A wind disturbance resistant unmanned aerial vehicle nonlinear flight control method and system

Abstract

This invention provides a nonlinear flight control method and system for wind-disrupted unmanned aerial vehicles (UAVs). It utilizes a pre-set weather radar to acquire wind field disturbance characteristics in real time; inputs these characteristics into a pre-set aerodynamic disturbance prediction model to generate predicted aerodynamic torque fluctuation values; applies a pre-set dynamic filtering process to the amplitude characteristics of these predicted aerodynamic torque fluctuation values ​​to generate a control command correction signal; and couples this correction signal with a pre-set nonlinear control law of the UAV flight control system to suppress UAV attitude deviation caused by wind disturbance in real time, thus performing nonlinear flight control of the UAV. This invention overcomes the combined defects of lag and filtering mismatch in traditional linear control under strong wind abrupt changes, achieving stable attitude control of the UAV during low-altitude wind shear and sudden gusts, significantly improving the UAV's anti-disturbance response speed, and greatly reducing the risk of UAV loss of control.

Description

Technical Field

[0001] This invention relates to the field of flight control technology, and in particular to a nonlinear flight control method and system for wind-resistant unmanned aerial vehicles. Background Technology

[0002] With the increasing application of drones in complex weather environments, sudden changes in strong winds pose a serious threat to flight stability. In such environments, drones need to have the ability to perceive wind field changes in real time and respond quickly to ensure flight safety and mission completion. Therefore, developing a nonlinear flight control method that can maintain flight attitude stability and improve control accuracy under sudden wind disturbances has become an urgent need for current technological development.

[0003] The current mainstream approach is an adaptive sliding mode control strategy based on inertial measurement unit feedback. This strategy involves high-speed acquisition of the UAV's angular velocity and acceleration information, combined with dynamic deviations caused by wind disturbances, to perform online parameter estimation and adjust the controller gain accordingly. This achieves active compensation for flight attitude and improves the system's robustness to external wind disturbances, especially demonstrating good control performance under low-to-medium frequency wind disturbances. However, existing solutions have some inherent drawbacks, including reliance on airframe state feedback, a lack of feedforward prediction capability for the wind disturbance source itself, and response lag and over-adjustment when facing wind field disturbances. The inherent high-frequency chattering characteristics of sliding mode control are easily amplified when dealing with complex wind disturbances, leading to decreased flight stability and limiting its practical application in extreme wind environments. Summary of the Invention

[0004] This invention provides a nonlinear flight control method and system for wind-resistant unmanned aerial vehicles (UAVs) to address the problems of existing technologies that rely on airframe state feedback, lack the ability to predict the source of wind disturbances themselves, and exhibit response lag and over-adjustment when facing wind field disturbances; the inherent high-frequency chattering characteristics in sliding mode control are easily amplified when dealing with complex wind disturbances, leading to decreased flight stability and limiting its practical application in extreme wind disturbance environments.

[0005] In a first aspect, the present invention provides a nonlinear flight control method for a wind-resistant unmanned aerial vehicle, comprising:

[0006] Real-time acquisition of wind field disturbance characteristics using a pre-set meteorological radar;

[0007] The wind field disturbance characteristics are input into a preset aerodynamic disturbance prediction model to generate aerodynamic torque fluctuation prediction values.

[0008] A preset dynamic filtering process is applied to the amplitude characteristics of the predicted aerodynamic torque fluctuation to generate a control command correction signal;

[0009] The control command correction signal is coupled with the nonlinear control law of the preset UAV flight control system to suppress the attitude deviation of the UAV caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

[0010] Optionally, wind field disturbance characteristics can be acquired in real time using a pre-set weather radar, including:

[0011] The airspace of the target area is scanned using a pre-set weather radar to obtain raw detection data.

[0012] A spatial vector set is extracted from the original detection data, and the spatial vector set is associated with the preset spatial coordinates of the UAV to generate a three-dimensional wind field distribution;

[0013] Extract the time-varying feature components of the three-dimensional wind field distribution, identify the disturbance distribution in the time-varying feature components, and generate wind field disturbance features.

[0014] Optionally, the wind field disturbance characteristics are input into a preset aerodynamic disturbance prediction model to generate predicted values ​​of aerodynamic torque fluctuations, including:

[0015] Extract the spatial feature set from the wind field disturbance features, wherein the spatial feature set includes wind field feature elements such as wind speed change rate and direction change abrupt regions;

[0016] The wind field features are mapped onto the preset UAV body coordinate system to generate a spatial projection distribution;

[0017] Calculate the intensity of the effect of the spatial projection distribution on the preset surface of the UAV, and generate a spatial intensity distribution;

[0018] The spatial action intensity distribution is combined with the preset real-time flight attitude parameters of the UAV to generate aerodynamic torque fluctuation prediction values.

[0019] Optionally, the wind field feature elements are mapped to the preset UAV body coordinate system to generate a spatial projection distribution, including:

[0020] Receive the preset real-time attitude angle parameters of the UAV to obtain the spatial orientation reference of the preset UAV body coordinate system;

[0021] Construct a vector transformation relationship between the wind field feature elements and the spatial orientation reference to generate dynamic transformation rules;

[0022] Based on the dynamic transformation rule, the spatial vectors of the wind field feature elements are projected to generate an initial projection distribution;

[0023] The projection delay portion of the initial projection distribution is compensated to generate a spatial projection distribution.

[0024] Optionally, a preset dynamic filtering process is applied to the amplitude characteristics of the predicted aerodynamic torque fluctuation to generate a control command correction signal, including:

[0025] Monitor the change pattern of the amplitude characteristics of the predicted aerodynamic torque fluctuation, and identify the start and end times of the sudden change in the amplitude characteristics;

[0026] Extract the amplitude change trajectory between the start time and the end time, quantize the amplitude change trajectory, and generate a sudden event intensity parameter;

[0027] Based on the mutation event intensity parameter, calculate the preset dynamic filtering response parameter adjustment amount, convert the response parameter adjustment amount into an instruction encoding format, and generate a filter parameter adjustment instruction;

[0028] The preset dynamic filtering processing operating parameters are updated using the filter parameter adjustment command to generate a dynamic filtering control state;

[0029] The high-frequency disturbance component in the amplitude characteristic is attenuated under the dynamic filtering control state to generate a control command correction signal.

[0030] Optionally, a preset adjustment amount for the response parameters of dynamic filtering is calculated based on the mutation event intensity parameter, and the adjustment amount of the response parameters is converted into an instruction encoding format to generate a filter parameter adjustment instruction, including:

[0031] Based on the data structure characteristics of the mutation event intensity parameter, the data composition of the mutation event intensity parameter is decomposed to obtain the response parameter type and response parameter dimension constraints.

[0032] The preset response rules, the response parameter types, and the response parameter dimension constraints are matched to generate a parameter adjustment calculation template.

[0033] The parameter adjustment calculation template is driven to process the mutation event intensity parameter to generate a preset dynamic filtering response parameter adjustment amount;

[0034] The adjustment amount of the response parameter is encoded based on a preset target data format to generate intermediate encoded data.

[0035] The intermediate encoded data is encapsulated into a preset communication protocol to generate a filter parameter adjustment instruction.

[0036] Optionally, the control command correction signal is coupled with a preset nonlinear control law of the UAV flight control system to suppress UAV attitude deviation caused by wind disturbance in real time, and to perform nonlinear flight control of the UAV, including:

[0037] The time-domain characteristics of the control command correction signal are analyzed to generate the correction signal intensity distribution characteristics;

[0038] Align the modified signal strength distribution characteristics with the gain scheduling parameters of the preset nonlinear control law of the UAV flight control system to generate coupling weight coefficients.

[0039] The coupling weight coefficients are fused with the control equations of the nonlinear control law to generate an updated control law;

[0040] Based on the updated control law, the preset control surface actions of the UAV are driven to generate attitude stabilization commands, so as to suppress the attitude deviation of the UAV caused by wind disturbance in real time.

[0041] In a second aspect, the present invention provides a nonlinear flight control system for a wind-resistant unmanned aerial vehicle, comprising:

[0042] The acquisition module is used to acquire wind field disturbance characteristics in real time using a preset weather radar;

[0043] The generation module is used to input the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate aerodynamic torque fluctuation prediction values.

[0044] An application module is used to apply a preset dynamic filtering process to the amplitude characteristics of the predicted aerodynamic torque fluctuation value to generate a control command correction signal.

[0045] The coupling module is used to couple the control command correction signal with the preset nonlinear control law of the UAV flight control system in order to suppress the attitude deviation of the UAV caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

[0046] Thirdly, the present invention provides a computing device including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a nonlinear flight control method for an anti-wind-affected unmanned aerial vehicle as described in any of the first aspects.

[0047] Fourthly, the present invention provides a computer storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the nonlinear flight control method for wind-resistant unmanned aerial vehicles as described in any one of the first aspects.

[0048] This invention captures wind field disturbance characteristics in real time using weather radar, predicts aerodynamic torque fluctuations using a fluid dynamics model, and generates correction signals using amplitude-driven dynamic filtering. Finally, a coupled nonlinear control law suppresses attitude drift in real time, effectively overcoming the combined defects of lag and filter mismatch in traditional linear control under sudden strong winds. Ultimately, it achieves stable attitude control of UAVs during low-altitude wind shear and sudden gusts, significantly improving disturbance response speed and greatly reducing the risk of loss of control.

[0049] Furthermore, by extracting wind field vector sets through spatial scanning of the target airspace, dynamically associating UAV spatial coordinates to construct a three-dimensional wind field distribution, and identifying disturbance distributions in time-varying features to generate high-precision wind field disturbance features, this provides an anti-interference data source for aerodynamic moment prediction, effectively overcoming the prediction lag defect caused by the disconnect between traditional meteorological data and flight attitude.

[0050] These or other aspects of the invention will become more apparent from the following description of the embodiments. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of a nonlinear flight control method for an unmanned aerial vehicle (UAV) designed to withstand wind disturbance, provided as an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of a nonlinear flight control system for an unmanned aerial vehicle (UAV) designed to withstand wind disturbance, provided as an embodiment of the present invention.

[0054] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0056] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Figure 1 A flowchart of a nonlinear flight control method for an anti-wind-disturbance UAV is provided as an embodiment of the present invention, such as... Figure 1 As shown, the method includes:

[0059] Existing technologies face serious shortcomings in scenarios with sudden strong wind changes: traditional UAV flight control methods rely on historical meteorological data or static models, failing to capture the instantaneous wind field disturbance characteristics of low-level wind shear and sudden gusts in real time; aerodynamic torque prediction uses a simplified linear model, ignoring the nonlinear strong coupling effect caused by sudden airflow changes; control signal processing uses fixed parameter filtering, resulting in phase lag and noise amplification when amplitude fluctuates drastically; and the correction signal is simply superimposed on the flight control law, leading to sluggish dynamic response and attitude instability. To address these issues, this invention proposes the following approach: real-time capture of wind field disturbance characteristics is achieved by dynamically scanning the target airspace with meteorological radar; an aerodynamic disturbance prediction model is constructed using fluid dynamics principles, converting wind field data into high-precision predicted aerodynamic torque fluctuations; adaptive filtering is driven based on the amplitude abrupt changes in the predicted values ​​to generate noise-resistant control command correction signals; finally, this signal is deeply coupled to the core equation of the nonlinear control law to offset the disturbance torque on the UAV's attitude caused by sudden strong wind changes in real time, forming a closed-loop system for wind disturbance resistance that coordinates meteorological perception, physical modeling, dynamic filtering, and control execution across four domains, completely overcoming the control failure bottleneck of traditional methods in sudden strong wind scenarios. Based on this, the present invention provides a nonlinear flight control method for wind-resistant unmanned aerial vehicles, such as... Figure 1 ,include:

[0060] Step 101: Use a pre-set weather radar to acquire wind field disturbance characteristics in real time.

[0061] In this step, wind field disturbance characteristics refer to the three-dimensional dynamic data of strong wind abrupt changes in the airspace detected and extracted by meteorological radar, including the wind speed change rate and direction change area distribution characteristics of the spatial vector set, which are used to characterize the instantaneous interference patterns of low-level wind shear or gust attacks.

[0062] In this embodiment of the invention, firstly, a real-time spatial scan of the UAV's flight airspace is performed using a preset weather radar to obtain raw detection data containing wind speed magnitude and direction components; secondly, a spatial vector set is separated from the raw detection data and dynamically associated with the UAV's current spatial coordinates; then, a three-dimensional wind field distribution model is constructed; finally, the time-varying feature components of the three-dimensional wind field distribution are extracted and the disturbance distribution areas are identified to generate wind field disturbance features characterizing the sudden change characteristics of strong winds.

[0063] Step 102: Input the wind field disturbance characteristics into the preset aerodynamic disturbance prediction model to generate aerodynamic torque fluctuation prediction values.

[0064] In this step, the preset aerodynamic disturbance prediction model refers to a computational model built based on the principles of fluid mechanics, which is used to map the characteristics of wind field disturbance to the distribution of fluid action intensity in the UAV body coordinate system, reflecting the nonlinear fluctuation law of aerodynamic torque; the predicted value of aerodynamic torque fluctuation refers to the quantitative parameter output by the aerodynamic disturbance prediction model, which is used to describe the instantaneous change amplitude and time sequence characteristics of the UAV roll or pitch torque caused by sudden strong wind changes.

[0065] In this embodiment of the invention, the wind field disturbance characteristics are first input into a preset aerodynamic disturbance prediction model to identify the wind speed change rate and direction change region in the characteristics; then the relationship between the above spatial characteristics and the UAV body coordinate system is calculated to determine the distribution of fluid action intensity; then the real-time flight attitude parameters of the UAV are combined; finally, the aerodynamic torque fluctuation prediction value of quantified nonlinear torque fluctuation is generated through fluid dynamics principles.

[0066] Step 103: Apply a preset dynamic filtering process to the amplitude characteristics of the predicted aerodynamic torque fluctuation to generate a control command correction signal.

[0067] In this step, amplitude characteristics refer to the amplitude characteristics of the predicted aerodynamic torque fluctuation over time, including the rise slope, peak intensity, and duration of abrupt events; preset dynamic filtering refers to the signal processing process that adaptively adjusts the cutoff frequency and phase parameters according to the amplitude abrupt change characteristics, used to suppress high-frequency noise and maintain the real-time performance of the control signal; control command correction signal refers to the optimized command generated by preset dynamic filtering, used to compensate for control deviations caused by wind disturbances.

[0068] In this embodiment of the invention, the amplitude characteristic change pattern of the predicted aerodynamic torque fluctuation is first monitored to identify the start and end times of amplitude mutation; then, the amplitude change trajectory within the time window is extracted and quantified to generate a mutation event intensity parameter; subsequently, the filter response parameter is dynamically adjusted based on the intensity parameter to generate a filter control command; finally, the high-frequency disturbance component in the amplitude characteristic is attenuated under the updated filter control state to generate a control command correction signal to suppress noise interference.

[0069] Step 104: Couple the control command correction signal with the preset nonlinear control law of the UAV flight control system to suppress the attitude deviation of the UAV caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

[0070] In this step, the preset UAV flight control system refers to a closed-loop system including sensors, controllers, and actuators, used to achieve stable flight attitude control; the nonlinear control law refers to the control equation using a variable gain scheduling mechanism, used to handle the strong aerodynamic coupling effect under sudden changes in strong winds; the coupling operation refers to the dynamic fusion process of embedding the control command correction signal into the nonlinear control law according to the weight coefficient, so as to realize the synergistic effect of the anti-disturbance signal and the control command; the UAV attitude deviation refers to the deviation of the pitch angle and roll angle of the aircraft from the target heading caused by sudden changes in strong winds.

[0071] In this embodiment of the invention, the time-domain distribution characteristics of the control command correction signal are first analyzed to determine its intensity variation law; then, the distribution is dynamically aligned with the gain scheduling parameters of the nonlinear control law to generate coupling weight coefficients; subsequently, the weight coefficients are fused into the core equation of the nonlinear control law to reconstruct the updated control law; finally, the updated control law is applied to drive the UAV control surface actuator to generate a stable flight command that can offset attitude deviation in real time.

[0072] This invention captures wind field disturbance characteristics in real time using weather radar and accurately predicts torque fluctuations using a fluid dynamics model; it uses amplitude characteristics to drive dynamic filtering to generate noise-resistant correction signals; and finally, it deeply couples nonlinear control laws to suppress attitude deviations in real time, significantly improving the flight stability and anti-disturbance capability of UAVs in low-altitude wind shear and gust attack scenarios.

[0073] To address the prediction lag caused by the disconnect between meteorological data and the spatial location of UAVs, this step acquires raw detection data through spatial scanning, dynamically correlates UAV coordinates to generate a three-dimensional wind field distribution, and identifies disturbance distributions in time-varying characteristics. This invention provides a specific embodiment, step 101, which utilizes a preset meteorological radar to acquire wind field disturbance characteristics in real time, specifically including the following steps:

[0074] Step 111: Use a pre-set weather radar to perform a spatial scan of the pre-set target airspace to obtain raw detection data.

[0075] In this step, the space scanning operation refers to the detection behavior of the weather radar emitting electromagnetic beams to perform a two-dimensional traversal of the target airspace in terms of azimuth and elevation angles, in order to obtain the Doppler frequency shift signal of airflow motion; the raw detection data refers to the digital information of the raw echo signal received by the space scanning after demodulation processing, including radial wind speed, signal-to-noise ratio and spectral width parameters.

[0076] In this embodiment of the invention, firstly, a spatial scanning operation is performed on the target airspace using a preset weather radar, which emits electromagnetic waves and receives reflected signals; secondly, the reflected signals are converted into raw detection data containing wind speed, wind direction, and turbulence intensity; finally, real-time data acquisition is completed for areas with sudden strong wind changes.

[0077] Step 112: Extract a spatial vector set from the original detection data, associate the spatial vector set with the preset spatial coordinates of the UAV, and generate a three-dimensional wind field distribution.

[0078] In this step, the spatial vector set refers to the set of wind speed vectors extracted from the original detection data. Each vector contains three-dimensional attributes of magnitude, direction, and spatial location. The association operation refers to the spatial mapping process of transforming the geographic coordinate system of the spatial vector set to the UAV body coordinate system, which is used to unify the data reference benchmark. The three-dimensional wind field distribution refers to the spatial gridded airflow model generated through the association operation. Each grid point contains a wind speed vector and a time stamp.

[0079] In this embodiment of the invention, wind speed and wind direction components are first separated from the original detection data to generate a spatial vector set; then, the real-time spatial coordinates of the UAV are obtained; subsequently, the spatial vector set and the UAV spatial coordinates are correlated by performing a geographic coordinate transformation operation; finally, a three-dimensional wind field distribution reflecting the three-dimensional dynamic distribution of airflow in the airspace is constructed.

[0080] Step 113: Extract the time-varying feature components of the three-dimensional wind field distribution, identify the disturbance distribution in the time-varying feature components, and generate wind field disturbance features.

[0081] In this step, the time-varying feature component refers to the differential characteristics of the three-dimensional wind field distribution in the time dimension, reflecting the trend of abrupt changes in airflow acceleration and direction at a specific location; the identification operation refers to the gradient threshold detection and regional clustering analysis based on the time-varying feature component to locate the spatial range of continuous airflow anomalies; the disturbance distribution refers to the non-uniform airflow region markers output by the identification operation, including the wind shear layer height, the range of the gust core area, and the vortex intensity level.

[0082] In this embodiment of the invention, the time-varying feature components that change with time in the three-dimensional wind field distribution are first extracted; then the spatial gradient change and time dimension abrupt change characteristics of the time-varying feature components are analyzed; then the spatial region of violent airflow disturbance is located by identification operation; finally, the disturbance distribution characterizing the core area of ​​wind shear or gust is generated and the wind field disturbance characteristics are output.

[0083] This invention acquires high-resolution wind field data through spatial scanning, dynamically correlates the pose of a UAV to generate a three-dimensional airflow model, accurately identifies the disturbance distribution area in time-varying features, and provides real-time wind field disturbance features that are resistant to projection errors for aerodynamic moment prediction.

[0084] To improve the accuracy of aerodynamic moment fluctuation prediction, this step extracts the spatial feature set from the wind field disturbance features, maps it to the body coordinate system to calculate the surface interaction intensity, and fuses real-time attitude parameters to generate predicted values. This invention provides a specific embodiment where step 102 involves inputting the wind field disturbance features into a preset aerodynamic disturbance prediction model to generate predicted aerodynamic moment fluctuation values, specifically including the following steps:

[0085] Step 201: Extract the spatial feature set from the wind field disturbance features, wherein the spatial feature set includes wind field feature elements such as wind speed change rate and direction change abrupt regions.

[0086] In this step, the spatial feature set refers to the set of physical quantities extracted from the wind field disturbance features, including the time derivative of the wind speed change rate and the spatial gradient of the direction change region, reflecting the core dynamic characteristics of strong wind changes; the wind speed change rate refers to the magnitude of wind speed change per unit time, used to characterize the acceleration characteristics of gust attacks; the direction change region refers to the spatial range in which the airflow direction changes beyond a threshold over a short distance, marking the core area of ​​wind shear; and the wind field feature elements refer to the specific physical parameters of the spatial feature set, including the scalar field of the wind speed change rate and the vector field of the direction change region.

[0087] In this embodiment of the invention, firstly, a spatial feature set is separated from the wind field disturbance characteristics. This set includes core elements such as the rate of change of wind speed and the region of sudden change in direction. Secondly, the time derivative of the rate of change of wind speed and the spatial gradient magnitude of the region of sudden change in direction are quantified. Finally, they are integrated into a set of wind field feature elements that describe the characteristics of sudden changes in strong winds.

[0088] Step 202: Map the wind field feature elements to the preset UAV body coordinate system to generate a spatial projection distribution.

[0089] In this step, the mapping operation refers to the mathematical process of transforming the wind field characteristics of the geographic coordinate system to the body coordinate system through a rotation transformation matrix; the body coordinate system refers to the right-handed coordinate system with the UAV's center of mass as the origin and the vertical axis pointing in the direction of the nose; the spatial projection distribution refers to the three-dimensional force distribution model generated after the mapping operation, which describes the force components of the airflow on each axis of the body.

[0090] In this embodiment of the invention, the real-time roll angle, pitch angle and yaw angle parameters of the UAV are first obtained; then the transformation relationship between the geographic coordinate system of the wind field characteristic elements and the UAV body coordinate system is established; then the wind speed change rate and the direction change region are projected onto the three axes of the body; finally, a spatial projection distribution reflecting the spatial distribution of airflow force is generated.

[0091] Step 203: Calculate the intensity of the effect of the spatial projection distribution on the preset surface of the UAV, and generate a spatial effect intensity distribution.

[0092] In this step, the spatial action intensity distribution refers to the aerodynamic force distribution per unit area obtained by integrating the pressure coefficient of the body surface.

[0093] In this embodiment of the invention, the normal and tangential components of the spatial projection distributed on the surface of the body are first analyzed; then, the pressure coefficient distribution of each component is calculated according to the principles of fluid mechanics; subsequently, the pressure coefficient is integrated to obtain the force per unit area; finally, all surface forces are combined to generate the spatial force intensity distribution.

[0094] Step 204: Combine the spatial action intensity distribution with the preset real-time flight attitude parameters of the UAV to generate aerodynamic torque fluctuation prediction values.

[0095] In this step, the real-time flight attitude parameters refer to the measured Euler angles and angular velocity values ​​of the UAV at the current moment; the combined operation refers to the vector synthesis process of merging the spatial action intensity distribution with the angular velocity after coordinate transformation according to the attitude parameters.

[0096] In this embodiment of the invention, the real-time flight attitude parameters of the UAV are first read, including angle of attack, sideslip angle and angular velocity; then the spatial action intensity distribution is rotated according to the attitude parameters; then the product relationship between the transformed intensity distribution and the angular acceleration is fused; finally, the predicted aerodynamic torque fluctuation values ​​with quantified torque fluctuation amplitude and direction are generated.

[0097] This invention significantly improves the accuracy of torque fluctuation modeling in strong wind abrupt change scenarios by extracting the core features of wind field abrupt change and accurately mapping them to the body coordinate system, calculating the dynamic intensity of airflow on the surface, and then fusing real-time attitude parameters to generate high-fidelity aerodynamic torque prediction.

[0098] To address the projection delay error during coordinate system transformation, this step constructs dynamic transformation rules based on real-time attitude, and compensates for time delay data after projection to generate an accurate spatial projection distribution. This invention provides a specific embodiment where step 202 maps the wind field feature elements to the preset UAV body coordinate system to generate a spatial projection distribution, specifically including the following steps:

[0099] Step 221: Receive the preset real-time attitude angle parameters of the UAV to obtain the spatial orientation reference of the preset UAV body coordinate system.

[0100] In this step, the real-time attitude angle parameters refer to the Euler angle measurements of the UAV at the current moment, including roll angle, pitch angle and yaw angle, which are used to determine the spatial orientation of the aircraft; the spatial orientation reference refers to the coordinate system reference frame calculated by the attitude angle parameters, including the position of the origin of the aircraft coordinate system and the orientation of the three axes.

[0101] In this embodiment of the invention, real-time attitude angle parameters, including roll angle, pitch angle and yaw angle, are first obtained through the UAV attitude sensor; then, the rotation matrix of the body coordinate system relative to the geographic coordinate system is calculated based on the attitude angle parameters; finally, the spatial orientation reference of the body coordinate system is determined based on the rotation matrix.

[0102] Step 222: Construct the vector transformation relationship between the wind field feature elements and the spatial orientation reference to generate dynamic transformation rules.

[0103] In this step, the construction operation refers to establishing the mathematical mapping relationship between the wind field vector and the body reference, including the generation of the rotation matrix and the definition of the coordinate transformation equation; the vector transformation relationship refers to the linear transformation rule describing the projection of the wind field vector from the geographic coordinate system to the body coordinate system; the dynamic transformation rule refers to the set of coordinate transformation parameters that are updated in real time with the attitude angle, including the rotation matrix elements and time-varying correction coefficients.

[0104] In this embodiment of the invention, the wind speed vector and the vector of the direction change region in the wind field feature elements are first analyzed; then the coordinate transformation relationship between the wind field vector and the spatial orientation reference is established; subsequently, the transformation matrix is ​​corrected by combining the real-time angular velocity parameters of the UAV; finally, dynamic transformation rules that adapt to attitude changes are generated.

[0105] Step 223: Project the spatial vectors of the wind field feature elements based on the dynamic transformation rules to generate an initial projection distribution.

[0106] In this step, the projection operation refers to the vector operation process of decomposing the wind field vector into three-axis components of the body coordinate system according to the transformation rules; the initial projection distribution refers to the uncompensated data generated by the projection operation, which includes spatiotemporal mismatch errors caused by transmission delay.

[0107] In this embodiment of the invention, the spatial vector of the wind field feature elements is first decomposed into three-axis components of the body according to the dynamic transformation rule; then, the normal projection value of each component on the surface of the body is calculated; then, all projection values ​​are integrated to form an initial projection distribution; finally, the spatial discretization modeling of the airflow force is completed.

[0108] Step 224: Compensate for the projection delay portion of the initial projection distribution to generate a spatial projection distribution.

[0109] In this step, the projection delay refers to the data segment in the initial projection distribution that has a time deviation from the current actual attitude, caused by sensor response delay; the compensation operation refers to the numerical reconstruction process that predicts and corrects the delayed data based on the attitude change trend.

[0110] In this embodiment of the invention, firstly, time-delayed data segments in the initial projection distribution caused by attitude angle update delay are detected; secondly, projection value deviation is compensated by extrapolation based on the attitude angle change rate; subsequently, the temporal continuity of the projection distribution is reconstructed; and finally, a spatial projection distribution with eliminated delay error is generated.

[0111] The embodiments of the present invention provide a spatiotemporally synchronized spatial projection distribution for aerodynamic torque prediction by dynamically establishing attitude-driven vector transformation rules, accurately projecting wind field characteristics to the body coordinate system, and compensating for transmission delay errors.

[0112] To improve the adaptability of dynamic filtering in scenarios with abrupt amplitude changes, this step identifies amplitude change events and quantifies intensity parameters. After dynamically adjusting the filtering parameters, high-frequency disturbances are attenuated to generate a correction signal. This invention provides a specific embodiment where step 103 applies a preset dynamic filtering process to the amplitude characteristics of the predicted aerodynamic torque fluctuation to generate a control command correction signal. This specifically includes the following steps:

[0113] Step 301: Monitor the change pattern of the amplitude characteristics of the predicted aerodynamic torque fluctuation, and identify the start and end times of the sudden change in the amplitude characteristics.

[0114] In this step, the change pattern refers to the trend characteristics of the amplitude characteristics over time, including the rise slope, peak intensity, and decay rate parameters.

[0115] In this embodiment of the invention, the amplitude characteristics of the predicted aerodynamic torque fluctuation are first monitored in real time to observe the change pattern over time; then, the rising and falling edges of the amplitude abrupt change are detected; subsequently, the starting moment when the amplitude characteristics exceed the dynamic threshold is located; and finally, the ending moment when the amplitude characteristics return to the stable range is determined.

[0116] Step 302: Extract the amplitude change trajectory between the start time and the end time, quantify the amplitude change trajectory, and generate a mutation event intensity parameter.

[0117] In this step, the amplitude change trajectory refers to the continuous change curve formed by the amplitude characteristics within the interval from the start to the end time; the quantization operation refers to the mathematical integration process of calculating the area enclosed by the amplitude change trajectory curve and the time axis; the mutation event intensity parameter refers to the scalar value output by the quantization operation, reflecting the total energy accumulation of the mutation process.

[0118] In this embodiment of the invention, firstly, the amplitude characteristic time series data from the start time to the end time is extracted; secondly, the amplitude change trajectory curve within this time period is extracted; then, the trajectory curve is integrally quantized to calculate the area value; finally, a mutation event intensity parameter characterizing the energy intensity of the mutation event is generated.

[0119] Step 303: Calculate the preset dynamic filtering response parameter adjustment amount based on the mutation event intensity parameter, convert the response parameter adjustment amount into an instruction encoding format, and generate a filter parameter adjustment instruction.

[0120] In this step, the response parameter adjustment amount refers to the modified value of the dynamic filtering processing parameters calculated based on the intensity parameters, including the cutoff frequency offset and gain correction coefficient; the conversion operation refers to the encoding process of converting the numerical adjustment amount into binary machine instructions that the flight control system can recognize; the instruction encoding format refers to the data structure that conforms to the UAV communication protocol, including the instruction type code, parameter value and check byte field; the filter parameter adjustment instruction refers to the encapsulated control command used to trigger the filter parameter update operation.

[0121] In this embodiment of the invention, firstly, a preset response parameter mapping table is queried based on the intensity parameter of the mutation event; secondly, the cutoff frequency adjustment amount and gain compensation value of the dynamic filtering process are calculated; then, the adjustment amount is converted into a binary instruction encoding format; finally, the check bit is encapsulated to generate a transmittable filter parameter adjustment instruction.

[0122] Step 304: Update the preset working parameters of the dynamic filtering process using the filter parameter adjustment command to generate a dynamic filtering control state.

[0123] In this step, the update operation refers to writing the new parameters into the filter register and activating the effective hardware control behavior; the operating parameters refer to the core configuration items of dynamic filtering, including cutoff frequency, stopband attenuation, and phase delay coefficient; the dynamic filtering control status refers to the operating mode flag that the filter enters after the parameter update is completed.

[0124] In this embodiment of the invention, the working parameter update item in the filter parameter adjustment instruction is first parsed; then the filter coefficient register of dynamic filtering is loaded; subsequently, the new cutoff frequency and gain parameters are written; finally, the parameters are activated to generate a dynamic filtering control status flag.

[0125] Step 305: Attenuate the high-frequency disturbance component in the amplitude characteristic under the dynamic filtering control state to generate a control command correction signal.

[0126] In this step, attenuation operation refers to the signal processing behavior of amplitude compression of a signal in a specific frequency band; high-frequency disturbance component refers to the noise signal component in the amplitude characteristics whose frequency is higher than a set threshold.

[0127] In this embodiment of the invention, the amplitude characteristic signal is first acquired under dynamic filtering control; then a high-pass filter is designed to separate the high-frequency disturbance component; subsequently, an attenuation operation is performed on the component to suppress the amplitude; finally, a smoothed control command correction signal is output.

[0128] The embodiments of the present invention significantly improve the stability of control signals under strong wind sudden change scenarios by accurately identifying amplitude change events and quantifying energy intensity, dynamically adjusting filtering parameters to suppress high-frequency noise, and generating anti-interference control command correction signals.

[0129] To ensure the real-time performance and compatibility of the filter parameter adjustment instructions, this step decomposes the intensity parameter data structure, matches response rules to generate a calculation template, and encapsulates it into executable instructions. This invention provides a specific embodiment: Step 303, based on the abrupt event intensity parameters, calculates the preset dynamic filtering response parameter adjustment amount, converts the response parameter adjustment amount into an instruction encoding format, and generates the filter parameter adjustment instructions, specifically including the following steps:

[0130] Step 331: Based on the data structure characteristics of the mutation event intensity parameter, decompose the data composition of the mutation event intensity parameter to obtain the response parameter type and response parameter dimension constraints.

[0131] In this step, data structure characteristics refer to the memory organization of the mutation event intensity parameters, including data dimensions, byte length, and storage order, reflecting the physical meaning and computational constraints of the parameters; response parameter type refers to the category of parameters that need to be adjusted in dynamic filtering, including control variables such as cutoff frequency, quality factor, and gain coefficient; response parameter dimensional constraints refer to the physical unit restriction rules for parameter types, such as mandatory specifications where the frequency unit is Hertz and the gain unit is decibel.

[0132] In this embodiment of the invention, the data structure characteristics of the mutation event intensity parameter are first analyzed, including data dimensions and unit systems; then, the data constituent elements are decomposed according to the feature type; subsequently, the parameter value range and dimensional attributes are separated; and finally, the response parameter type and its dimensional constraint rules are output.

[0133] Step 332: Match the preset response rules, the response parameter types, and the response parameter dimension constraints to generate a parameter adjustment calculation template.

[0134] In this step, the preset response rules refer to the predefined intensity parameter-adjustment mapping relationship library, which includes calculation strategies such as linear scaling and logarithmic transformation; the matching operation refers to the verification process that verifies the logical consistency between the response rules, parameter types, and dimensional constraints; the parameter adjustment calculation template refers to the executable calculation process generated by the matching operation, which includes the input interface, operation logic, and output format definition.

[0135] In this embodiment of the invention, a preset response rule library is first invoked; then the response rules are logically matched with the parameter types; subsequently, the compatibility of dimensional constraints is verified; and finally, a parameter adjustment calculation template containing calculation logic is generated.

[0136] Step 333: Drive the parameter adjustment calculation template to process the mutation event intensity parameter and generate a preset dynamic filtering response parameter adjustment amount.

[0137] In this step, the processing operation refers to the driving behavior of injecting the mutation event intensity parameter into the calculation template to perform numerical transformation.

[0138] In this embodiment of the invention, firstly, a parameter adjustment calculation template is loaded; secondly, the intensity parameter of the mutation event is input to the template calculation unit; then, the calculation process defined by the template is executed; finally, the preset dynamic filtering response parameter adjustment amount is output.

[0139] Step 334: Encode the adjustment amount of the response parameter based on the preset target data format to generate intermediate encoded data.

[0140] In this step, the preset target data format refers to the binary encoding specification required by the flight control system, including data bit width, byte order, and floating-point representation standard; the encoding operation refers to the conversion process of converting numerical parameters into machine code according to the target data format; the intermediate encoded data refers to the original binary sequence generated by the encoding operation, without the addition of communication protocol elements.

[0141] In this embodiment of the invention, the preset target data format specification is first read; then the response parameter adjustment amount is decomposed bit by bit; subsequently converted into a binary encoding sequence; and finally, intermediate encoded data that conforms to machine recognition is generated.

[0142] Step 335: Encapsulate the intermediate encoded data into a preset communication protocol to generate a filter parameter adjustment instruction.

[0143] In this step, the encapsulation operation refers to the protocol-based packaging behavior of adding frame headers, address codes, and check codes to intermediate encoded data; the preset communication protocol refers to the message structure defined by the UAV bus transmission standard, such as CAN 2.0B or MAVLink protocol.

[0144] In this embodiment of the invention, a preset communication protocol framework is first obtained; then a protocol header and a checksum are added to the intermediate encoded data; subsequently, the data packet structure is encapsulated; and finally, a transmittable filter parameter adjustment instruction is generated.

[0145] This invention generates a calculation template through intelligent matching rules, achieving precise conversion of intensity parameters into adjustment quantities. The template is then encoded and encapsulated to form control commands that can be transmitted in real time, ensuring zero-delay dynamic adjustment of filter parameters in scenarios of sudden strong wind changes.

[0146] To overcome the response hysteresis caused by the simple superposition of the correction signal and the control law, this step analyzes the time-domain characteristics of the signal to generate coupling weights, and then deeply fuses the nonlinear control law to drive the control surface to suppress attitude deviation. This invention provides a specific embodiment: Step 104, coupling the control command correction signal with the preset nonlinear control law of the UAV flight control system to suppress UAV attitude deviation caused by wind disturbance in real time, performing nonlinear flight control of the UAV, specifically including the following steps:

[0147] Step 401: Analyze the time-domain characteristics of the control command correction signal to generate the correction signal intensity distribution characteristics.

[0148] In this step, time-domain characteristics refer to the waveform attributes of the control command correction signal in the time dimension, including rise time, peak duration, and attenuation slope; analytical operation refers to the mathematical process of performing piecewise integration and differentiation calculations on the signal waveform to extract energy distribution characteristics; and corrected signal strength distribution characteristics refer to the non-uniform distribution model of signal energy on the time axis, reflecting the temporal intensity of the anti-disturbance control force.

[0149] In this embodiment of the invention, firstly, time-domain waveform data of the control command correction signal is acquired; secondly, the rate of change and duration characteristics of the signal amplitude over time are extracted; then, the energy integral value within a unit time window is calculated; finally, the corrected signal strength distribution characteristics reflecting the spatial distribution of the signal strength are generated.

[0150] Step 402: Align the modified signal strength distribution characteristics with the gain scheduling parameters of the preset nonlinear control law of the UAV flight control system to generate coupling weight coefficients.

[0151] In this step, the gain scheduling parameter refers to the set of proportional, integral, and derivative coefficients that are dynamically adjusted according to the flight state in the nonlinear control law; the alignment operation refers to the optimization process of matching the overlap between the signal strength distribution frequency band and the effective frequency band of the gain parameter; and the coupling weight coefficient refers to the correction factor of the signal strength characteristics on the gain parameter in the control law fusion.

[0152] In this embodiment of the invention, the gain scheduling parameter set of the nonlinear control law is first read; then the frequency band coverage of the corrected signal strength distribution characteristics and the gain parameters is matched; then the correlation coefficient between the characteristic parameters and the gain parameters is calculated; finally, the control law coupling weight coefficient is generated.

[0153] Step 403: Fuse the coupling weight coefficients with the control equations of the nonlinear control law to generate an updated control law.

[0154] In this step, the control equations refer to the set of differential equations describing the attitude dynamics of the UAV, which includes position loop and attitude loop coupling terms; the fusion operation refers to the coefficient update behavior of writing weight coefficients into the gain adjustment matrix of the control equations; the updated control law refers to the set of variable gain differential equations reconstructed by the weight coefficients, which enhances the disturbance rejection capability.

[0155] In this embodiment of the invention, the control equation structure of the nonlinear control law is first analyzed; then, the coupling weight coefficients are embedded into the gain adjustment term of the control equation; subsequently, the variable gain differential equation is reconstructed; and finally, the nonlinear control law with updated parameters is generated.

[0156] Step 404: Drive the preset control surface actions of the UAV based on the updated control law to generate attitude stabilization commands, so as to suppress the attitude deviation of the UAV caused by wind disturbance in real time.

[0157] In this step, control surface actions refer to the physical deflection behavior of the UAV control surface mechanism, including aileron roll control, elevator pitch control, and rudder yaw control; attitude stabilization commands refer to the deflection angle and directional control amount that drive the control surfaces to generate deflection angles and directional control amounts to counteract wind disturbance torque.

[0158] In this embodiment of the invention, the control interface of the UAV's control surface mechanism is first obtained; then the updated control law is discretized into a sequence of control surface deflection commands; subsequently, the ailerons, elevators, and rudder are driven to coordinate their actions; finally, a real-time stabilization command to eliminate attitude deviation is generated.

[0159] This invention achieves zero-offset stable control of UAV attitude under strong wind sudden change scenarios by dynamically aligning the correction signal and the gain parameter to generate coupling weights, deeply integrating and reconstructing the nonlinear control law, and driving the control surface to accurately counteract wind disturbance torque.

[0160] Figure 2 This invention provides a schematic diagram of the structure of a wind-resistant unmanned aerial vehicle (UAV) nonlinear flight control system, as shown in the embodiment of the invention. Figure 2 As shown, the system includes:

[0161] The acquisition module 21 is used to acquire wind field disturbance characteristics in real time using a preset weather radar;

[0162] The generation module 22 is used to input the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate aerodynamic torque fluctuation prediction values.

[0163] The application module 23 is used to apply a preset dynamic filtering process to the amplitude characteristics of the predicted aerodynamic torque fluctuation value to generate a control command correction signal.

[0164] The coupling module 24 is used to couple the control command correction signal with the preset nonlinear control law of the UAV flight control system in order to suppress the attitude deviation of the UAV caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

[0165] Figure 2 The aforementioned wind-resistant UAV nonlinear flight control system can perform... Figure 1 The implementation principle and technical effects of the wind-resistant UAV nonlinear flight control method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the wind-resistant UAV nonlinear flight control system described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0166] In one possible design, Figure 2 The wind-resistant unmanned aerial vehicle (UAV) nonlinear flight control system of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0167] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0168] The processing component 32 is used to: acquire wind field disturbance characteristics in real time using a preset weather radar; input the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate aerodynamic torque fluctuation prediction values; apply a preset dynamic filtering process to the amplitude characteristics of the aerodynamic torque fluctuation prediction values ​​to generate control command correction signals; and couple the control command correction signals with a preset nonlinear control law of the UAV flight control system to suppress UAV attitude deviation caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

[0169] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0170] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0171] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0172] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0173] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0174] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0175] This invention also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a nonlinear flight control method for a wind-resistant unmanned aerial vehicle (UAV).

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0177] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0178] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A nonlinear flight control method for wind-resistant unmanned aerial vehicles (UAVs), characterized in that, include: Real-time acquisition of wind field disturbance characteristics using a pre-set meteorological radar; The process involves inputting the wind field disturbance features into a preset aerodynamic disturbance prediction model to generate predicted aerodynamic torque fluctuation values. This includes: extracting a spatial feature set from the wind field disturbance features, wherein the spatial feature set includes wind field feature elements such as wind speed change rate and direction change abrupt regions; mapping the wind field feature elements onto the preset UAV body coordinate system to generate a spatial projection distribution; calculating the intensity of the effect of the spatial projection distribution on the preset UAV body surface to generate a spatial effect intensity distribution; and combining the spatial effect intensity distribution with the preset real-time flight attitude parameters of the UAV to generate predicted aerodynamic torque fluctuation values. A preset dynamic filtering process is applied to the amplitude characteristics of the predicted aerodynamic torque fluctuation to generate a control command correction signal; The control command correction signal is coupled with the nonlinear control law of the preset UAV flight control system to suppress the attitude deviation of the UAV caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

2. The method according to claim 1, characterized in that, The characteristics of wind field disturbances are acquired in real time using a pre-set weather radar, including: The airspace of the target area is scanned using a pre-set weather radar to obtain raw detection data. A spatial vector set is extracted from the original detection data, and the spatial vector set is associated with the preset spatial coordinates of the UAV to generate a three-dimensional wind field distribution; Extract the time-varying feature components of the three-dimensional wind field distribution, identify the disturbance distribution in the time-varying feature components, and generate wind field disturbance features.

3. The method according to claim 1, characterized in that, Mapping the wind field features onto the preset UAV body coordinate system to generate a spatial projection distribution includes: Receive the preset real-time attitude angle parameters of the UAV to obtain the spatial orientation reference of the preset UAV body coordinate system; Construct a vector transformation relationship between the wind field feature elements and the spatial orientation reference to generate dynamic transformation rules; Based on the dynamic transformation rule, the spatial vectors of the wind field feature elements are projected to generate an initial projection distribution; The projection delay portion of the initial projection distribution is compensated to generate a spatial projection distribution.

4. The method according to claim 1, characterized in that, Applying a preset dynamic filtering process to the amplitude characteristics of the predicted aerodynamic torque fluctuation to generate a control command correction signal includes: Monitor the change pattern of the amplitude characteristics of the predicted aerodynamic torque fluctuation, and identify the start and end times of the sudden change in the amplitude characteristics; Extract the amplitude change trajectory between the start time and the end time, quantize the amplitude change trajectory, and generate a sudden event intensity parameter; Based on the mutation event intensity parameter, calculate the preset dynamic filtering response parameter adjustment amount, convert the response parameter adjustment amount into an instruction encoding format, and generate a filter parameter adjustment instruction; The preset dynamic filtering processing operating parameters are updated using the filter parameter adjustment command to generate a dynamic filtering control state; The high-frequency disturbance component in the amplitude characteristic is attenuated under the dynamic filtering control state to generate a control command correction signal.

5. The method according to claim 4, characterized in that, Based on the mutation event intensity parameter, a preset dynamic filtering response parameter adjustment amount is calculated, and the response parameter adjustment amount is converted into an instruction encoding format to generate a filter parameter adjustment instruction, including: Based on the data structure characteristics of the mutation event intensity parameter, the data composition of the mutation event intensity parameter is decomposed to obtain the response parameter type and response parameter dimension constraints. The preset response rules, the response parameter types, and the response parameter dimension constraints are matched to generate a parameter adjustment calculation template. The parameter adjustment calculation template is driven to process the mutation event intensity parameter and generate a preset dynamic filtering response parameter adjustment amount. The adjustment amount of the response parameter is encoded based on a preset target data format to generate intermediate encoded data. The intermediate encoded data is encapsulated into a preset communication protocol to generate a filter parameter adjustment instruction.

6. The method according to claim 1, characterized in that, The control command correction signal is coupled with a preset nonlinear control law of the UAV flight control system to suppress UAV attitude deviation caused by wind disturbance in real time, and to perform nonlinear flight control of the UAV, including: The time-domain characteristics of the control command correction signal are analyzed to generate the correction signal intensity distribution characteristics; Align the modified signal strength distribution characteristics with the gain scheduling parameters of the preset nonlinear control law of the UAV flight control system to generate coupling weight coefficients. The coupling weight coefficients are fused with the control equations of the nonlinear control law to generate an updated control law; Based on the updated control law, the preset control surface actions of the UAV are driven to generate attitude stabilization commands, so as to suppress the attitude deviation of the UAV caused by wind disturbance in real time.

7. A nonlinear flight control system for a wind-resistant unmanned aerial vehicle (UAV), characterized in that, include: The acquisition module is used to acquire wind field disturbance characteristics in real time using a preset weather radar; The generation module is used to input the wind field disturbance features into a preset aerodynamic disturbance prediction model to generate aerodynamic torque fluctuation prediction values. This includes: extracting a spatial feature set from the wind field disturbance features, wherein the spatial feature set includes wind field feature elements such as wind speed change rate and direction change abrupt regions; mapping the wind field feature elements to the preset UAV body coordinate system to generate a spatial projection distribution; calculating the intensity of the effect of the spatial projection distribution on the preset UAV body surface to generate a spatial effect intensity distribution; and combining the spatial effect intensity distribution with the preset real-time flight attitude parameters of the UAV to generate aerodynamic torque fluctuation prediction values. An application module is used to apply a preset dynamic filtering process to the amplitude characteristics of the predicted aerodynamic torque fluctuation value to generate a control command correction signal. The coupling module is used to couple the control command correction signal with the preset nonlinear control law of the UAV flight control system in order to suppress the attitude deviation of the UAV caused by wind disturbance in real time and perform nonlinear flight control of the UAV.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the nonlinear flight control method for wind-resistant unmanned aerial vehicles as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a nonlinear flight control method for wind-resistant unmanned aerial vehicles as described in any one of claims 1 to 6.

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