Non-linear flight control method and system for anti-wind disturbance unmanned aerial vehicle

Abstract

The invention provides a non-linear flight control method and system for an anti-wind disturbance unmanned aerial vehicle. The method comprises the following steps: acquiring wind field disturbance characteristics in real time by using a preset meteorological radar; inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic torque fluctuation prediction value; preset dynamic filtering processing is applied to the amplitude characteristic of the aerodynamic torque fluctuation predicted value so as to generate a control instruction correction signal; and the control instruction correction signal is coupled with a preset nonlinear control law of an unmanned aerial vehicle flight control system, so that unmanned aerial vehicle attitude offset caused by wind disturbance is inhibited in real time, and unmanned aerial vehicle nonlinear flight control is carried out. According to the method, the composite defect of hysteresis and filtering mismatch of traditional linear control in a strong wind sudden change scene is overcome, stable attitude control of the unmanned aerial vehicle in low-altitude wind shear and gust sudden attack is realized, the anti-interference response speed of the unmanned aerial vehicle is remarkably improved, and the out-of-control risk of the unmanned aerial vehicle is greatly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flight control, and in particular to a wind disturbance resistant unmanned aerial vehicle nonlinear flight control method and system. BACKGROUND

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

[0003] The current mainstream solution is an adaptive sliding mode control strategy based on inertial measurement unit feedback. By collecting the angular velocity and acceleration information of the unmanned aerial vehicle body at high speed, online parameter estimation is performed in combination with the dynamic deviation caused by wind disturbance, and the controller gain is adjusted accordingly, thereby achieving active compensation of the flight attitude and improving the robustness of the system to external wind disturbance. Especially under the action of medium and low frequency wind disturbance, it shows good control effect. The existing scheme has some inherent defects, including dependence on body state feedback, lack of feedforward prediction ability for the source of wind disturbance, and response lag and over-regulation phenomenon when facing wind field disturbance; the inherent high-frequency chattering characteristic of sliding mode control is easily amplified when dealing with complex wind disturbance, leading to a decrease in flight stability and limiting its practical application effect in extreme wind disturbance environments. SUMMARY

[0004] The present application provides a wind disturbance resistant unmanned aerial vehicle nonlinear flight control method and system to solve the problems of the prior art, such as dependence on body state feedback, lack of feedforward prediction ability for the source of wind disturbance, and response lag and over-regulation phenomenon when facing wind field disturbance; the inherent high-frequency chattering characteristic of sliding mode control is easily amplified when dealing with complex wind disturbance, leading to a decrease in flight stability and limiting its practical application effect in extreme wind disturbance environments.

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

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

[0007] Inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value;

[0008] Applying a preset dynamic filtering process to the amplitude characteristics of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal;

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

[0010] Optionally, a preset weather radar is used to obtain wind field disturbance features in real time, including:

[0011] The preset weather radar is used to perform spatial scanning on a preset target airspace to obtain original detection data.

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

[0013] Time-varying feature components of the three-dimensional wind field distribution are extracted, and disturbance distribution in the time-varying feature components is identified to generate wind field disturbance features.

[0014] Optionally, the wind field disturbance features are input into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value, including:

[0015] A set of spatial features in the wind field disturbance features is extracted, where the set of spatial features includes wind speed variation rate and direction mutation region and other wind field feature elements.

[0016] The wind field feature elements are mapped into a body coordinate system of the preset UAV to generate a spatial projection distribution.

[0017] An action intensity distribution of the spatial projection distribution on a body surface of the preset UAV is calculated to generate a spatial action intensity distribution.

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

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

[0020] Real-time attitude angle parameters of the preset UAV are received to obtain a spatial orientation reference of the body coordinate system of the preset UAV.

[0021] A vector conversion relationship between the wind field feature elements and the spatial orientation reference is constructed to generate a dynamic conversion rule.

[0022] The spatial vectors of the wind field feature elements are projected based on the dynamic conversion rule to generate an initial projection distribution.

[0023] A projection delay part 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 characteristic of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal, including:

[0025] A change mode of the amplitude characteristic of the aerodynamic moment fluctuation prediction value is monitored to identify a starting time and an ending time of a mutation of the amplitude characteristic;

[0026] An amplitude change trajectory between the starting time and the ending time is extracted, and the amplitude change trajectory is quantized to generate a mutation event intensity parameter;

[0027] A response parameter adjustment amount of the preset dynamic filtering process is calculated based on the mutation event intensity parameter, the response parameter adjustment amount is converted into an instruction code format to generate a filtering parameter adjustment instruction;

[0028] The working parameters of the preset dynamic filtering process are updated using the filtering parameter adjustment instruction to generate a dynamic filtering control state;

[0029] High-frequency disturbance components in the amplitude characteristic are attenuated in the dynamic filtering control state to generate a control instruction correction signal.

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

[0031] The data structure of the mutation event intensity parameter is decomposed based on the data structure characteristics of the mutation event intensity parameter to obtain a response parameter type and a response parameter dimension constraint;

[0032] A parameter adjustment amount calculation template is generated by matching a preset response rule, the response parameter type, and the response parameter dimension constraint;

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

[0034] The response parameter adjustment amount 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 filtering parameter adjustment instruction.

[0036] Optionally, the control instruction correction signal is coupled with a preset nonlinear control law of an unmanned aerial vehicle flight control system to real-time suppress attitude deviation of the unmanned aerial vehicle caused by wind disturbance and perform nonlinear flight control of the unmanned aerial vehicle, including:

[0037] analyzing time domain features of the control instruction correction signal to generate a correction signal intensity distribution feature;

[0038] aligning the correction signal intensity distribution feature with preset gain scheduling parameters of a nonlinear control law of a UAV flight control system to generate a coupling weight coefficient;

[0039] fusing the coupling weight coefficient with a control equation of the nonlinear control law to generate an updated control law;

[0040] driving a preset control surface action of a UAV based on the updated control law to generate an attitude stabilization instruction to real-time suppress UAV attitude deviation caused by wind disturbance.

[0041] In a second aspect, the present application provides an anti-wind-disturbance UAV nonlinear flight control system, comprising:

[0042] an acquisition module configured to acquire wind field disturbance features in real time by using a preset weather radar;

[0043] a generation module configured to input the wind field disturbance features into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value;

[0044] an application module configured to apply a preset dynamic filtering process to an amplitude characteristic of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal;

[0045] a coupling module configured to couple the control instruction correction signal with a nonlinear control law of a preset UAV flight control system to real-time suppress UAV attitude deviation caused by wind disturbance and perform UAV nonlinear flight control.

[0046] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the anti-wind-disturbance UAV nonlinear flight control method according to any one of the first aspect.

[0047] In a fourth aspect, the present application provides a computer storage medium storing computer program instructions, wherein the computer program instructions are executed by a processor to implement the anti-wind-disturbance UAV nonlinear flight control method according to any one of the first aspect.

[0048] The application captures the wind field disturbance characteristics in real time through the weather radar, combines the fluid mechanics model to predict the aerodynamic moment fluctuation, adopts the amplitude characteristic driven dynamic filtering to generate the correction signal, and finally couples the nonlinear control law to suppress the attitude deviation in real time, effectively overcomes the composite defects of the traditional linear control hysteresis and filter mismatch in the strong wind mutation scene.

[0049] Further, the wind field vector set is extracted through the space scanning of the target airspace, the three-dimensional wind field distribution is constructed by dynamically correlating the spatial coordinates of the unmanned aerial vehicle, and the disturbance distribution in the time-varying characteristics is identified to generate high-precision wind field disturbance characteristics, so as to provide an anti-interference data source for the aerodynamic moment prediction, and effectively overcome the prediction lag defect caused by the disconnection between the traditional weather data and the flight posture.

[0050] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0052] Figure 1 A flow chart of an anti-wind interference unmanned aerial vehicle nonlinear flight control method provided by the embodiment of the present application;

[0053] Figure 2 A structural schematic diagram of an anti-wind interference unmanned aerial vehicle nonlinear flight control system provided by the embodiment of the present application;

[0054] Figure 3 A structural schematic diagram of a computing device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings of the embodiments of the present application.

[0056] In some of the flowcharts described in the description, claims, and drawings of the present application and above, a plurality of operations are included in a particular order, but it should be clearly understood that these operations can be performed in the order they appear in this paper or in parallel, the serial numbers of the operations such as 101, 102, etc. are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these flowcharts can include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this paper are used to distinguish different messages, devices, modules, etc. and do not represent the order and do not limit that "first" and "second" are different types.

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application combined with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0058] Figure 1 A flowchart of an anti-wind interference unmanned aerial vehicle nonlinear flight control method is provided for the embodiments of the present application, as shown in Figure 1 , the method comprises:

[0059] The prior art faces serious defects in the strong wind mutation scene: the traditional unmanned aerial vehicle flight control method relies on historical meteorological data or static model, and cannot capture the instantaneous wind field disturbance characteristics of low altitude wind shear and gust attack in real time; the aerodynamic moment prediction adopts a linear simplified model, ignoring the nonlinear strong coupling effect caused by sudden airflow; the control signal processing uses fixed parameter filtering, which produces phase lag and noise amplification when the amplitude fluctuates sharply; the correction signal and the flight control law are simply superimposed, resulting in dynamic response delay and attitude instability. To solve these problems, the research and development idea of the present application is: real-time capture of wind field disturbance characteristics by meteorological radar dynamic scanning of target airspace, construction of aerodynamic disturbance prediction model combined with fluid mechanics principles, conversion of wind field data into high-precision aerodynamic moment fluctuation prediction value; generate noise-resistant control instruction correction signal based on the amplitude mutation characteristics of the prediction value; finally, deeply couple the signal to the core equation of the nonlinear control law, real-time offset the interference moment of strong wind mutation on the attitude of the unmanned aerial vehicle, form an anti-wind interference closed-loop system of meteorological perception, physical modeling, dynamic filtering and control execution four domains, and completely break through the control failure bottleneck of traditional methods in sudden strong wind scene. Based on this, the present application provides an anti-wind interference unmanned aerial vehicle nonlinear flight control method, as shown in Figure 1 , comprising:

[0060] Step 101: Real-time acquisition of wind field disturbance characteristics by using a preset meteorological radar.

[0061] In this step, the wind field disturbance feature refers to the three-dimensional dynamic data of strong wind mutation space detected and extracted by the meteorological radar, including the wind speed change rate and the distribution characteristics of the direction mutation region of the space vector set, for characterizing the instantaneous interference mode of low-altitude wind shear or gust attack. In the embodiment of the present application, firstly, the preset meteorological radar performs real-time spatial scanning on the flight space of the unmanned aerial vehicle to obtain original detection data containing wind speed and direction components; secondly, the space vector set is separated from the original detection data, and the space vector set is dynamically associated with the current spatial coordinates of the unmanned aerial vehicle; 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 region is identified, and the wind field disturbance feature characterizing the strong wind mutation is generated.

[0063] Step 102: input the wind field disturbance feature into the preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value.

[0064] In this step, the preset aerodynamic disturbance prediction model refers to a calculation model constructed based on the principle of fluid mechanics, which is used to map the wind field disturbance feature into the fluid action intensity distribution under the body coordinate system of the unmanned aerial vehicle, reflecting the nonlinear fluctuation law of the aerodynamic moment; the aerodynamic moment fluctuation prediction value refers to a quantitative parameter output by the aerodynamic disturbance prediction model, which is used to describe the instantaneous change amplitude and time sequence characteristics of the rolling or pitching moment of the unmanned aerial vehicle caused by strong wind mutation.

[0065] In the embodiment of the present application, firstly, the wind field disturbance feature is input into the preset aerodynamic disturbance prediction model to identify the wind speed change rate and the direction mutation region in the feature; secondly, the action relationship of the above-mentioned spatial feature on the body coordinate system of the unmanned aerial vehicle is calculated to determine the fluid action intensity distribution; then the real-time flight attitude parameters of the unmanned aerial vehicle are combined; finally, the aerodynamic moment fluctuation prediction value of the quantitative nonlinear moment fluctuation is generated through the principle of fluid mechanics.

[0066] Step 103: apply a preset dynamic filtering process to the amplitude characteristics of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal.

[0067] In this step, the amplitude characteristics refer to the amplitude characteristics of the aerodynamic moment fluctuation prediction value changing with time, including the rising slope, peak intensity and duration of the mutation event; the preset dynamic filtering process refers to a signal processing process of adaptively adjusting the cutoff frequency and phase parameters according to the amplitude mutation characteristics, which is used to suppress high-frequency noise and maintain the real-time nature of the control signal; the control instruction correction signal refers to the optimized instruction generated by the preset dynamic filtering process, which is used to compensate for the control deviation caused by wind disturbance.

[0068] In the embodiment of the application, firstly, the amplitude characteristic change mode of the aerodynamic moment fluctuation prediction value is monitored, and the starting and ending moments of amplitude mutation are identified; secondly, the amplitude change trajectory in the time window is extracted and quantitatively calculated to generate a mutation event strength parameter; then, the filter response parameter is dynamically adjusted according to the strength parameter to generate a filter control instruction; finally, the high-frequency disturbance component in the amplitude characteristic is attenuated under the updated filter control state to generate a control instruction correction signal for suppressing noise interference.

[0069] Step 104: coupling the control instruction correction signal with the preset nonlinear control law of the unmanned aerial vehicle flight control system to generate a control instruction correction signal for suppressing the attitude deviation of the unmanned aerial vehicle caused by wind disturbance in real time and performing nonlinear flight control of the unmanned aerial vehicle.

[0070] In this step, the preset unmanned aerial vehicle flight control system includes a closed-loop system of sensors, controllers and actuators for realizing flight attitude stability control; the nonlinear control law refers to a control equation using a variable gain scheduling mechanism for processing aerodynamic strong coupling effects under strong wind mutation; the coupling operation refers to embedding the control instruction correction signal into the dynamic fusion process of the nonlinear control law according to a weight coefficient to realize the synergistic effect of the anti-interference signal and the control instruction; the attitude deviation of the unmanned aerial vehicle refers to the deviation of the body pitch angle and roll angle from the target heading caused by strong wind mutation.

[0071] In the embodiment of the application, firstly, the time domain distribution characteristics of the control instruction correction signal are analyzed to determine the strength variation law; secondly, the distribution is dynamically aligned with the gain scheduling parameter of the nonlinear control law to generate a coupling weight coefficient; then, the weight coefficient is 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 unmanned aerial vehicle rudder actuator to generate a stable flight instruction for real-time offset of the attitude deviation.

[0072] The embodiment of the application captures the wind field disturbance characteristics in real time by using a weather radar, accurately predicts the moment fluctuation by combining a fluid mechanics model, generates an anti-noise correction signal by using the amplitude characteristics to drive dynamic filtering, and finally suppresses the attitude deviation in real time by deeply coupling the nonlinear control law, thereby significantly improving the flight stability and anti-interference ability of the unmanned aerial vehicle in the scene of low-altitude wind shear and gust attack.

[0073] In order to solve the prediction lag problem caused by the disconnection of meteorological data and the spatial position of the unmanned aerial vehicle, the original detection data is obtained by spatial scanning, and the disturbance distribution in the time-varying characteristics is dynamically associated with the unmanned aerial vehicle coordinates to generate a three-dimensional wind field distribution. The application provides a specific embodiment, step 101, which uses a preset weather radar to obtain wind field disturbance characteristics in real time, specifically including the following steps:

[0074] Step 111: using a preset weather radar to perform spatial scanning on a preset target airspace to obtain original detection data.

[0075] In this step, the space scanning operation refers to the detection behavior of the meteorological radar emitting electromagnetic beams to traverse the target airspace in azimuth and elevation angles for obtaining the Doppler frequency shift signal of the air flow movement; the original detection data refer to the digital information of the original echo signal received by the space scanning after demodulation processing, including the radial wind speed, signal-to-noise ratio and spectrum width parameters.

[0076] In the embodiment of the present application, first, the space scanning operation is performed on the target airspace by the preset meteorological radar, electromagnetic waves are emitted and reflected signals are received; second, the reflected signals are converted into original detection data containing wind speed, wind direction and turbulence intensity; and finally, the real-time data acquisition of the strong wind mutation area is completed.

[0077] Step 112: Extracting a set of spatial vectors from the original detection data, associating the set of spatial vectors with the spatial coordinates of the preset unmanned aerial vehicle, and generating a three-dimensional wind field distribution.

[0078] In this step, the set of spatial vectors refers to the set of wind speed vectors extracted from the original detection data, each vector containing three-dimensional attributes of size, direction and spatial position; the association operation refers to the spatial mapping process of converting the geographical coordinate system of the set of spatial vectors to the body coordinate system of the unmanned aerial vehicle, for unifying the data reference datum; the three-dimensional wind field distribution refers to the spatial gridded air flow model generated by the association operation, each grid point containing a wind speed vector and a time stamp.

[0079] In the embodiment of the present application, first, the wind speed and wind direction components are separated from the original detection data to generate a set of spatial vectors; second, the real-time spatial coordinates of the unmanned aerial vehicle are obtained; then, the set of spatial vectors and the real-time spatial coordinates of the unmanned aerial vehicle are associated by geographical coordinate conversion; and finally, a three-dimensional wind field distribution reflecting the three-dimensional dynamic distribution of the airspace air flow is constructed.

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

[0081] In this step, the time-varying feature component refers to the differential feature of the three-dimensional wind field distribution in the time dimension, reflecting the air flow acceleration and direction mutation trend at a specific position; the identification operation refers to the gradient threshold detection and regional clustering analysis based on the time-varying feature component, locating the spatial range of the continuous air flow anomaly; the disturbance distribution refers to the non-uniform air flow area label 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 the embodiment of the present application, firstly, the time-varying characteristic component changing with time in the three-dimensional wind field distribution is extracted; secondly, the spatial gradient change and time dimension mutation characteristics of the time-varying characteristic component are analyzed; then, the spatial region of airflow severe disturbance is located through the identification operation; finally, the disturbance distribution representing the wind shear or gust core region is generated and the wind field disturbance feature is output.

[0083] The embodiment of the present application obtains high-resolution wind field data through spatial scanning, generates a three-dimensional airflow model by dynamically correlating the unmanned aerial vehicle pose, accurately identifies the disturbance distribution region in the time-varying characteristic, and provides the real-time wind field disturbance feature resistant to projection error for aerodynamic moment prediction.

[0084] In order to improve the accuracy of aerodynamic moment fluctuation prediction, the spatial feature set in the wind field disturbance feature is extracted, mapped to the body coordinate system to calculate the surface action intensity, and fused with real-time attitude parameters to generate a prediction value. The present application provides a specific embodiment, step 102, inputting the wind field disturbance feature into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value, specifically including the following steps:

[0085] Step 201: Extracting a spatial feature set in the wind field disturbance feature, wherein the spatial feature set contains wind speed change rate and direction mutation region and other wind field characteristic elements.

[0086] In this step, the spatial feature set refers to the physical quantity set extracted from the wind field disturbance feature, including the time derivative of the wind speed change rate and the spatial gradient of the direction mutation region, reflecting the core dynamic characteristics of strong wind mutation; the wind speed change rate refers to the change amplitude of the wind speed per unit time, used to characterize the acceleration characteristics of the gust attack; the direction mutation region refers to the spatial range in which the airflow direction changes by more than a threshold value in a short distance, marking the wind shear core region; the wind field characteristic element refers to the specific physical parameter of the spatial feature set, including the wind speed change rate scalar field and the direction mutation region vector field.

[0087] In the embodiment of the present application, firstly, the spatial feature set is separated from the wind field disturbance feature, which contains core elements such as wind speed change rate and direction mutation region; secondly, the time differential value of the wind speed change rate and the spatial gradient amplitude of the direction mutation region are quantified; finally, the wind field characteristic element set describing the strong wind mutation characteristics is integrated.

[0088] Step 202: Mapping the wind field characteristic element to the body coordinate system of the preset unmanned aerial vehicle to generate a spatial projection distribution.

[0089] In this step, the mapping operation refers to a mathematical process of converting the wind field feature elements of the geographic coordinate system to the body coordinate system through a rotation transformation matrix; the body coordinate system refers to a right-handed coordinate system with the center of mass of the unmanned aerial vehicle as the origin and the longitudinal axis pointing to the head direction; the spatial projection distribution refers to a three-dimensional force distribution model generated after the mapping operation, describing the force components of the airflow on each axis of the body.

[0090] In the embodiment of the application, firstly, real-time roll angle, pitch angle and yaw angle parameters of the unmanned aerial vehicle are acquired; secondly, a conversion relationship between the geographic coordinate system of the wind field feature elements and the body coordinate system of the unmanned aerial vehicle is established; then, the wind speed variation rate and the direction mutation region are projected to the three-axis direction of the body; finally, a spatial projection distribution reflecting the spatial distribution of the airflow force is generated.

[0091] Step 203: calculating the action intensity of the spatial projection distribution on the preset body surface of the unmanned aerial vehicle, and generating a spatial action intensity distribution.

[0092] In this step, the spatial action intensity distribution refers to the unit area aerodynamic force distribution obtained by integrating the normal component and the tangential component of the body surface.

[0093] In the embodiment of the application, firstly, the normal component and the tangential component of the spatial projection distribution on the body surface are analyzed; secondly, the pressure coefficient distribution of each component is calculated according to the principle of fluid mechanics; then, the unit area force is obtained by integrating the pressure coefficient; finally, the spatial action intensity distribution is generated by synthesizing all the surface forces.

[0094] Step 204: combining the spatial action intensity distribution with the preset real-time flight attitude parameters of the unmanned aerial vehicle to generate an aerodynamic moment fluctuation prediction value.

[0095] In this step, the real-time flight attitude parameters refer to the Euler angle and angular velocity measurement values of the unmanned aerial vehicle at the current time; the combination operation refers to a vector synthesis process of combining the spatial action intensity distribution after coordinate transformation according to the attitude parameters with the angular velocity.

[0096] In the embodiment of the application, firstly, the real-time flight attitude parameters of the unmanned aerial vehicle are read, including the angle of attack, the sideslip angle and the angular velocity; secondly, the spatial action intensity distribution is rotated and transformed according to the attitude parameters; then, the product relationship between the transformed intensity distribution and the angular acceleration is fused; finally, the aerodynamic moment fluctuation prediction value quantifying the amplitude and direction of the moment fluctuation is generated.

[0097] The embodiment of the application significantly improves the modeling accuracy of the moment fluctuation in the strong wind mutation scene by extracting the mutation core features of the wind field and accurately mapping them to the body coordinate system, calculating the dynamic action intensity of the airflow on the surface, and then fusing the real-time attitude parameters to generate a high-fidelity aerodynamic moment prediction.

[0098] In order to solve the projection delay error in the coordinate system conversion process, a dynamic conversion rule is constructed based on the real-time attitude in this step, and the time delay data is compensated to generate an accurate spatial projection distribution. The present application provides a specific embodiment, step 202, mapping the wind field characteristic elements to the preset unmanned aerial vehicle body coordinate system to generate a spatial projection distribution, specifically including the following steps:

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

[0100] In this step, the real-time attitude angle parameter refers to the Euler angle measurement value of the unmanned aerial vehicle at the current time, including the roll angle, pitch angle and yaw angle, which is used to determine the body spatial orientation; the spatial orientation reference refers to the coordinate system reference frame calculated by the attitude angle parameter, including the body coordinate system origin position and three-axis pointing.

[0101] In the embodiment of the present application, first, the real-time attitude angle parameter is obtained by the unmanned aerial vehicle attitude sensor, including the roll angle, pitch angle and yaw angle; second, the rotation matrix of the body coordinate system relative to the geographic coordinate system is calculated according to the attitude angle parameter; finally, the spatial orientation reference of the body coordinate system is determined based on the rotation matrix.

[0102] Step 222: Construct the vector conversion relationship between the wind field characteristic elements and the spatial orientation reference to generate a dynamic conversion rule.

[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 rotation matrix generation and the coordinate transformation equation definition; the vector conversion relationship refers to the linear transformation rule describing the projection of the geographic coordinate system wind field vector to the body coordinate system; the dynamic conversion rule refers to the coordinate transformation parameter set updated in real time with the attitude angle, including the rotation matrix elements and the time-varying correction coefficient.

[0104] In the embodiment of the present application, first, the wind speed vector and the direction mutation area vector in the wind field characteristic elements are analyzed; second, the coordinate transformation relationship between the wind field vector and the spatial orientation reference is established; then the transformation matrix is corrected combined with the real-time angular velocity parameter of the unmanned aerial vehicle; finally, the dynamic conversion rule suitable for attitude change is generated.

[0105] Step 223: Project the spatial vector of the wind field characteristic elements based on the dynamic conversion rule 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 the three-axis components of the body coordinate system according to the conversion rule; the initial projection distribution refers to the uncompensated data generated by the projection operation, which contains the space-time mismatch error caused by transmission delay.

[0107] In the embodiment of the present application, first, the spatial vector of the wind field characteristic element is decomposed into three-axis components of the body according to the dynamic conversion rule; second, the normal projection values of each component on the body surface are calculated; then, all the 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 the projection delay part of the initial projection distribution to generate a spatial projection distribution.

[0109] In this step, the projection delay part 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 of predicting and correcting the delay data based on the attitude change trend.

[0110] In the embodiment of the present application, first, the time lag data segment caused by the attitude angle update delay in the initial projection distribution is detected; second, the projection value deviation is extrapolated and compensated according to the attitude angle change rate; then, the time continuity of the projection distribution is reconstructed; finally, the spatial projection distribution that eliminates the delay error is generated.

[0111] The embodiment of the present application dynamically establishes the attitude-driven vector conversion rule, accurately projects the wind field characteristics into the body coordinate system, and compensates for the transmission delay error, thereby providing a spatial projection distribution that is time and space synchronized for aerodynamic moment prediction.

[0112] In order to improve the adaptability of dynamic filtering in the amplitude mutation scene, this step identifies the amplitude mutation event and quantifies the intensity parameter, and generates a correction signal after dynamically adjusting the filtering parameter to attenuate high-frequency disturbance. The present application provides a specific embodiment, step 103, applying a preset dynamic filtering process to the amplitude characteristics of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal, specifically including the following steps:

[0113] Step 301: monitor the change mode of the amplitude characteristics of the aerodynamic moment fluctuation prediction value, and identify the starting time and ending time of the amplitude characteristics mutation.

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

[0115] In the embodiment of the present application, first, the change mode of the amplitude characteristics of the aerodynamic moment fluctuation prediction value over time is monitored in real time; second, the rising edge and falling edge inflection points of the amplitude mutation are detected; then, the starting time of the amplitude characteristics exceeding the dynamic threshold is located; finally, the ending time of the amplitude characteristics returning to the stable interval is determined.

[0116] Step 302: extract the amplitude change trajectory between the starting time and the ending time, and generate a mutation event intensity parameter by quantizing the amplitude change trajectory.

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

[0118] In the embodiment of the present application, first, the amplitude characteristic time series data from the start time to the end time is intercepted; second, the amplitude variation trajectory curve in this period is extracted; then, the integral quantization operation is performed on the trajectory curve to calculate the area value; and finally, the mutation event intensity parameter representing the energy intensity of the mutation event is generated.

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

[0120] In this step, the response parameter adjustment amount refers to the dynamic filtering process parameter modification value calculated according to the intensity parameter, including the cutoff frequency offset and the gain correction coefficient; the conversion operation refers to the encoding process of converting the numerical adjustment amount into binary machine instructions recognizable by the flight control system; the instruction code format refers to the data structure conforming to the unmanned aerial vehicle communication protocol, including the instruction type code, the parameter value, and the check byte segment; and the filter parameter adjustment instruction refers to the encapsulated control command used to trigger the filter parameter update operation.

[0121] In the embodiment of the present application, first, the preset response parameter mapping table is queried according to the mutation event intensity parameter; second, the cutoff frequency adjustment amount and the gain compensation value of the dynamic filtering process are calculated; then, the adjustment amount is converted into a binary instruction code format; and finally, the check bit is encapsulated to generate a transmittable filter parameter adjustment instruction.

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

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

[0124] In the embodiment of the present application, first, the working parameter update item in the filter parameter adjustment instruction is parsed; second, the filter coefficient register of the dynamic filtering process is loaded; then, the new cutoff frequency and gain parameters are written; and finally, the parameter takes effect to generate a dynamic filtering control state flag.

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

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

[0127] In the embodiment of the application, first, the amplitude characteristic signal is collected in the dynamic filtering control state; second, a high-pass filter is designed to separate the high-frequency disturbance component; then, the attenuation operation is performed on the component to suppress the amplitude; and finally, the control instruction correction signal after the smoothing processing is output.

[0128] The embodiment of the application can significantly improve the stability of the control signal in the strong wind mutation scene by accurately identifying the amplitude mutation event and quantifying the energy intensity, dynamically adjusting the filtering parameter to suppress the high-frequency noise, and generating the anti-interference control instruction correction signal.

[0129] To ensure the real-time and compatibility of the filtering parameter adjustment instruction, the intensity parameter data structure is decomposed in this step, the calculation template is generated by matching the response rule, and the executable instruction is formed by encoding and packaging. The application provides a specific embodiment, step 303, based on the mutation event intensity parameter, the response parameter adjustment amount of the preset dynamic filtering processing is calculated, the response parameter adjustment amount is converted into an instruction code format, the filtering parameter adjustment instruction is generated, and the specific steps include the following steps:

[0130] Step 331: based on the data structure characteristics of the mutation event intensity parameter, the data structure of the mutation event intensity parameter is decomposed to obtain the response parameter type and the response parameter dimension constraint.

[0131] In this step, the data structure characteristics refer to the memory organization form of the mutation event intensity parameter, including the data dimension, the byte length, and the storage order, reflecting the physical meaning and the calculation constraint of the parameter; the response parameter type refers to the parameter category that needs to be adjusted in the dynamic filtering processing, including the control variables such as the cutoff frequency, the quality factor, and the gain coefficient; and the response parameter dimension constraint refers to the parameter type physical unit limit rule, such as the mandatory specification of the frequency unit in hertz and the gain unit in decibels.

[0132] In the embodiment of the application, first, the data structure characteristics of the mutation event intensity parameter are analyzed, including the data dimension and the unit system; second, the data structure elements are decomposed according to the characteristic type; then, the parameter value domain and the dimension attribute are separated; and finally, the response parameter type and the dimension constraint rule are output.

[0133] Step 332: matching the preset response rule, the response parameter type, and the response parameter dimension constraint to generate a parameter adjustment amount calculation template.

[0134] In this step, the preset response rule refers to a predefined intensity parameter-adjustment mapping relationship library, including linear scaling, logarithmic conversion and other calculation strategies; the matching operation refers to a verification process of verifying the logical consistency of the response rule, parameter type and dimension constraint; and the parameter adjustment amount calculation template refers to an executable calculation flow generated by the matching operation, including input interface, operation logic and output format definition.

[0135] In the embodiment of the present application, firstly, a preset response rule library is called; secondly, the response rule is logically matched with the parameter type; then, the compatibility of the dimension constraint is verified; and finally, a parameter adjustment amount calculation template containing calculation logic is generated.

[0136] Step 333: driving the parameter adjustment amount calculation template to process the mutation event intensity parameter to generate a preset dynamically filtered 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 conversion.

[0138] In the embodiment of the present application, firstly, the parameter adjustment amount calculation template is loaded; secondly, the mutation event intensity parameter is input to the template operation unit; then, the calculation flow defined by the template is executed; and finally, the preset dynamically filtered response parameter adjustment amount is output.

[0139] Step 334: encoding the response parameter adjustment amount based on a 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 sequence 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; and the intermediate encoded data refers to the original binary sequence generated by the encoding operation, which has not yet added communication protocol elements.

[0141] In the embodiment of the present application, firstly, the preset target data format specification is read; secondly, the response parameter adjustment amount is bit-decomposed; then, it is converted into a binary encoding sequence; and finally, intermediate encoded data conforming to machine recognition is generated.

[0142] Step 335: encapsulating 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 protocolized packaging behavior of adding a frame header, address code and check code to the intermediate encoded data; and the preset communication protocol refers to the unmanned aerial vehicle bus transmission standard, such as the CAN 2.0B or MAVLink protocol defined message structure.

[0144] In the embodiment of the application, first, a preset communication protocol framework is acquired; second, a protocol header and a check bit are added to the intermediate coded data; then, a data packet structure is encapsulated; and finally, a transmittable filter parameter adjustment instruction is generated.

[0145] The embodiment of the application realizes accurate conversion of the strength parameter to the adjustment amount by intelligently matching the rule to generate a calculation template, forms a control instruction that can be transmitted in real time through coding and encapsulation, and ensures zero delay of dynamic adjustment of the filter parameter in a strong wind mutation scenario.

[0146] In order to overcome the response delay caused by the simple superposition of the correction signal and the control law, the signal time domain characteristics are analyzed to generate a coupling weight, and the control surface is driven after deep fusion of the nonlinear control law to suppress the attitude deviation. The application provides a specific embodiment, step 104, coupling the control instruction correction signal with the preset nonlinear control law of the unmanned aerial vehicle flight control system to suppress the attitude deviation of the unmanned aerial vehicle caused by the wind disturbance in real time, and performing nonlinear flight control of the unmanned aerial vehicle, specifically including the following steps:

[0147] Step 401: analyzing the time domain characteristics of the control instruction correction signal to generate a correction signal strength distribution characteristic.

[0148] In this step, the time domain characteristics refer to the waveform attributes of the control instruction correction signal in the time dimension, including the rise time, peak duration and decay slope; the analysis operation refers to the segmented integral and differential calculation of the signal waveform, and the mathematical process of extracting the energy distribution characteristics; the correction signal strength distribution characteristic refers to the non-uniform distribution model of the signal energy on the time axis, reflecting the time sequence action strength of the disturbance control force.

[0149] In the embodiment of the application, first, the time domain waveform data of the control instruction correction signal is collected; second, the signal amplitude change rate and the duration characteristics are extracted; then, the energy integral value in a unit time window is calculated; and finally, the correction signal strength distribution characteristic reflecting the signal strength spatial distribution is generated.

[0150] Step 402: aligning the correction signal strength distribution characteristic with the gain scheduling parameter of the preset nonlinear control law of the unmanned aerial vehicle flight control system to generate a coupling weight coefficient.

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

[0152] In the embodiment of the present application, first, the gain scheduling parameter set of the nonlinear control law is read; second, the strength distribution characteristics of the correction signal are matched with the frequency band coverage of the gain parameters; then, the correlation coefficient of the characteristic parameters and the gain parameters is calculated; finally, the control law coupling weight coefficient is generated.

[0153] Step 403: The coupling weight coefficient is fused with the control equation of the nonlinear control law to generate an updated control law.

[0154] In this step, the control equation refers to a differential equation set describing the attitude dynamics relationship of the unmanned aerial vehicle, including the position loop and attitude loop coupling term; the fusion operation refers to the coefficient updating behavior of writing the weight coefficient into the gain adjustment matrix of the control equation; the updated control law refers to the variable gain differential equation set reconstructed by the weight coefficient, which enhances the anti-interference ability.

[0155] In the embodiment of the present application, first, the control equation structure of the nonlinear control law is analyzed; second, the coupling weight coefficient is embedded into the gain adjustment term of the control equation; then, the variable gain differential equation is reconstructed; finally, the nonlinear control law after parameter update is generated.

[0156] Step 404: Based on the updated control law, the control surface action of the preset unmanned aerial vehicle is driven to generate an attitude stabilization instruction to real-time suppress the attitude deviation of the unmanned aerial vehicle caused by wind disturbance.

[0157] In this step, the control surface action refers to the physical deflection behavior of the rudder mechanism of the unmanned aerial vehicle, including aileron roll control, elevator pitch control and rudder yaw control; the attitude stabilization instruction refers to the deflection angle and direction control quantity of the rudder surface driven to generate a counteracting wind disturbance torque.

[0158] In the embodiment of the present application, first, the control interface of the rudder mechanism of the unmanned aerial vehicle is obtained; second, the updated control law is discretized into a rudder deflection instruction sequence; then, the aileron, elevator and rudder are driven to act in coordination; finally, the real-time stabilization instruction to eliminate the attitude deviation is generated.

[0159] The embodiment of the present application generates the coupling weight by dynamically aligning the correction signal and the gain parameter, deeply fuses and reconstructs the nonlinear control law, drives the rudder surface to accurately counteract the wind disturbance torque, and realizes the attitude zero deviation stable control of the unmanned aerial vehicle in the strong wind mutation scene.

[0160] Figure 2 A structural schematic diagram of an anti-wind interference unmanned aerial vehicle nonlinear flight control system is provided for the embodiment of the present application, as shown in FIG. Figure 2 The system comprises:

[0161] The acquisition module 21 is configured to acquire the wind field disturbance characteristics in real time by using a preset weather radar.

[0162] The generating module 22 is configured to input the wind field disturbance feature into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value.

[0163] The applying module 23 is configured to apply preset dynamic filtering processing to a magnitude characteristic of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal.

[0164] The coupling module 24 is configured to couple the control instruction correction signal with a preset nonlinear control law of a UAV flight control system to realize real-time suppression of UAV attitude deviation caused by wind disturbance and nonlinear flight control of the UAV.

[0165] Figure 2 The anti-wind-disturbance UAV nonlinear flight control system can perform Figure 1 The anti-wind-disturbance UAV nonlinear flight control method of the embodiment has the same implementation principles and technical effects as those of the anti-wind-disturbance UAV nonlinear flight control system. The specific operation modes of each module and unit of the anti-wind-disturbance UAV nonlinear flight control system in the above embodiment have been described in detail in the embodiment of the method, and will not be described in detail here.

[0166] In one possible design, Figure 2 The anti-wind-disturbance UAV nonlinear flight control system of the embodiment can be implemented as a computing device, such as a server. Figure 3 As shown, the computing device can 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 called and executed by the processing component 32.

[0168] The processing component 32 is configured to: acquire wind field disturbance features in real time by using a preset weather radar; input the wind field disturbance features into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value; apply preset dynamic filtering processing to a magnitude characteristic of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal; and couple the control instruction correction signal with a preset nonlinear control law of a UAV flight control system to realize real-time suppression of UAV attitude deviation caused by wind disturbance and nonlinear flight control of the UAV.

[0169] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be 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, micro-controllers, microprocessors or other electronic components, configured to perform the methods described above.

[0170] The storage component 31 is configured to store various types of data to support the operation of 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, the computing device can also include other components, such as an input / output interface, a display component, a communication component, etc.

[0172] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0173] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0174] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be a basic server resource rented or purchased from the cloud computing platform.

[0175] The embodiment of the application also provides a computer storage medium, which stores a computer program, and the computer program can implement the above-mentioned Figure 1 The embodiment shown in the figure is an anti-wind interference unmanned aerial vehicle nonlinear flight control method.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0177] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0178] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment 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 application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A wind disturbance resistant unmanned aerial vehicle (UAV) nonlinear flight control method, characterized in that, The method comprises the following steps: Real-time acquisition of wind field disturbance characteristics by using a preset weather radar; Inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value; Applying a preset dynamic filtering process to the amplitude characteristics of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal; Coupling the control instruction correction signal with a preset nonlinear control law of the unmanned aerial vehicle flight control system to real-time suppress the unmanned aerial vehicle attitude deviation caused by wind disturbance and perform nonlinear flight control of the unmanned aerial vehicle.

2. The method of claim 1, wherein, Real-time acquisition of wind field disturbance characteristics by using a preset weather radar, comprising: Spatial scanning of a preset target airspace by using a preset weather radar to obtain original detection data; Extracting a spatial vector set from the original detection data, associating the spatial vector set with a spatial coordinate of a preset unmanned aerial vehicle, and generating a three-dimensional wind field distribution; Extracting time-varying characteristic components of the three-dimensional wind field distribution, identifying disturbance distribution in the time-varying characteristic components, and generating wind field disturbance characteristics.

3. The method of claim 1, wherein, Inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value, comprising: Extracting a spatial feature set from the wind field disturbance characteristics, wherein the spatial feature set contains wind speed variation rate and direction mutation area and other wind field feature elements; Mapping the wind field feature elements to a body coordinate system of the preset unmanned aerial vehicle to generate a spatial projection distribution; Calculating the action intensity of the spatial projection distribution on the body surface of the preset unmanned aerial vehicle to generate a spatial action intensity distribution; Combining the spatial action intensity distribution with real-time flight attitude parameters of the preset unmanned aerial vehicle to generate an aerodynamic moment fluctuation prediction value.

4. The method of claim 3, wherein, Mapping the wind field feature elements to the body coordinate system of the preset unmanned aerial vehicle to generate a spatial projection distribution, comprising: Receiving real-time attitude angle parameters of the preset unmanned aerial vehicle to obtain a spatial orientation reference of the body coordinate system of the preset unmanned aerial vehicle; Constructing a vector conversion relationship between the wind field feature elements and the spatial orientation reference to generate a dynamic conversion rule; Projecting the spatial vectors of the wind field feature elements based on the dynamic conversion rule to generate an initial projection distribution; Compensating for the projection delay part of the initial projection distribution to generate a spatial projection distribution.

5. The method of claim 1, wherein, Applying a preset dynamic filtering process to the amplitude characteristics of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal, comprising: Monitoring the change mode of the amplitude characteristics of the aerodynamic moment fluctuation prediction value, identifying the starting time and ending time of the amplitude characteristics mutation; Extracting the amplitude change trajectory between the starting time and the ending time, quantizing the amplitude change trajectory to generate a mutation event intensity parameter; Calculating the response parameter adjustment amount of the preset dynamic filtering process based on the mutation event intensity parameter, converting the response parameter adjustment amount into an instruction code format to generate a filtering parameter adjustment instruction; Updating the working parameters of the preset dynamic filtering process by using the filtering parameter adjustment instruction to generate a dynamic filtering control state; The high-frequency disturbance component in the amplitude characteristic is attenuated in the dynamic filtering control state to generate a control instruction correction signal.

6. The method of claim 5, wherein, The response parameter adjustment amount of the preset dynamic filtering processing is calculated based on the mutation event intensity parameter, the response parameter adjustment amount is converted into an instruction code format, a filtering parameter adjustment instruction is generated, and the method comprises the following steps: The data structure characteristics of the mutation event intensity parameter are used to decompose the data structure of the mutation event intensity parameter to obtain a response parameter type and a response parameter dimension constraint; A parameter adjustment amount calculation template is generated by matching a preset response rule, the response parameter type, and the response parameter dimension constraint; The parameter adjustment amount calculation template is used to process the mutation event intensity parameter to generate a response parameter adjustment amount of the preset dynamic filtering processing; The response parameter adjustment amount 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 filtering parameter adjustment instruction.

7. The method of claim 1, wherein, The control instruction correction signal is coupled with a nonlinear control law of a preset unmanned aerial vehicle flight control system to real-time suppress the unmanned aerial vehicle attitude deviation caused by wind disturbance and perform unmanned aerial vehicle nonlinear flight control, and the method comprises the following steps: The time domain characteristics of the control instruction correction signal are analyzed to generate a correction signal intensity distribution characteristic; The correction signal intensity distribution characteristic is aligned with a gain scheduling parameter of the nonlinear control law of the preset unmanned aerial vehicle flight control system to generate a coupling weight coefficient; The coupling weight coefficient is fused with a control equation of the nonlinear control law to generate an updated control law; The control surface action of the preset unmanned aerial vehicle is driven based on the updated control law to generate an attitude stabilization instruction to real-time suppress the unmanned aerial vehicle attitude deviation caused by wind disturbance.

8. A wind disturbance resistant unmanned aerial vehicle nonlinear flight control system, characterized in that, The method comprises the following steps: An acquisition module is used to acquire wind field disturbance characteristics in real time by using a preset weather radar; A generation module is used to input the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic moment fluctuation prediction value; An application module is used to apply a preset dynamic filtering processing to the amplitude characteristic of the aerodynamic moment fluctuation prediction value to generate a control instruction correction signal; A coupling module is used to couple the control instruction correction signal with a nonlinear control law of a preset unmanned aerial vehicle flight control system to real-time suppress the unmanned aerial vehicle attitude deviation caused by wind disturbance and perform unmanned aerial vehicle nonlinear flight control.

9. A computing device, comprising: The method comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the anti-wind-disturbance unmanned aerial vehicle nonlinear flight control method in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, the anti-wind-disturbance unmanned aerial vehicle nonlinear flight control method in any one of claims 1 to 7 is implemented.

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