis of the position loop of the quadrotor unmanned aerial vehicle in the experiment of an embodiment of the application is shown in the figure;

[0056] Figure 7 The attitude tracking comparison curve of the Euler angle ψ of the quadrotor unmanned aerial vehicle in the experiment of an embodiment of the application is shown in the figure;

[0057] Figure 8 The attitude tracking comparison curve of the Euler angle φ of the quadrotor unmanned aerial vehicle in the experiment of an embodiment of the application is shown in the figure;

[0058] Figure 9 Figure 4 is a comparison curve diagram of the attitude tracking of the Euler angle θ of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application;

[0059] Figure 10 Figure 5 is a comparison curve diagram of the position tracking error on the x-axis of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application;

[0060] Figure 11 Figure 6 is a comparison curve diagram of the position tracking error on the y-axis of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application;

[0061] Figure 12 Figure 7 is a comparison curve diagram of the position tracking error on the z-axis of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application;

[0062] Figure 13 Figure 8 is a comparison curve diagram of the attitude tracking error of the Euler angle ψ of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application;

[0063] Figure 14 Figure 9 is a comparison curve diagram of the attitude tracking error of the Euler angle φ of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application;

[0064] Figure 15 Figure 10 is a comparison curve diagram of the attitude tracking error of the Euler angle θ of the quad-rotor unmanned aerial vehicle in the experiment of an embodiment of the present application. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present application.

[0066] Embodiment 1

[0067] The embodiment discloses a quad-rotor unmanned aerial vehicle active disturbance rejection control method for trajectory tracking of a wind turbine blade, and the method comprises steps S1-S6, and the specific descriptions of the steps are as follows: Figure 1

[0068] In step S1, a mathematical model of the quad-rotor unmanned aerial vehicle and the wind turbine blade is established based on actual data of the quad-rotor unmanned aerial vehicle and the wind turbine.

[0069] In the embodiment, a wind turbine blade model diagram is shown in Figure 2 , and a simplified structure example diagram of the quad-rotor unmanned aerial vehicle is shown in Figure 3 . Figure 2 In the embodiment, a wind turbine blade model diagram is shown in ​represents a wind turbine, the length of the wind turbine blade is , the rotating speed of the wind turbine blade is , the origin of the wind turbine blade is , the bottom of the wind turbine is marked as O as the origin of the coordinate system, and the length of the wind turbine rod body is H. Figure 3 In the formula, represents a quadcopter, the origin O of the coordinate system is the center of mass of the quadcopter, the four corners of , , , are the lift forces generated by the four motors of the quadcopter respectively.

[0070] The mathematical model of the wind turbine blade adopts a mathematical geometric analysis method, is established based on the horizontal component and the vertical component of the wind turbine blade rotating track, the length of the wind turbine blade, the rotating angular velocity of the wind turbine blade, and the height of the wind turbine tower;

[0071] The mathematical model of the quadcopter adopts Newton-Euler equation, is established based on the position variable of the quadcopter, the Euler angle, the force component of the total lift force on the position variable, the lift force generated by the motor, the moment of inertia, the air resistance coefficient, and the torque coefficient.

[0072] In the embodiment, the models are all established on the basis of the earth coordinate system, for the quadcopter, the x axis represents the yaw foot, the y axis direction represents the horizontal component, and the z axis represents the vertical component, for the wind turbine blade, only the y axis direction represents the horizontal component, and the z axis represents the vertical component.

[0073] In the embodiment, the expression of the wind turbine blade model is specifically:

[0074]

[0075] wherein, represents the horizontal component of the wind turbine blade rotating track, represents the vertical component of the wind turbine blade rotating track, represents the length of the wind turbine blade, represents the rotating angular velocity of the wind turbine blade, H represents the height of the wind turbine tower, represents time.

[0076] The expression of the quadcopter model is specifically:

[0077]

[0078] wherein, , , are position variables of the quadrotor UAV respectively , , first derivative, , , second derivative of the position variables respectively, , , are three Euler angles of the quadrotor UAV respectively , , first derivative of the three Euler angles respectively, , , second derivative of the three Euler angles respectively, , , are force components of total lift of the quadrotor UAV on the position variables respectively, , , , are lift forces generated by four motors of the quadrotor UAV respectively, , , are three moments of inertia of the quadrotor UAV, is an air resistance coefficient, is a moment coefficient, is a moment.

[0079] Step S2, considering the characteristics of each quadrotor UAV itself, based on the mathematical model and underactuation of the quadrotor UAV, a virtual variable is introduced to design an input parameter, and a flight control framework of the quadrotor UAV is constructed.

[0080] The quadrotor UAV has 6 degrees of freedom but is controlled by four motors, therefore, the quadrotor UAV is an underactuated system. The present application designs a quadrotor UAV control framework, and introduces a virtual variable to realize the control of six output variables by four control inputs.

[0081] The flight control framework of the quadrotor UAV introduces six virtual variables, and establishes a control relationship between the virtual variables and the model of the quadrotor UAV, and utilizes the conversion relationship between the variables to realize the control of the underactuated system of the quadrotor UAV, and the specific expression is:

[0082]

[0083] wherein, , , are virtual control input variables of the quadrotor UAV on the position variables respectively, , , are virtual control inputs on three Euler angle variables of the quadrotor UAV.

[0084] The relationship between the introduced virtual variables and the original quadrotor UAV model is shown as follows:

[0085]

[0086]

[0087] wherein, is a total lift virtual input, is the distance from the motor to the center of mass.

[0088] The virtual variables are introduced and the control relationship between the virtual variables and the quadrotor UAV model is established, and the control of the under-actuated system of the quadrotor UAV is realized by using the conversion relationship between the variables.

[0089] Step S3, a coupling relationship between the quadrotor UAV and the wind turbine blade is established.

[0090] The process of establishing the coupling relationship between the quadrotor UAV and the wind turbine blade includes:

[0091] The variable of the wind turbine blade in the vertical direction on the earth coordinate system is bound to the variable of the quadrotor UAV in the vertical direction on the earth coordinate system, and the variable of the wind turbine blade in the horizontal direction on the earth coordinate system is bound to the variable of the quadrotor UAV in the horizontal direction on the earth coordinate system.

[0092] The quadrotor UAV variable is kept unchanged, the uncertainty disturbance encountered in the actual external flight tracking process of the quadrotor UAV and the environmental condition disturbance suffered by the wind turbine blade are considered, the quadrotor UAV maintains a stable attitude during flight and tracks the trajectory of the wind turbine blade in real time, and the coupling relationship between the quadrotor UAV and the wind turbine blade is established.

[0093] Specifically, the present application is established on the condition that the quadrotor UAV works under the normal operation of the wind turbine blade, and is used for tracking the motion trajectory of the wind turbine blade under uncertain environment. Therefore, in the embodiment, the output variable of the quadrotor UAV control model and the motion trajectory of the wind turbine blade are related as follows:

[0094]

[0095] wherein, , respectively represent the variables of the wind turbine blade and the quadrotor UAV in the vertical direction on the earth coordinate system, , These represent the horizontal variables of the wind turbine blades and the quadcopter drone in the Earth coordinate system, respectively.

[0096] By keeping the above variables equal and keeping the variables of the quadcopter drone constant, it can be ensured that the quadcopter drone can maintain a stable attitude and track the trajectory of the wind turbine blades in real time during flight.

[0097] Step S4: To address the uncertainties encountered by the quadcopter drone during actual external flight tracking and the environmental interference experienced by the wind turbine blades, an interference function is established, and mathematical models of the quadcopter drone and wind turbine blades are introduced to characterize the magnitude and type of interference.

[0098] Uncertainty interference includes environmental airflow interference, measurement system interference, and communication and equipment interference. Environmental condition interference includes airflow interference, structural and vibration interference, and additional interference.

[0099] Specifically, the uncertainties and interferences encountered by quadcopter drones during actual external flight tracking include:

[0100] Environmental airflow disturbances: random gusts, blade wake turbulence, and slow drift of wind speed and direction in the continuous wind field;

[0101] Interference in the measurement system: GPS signal blockage, multipath effect, IMU zero drift, temperature drift and visual sensor noise;

[0102] Communication and equipment interference: electromagnetic interference, motor output fluctuations, and battery voltage degradation;

[0103] The environmental interferences include:

[0104] Airflow interference: lateral / longitudinal gust impact and atmospheric turbulence;

[0105] Structural and vibration disturbances: blade vibration and tower swaying;

[0106] Additional disturbances: deposits on the blade surface and fatigue damage to the blade.

[0107] Interference from ambient airflow can cause positional deviation, lift instability, attitude oscillation, and accumulation of tracking errors; interference with the measurement system can cause the UAV to perceive a deviation between its own position / attitude and its actual state, leading to misinterpretation of control commands; interference with communication and equipment can cause delays in the transmission of control commands, imbalances in power output, and sluggish attitude control response.

[0108] The interference core of the blade is trajectory deviation and motion instability. The airflow interference will make the actual trajectory of the blade deviate from the theoretical rotating trajectory. The target fluctuation expected to be tracked by the unmanned aerial vehicle, the structure and vibration interference will make the blade trajectory appear high-frequency small deviation, increase the tracking difficulty of the unmanned aerial vehicle, the additional interference will make the blade rotation angular velocity fluctuate, the trajectory consistency decreases, and the tracking error increases.

[0109] The interference function of the quadrotor unmanned aerial vehicle is constructed based on the influence of uncertain interference on the y-axis and z-axis position variables and the yaw angle of the quadrotor unmanned aerial vehicle. The interference function of the wind turbine blade is constructed based on the influence of environmental condition interference on the y-axis and z-axis directions of the wind turbine blade.

[0110] The expression of the interference function of the quadrotor unmanned aerial vehicle is:

[0111] ;

[0112] Wherein, is the interference on the z-axis position variable of the quadrotor unmanned aerial vehicle, is the interference on the y-axis position variable of the quadrotor unmanned aerial vehicle, is the interference on the yaw angle of the quadrotor unmanned aerial vehicle, and is the intensity amplitude calibration value of the interference on the z-axis position variable of the unmanned aerial vehicle, and is the intensity amplitude calibration value of the interference on the y-axis position variable of the unmanned aerial vehicle, is the intensity amplitude calibration value of the environmental airflow interference on the yaw angle of the unmanned aerial vehicle, is the pulsation frequency of the environmental airflow interference, is the intensity amplitude calibration value of the measurement system interference or communication and device interference on the yaw angle of the unmanned aerial vehicle, t is the current time;

[0113] The expression of the interference function of the wind turbine blade is:

[0114] ;

[0115] Wherein, is the interference on the y-axis direction of the wind turbine blade in the earth coordinate system, is the interference on the z-axis direction of the wind turbine blade in the earth coordinate system, is the maximum deviation amplitude of the y-axis direction of the wind turbine blade under interference, is the maximum deviation amplitude of the z-axis direction of the wind turbine blade under interference.

[0116] In the interference function of the quadrotor unmanned aerial vehicle, the intensity amplitude calibration value of the interference on the z-axis and y-axis position variables, the intensity amplitude calibration value of the environmental airflow interference on the yaw angle, and the intensity amplitude calibration value of the measurement system interference or communication and device interference are obtained based on the measured interference data of the unmanned aerial vehicle in the target wind farm, in combination with the device parameter correction of the unmanned aerial vehicle; in the interference function of the wind turbine blade, the maximum offset amplitude of the wind turbine blade in the y-axis direction and the z-axis direction under interference is obtained by collecting the maximum offset amplitude in the y-axis direction and the z-axis direction when the blade is stably rotating and fitting.

[0117] Specifically, in the embodiment, the process of obtaining the intensity amplitude calibration value of the interference on the z-axis and y-axis position variables is as follows:

[0118] Actual measurement basis: In the target wind farm, the unmanned aerial vehicle is hovered in the blade tracking area (without tracking task), and the position deviation in the z-axis and y-axis directions, i.e. the difference between the expected hovering position and the actual position, is continuously collected, and the maximum fluctuation amplitude of the interference in the statistical data is obtained.

[0119] Calibration logic: Take the measured maximum amplitude × 0.75~0.8 as the basic coefficient, reserve redundancy to cover extreme interference, and finally obtain the calibration value 0.6, which corresponds to the measured maximum amplitude of z / y direction interference about 0.8m, 0.8×0.75=0.6.

[0120] Device adaptation: In combination with the measured parameters of the unmanned aerial vehicle in the patent, it is verified that the lift / torque of the unmanned aerial vehicle under this amplitude can be effectively compensated by the active disturbance rejection controller to avoid subsequent interference overload.

[0121] Specifically, in the embodiment, the process of obtaining the intensity amplitude calibration value of the environmental airflow interference on the yaw angle and the intensity amplitude calibration value of the measurement system interference or communication and device interference is as follows:

[0122] The intensity amplitude calibration value of the environmental airflow interference on the yaw angle: The measured interference of the yaw angle on the wake turbulence is periodic fluctuation, and the amplitude is taken as the fitting value 0.5.

[0123] The intensity amplitude calibration value of the measurement system interference or communication and device interference: The measured interference of the yaw angle on the electromagnetic interference and IMU drift is stepwise fluctuation, and 0.75~0.8 of the maximum value of the amplitude is taken as the safety redundancy value, and the calibration value is obtained as 2.

[0124] Verification: The interference is substituted into the extended state observer (ESO) of the active disturbance rejection controller through simulation to ensure that the interference estimation error is ≤10%.

[0125] Specifically, in the embodiment, the process of obtaining the pulsation frequency of the environmental airflow interference is as follows:

[0126] The measured target wind farm gust fluctuation frequency (e.g., the gust completes a periodic fluctuation every 7.85 seconds on average, corresponding to an angular frequency The angular frequency of the measured gust is directly taken as the coefficient.

[0127] Specifically, in the embodiment, the process of obtaining the maximum deviation amplitude of the wind turbine blade in the y-axis direction and the z-axis direction under interference is as follows:

[0128] The y-axis direction interference amplitude of the blade is:

[0129] The measurement basis is that the y-axis direction trajectory of the blade during stable rotation is collected by the laser radar fixed on the tower body, and the deviation of the actual trajectory from the theoretical rotation trajectory is compared to statistically obtain the maximum deviation amplitude in the y-axis direction under the gust, e.g., the measured maximum deviation is about 12 m.

[0130] The calibration logic is that the measured maximum deviation x 0.8~0.9 is taken as the fitting amplitude, so 10 is selected.

[0131] The correlation characteristic is that the coefficient is strongly related to the angular velocity of the blade rotation, and in the embodiment, the measured angular velocity of the blade is about 10 rad / s, the interference frequency is synchronized with the blade speed, so the amplitude is matched to 10.

[0132] The z-axis direction interference amplitude of the blade is:

[0133] The measurement basis is that the trajectory deviation of the blade in the z-axis direction is collected by the laser radar, and the maximum deviation amplitude in the z-axis direction under the gust is statistically obtained, e.g., the measured maximum deviation is about 1 m.

[0134] The calibration logic is that the measured maximum deviation x 0.8 is taken as the fitting value, so 0.8 is selected.

[0135] The verification is that the interference is substituted into the blade trajectory model, the position error of the unmanned aerial vehicle during tracking is simulated to be ≤0.5 m, which meets the engineering requirements.

[0136] In summary, in the embodiment, the following function is used as an example to describe the interference encountered during flight:

[0137]

[0138] The following function is used as an example to describe the interference encountered in the actual motion of the wind turbine blade:

[0139]

[0140] The designed interference function is introduced into the previously established quadrotor unmanned aerial vehicle and wind turbine blade model to obtain a more actual quadrotor unmanned aerial vehicle flight control model and a mathematical model of the wind turbine blade motion trajectory.

[0141] ​Step S5, in the case of external interference, a double closed-loop active disturbance rejection control algorithm is designed based on a traditional active disturbance rejection control algorithm, and based on the double closed-loop active disturbance rejection control algorithm, a quadrotor unmanned aerial vehicle flight control framework and mathematical models of the quadrotor unmanned aerial vehicle and the wind turbine blade, double closed-loop active disturbance rejection control of the quadrotor unmanned aerial vehicle is carried out, so that tracking control of the quadrotor unmanned aerial vehicle can still be realized in the presence of interference.

[0142] The double closed-loop active disturbance rejection control algorithm adopts a tracking differentiator to suppress environmental condition interference, adopts an extended state observer to suppress uncertain interference, and calculates a final quadrotor unmanned aerial vehicle control input through a linear feedback law to realize control of the quadrotor unmanned aerial vehicle.

[0143] Specifically, in the actual control process, the tracking differentiator is used to track the wind turbine blade first to suppress uncertain interference and extract a clean expected trajectory signal (including the trajectory itself and the derivative) to avoid interference-induced control oscillation. Taking the unmanned aerial vehicle x-direction position control as an example (the y and z directions are the same):

[0144] From the above modeling and interference function setting, we can obtain the blade trajectory with interference and , the unmanned aerial vehicle itself is disturbed , and , and the wind turbine blade with interference model after adding interference is:

[0145]

[0146] The quadrotor unmanned aerial vehicle with interference model (simplified version) is:

[0147]

[0148] The unmanned aerial vehicle-blade coupling relationship is based on the previously established coupling relationship to force the alignment of the unmanned aerial vehicle position and the blade trajectory.

[0149] Define the expected trajectory , that is, the unmanned aerial vehicle target position after the blade with interference trajectory is mapped through the coupling relationship, and design a tracking differentiator to track the expected trajectory and output the tracking signal and its derivative :

[0150]

[0151] wherein is the tracking signal output by the tracking differentiator, is the derivative signal of , and is the second derivative signal of , This is the fastest synthesis function, which is also the core control function of the tracking differentiator. For the desired trajectory, To track the filter coefficients of the differentiator, To track the sampling step size of the differentiator;

[0152]

[0153] in, As a comprehensive error variable, For boundary layer parameters, For smooth switching function, The length of the wind turbine blade. The derivative compensation term, related to the desired velocity, is used to improve tracking real-time performance. For the linear term of position error, This is the nonlinear term of the position error.

[0154] Through the above design of the tracking differentiator, we can extract the effective signal from the disturbance and use it as the desired input to the controller. Furthermore, this invention utilizes an extended state observer to address uncertain disturbances. By defining the disturbance as a new state variable and observing it along with position and velocity, real-time disturbance compensation is achieved. The following example still uses the x-direction:

[0155] Original state: , ;

[0156] In the expanded state: ;in, For the total disturbance, including , , And model uncertainty.

[0157] Design the ESO observation equation:

[0158]

[0159] in, yes The estimated value, yes The derivative of This is an estimate of the total disturbance. , , It is the adjustable gain of the extended state observer, typically set to... , , , For the observer poles, take values ​​from 10 to 50; Virtual control input (to be solved). Position observation error for driving the observer to converge.

[0160] Combined with the linear feedback law, the feedback law is used to obtain a suitable control input in the x-axis direction:

[0161]

[0162] wherein, is the position error feedback gain, the proportional term, usually 5~20, reducing the position deviation, is the speed error feedback gain differential term, usually 1~5, to suppress overshoot, is the disturbance compensation term, directly offsetting the total disturbance estimated by ESO , the core anti-interference mechanism, is the position tracking error, i.e. the difference between the tracking differentiator and the ESO observation value, is the speed tracking error, used to improve the dynamic response speed.

[0163] Then based on the above x-axis direction for example of the solution of the control input, analogy of the double closed loop extension, covering the position and attitude control.

[0164] Position loop ADRC: for y(t), z(t), repeat the above processing, get the position direction virtual control input of and ;

[0165] Attitude loop ADRC: for and , the tracking differentiator + ESO observation equation + feedback law of the extended state observer are designed in the same way, to get the attitude direction virtual control input , , .

[0166] Based on the previously constructed quadrotor unmanned aerial vehicle flight control framework, the virtual input is mapped to the motor lift to realize underactuated control:

[0167]

[0168]

[0169] wherein, is the total lift virtual input, associated with balance gravity, is the distance from the motor to the center of mass.

[0170] The solving and derivation process between the above formulas can be obtained based on existing mathematical derivation techniques, and this embodiment will not be repeated here.

[0171] Step S6, by constructing the system state error equation, the sufficient condition of the error system's asymptotic stability is solved, and it is proved that the tracking error tends to be bounded with the increase of the observer pole and the controller pole, ensuring the effectiveness of the algorithm.

[0172] Specifically: according to the active disturbance rejection control algorithm established in steps S1-S5, the error dynamics is established, the error dynamic equation is constructed, and the sufficient condition of the error system's asymptotic stability is solved.

[0173] Lemma 1: Assuming that the total disturbance is bounded, there exist constants and a finite time such that for all . In addition, where k is a positive integer.

[0174] The extended state observer estimates the error system dynamics:

[0175]

[0176] where ( ) represents the error between different state variables, and the above formula can be expressed as:

[0177]

[0178] where represents the total disturbance.

[0179] Let , the equation can also be rewritten as:

[0180]

[0181] where , , is a Hermitian matrix.

[0182] The solution of the above equation is as follows:

[0183]

[0184] Let ;

[0185] Since is bounded, , is a constant, the following expression can be derived:

[0186]

[0187] From the matrix , we can derive the inverse matrix as follows:

[0188]

[0189] Thus we can get:

[0190]

[0191] Since is a Hurwitz matrix, there exists a finite time such that .

[0192] Therefore, we can get:

[0193]

[0194] In summary, we can get the following results:

[0195]

[0196] And derive the following inequalities:

[0197]

[0198] Finally, we get: where .

[0199] Based on and the inequalities derived, we can derive the following results:

[0200] .

[0201] Lemma 2: Assume is bounded and differentiable, then there exists a constant and a finite time such that for all . In addition, where is a positive integer.

[0202] Construct the system state error equation:

[0203]

[0204] where represents the motion trajectory of the wind turbine blade with disturbance and its first and second derivatives, is the position state variable of the quadrotor unmanned aerial vehicle and its first and second derivatives.

[0205] Combining the above linear feedback law, the error state equation above is expanded as the following equation:

[0206]

[0207] Rewriting the state equation as a differential equation:

[0208]

[0209] where, .

[0210] Solving the above differential equation gives:

[0211]

[0212] Let , thus we have:

[0213] .

[0214] From Lemma 1, , thus we have:

[0215]

[0216] Let , we have:

[0217] .

[0218] Since is a Hurwitz matrix, there exists a finite time such that

[0219]

[0220]

[0221] Thus, from the above equation we have:

[0222]

[0223] We can derive that:

[0224]

[0225] where, , represents the error between the wind turbine blade trajectory and the quadrotor output.

[0226] By solving the above two error equations, we have:

[0227]

[0228]

[0229] wherein, , are configured poles of the extended state observer and the system controller respectively, , is an initial tracking error, , is an error synthesis boundary.

[0230] From the above formula, the observation error of the system and the tracking error of the system tend to a bounded value as the value of and increases, which indicates that the active disturbance rejection controller designed in the application can achieve better control effect on the quadrotor unmanned aerial vehicle under the condition that the external disturbance is bounded.

[0231] Embodiment 2

[0232] This embodiment is based on the active disturbance rejection control method for the quadrotor unmanned aerial vehicle tracking the trajectory of the wind turbine blade disclosed in the above embodiment 1, and discloses the actual experimental verification process thereof.

[0233] The steps in embodiment 1 are repeated, and a double closed loop active disturbance rejection controller carrying a double closed loop active disturbance rejection control algorithm is designed in this embodiment, which is hereinafter referred to as ADRC controller or ADRC. In order to fully demonstrate the theoretical effect of the active disturbance rejection controller for the quadrotor unmanned aerial vehicle tracking the trajectory of the wind turbine blade through experiments, a traditional PID controller is set as a control group.

[0234] Under the condition of disturbance, the quadrotor unmanned aerial vehicle can still achieve good tracking effect on the wind turbine blade and is compared with the traditional PID experiment, and the active disturbance rejection control shows better effect in disturbance rejection. The specific experimental parameters are shown in Table 1.

[0235] Table 1 Experimental parameters

[0236]

[0237] The experimental results are shown in Figures 4-15 , which shows the position and attitude tracking effect of the ADRC control scheme in the tracking of the trajectory of the wind turbine blade based on the quadrotor unmanned aerial vehicle, and the parameters and are set to 10 and 90 respectively. The Reference curve in each figure is a reference curve.

[0238] wherein Figure 4, Figure 5 and Figure 6 show the trajectory tracking of position loop (x, y, z) respectively. In the figures, the curves of ADRC controller are basically coincided with the reference curves, realizing accurate tracking with minimal overshoot and fast convergence. In contrast, the traditional PID controller has slower response time and larger tracking error, especially in the presence of disturbance, highlighting the advantage of ADRC in anti-interference ability.

[0239] Figure 7 , Figure 8 and Figure 9 show the attitude tracking of Euler angles ψ, φ and θ respectively. In the figures, ADRC controller quickly aligns the angles with the reference values, while the PID controller performs poorly in terms of delay and increased steady-state error. The fast convergence of ADRC further emphasizes its robustness in handling attitude control under dynamic conditions.

[0240] Figure 10 , Figure 11 and Figure 12 compare the position tracking errors on x-axis, y-axis and z-axis respectively, Figure 13 , Figure 14 and Figure 15 compare the attitude tracking errors on three Euler angles ψ, φ and θ respectively. As can be seen from the figures, under external disturbance, the design of ADRC controller enables the quadrotor UAV to maintain a small tracking error throughout the process of tracking the wind turbine blade trajectory and converge to a bounded error value in a relatively short time, and can compensate for external disturbances in real time, demonstrating the ability of ADRC controller to handle external disturbances. In contrast, when subjected to unknown bounded disturbance, the quadrotor UAV under PID controller has a larger tracking deviation during tracking, and the convergence time is longer than that of ADRC controller, and it cannot converge to a small error value in a short time, and the control effect cannot reach the ideal state.

[0241] Embodiment 3

[0242] On the basis of embodiment 1, the electronic device provided in the embodiment comprises one or more processors and a memory, the memory has one or more programs stored therein, and the one or more programs comprise instructions for executing the self-disturbance control method of the quadrotor UAV for tracking the wind turbine blade trajectory as described above.

[0243] At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course, other hardware required by the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the above-mentioned wind turbine blade trajectory tracking four-rotor unmanned aerial vehicle active disturbance rejection control method. Of course, in addition to the software implementation, the present application does not exclude other implementation manners, such as logic devices or software and hardware combined manner, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but also can be hardware or logic device.

[0244] The memory can include non-permanent memory in the computer readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer readable medium.

[0245] The computer readable medium includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition in this paper, the computer readable medium does not include transitory computer readable media such as modulated data signals and carriers.

[0246] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for active disturbance rejection control of a quadcopter UAV for tracking the trajectory of wind turbine blades, characterized in that, The method includes: Based on actual data from quadrotor drones and wind turbines, mathematical models of quadrotor drones and wind turbine blades, including disturbance functions, are established to address the uncertainties encountered by quadrotor drones during actual external flight tracking and the environmental disturbances experienced by wind turbine blades. Based on the mathematical model and the underactuated nature of the quadcopter UAV, virtual variables are introduced to design input parameters, and a flight control framework for the quadcopter UAV is constructed. A coupling relationship between a quadrotor UAV and wind turbine blades is established. Based on a dual-closed-loop active disturbance rejection control algorithm, the quadrotor UAV's dual-closed-loop active disturbance rejection control is performed using the flight control framework of the quadrotor UAV and the mathematical models of the quadrotor UAV and wind turbine blades. The uncertainties include environmental airflow interference, measurement system interference, and communication and equipment interference. The environmental conditions include airflow interference, structural and vibration interference, and additional interference. The dual-closed-loop active disturbance rejection control algorithm uses a tracking differentiator to suppress environmental conditions and an extended state observer to suppress uncertainties. The final control input of the quadcopter UAV is calculated through a linear feedback law to achieve control of the quadcopter UAV. The interference functions include interference functions for quadcopter drones and interference functions for wind turbine blades; wherein... The quadcopter UAV interference function is constructed based on the influence of uncertainty interference on the quadcopter UAV's y-axis and z-axis position variables and yaw angle. The wind turbine blade interference function is constructed based on the influence of environmental condition interference on the wind turbine blade's y-axis and z-axis directions. The expression for the interference function of the quadcopter UAV is: ； in, The disturbances affecting the z-axis position variable of the quadcopter drone. The disturbances affecting the y-axis position variable of the quadcopter drone. The interference affecting the yaw angle of a quadcopter drone. and This is a calibration value for the intensity amplitude of the disturbance received by the UAV's z-axis position variable. and This is a calibration value for the intensity and amplitude of the disturbance received by the UAV's y-axis position variable. This is a calibration value for the intensity and amplitude of environmental airflow interference affecting the yaw angle of the UAV. The frequency of the fluctuations caused by environmental airflow interference. This is a calibration value for the intensity and amplitude of interference from the measurement system or communication and equipment that affects the yaw angle of the UAV. t The current moment; The expression for the wind turbine blade interference function is as follows: ； in, This represents the disturbance experienced by the wind turbine blades along the y-axis in the Earth coordinate system. This represents the disturbance experienced by the wind turbine blades along the z-axis in the Earth coordinate system. This represents the maximum y-axis displacement of the wind turbine blade under disturbance. This represents the maximum displacement of the wind turbine blade along the z-axis under disturbance.

2. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, The environmental airflow disturbances include: random gusts, blade wake turbulence, and slow drift of wind speed and direction in the continuous wind field. The interferences to the measurement system include: GPS signal blockage, multipath effect, IMU zero drift, temperature drift, and visual sensor noise; The communication and equipment interference includes: electromagnetic interference, motor output fluctuations, and battery voltage attenuation; The airflow disturbances include: lateral / longitudinal gust impacts and atmospheric turbulence. The structural and vibration disturbances include: blade vibration and tower swaying; The additional disturbances include: blade surface deposits and blade fatigue damage.

3. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, In the quadcopter UAV interference function, the intensity amplitude calibration values ​​of the interference on the z-axis and y-axis position variables, the intensity amplitude calibration value of the interference on the yaw angle from the ambient airflow, and the intensity amplitude calibration values ​​of the interference from the measurement system or communication and equipment are obtained based on the measured interference data of the UAV at the target wind farm, combined with the UAV equipment parameters for correction; in the wind turbine blade interference function, the maximum offset amplitude of the wind turbine blade in the y-axis and z-axis directions under interference is obtained by collecting the maximum offset amplitude in the y-axis and z-axis directions when the blade is rotating stably and fitting the data.

4. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, The mathematical model of the wind turbine blade is established using mathematical geometric analysis methods, based on the horizontal and vertical components of the wind turbine blade's rotation trajectory, the length of the wind turbine blade, the rotational angular velocity of the wind turbine blade, and the height of the wind turbine tower. The mathematical model of the quadcopter UAV adopts the Newton-Euler equations and is based on the position variables, Euler angles, force components generated by the total lift on the position variables, lift generated by the motor, moment of inertia, air drag coefficient, and torque coefficient of the quadcopter UAV.

5. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, The process of establishing the coupling relationship between the quadcopter UAV and the wind turbine blades includes: The variables of the wind turbine blades in the vertical direction in the Earth coordinate system are bound to the variables of the quadcopter drone in the vertical direction in the Earth coordinate system, and the variables of the wind turbine blades in the horizontal direction in the Earth coordinate system are bound to the variables of the quadcopter drone in the horizontal direction in the Earth coordinate system. Keeping the variables of the quadcopter drone constant, and considering the uncertainties encountered by the quadcopter drone during actual external flight tracking and the environmental conditions affecting the wind turbine blades, the quadcopter drone maintains a stable attitude during flight and tracks the trajectory of the wind turbine blades in real time, thus establishing the coupling relationship between the quadcopter drone and the wind turbine blades.

6. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, The quadcopter UAV flight control framework introduces six virtual variables and establishes a control relationship between the virtual variables and the quadcopter UAV model. It utilizes the transformation relationship between the variables to achieve control of the quadcopter UAV's underactuated system.

7. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, The specific process of performing dual-closed-loop active disturbance rejection control (ADRC) for a quadcopter UAV using the aforementioned dual-closed-loop ADRC algorithm is as follows: Collect the interference-laden trajectory of wind turbine blades and the real-time status of UAVs; The tracking differentiator suppresses environmental interference in the interfering trajectory and extracts the smooth desired trajectory of the wind turbine blade. By using the extended state observer to treat uncertainty disturbances as new state variables, the observation of the UAV's real-time state is extended, and the uncertainty disturbances, the UAV's position and velocity are observed simultaneously to establish the ESO observation equation. Combining the desired trajectory of the wind turbine blades with the ESO observation equations, a virtual input is generated through a linear feedback law. The virtual input is then converted into the actual lift of the four motors of the quadcopter through the flight control framework of the quadcopter and the mathematical models of the quadcopter and the wind turbine blades, thus achieving underactuated control.

8. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 1, characterized in that, This method also includes: constructing the system state error equation, and proving that the dual-loop active disturbance rejection control algorithm can achieve stable tracking under disturbance by solving the sufficient conditions for the asymptotic stability of the error system.

9. The method for active disturbance rejection control of a quadcopter UAV for tracking wind turbine blade trajectory according to claim 8, characterized in that, The system state error equation includes the observation error of the extended state observer and the trajectory tracking error of the tracking differentiator. The process of constructing the system state error equation and solving for the sufficient conditions for the asymptotic stability of the error system includes: The observation error of the extended state observer and the trajectory tracking error of the tracking differentiator are defined respectively. The system state error equation is established based on the dual closed-loop active disturbance rejection control algorithm. The system state error equation is expanded and solved by linear feedback law to obtain the tracking error inequality. Based on the tracking error inequality, it is determined whether the tracking error tends to a bounded value when the pole configuration parameters of the extended state observer and the tracking differentiator increase. If it tends to a bounded value, it proves that the dual-loop active disturbance rejection control algorithm can achieve stable tracking under disturbance.

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