Control method and device for wind load resistance of mooring unmanned aerial vehicle and storage medium

Abstract

The invention relates to a control method and device for wind load resistance of a mooring unmanned aerial vehicle and a storage medium. The method comprises the following steps: collecting and modeling a mooring unmanned aerial vehicle to obtain a system dynamics model; state estimation is carried out to obtain system wind load disturbance data and expected attitude data; performing optimal control solution on the plurality of mooring points to obtain tension distribution control data of each mooring point; inputting into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data; performing adaptive robust control on the attitude of the mooring unmanned aerial vehicle to obtain a rotating speed control instruction of each motor of the four rotors; and performing safety threshold judgment, and when any monitoring data exceeds a preset threshold, outputting a working mode switching instruction and executing safe landing control. According to the invention, a multi-stage safety protection mechanism is established, and reliable operation of the mooring unmanned aerial vehicle under extreme conditions is ensured through real-time monitoring and mode switching.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a control method, a device and a storage medium for the wind load resistance of a tethered unmanned aerial vehicle. Background Art

[0002] In strong wind environments, tethered drones face serious wind load interference problems, which not only affects their flight stability, but may also cause excessive or uneven force on the tethered ropes, endangering flight safety.

[0003] At present, the traditional control method of wind load of tethered UAV mainly adopts fixed-gain PID control or simple adaptive control strategy, which is difficult to effectively deal with strong nonlinear disturbances in complex wind field environment. At the same time, due to the strong coupling relationship between the force on the tethering point and the posture of the aircraft, a single control strategy is difficult to take into account the optimization of attitude stability and the tension distribution of the tethered rope at the same time. In addition, the existing control methods often ignore the guarantee mechanism of system safety and lack the ability to respond and handle abnormal situations such as sudden changes in wind load and overload of tethering points in a timely manner, which is very likely to cause safety accidents in practical applications. Summary of the invention

[0004] The main purpose of the present invention is to provide a control method, device and storage medium for the wind load resistance of a tethered UAV. The present invention establishes a multi-level safety protection mechanism, which ensures the reliable operation of the tethered UAV under extreme conditions through real-time monitoring and mode switching.

[0005] To achieve the above object, the present invention provides a method for controlling the wind load of a tethered UAV, comprising the following steps: The force data of multiple tethering points of the tethered UAV, the body posture data and the environmental wind field data are collected and modeled to obtain the system dynamics model; Perform state estimation based on the system dynamics model to obtain system wind load disturbance data and expected attitude data; Performing optimal control solutions for the multiple mooring points according to the system wind load disturbance data and the desired attitude data to obtain tension distribution control data of each mooring point; Inputting the system wind load disturbance data and the tension distribution control data into a discrete time neural network controller to perform attitude compensation calculation to obtain thrust vector compensation control data; Adaptively and robustly controlling the attitude of the tethered UAV according to the thrust vector compensation control data to obtain a speed control instruction for each motor of the quadrotor; A safety threshold is determined for the aircraft body posture data, the force data of the mooring point, and the environmental wind field data. When any of the monitoring data exceeds a preset threshold, a working mode switching instruction is output and safe landing control is executed.

[0006] The present invention also provides a control device for wind load resistance of a tethered UAV, comprising: A modeling module is used to collect and model the force data of multiple tethering points of the tethered UAV, the body posture data, and the environmental wind field data to obtain a system dynamics model; A state estimation module, used to perform state estimation based on the system dynamics model to obtain system wind load disturbance data and expected attitude data; A solution module, used for performing optimal control solution on the multiple mooring points according to the system wind load disturbance data and the desired attitude data, and obtaining tension distribution control data of each mooring point; A calculation module, used for inputting the system wind load disturbance data and the tension distribution control data into a discrete time neural network controller to perform attitude compensation calculation to obtain thrust vector compensation control data; A control module, used to perform adaptive robust control on the attitude of the tethered UAV according to the thrust vector compensation control data, and obtain a speed control instruction for each motor of the quadrotor; The judgment module is used to perform safety threshold judgment on the body posture data, the force data of the mooring point and the environmental wind field data. When any monitoring data exceeds a preset threshold, a working mode switching instruction is output and a safe landing control is executed.

[0007] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0008] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0009] In summary, the technical solution provided by the present invention realizes an accurate description of the motion characteristics of the tethered UAV in a strong wind environment by establishing a system dynamics model of multi-source sensor data fusion, and adopts a distributed model reference adaptive event-triggered control architecture to reduce the computational burden of the system and improve the compensation accuracy of wind load interference; the Hamilton-Jacobi-Bellman equation is introduced to optimally distribute the tension of the mooring points, which effectively solves the problem of force balance at multiple mooring points and avoids local overload; a multi-level discrete-time neural network controller is designed, and the system's adaptability to unknown wind load interference is enhanced through online learning and adaptive mechanisms; an adaptive robust control strategy is adopted to improve the control stability of the system under conditions of a wide range of wind speed changes; a multi-level safety protection mechanism is established, and the reliable operation of the tethered UAV under extreme conditions is ensured through real-time monitoring and mode switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 1 is a schematic diagram of the steps of a method for controlling the wind load of a tethered UAV in one embodiment of the present invention; Figure 2 This is a structural block diagram of a control device for wind load resistance of a tethered UAV in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0011] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0013] Reference Figure 1 This embodiment provides a method for controlling the wind load of a tethered UAV, comprising the following steps: S1, collecting and modeling the force data of multiple tethering points of the tethered UAV, the body posture data and the environmental wind field data to obtain a system dynamics model; Among them, the force signals of the torque sensors installed at multiple mooring points of the tethered UAV are sampled in real time to obtain the real-time force data of each mooring point. These data include the magnitude and direction of tension, and the timeliness and accuracy of the data are ensured by high-frequency sampling. While collecting the mooring point data, the three-axis acceleration sensor and angular velocity sensor on the tethered UAV body are used to collect the body motion data. Through the sensor fusion technology, the acceleration and angular velocity data are comprehensively processed to eliminate the errors caused by sensor noise or drift, and generate accurate body attitude motion data to reflect the attitude changes of the UAV in space, including the dynamic response of pitch, roll and yaw angle. At the same time, an array of wind speed and direction sensors is arranged in the UAV operation area. Through the collection of local wind speed and wind direction data by each sensor in the array, the data is processed using a spatial interpolation algorithm to generate a refined model of the environmental wind field distribution. The wind field model can reflect the distribution of wind speed and wind direction in space, and can take into account the turbulence characteristics in the wind field, providing accurate wind load information for subsequent calculations. Based on the real-time collected mooring point force data and environmental wind field distribution data, mooring mechanics calculations are performed to establish a force model for the mooring rope. In this process, the nonlinear characteristics of the mooring rope, including its stiffness, damping and bending effects, are considered to construct a physical model that can accurately describe the dynamic behavior of the rope. This model can predict the tension change and deformation of the mooring rope under wind load. The body attitude motion model is constructed by performing kinematic analysis on the body attitude motion data and the real-time force data of the mooring point. The model is based on the inertial coordinate system of the UAV and describes the motion law of the body under the constraints of the mooring point and external disturbances. The model integrates rotational dynamics and linear kinematic equations to capture the attitude changes and displacement characteristics of the UAV. The aerodynamic torque of the body is calculated according to the environmental wind field distribution data to obtain the wind load interference model. The wind load interference model can reflect the force and torque effects of the wind field on the UAV, including lift, drag and lateral force, as well as the aerodynamic torque generated by these forces. The model integrates the influence of wind speed, body geometry and attitude to ensure that the description of wind load disturbance has physical meaning. The tethered rope force model, the body attitude motion model and the wind load interference model are combined to form a comprehensive dynamic model of the system. The force conditions of the tethered point, the motion state of the drone and the comprehensive effect of the environmental wind field are incorporated into a unified framework to fully describe the dynamic behavior of the tethered drone. In order to improve the accuracy and adaptability of the model, the parameters in the system comprehensive dynamic model are optimized through parameter identification technology, and the model parameters are corrected using experimental data or field operation data, so that the final system dynamic model can better match the actual application scenario.

[0014] S2, perform state estimation based on the system dynamics model to obtain the system wind load disturbance data and expected attitude data; Specifically, the system dynamics model is input into the distributed control network, and the entire dynamic system is decomposed by the network partitioning algorithm to obtain multiple subsystem control units. The complex global dynamic behavior is dispersed into different subunits to achieve high efficiency and modular management of distributed control. On the basis of decomposition, the event trigger threshold is set for the state data of each subsystem, and the trigger condition criterion is set to ensure that each subsystem updates the state only when necessary, thereby effectively reducing the computational overhead and communication burden. The system dynamics model is input into the reference model for response characteristic calculation. The reference model generates the expected dynamic response data of the system according to the design objectives to ensure the stability and wind resistance of the drone under various disturbances. By analyzing the dynamic response characteristics of the system under ideal conditions, the expected behavior of each subsystem in different states is obtained. These expected dynamic response data provide a reference benchmark for the evaluation of the actual state. By comparing the actual states of multiple subsystem control units with the expected dynamic response data provided by the reference model, the state tracking error data of each subsystem is calculated. These error data reflect the deviation between the actual system and the ideal response. The state tracking error data is updated by applying the adaptive law to infer the wind load disturbance data of the system. The adaptive law adjusts the control gain and parameters in real time, and uses the implicit information in the error data to dynamically estimate the size and direction of the wind disturbance, thereby providing accurate data support for compensation control and attitude optimization. This process can not only effectively capture the changes in wind disturbance, but also improve the system's adaptability to complex wind fields, so that the UAV can still maintain stable flight under high wind load conditions. After the wind disturbance data is estimated, the state tracking error data and the preset trigger condition criteria are input into the event judgment module for comparison. When the trigger condition is met, the module generates a state update trigger instruction. These instructions are used to activate the state update mechanism of the subsystem control unit to respond to changes in external disturbances in a timely manner. At the same time, the UAV's windward attitude is optimized based on the generated state update trigger instructions and wind disturbance data. In this process, the optimal attitude angle data of the UAV is calculated by the optimization algorithm, and its windward direction relative to the wind field is adjusted to minimize the influence of wind resistance and unstable torque. The wind resistance characteristics of the optimal attitude angle data are analyzed to evaluate the wind load performance of the UAV under this attitude, including key parameters such as wind resistance and aerodynamic torque. Based on these analysis results, the final expected attitude data is generated.

[0015] S3, performing optimal control solutions for multiple mooring points according to the system wind load disturbance data and the desired attitude data, and obtaining the tension distribution control data of each mooring point; It should be noted that the system wind load disturbance data and the desired attitude data are input into the HJB equation, and the entire control problem is divided into time intervals through its solution process, the changing trend of the system state in different stages is identified, and the originally complex dynamic problem is divided into several more manageable small time intervals, which is convenient for the subsequent control target setting and calculation accuracy optimization. The time interval division data is refined, and clear local control targets and constraints are set in each time sub-interval. These conditions include the balance requirements for attitude maintenance, the force range of each mooring point, and the dynamic impact of wind load disturbance on the mooring rope. In this way, each sub-interval is given a clear physical meaning, providing a constraint basis for the subsequent tension distribution calculation. In this process, the design of the local control target will comprehensively consider the changing trend of the desired attitude data to ensure the attitude stability of the drone at each stage, while minimizing the energy consumption of the system and the unevenness of the tension distribution. According to the set local control constraints, the position distribution data of each mooring point relative to the wind direction is calculated. Based on the relative relationship between the real-time wind direction and the position of the drone, the distribution of each mooring point in three-dimensional space is determined through geometric models and mechanical analysis, thereby providing accurate input parameters for tension calculation. On this basis, the position distribution data of the mooring point is input into the tension calculation module, and the stress state of the mooring rope is accurately analyzed in combination with the wind load disturbance and the desired attitude requirements. By establishing a tension-position-wind load correlation model, the ideal tension data of each mooring point is calculated, which reflects the optimal force distribution of the mooring rope under the current wind load and attitude requirements. After obtaining the ideal tension data, the HJB equation is used again to optimize the tension distribution scheme. The solution process of the HJB equation can dynamically balance the forces of various parts of the system and ensure the calculation of the global optimal solution. Through this process, the optimal tension distribution scheme generated can effectively reduce the structural instability problem caused by uneven tension distribution in the system, and resist the interference of external wind load to the greatest extent. The generated optimal tension distribution scheme is input into the position actuator to drive each mooring point to adjust the tension and achieve precise control. The position actuator ensures that the actual tension is consistent with the target tension by adjusting the length and angle of the rope in real time. During the execution of the tension control scheme, in order to prevent the tension over-limit problem caused by extreme wind load or accidental interference from the outside, the tension control amount of the mooring point is protected from over-limit. By setting the tension protection threshold data, the tension changes of each mooring point are monitored in real time, and the tension that exceeds the threshold range is limited. The limit operation can effectively avoid the risk of rope breakage or uncontrolled attitude of the drone due to excessive local tension. The tension distribution control data after limit adjustment is transmitted to each mooring point.

[0016] S4, inputting the system wind load disturbance data and tension distribution control data into the discrete time neural network controller to perform attitude compensation calculation to obtain thrust vector compensation control data; Specifically, the system wind load disturbance data and tension distribution control data are input into the input layer of the discrete time neural network controller, which consists of 12 neuron nodes, each of which corresponds to three-dimensional wind load data (three components of wind speed and direction) and tension data of four mooring points. The data of the input layer is passed to the first hidden layer for processing. The first hidden layer contains 24 neuron nodes and uses the ReLU activation function to achieve nonlinear mapping. The input data is preliminarily extracted to convert the complex input relationship into the first layer of feature mapping data representing the interaction between wind load disturbance and tension distribution. Through the nonlinear transformation of the ReLU function, the response ability of the neural network to disturbances of different intensities is enhanced, so that it can identify potential nonlinear features. The first layer of feature mapping data is input into the wind load compensation control layer for processing. The wind load compensation control layer is a key layer in the neural network, which contains 36 neuron nodes and uses the Tanh activation function to perform nonlinear transformation on the data to calculate the thrust compensation required by the system. This layer generates wind load compensation data that matches the current wind field conditions according to the dynamic changes of wind load disturbance and tension distribution. These data directly reflect the external force compensation requirements required for the attitude stability of the drone. In this process, the use of Tanh activation function ensures that the output data has a smoother range of variation and can effectively handle the complex features in the wind load data. The wind load compensation data is input into the second hidden layer for processing. The second hidden layer also contains 24 neuron nodes and uses ReLU activation function for feature extraction. At this stage, the network extracts the implicit features in the wind load compensation data and generates the second layer of feature mapping data. Through the multi-layer feature extraction structure, the deep learning ability of the neural network for perturbation data is ensured, so that it can effectively capture the dynamic changes of the system. The second layer of feature mapping data is input into the attitude tracking control layer for processing. The attitude tracking control layer contains 18 neuron nodes and uses Sigmoid activation function to calculate the desired attitude angle by transforming the feature mapping data. The output of this layer is the attitude compensation data, which represents the attitude adjustment information required by the drone under the combined action of wind load and tension distribution. The use of Sigmoid activation function in this layer enables the attitude compensation data to be mapped into a limited range, ensuring the stability and controllability of the calculation results. The generated attitude compensation data is input into the third hidden layer for processing. The third hidden layer contains 12 neuron nodes, and uses the ReLU activation function to complete the dimensionality reduction mapping, compressing high-dimensional features into low-dimensional features suitable for thrust distribution. Through this dimensionality reduction process, the network simplifies the complexity of subsequent calculations while ensuring that important data information is not lost. The reduced-dimensional feature data is input into the thrust distribution layer for processing. The thrust distribution layer contains 8 neuron nodes, and uses a linear activation function to directly calculate the thrust component of each rotor. Through the processing of this layer, the thrust component of each rotor can match the wind load compensation requirements and attitude compensation requirements, thereby achieving precise control of the drone's attitude.The thrust distribution data is passed to the output layer for processing. The output layer contains 4 neuron nodes, and the Tanh activation function is used to normalize the data to finally generate thrust vector compensation control data.

[0017] S5, performing adaptive robust control on the attitude of the tethered UAV according to the thrust vector compensation control data, and obtaining the speed control command of each motor of the quadrotor; Among them, the thrust vector compensation control data is input into the nominal controller for decomposition calculation. The nominal controller solves the expected angle data of the roll angle, pitch angle and yaw angle according to the characteristics of the compensation data and the attitude requirements. These expected angle data indicate the attitude target that the drone should achieve under ideal conditions, and provide the direction and amplitude of attitude adjustment. The expected angle data is compared with the actual attitude data to calculate the three-axis attitude angle error and the corresponding angular velocity error. These error data reflect the deviation between the current attitude state of the drone and the target attitude. The attitude angle error and angular velocity error are input into the adaptive robust controller for tracking control calculation. The adaptive robust controller dynamically adjusts the control parameters according to the real-time state and error characteristics of the system, and outputs the attitude angular acceleration control quantity that can respond quickly and has high robustness by estimating and compensating for disturbances and uncertainties. These control quantities can correct the attitude error in real time and cope with the influence of external disturbances such as wind load disturbances, thereby improving the attitude stability of the drone. The attitude angular acceleration control quantity is dynamically inverse mapped and converted into the overall thrust control quantity and three-axis torque control quantity of the quadrotor that are more suitable for physical execution. Using the UAV's dynamic model, the controller's output is mapped from the form of angular acceleration to the form of thrust and torque to match the actual force of the rotor. The overall thrust control reflects the lift demand of the UAV in the vertical direction, while the three-axis torque control determines the rotational torque distribution of the UAV in the roll, pitch and yaw directions. The thrust mapping matrix is ​​solved according to the overall thrust control and three-axis torque control of the quadrotor. The thrust mapping matrix distributes the overall thrust and torque requirements to the thrust of the four rotors through mathematical transformation, ensuring that the four rotors can meet the system's balance requirements and attitude control requirements under the joint action. This process not only considers the linear distribution of thrust, but also needs to integrate factors such as rotor position, arm length and relative angle to make the thrust distribution physically feasible and mechanically balanced. The thrust distribution data of the four rotor motors is input into the rotor's characteristic curve for speed mapping. The characteristic curve describes the nonlinear relationship between the motor speed and the output thrust. Through this mapping process, the thrust demand is converted into the corresponding motor equivalent speed data to ensure the consistency between the controller output and the actual motor drive parameters. At this stage, the equivalent speed data is dead-zone compensated and saturated to eliminate the nonlinear response of the motor in the small thrust range and prevent the motor speed from exceeding the safe range, resulting in mechanical failure or reduced efficiency. The safe speed range data processed by dead-zone compensation and saturation limiting is input into the ESC drive module, and the specific control signal for driving the motor is generated through PWM signal conversion. The ESC drive module converts the input speed command into the PWM signal required by the motor. These signals adjust the power supply voltage and frequency of the motor to ultimately achieve precise speed control of the four rotors.

[0018] S6, make safety threshold judgment on the aircraft attitude data, the force data of the mooring point and the environmental wind field data. When any monitoring data exceeds the preset threshold, output the working mode switching instruction and execute the safe landing control.

[0019] Specifically, the safety threshold is set for the body attitude data, and the safety area within the attitude angle range is defined, such as the limit range of the roll angle, pitch angle and yaw angle, and the maximum allowable value of the angular velocity, to generate the attitude threshold range data. At the same time, the safety threshold is set for the force data of the mooring point to monitor the tension range of the mooring rope during wind load and dynamic adjustment of the UAV. According to the rope material properties, mooring point distribution and dynamic load calculation, the tension threshold range data of each mooring point is set, including the maximum tension limit to prevent breakage and the minimum tension limit to avoid relaxation or failure. Similarly, the safety threshold is set for the environmental wind field data, and the safety range of wind speed, wind direction and turbulence intensity is set according to the wind resistance of the UAV design and the mission scenario requirements, and the wind field threshold range data is generated. These threshold range data together constitute the safe operation boundary of the UAV. The attitude threshold range data, the mooring tension threshold range data and the wind field threshold range data are input into the monitoring module for multi-threshold judgment. The monitoring module collects the status data and external environment data of the UAV in real time and compares them with the preset threshold range. The multi-threshold judgment result data can identify whether the current system is in a safe state. If any data is detected to exceed the threshold, the working mode switching command will be triggered to switch the drone from the normal operation mode to the emergency control mode. In the emergency control mode, the multi-threshold judgment result data is input into the attitude protection module for calculation. The attitude protection module calculates the expected attitude angle and angular velocity through the self-stabilization algorithm and generates attitude self-stabilization data. These data ensure that the drone can maintain basic stability under extreme conditions, such as restoring excessive pitch or roll angles to a safe range, while reducing drastic changes in angular velocity to avoid attitude loss of control. Self-stabilization data provides a reliable attitude reference for the planning of subsequent landing trajectories. Based on the attitude self-stabilization data, the landing trajectory is planned to determine the descent path and speed control strategy of the drone. At this stage, the current wind field conditions, the height of the drone and the dynamic characteristics are comprehensively considered, and the descent speed control data is generated through the optimization algorithm to ensure that the drone can gradually approach the ground at a controllable speed within a safe range. The descent speed control data also needs to adapt to the dynamic disturbances to which the drone is subjected during the landing process to ensure the smoothness and accuracy of the landing trajectory. The descent speed control data is input into the motor control distribution module to calculate the speed adjustment instructions of each motor of the quadrotor, thereby achieving safe landing control. The motor control distribution module adjusts the speed of each motor in real time according to the UAV's dynamic model and current attitude adjustment requirements to balance the attitude of the UAV and control the descent speed. At the same time, dead zone compensation and limit operations are performed to ensure that the motor operates within a safe range to prevent overload or failure.

[0020] In one example, the force data of multiple tethering points of a tethered UAV, the body posture data, and the environmental wind field data are collected and modeled to obtain a system dynamics model, including: The force signals of the torque sensors at multiple mooring points are sampled to obtain the real-time force data of the mooring points, and the data of the three-axis acceleration sensor and angular velocity sensor of the tethered drone are fused to obtain the body posture motion data; Perform spatial interpolation on the data collected by the wind speed and direction sensor array in the tethered UAV operation area to obtain the environmental wind field distribution data; Based on the real-time force data of the mooring point and the environmental wind field distribution data, the mooring mechanics calculation is performed to obtain the mooring rope force model, and the body posture motion data and the real-time force data of the mooring point are kinematically analyzed to obtain the body posture motion model; The aerodynamic moment is calculated for the environmental wind field distribution data to obtain the wind load interference model; The mooring rope force model, the body posture motion model and the wind load interference model are combined to obtain the system comprehensive dynamics model, and the system comprehensive dynamics model is subjected to parameter identification to obtain the system dynamics model.

[0021] In this example, torque sensors installed at multiple mooring points are used to sample force signals at the mooring points. These signals include the tension of the mooring ropes. and direction vector ,in Indicates the number of the mooring point, there are Using these sensor data, the force condition of each mooring point is obtained in real time, forming the real-time force data of the mooring point. At the same time, the three-axis acceleration sensor and angular velocity sensor on the drone body collect data through the inertial measurement unit, and record the linear acceleration of the body respectively. and angular velocity These data are processed by sensor fusion algorithms, such as Kalman filtering or extended Kalman filtering, which combines acceleration and angular velocity data, filters out noise and drift, and obtains dynamic motion data of the body attitude, including attitude angle (pitch angle , Roll Angle , yaw angle ) and attitude change rate. After completing the collection of the body attitude and the force data of the tethered point, the wind field data of the UAV operation area is processed. The local wind speed and direction sensor array arranged in the operation area is used to collect the local wind speed in real time. Wind direction These data are processed by spatial interpolation algorithms (such as Kriging interpolation or inverse distance weighted interpolation) to generate a wind field distribution model. ,in is a spatial coordinate. This model describes the wind speed and direction at any point and captures the changing characteristics of the wind field. Based on the real-time force data of the mooring point and the environmental wind field distribution data, the mooring mechanics calculation is performed to obtain the mooring rope force model. The mooring rope force model is based on the tension and direction vector of each rope, and reflects the force of the rope by calculating the resultant force and torque applied by each mooring point to the drone. Its basic formula is: ; ; in, is the total tethering force; is the total torque; It is The displacement vector of each tethered point relative to the center of mass of the drone; represents the cross product operation. At the same time, the body posture motion data and the real-time force data of the tethering point are subjected to kinematic analysis to obtain the body posture motion model. The motion model is described by the Newton-Euler equation as follows: ; ; in, It’s the drone quality; is the inertia matrix; is the inertial damping term; is the gravity term; is the wind force; is the torque of the wind field. Based on the wind field distribution data, the aerodynamic torque model generated by the wind field on the UAV is calculated. The aerodynamic torque calculation formula is: ; in, is the pressure distribution of the wind field on the surface of the drone; is the microelement area. By combining the tethered rope force model, the body attitude motion model and the wind load interference model, a complete system comprehensive dynamics model is constructed. This model describes the comprehensive behavior of the UAV under the action of tethered force, wind load force and body dynamics. In order to improve the accuracy of the model, the parameter identification technology is used to optimize the key parameters in the comprehensive dynamics model to obtain the system dynamics model. The actual data measured by the experiment is compared with the model output data, and the model parameters are adjusted using the least squares method or recursive least squares method, such as the tethered rope stiffness, damping coefficient, UAV inertia matrix, etc., so that the model can accurately reflect the actual operation.

[0022] In one example, state estimation is performed based on the system dynamics model to obtain system wind load disturbance data and expected attitude data, including: The system dynamics model is input into the distributed control network for node division to obtain multiple subsystem control units, and event trigger thresholds are set for the state data of the multiple subsystem control units to obtain trigger condition criteria; The system dynamics model is input into the reference model to calculate the response characteristics to obtain the expected dynamic response data, and the actual states of the control units of the multiple subsystems are calculated according to the expected dynamic response data to obtain the state tracking error data; The state tracking error data is updated by adaptive law to obtain the system wind load disturbance data; The state tracking error data and the trigger condition criterion are input into the event judgment module for comparison to obtain the state update trigger instruction; The windward attitude of the aircraft body is optimized according to the state update trigger command and the system wind load disturbance data to obtain the optimal attitude angle data, and the wind resistance characteristics of the optimal attitude angle data are analyzed to obtain the expected attitude data.

[0023] In this example, the system dynamics model Input the distributed control network, decompose the system nodes through the network partitioning algorithm (such as graph theory or clustering algorithm), and divide the overall model into multiple subsystem control units. These subsystem control units correspond to different control modules of the drone, such as pitch control unit, roll control unit and yaw control unit, or are divided according to the mechanical characteristics of each tethering point. Assume that the system dynamics model is expressed in matrix form as: ; in Representation Subsystem The local dynamics of Representation Subsystem With subsystem Through the node partitioning algorithm, the complex global model is decomposed into multiple relatively independent subsystem control units to facilitate parallel processing and local optimization. After the subsystem division is completed, the event trigger threshold is set for the state data of each subsystem to construct the trigger condition criterion. Assuming that the subsystem The state vector of , and its trigger threshold is expressed as: ; in is the reference state, It is the threshold parameter of the subsystem, which is used to determine whether the system needs to trigger a state update. Each subsystem determines whether the control input needs to be recalculated by comparing the deviation between the current state and the reference state. The system dynamics model is input into the reference model for response characteristic calculation. The reference model generates the expected dynamic response data by simulating the dynamic behavior of the drone under ideal conditions. These data include the desired attitude angles (such as pitch , Roll Angle , yaw angle ) and its rate of change. The expected dynamic response is calculated by model predictive control or optimal control method, which is specifically expressed as: ; in is the control input, is the system status, is the reference state. Based on the expected dynamic response data, the actual states of multiple subsystems are calculated to obtain the state tracking error data. The state tracking error is defined as: ; in It is a subsystem The error data is input into the adaptive law for updating. The adaptive law is used to dynamically adjust the control gain of the system to compensate for the error caused by external disturbances or system uncertainty. The update formula of the adaptive law is expressed as: ; in It is a subsystem The control gain matrix is is the learning rate parameter. After the adaptive law is updated, the state tracking error data and the trigger condition criterion are input into the event judgment module for comparison. If the error exceeds the set trigger threshold, that is , the module will generate a state update trigger instruction. This instruction is used to activate the control algorithm to recalculate the control input of the subsystem, so as to respond to the change of the system state in real time. According to the state update trigger instruction and the system wind load disturbance data, the windward attitude of the UAV is optimized. The goal of windward attitude optimization is to minimize the impact of wind load on the UAV by adjusting the attitude angle. The optimization problem is expressed as: ; in is the vector function of wind load. By solving this optimization problem, we can get the optimal attitude angle data. The wind resistance characteristic analysis is performed on the optimal attitude angle data to calculate the expected aerodynamic torque and wind resistance torque of the UAV in this attitude. The wind resistance characteristic analysis is achieved through the following formula: ; in is the wind resistance moment, is the displacement vector of the point of action, It is the wind force acting on the surface of the drone. The results of the wind resistance characteristic analysis are combined with the attitude optimization data to generate the expected attitude data to ensure that the drone can maintain stable operation in complex wind fields.

[0024] In one example, optimal control solutions are performed on multiple mooring points based on system wind load disturbance data and desired attitude data to obtain tension distribution control data of each mooring point, including: Input the system wind load disturbance data and expected attitude data into the Hamilton-Jacobi-Bellman equation to obtain the time interval division data; The sub-interval control target is set for the time interval division data to obtain the local control constraint conditions; The position of each mooring point relative to the wind direction is calculated according to the local control constraint conditions to obtain the mooring point position distribution data; Input the mooring point position distribution data into the tension calculation module for force analysis to obtain the ideal tension data of the mooring point; The Hamilton-Jacobi-Bellman equation is solved for the ideal tension data of the mooring point to obtain the optimal tension distribution scheme, and the optimal tension distribution scheme is input into the position actuator for tension adjustment to obtain the tension control value of the mooring point; The tension control amount of the mooring point is protected against tension over-limit, tension protection threshold data is obtained, and the tension control amount of the mooring point is limited according to the tension protection threshold data to obtain tension distribution control data of each mooring point.

[0025] In this example, the system wind load disturbance data and the desired attitude data are input into the HJB equation. The HJB equation uses dynamic programming to find an optimal control strategy from the current state to the desired state, and its form is: ; in is the cost function, is a state variable, is the control input, is the price item, is the state transfer function. By solving the HJB equation, we get the time interval division data, which is used to divide the global dynamic process into several time sub-intervals. The control target in each sub-interval is clearer. After the time interval division is completed, the control target is set for each time sub-interval to obtain the local control constraint. For example, for a time sub-interval, the goal is to maintain a certain posture of the drone (such as pitch angle The local control constraint is expressed as: ; ; in It is The tension of each mooring point, is the tension direction vector, is the position vector of the tethered point relative to the center of mass of the drone, is the wind load force, is the wind load moment. According to the local control constraints, the position distribution data of each mooring point relative to the wind direction is calculated. This calculation process is based on geometric relationships and mechanical equilibrium. For example, if the wind load mainly comes from -axis direction, the position distribution of the tethering points needs to be adjusted to offset the wind load torque so that the drone attitude remains stable. The coordinates of the anchor points are , solved by optimizing the problem: ; ; in is the sum of the tethering forces, is a set of position constraints. The mooring point position distribution data is input into the tension calculation module, and the force analysis is performed based on the mechanical properties of the mooring rope to obtain the ideal tension data. These ideal tension data are obtained by solving the following equations: ; in It is The HJB equation is used again to optimize the ideal tension data and generate the optimal tension distribution solution. This process needs to consider the uniformity and efficiency of tension distribution based on the global cost function. For example: ; in is the weight parameter, is the reference position. The HJB equation provides a step-by-step optimization strategy at this stage to ensure that the tension distribution scheme can balance the wind load disturbance and the UAV attitude requirements. The optimal tension distribution scheme is input into the position actuator, and the adjustment is achieved by adjusting the length and tension of the mooring rope to generate the mooring point tension control amount. In order to ensure the safety of tension control, the tension control amount is protected from over-limit. Set the tension protection threshold data , limit the tension beyond the range: ; By limiting the operation, it is ensured that the mooring rope will not break (exceed ), nor too loose (less than ), thereby maintaining the stability of the drone. Through the above calculations, the tension distribution control data of each mooring point is obtained.

[0026] In one example, the system wind load disturbance data and tension distribution control data are input into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data, including: The system wind load disturbance data and tension distribution control data are input into the input layer of the discrete time neural network controller. The input layer includes 12 neuron nodes, corresponding to the three-dimensional wind load data and the tension data of the four mooring points respectively. The data of the input layer is processed by the first hidden layer. The first hidden layer contains 24 neuron nodes and uses the ReLU activation function for nonlinear mapping to obtain the first layer feature mapping data; The first layer feature mapping data is input into the wind load compensation control layer for processing. The wind load compensation control layer contains 36 neuron nodes and uses the Tanh activation function to calculate the required thrust compensation amount to obtain the wind load compensation data. The wind load compensation data is processed by the second hidden layer, which contains 24 neuron nodes and uses the ReLU activation function to extract features to obtain the second layer feature mapping data; The second layer feature map data is input into the posture tracking control layer for processing. The posture tracking control layer contains 18 neuron nodes and uses the Sigmoid activation function to calculate the expected posture angle to obtain the posture compensation data. The posture compensation data is processed by the third hidden layer, which contains 12 neuron nodes and uses the ReLU activation function for dimensionality reduction mapping to obtain the third layer feature mapping data; The feature map data of the third layer is input into the thrust distribution layer for processing. The thrust distribution layer contains 8 neuron nodes and uses a linear activation function to calculate the thrust component of each rotor to obtain the thrust distribution data. The thrust distribution data is processed by the output layer, which contains 4 neuron nodes and is normalized using the Tanh activation function to obtain the thrust vector compensation control data.

[0027] In this example, the system wind load disturbance data and tension distribution control data are input into the input layer of the discrete time neural network, which contains 12 neuron nodes. The three-dimensional wind load data is represented as , reflecting the wind load on the three axes of the drone. The tension data of the four mooring points are , indicating the real-time tension of each tethered point. The input layer passes these input values ​​to the next layer through simple linear operations of neurons. The data of the input layer is passed to the first hidden layer for processing. The first hidden layer contains 24 neuron nodes, each of which is nonlinearly mapped through the ReLU activation function. The form of the ReLU function is: ; in is the input data. This function enhances the network's ability to express nonlinear features by truncating negative values ​​to zero. In this layer, the interaction between wind load data and tension data is extracted as the first layer of feature mapping data, recorded as , these data contain preliminary characteristics of the dynamic effects of wind load disturbance and tension distribution on the UAV system. The first layer of feature map data is input into the wind load compensation control layer, which contains 36 neuron nodes and is processed by the Tanh activation function. The Tanh activation function is defined as: ; Its output range is between [-1,1], which is suitable for nonlinear mapping with smooth transition. The task of the wind load compensation control layer is to calculate the thrust compensation required by the UAV based on the feature mapping data, which is recorded as These thrust compensation data are used to offset the disturbance of wind load on the attitude of the drone, ensuring that the attitude of the drone can remain stable. The wind load compensation data is passed to the second hidden layer, which contains 24 neuron nodes and uses the ReLU activation function for feature extraction. Similar to the first hidden layer, the second hidden layer further mines the potential information in the wind load compensation data through nonlinear mapping to generate the second layer of feature mapping data, recorded as The second layer feature map data is input to the posture tracking control layer for processing. This layer contains 18 neuron nodes and uses the Sigmoid activation function to calculate the desired posture angle. The Sigmoid function is defined as: ; Its output range is [0,1], which is suitable for generating attitude compensation data. These data represent the desired pitch angle, roll angle and yaw angle, denoted as The attitude compensation data provides the attitude adjustment target for thrust distribution. The attitude compensation data is passed to the third hidden layer for dimensionality reduction mapping. The third hidden layer contains 12 neuron nodes and uses the ReLU activation function. Through nonlinear mapping, the high-dimensional features are compressed into data dimensions suitable for thrust distribution while retaining key information. The third layer feature mapping data is recorded as The third layer feature map data is input to the thrust distribution layer for processing. The thrust distribution layer contains 8 neuron nodes and uses a linear activation function to calculate the thrust component of each rotor. The output of the thrust distribution layer is recorded as ,in Indicates The thrust distribution relationship is expressed in the following matrix form: ; in is the thrust distribution matrix, which describes the linear relationship between the feature map data and the thrust component. The thrust distribution data is passed to the output layer for normalization. The output layer contains 4 neuron nodes and uses the Tanh activation function. The output of the Tanh function normalizes the thrust distribution data to the range of [-1,1] to ensure that the data adapts to the motor drive control requirements to generate the final thrust vector compensation control data. .

[0028] In one example, the attitude of the tethered drone is adaptively and robustly controlled according to the thrust vector compensation control data to obtain the speed control instructions of each motor of the quadrotor, including: The thrust vector compensation control data is input into the nominal controller for decomposition calculation to obtain the desired angle data of the roll angle, pitch angle and yaw angle; The attitude error is calculated for the expected angle data to obtain the three-axis attitude angle error and angular velocity error data, and the three-axis attitude angle error and angular velocity error data are input into the adaptive robust controller for tracking control calculation to obtain the attitude angular acceleration control quantity; Perform dynamic inverse mapping on the attitude angular acceleration control quantity to obtain the overall thrust control quantity and three-axis torque control quantity of the quadrotor; The thrust mapping matrix is ​​solved according to the overall thrust control quantity of the quadrotor and the three-axis torque control quantity to obtain the thrust distribution data of the four rotor motors; The thrust distribution data of the four rotor motors are input into the characteristic curve for speed mapping to obtain the equivalent speed data of the four motors, and the equivalent speed data of the four motors are subjected to dead zone compensation and saturation limiting to obtain the safe speed range data; The safe speed range data is input into the ESC drive module for PWM signal conversion to obtain the speed control instructions of each motor of the quadrotor.

[0029] In this example, the thrust vector compensation control data The input is sent to the nominal controller for decomposition calculation. Based on the dynamic characteristics of the drone, the nominal controller solves the relationship between the input thrust vector and the attitude angle to obtain the desired roll angle. , Pitch angle and yaw angle . This decomposition is achieved through the following relationship: ; ; ; in is the total thrust, offset is the reference value of the yaw angle, is the yaw sensitivity coefficient. Compare with the current attitude angle data to calculate the three-axis attitude angle error and angular velocity error. Assume the current attitude angle is , the angular velocity is , the error calculation formula is: ; ; These error data reflect the deviation between the current attitude state and the target, and are the core basis for the control system adjustment. The attitude angle error and angular velocity error are input into the adaptive robust controller, which compensates for external disturbances and system uncertainties by adjusting the control parameters in real time, thereby generating attitude angular acceleration control quantities. The adaptive law is implemented by the following formula: ; ; ; in are the proportional and derivative gains, It is an adaptive compensation term used to deal with nonlinearity and disturbance. After obtaining the attitude angular acceleration control quantity, the dynamic inverse mapping is performed to convert it into the overall thrust control quantity of the quadrotor. And the three-axis torque control quantity The dynamic relationship of the quadrotor is as follows: ; ; in is the arm length of the rotor relative to the center of mass, is the torque coefficient. According to the above relationship between thrust and torque, the thrust mapping matrix is ​​constructed: ; in is the thrust distribution matrix, which describes the mapping relationship between the rotor thrust and the overall thrust and torque. Input to the rotor characteristic curve for speed mapping. The characteristic curve relates thrust to rotor speed, which is converted by the following formula: ; in It is The rotation speed of the rotor, is the lift coefficient. To ensure the reliability and efficiency of the motor operation, the speed data is subjected to dead zone compensation and saturation limit processing. Dead zone compensation is used to eliminate the non-response interval of the motor under small input, while limit prevents the speed from exceeding the motor design range. The formula is: ; in and The processed safe speed range data is input into the ESC drive module, and the motor control instructions are generated through PWM signal conversion to drive the quadrotor system to achieve the desired attitude and motion control.

[0030] In one example, a safety threshold is determined for the aircraft attitude data, the force data of the mooring point, and the environmental wind field data. When any of the monitoring data exceeds the preset threshold, a working mode switching instruction is output and a safe landing control is executed, including: Setting a safety threshold for the body posture data to obtain posture threshold range data; Setting a safety threshold for the force data of the mooring point to obtain mooring tension threshold range data; Setting safety thresholds for environmental wind field data to obtain wind field threshold range data; Inputting the attitude threshold range data, the mooring tension threshold range data and the wind field threshold range data into the monitoring module for multi-threshold judgment to obtain multi-threshold judgment result data; The control mode is switched according to the multi-threshold judgment result data to obtain the emergency control mode data; The emergency control mode data is input into the attitude protection module for calculation to obtain attitude self-stabilization data, which includes the expected attitude angle and angular velocity; The landing trajectory is planned for the attitude self-stabilization data to obtain the descent speed control data, and the motor control allocation is performed on the descent speed control data to obtain the safe landing control instructions for each motor of the quadrotor.

[0031] In this example, the safety threshold is set for the body attitude data. By analyzing the stability and mission requirements of the drone, the attitude angle range and its change rate limit are determined. )、Roll angle( ) and yaw angle ( ) is expressed as: ; The angular velocity constraint is expressed as: ; These data constitute the attitude threshold range, which is used to determine whether the drone exceeds the stable working range. At the same time, the safety threshold is set for the force data of the mooring point. By analyzing the mechanical properties and ultimate load-bearing capacity of the mooring rope, the allowable range of tension at each mooring point is set. Assuming that the total number of mooring points is , No. The tension at each mooring point is , then its safety threshold is: ; in is the minimum tension to prevent the rope from slacking. is the maximum tension to avoid rope breakage. Set safety thresholds for environmental wind field data and determine the wind resistance of the drone by analyzing the range of wind speed, wind direction, and turbulence intensity. Assume the wind speed is , the wind direction angle is , the turbulence intensity is , then its safety threshold is expressed as: ; These wind field threshold ranges are used to evaluate whether the drone can work normally under the current wind field conditions. The attitude threshold range, tether tension threshold range and wind field threshold range data are input into the monitoring module, which collects the status data of the drone in real time and performs multi-threshold judgment. When the multi-threshold judgment result data indicates that the drone is out of the safety range, the emergency control mode is triggered, and the mode data is input into the attitude protection module for calculation. The attitude protection module generates the desired attitude angle through the attitude self-stabilization algorithm. and angular velocity The self-stabilization algorithm is based on the UAV's dynamic model and calculates the safe attitude target by minimizing attitude errors and disturbance effects: ; in is the target posture, is the current posture, is the control input. The landing trajectory is planned based on the attitude self-stabilization data. By planning the descent path and speed control strategy, the drone can gradually reduce its altitude in a safe manner. Assume that the current altitude is , the target landing altitude is The planned descent speed is , then the trajectory planning is expressed as: ; ; in is the initial height, is the descent speed control gain. The planned descent speed control data is input into the motor control allocation module to calculate the thrust requirements of the quadrotor motors. The thrust allocation distributes the overall thrust and torque requirements to each motor according to the dynamic characteristics of the drone: ; in is the thrust distribution matrix, is the total thrust, They are roll, pitch and yaw moments respectively. The calculated thrust distribution data is mapped to speed, and the thrust is converted into motor speed through the rotor characteristic curve: ; in is the lift coefficient. To ensure the speed safety, the speed is compensated for dead zone and saturated limited: ; in and is the minimum and maximum speed allowed by the motor. Finally, the safe landing control instructions for each motor of the quadrotor are obtained.

[0032] Reference Figure 2 This embodiment provides a control device for tethered UAV wind load resistance, including: Modeling module 1 is used to collect and model the force data, body posture data and environmental wind field data of multiple tethered points of the tethered UAV to obtain a system dynamics model; State estimation module 2, used to perform state estimation based on the system dynamics model to obtain system wind load disturbance data and expected attitude data; A solution module 3 is used to solve the optimal control of multiple mooring points according to the system wind load disturbance data and the expected attitude data, and obtain the tension distribution control data of each mooring point; Calculation module 4, used for inputting system wind load disturbance data and tension distribution control data into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data; The control module 5 is used to perform adaptive robust control on the attitude of the tethered UAV according to the thrust vector compensation control data, and obtain the speed control instructions of each motor of the quadrotor; The judgment module 6 is used to judge the safety threshold of the aircraft body posture data, the force data of the mooring point and the environmental wind field data. When any monitoring data exceeds the preset threshold, it outputs the working mode switching instruction and executes the safe landing control.

[0033] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0034] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0035] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0036] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0037] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0038] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0039] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for controlling wind load of a tethered UAV, characterized in that: The following steps are involved: The force data of multiple tethering points of the tethered UAV, the body posture data and the environmental wind field data are collected and modeled to obtain the system dynamics model; Perform state estimation based on the system dynamics model to obtain system wind load disturbance data and expected attitude data; Performing optimal control solutions for the multiple mooring points according to the system wind load disturbance data and the desired attitude data to obtain tension distribution control data of each mooring point; Inputting the system wind load disturbance data and the tension distribution control data into a discrete time neural network controller to perform attitude compensation calculation to obtain thrust vector compensation control data; Adaptively and robustly controlling the attitude of the tethered UAV according to the thrust vector compensation control data to obtain a speed control instruction for each motor of the quadrotor; A safety threshold is determined for the aircraft body posture data, the force data of the mooring point, and the environmental wind field data. When any of the monitoring data exceeds a preset threshold, a working mode switching instruction is output and safe landing control is executed.

2. The method for controlling wind load of a tethered UAV according to claim 1, characterized in that: The force data of multiple tethering points of the tethered UAV, the body posture data and the environmental wind field data are collected and modeled to obtain a system dynamics model, including: Sampling the force signals of the torque sensors of the multiple mooring points to obtain real-time force data of the mooring points, and fusing the data of the three-axis acceleration sensor and angular velocity sensor of the moored drone to obtain the body posture motion data; Performing spatial interpolation on the data collected by the wind speed and direction sensor array in the operating area of ​​the tethered UAV to obtain environmental wind field distribution data; Based on the real-time force data of the mooring point and the environmental wind field distribution data, mooring mechanics calculation is performed to obtain a mooring rope force model, and kinematic analysis is performed on the body posture motion data and the real-time force data of the mooring point to obtain a body posture motion model; Performing aerodynamic moment calculation on the environmental wind field distribution data to obtain a wind load interference model; The mooring rope force model, the body posture motion model and the wind load interference model are combined to obtain a system comprehensive dynamics model, and parameter identification is performed on the system comprehensive dynamics model to obtain a system dynamics model.

3. The method for controlling wind load of a tethered UAV according to claim 2, characterized in that: The state estimation is performed based on the system dynamics model to obtain system wind load disturbance data and expected attitude data, including: Inputting the system dynamics model into a distributed control network for node division to obtain a plurality of subsystem control units, and setting event trigger thresholds for state data of the plurality of subsystem control units to obtain a trigger condition criterion; Inputting the system dynamics model into a reference model to calculate response characteristics to obtain expected dynamic response data, and performing error calculation on actual states of the plurality of subsystem control units according to the expected dynamic response data to obtain state tracking error data; Adaptively updating the state tracking error data to obtain system wind load disturbance data; The state tracking error data and the trigger condition criterion are input into an event judgment module for comparison to obtain a state update trigger instruction; The windward posture of the aircraft body is optimized according to the state update trigger instruction and the system wind load disturbance data to obtain optimal posture angle data, and the wind resistance characteristics of the optimal posture angle data are analyzed to obtain expected posture data.

4. The method for controlling wind load of a tethered UAV according to claim 3, characterized in that: The optimal control solution of the multiple mooring points is performed according to the system wind load disturbance data and the desired posture data to obtain the tension distribution control data of each mooring point, including: Inputting the system wind load disturbance data and the desired attitude data into the Hamilton-Jacobi-Bellman equation to obtain time interval division data; Setting sub-interval control targets for the time interval division data to obtain local control constraints; Calculating the position of each mooring point relative to the wind direction according to the local control constraint condition to obtain mooring point position distribution data; Input the mooring point position distribution data into the tension calculation module for force analysis to obtain the ideal tension data of the mooring point; Solving the Hamilton-Jacobi-Bellman equation for the ideal tension data of the mooring point to obtain an optimal tension distribution scheme, and inputting the optimal tension distribution scheme into a position actuator for tension adjustment to obtain a mooring point tension control amount; The tension control amount of the mooring point is subjected to tension over-limit protection to obtain tension protection threshold data, and the tension control amount of the mooring point is limited according to the tension protection threshold data to obtain tension distribution control data of each mooring point.

5. The method for controlling wind load of a tethered UAV according to claim 4, characterized in that: The step of inputting the system wind load disturbance data and the tension distribution control data into a discrete time neural network controller for posture compensation calculation to obtain thrust vector compensation control data comprises: Inputting the system wind load disturbance data and the tension distribution control data into the input layer of the discrete time neural network controller, wherein the input layer includes 12 neuron nodes corresponding to the three-dimensional wind load data and the tension data of the four mooring points respectively; The data of the input layer is processed by the first hidden layer, wherein the first hidden layer includes 24 neuron nodes and uses the ReLU activation function for nonlinear mapping to obtain the first layer feature mapping data; The first layer of feature mapping data is input into the wind load compensation control layer for processing. The wind load compensation control layer includes 36 neuron nodes and uses the Tanh activation function to calculate the required thrust compensation amount to obtain wind load compensation data. Performing second hidden layer processing on the wind load compensation data, the second hidden layer includes 24 neuron nodes, and adopts ReLU activation function to perform feature extraction to obtain second layer feature mapping data; The second layer feature map data is input into the posture tracking control layer for processing, the posture tracking control layer includes 18 neuron nodes, and the Sigmoid activation function is used to calculate the expected posture angle to obtain the posture compensation data; Performing third hidden layer processing on the posture compensation data, the third hidden layer includes 12 neuron nodes, and adopts ReLU activation function to perform dimensionality reduction mapping to obtain third layer feature mapping data; Inputting the third layer feature map data into the thrust distribution layer for processing, the thrust distribution layer comprises 8 neuron nodes, and uses a linear activation function to calculate the thrust component of each rotor to obtain thrust distribution data; The thrust distribution data is processed by an output layer, wherein the output layer includes four neuron nodes and is normalized by using a Tanh activation function to obtain thrust vector compensation control data.

6. The method for controlling wind load of a tethered UAV according to claim 5, characterized in that: The method of performing adaptive robust control on the attitude of the tethered drone according to the thrust vector compensation control data to obtain a speed control instruction for each motor of the quadrotor includes: Inputting the thrust vector compensation control data into a nominal controller for decomposition calculation to obtain desired angle data of roll angle, pitch angle and yaw angle; Performing attitude error calculation on the expected angle data to obtain three-axis attitude angle error and angular velocity error data, and inputting the three-axis attitude angle error and angular velocity error data into an adaptive robust controller for tracking control calculation to obtain an attitude angular acceleration control amount; Performing dynamic inverse mapping on the attitude angular acceleration control quantity to obtain the overall thrust control quantity and three-axis torque control quantity of the quadrotor; Solving the thrust mapping matrix according to the overall thrust control amount of the quadrotor and the three-axis torque control amount to obtain thrust distribution data of the four rotor motors; Inputting the thrust distribution data of the four rotor motors into the characteristic curve for speed mapping to obtain equivalent speed data of the four motors, and performing dead zone compensation and saturation limiting on the equivalent speed data of the four motors to obtain safe speed range data; The safe speed range data is input into the electric speed controller driving module for PWM signal conversion to obtain the speed control instructions of each motor of the quadrotor.

7. The method for controlling wind load of a tethered UAV according to claim 6, characterized in that: The safety threshold judgment is performed on the body posture data, the force data of the mooring point and the environmental wind field data, and when any monitoring data exceeds a preset threshold, a working mode switching instruction is output and a safe landing control is executed, including: Setting a safety threshold for the body posture data to obtain posture threshold range data; Setting a safety threshold for the force data of the mooring point to obtain mooring tension threshold range data; Setting a safety threshold for the environmental wind field data to obtain wind field threshold range data; Inputting the attitude threshold range data, the mooring tension threshold range data and the wind field threshold range data into a monitoring module for multi-threshold judgment to obtain multi-threshold judgment result data; Switching the control mode according to the multi-threshold judgment result data to obtain emergency control mode data; Inputting the emergency control mode data into the attitude protection module for calculation to obtain attitude self-stabilization data, wherein the attitude self-stabilization data includes a desired attitude angle and an angular velocity; The attitude self-stabilization data is used for landing trajectory planning to obtain descent speed control data, and the descent speed control data is used for motor control allocation to obtain safe landing control instructions for each motor of the quadrotor.

8. A control device for wind load resistance of a tethered UAV, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the device comprises: A modeling module is used to collect and model the force data of multiple tethering points of the tethered UAV, the body posture data, and the environmental wind field data to obtain a system dynamics model; A state estimation module, used to perform state estimation based on the system dynamics model to obtain system wind load disturbance data and expected attitude data; A solution module, used for performing optimal control solution on the multiple mooring points according to the system wind load disturbance data and the desired attitude data, and obtaining tension distribution control data of each mooring point; A calculation module, used for inputting the system wind load disturbance data and the tension distribution control data into a discrete time neural network controller to perform attitude compensation calculation to obtain thrust vector compensation control data; A control module, used for performing adaptive robust control on the attitude of the tethered UAV according to the thrust vector compensation control data, and obtaining a speed control instruction for each motor of the quadrotor; The judgment module is used to perform safety threshold judgment on the body posture data, the force data of the mooring point and the environmental wind field data. When any monitoring data exceeds a preset threshold, a working mode switching instruction is output and a safe landing control is executed.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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