An unmanned aerial vehicle wind-resistant attitude regulation method and device based on a porous pneumatic probe

Abstract

The present application provides a kind of unmanned aerial vehicle wind-resistant attitude regulation and control method and device based on porous aerodynamic probe, method includes based on the information and environmental information, in wind tunnel, the attitude change of unmanned aerial vehicle equipped with porous aerodynamic probe is calibrated, the force discrete data of unmanned aerial vehicle under different working conditions is determined, to determine the correlation calibration model of porous aerodynamic probe pressure value and the force of unmanned aerial vehicle;Based on porous aerodynamic probe and preset sensor, obtain environmental data and pressure data when unmanned aerial vehicle flies, to determine the force characteristic data of each propeller of unmanned aerial vehicle according to environmental data, pressure data and correlation calibration model;Based on force characteristic data, determine attitude regulation and control instruction, to adjust propeller parameter to offset the additional force caused by wind field, realize attitude pre-adjustment.The present application solves the problem that there is no kind of precise, fast pre-adjustment of unmanned aerial vehicle attitude based on wind field early perception based on porous aerodynamic probe unmanned aerial vehicle wind-resistant attitude regulation and control method in prior art.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) automatic control technology, and in particular to a method and device for controlling the wind-resistant attitude of UAVs based on a porous aerodynamic probe. Background Technology

[0002] Most low-altitude drones have limited aerodynamic design and power reserves, making them naturally sensitive to wind interference. They need to compensate for these shortcomings with improved wind resistance. Furthermore, the complexity of their application scenarios, the unique characteristics of the low-altitude environment, and the inherent limitations of drones on flight safety are critical prerequisites for ensuring mission completion and equipment safety. Currently, drones have expanded from consumer entertainment to professional operations, with many scenarios requiring missions to be performed in complex wind environments. Wind resistance directly determines mission success or failure. For example, logistics delivery requires navigating narrow tunnels between tall buildings in urban areas, and power / oil / gas pipeline inspections may encounter turbulence around lines / pipes that could cause equipment collisions. Therefore, wind resistance has become a core indicator for differentiated competition among low-altitude drones.

[0003] Traditional drone wind-resistant technology typically uses gyroscopes as the "sensing core" for attitude adjustment. By detecting real-time changes in the angular velocity of the fuselage around its roll, pitch, and yaw axes, it provides attitude data to the flight control system, which then drives the motors to adjust thrust to achieve flight stability. However, this technology has significant limitations. Gyroscopes cannot autonomously correct long-term accumulated angular deviations. After prolonged continuous flight, relying solely on gyroscopes can lead to attitude angle deviations of 1°-2°. Furthermore, gyroscopes can only measure relative rotational speed and cannot directly obtain the absolute angle of the fuselage relative to the ground. This wind-resistant technology can only activate attitude adjustment after the fuselage has veered. In strong crosswinds, rapid changes in the drone's attitude can cause lag in gyroscope response and control, leading to flight safety issues.

[0004] Currently, manned aircraft primarily use pitot tubes (dynamic pressure probes) to monitor flight speed. These probes acquire the total and static pressure of the incoming flow through a forward-extending pressure tube. The flow velocity is calculated using the total static pressure difference and Bernoulli's equation (Mach number < 0.3), or the Mach number (Mach number > 0.3) is calculated using the total static pressure ratio and the definition of Mach number. However, the accuracy of total and static pressure measurements using this method is significantly affected by the airflow angle. When the airflow angle exceeds the probe's insensitive angle range (typically ±10°), the pressure measurement error increases dramatically, leading to a corresponding increase in the error of the flow velocity calculated based on the total static pressure difference / ratio. Furthermore, dynamic pressure probes can only obtain the velocity magnitude and cannot sense the airflow direction, thus hindering rapid response and control of flight attitude. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a method and device for wind-resistant attitude control of unmanned aerial vehicles (UAVs) based on a porous aerodynamic probe, which aims to solve the problem that there is a lack of a method for wind-resistant attitude control of UAVs based on a porous aerodynamic probe that can accurately and quickly pre-adjust the attitude of UAVs based on early wind field perception in the prior art.

[0006] A method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe according to an embodiment of the present invention, the method comprising:

[0007] Based on incoming flow information and environmental information, the attitude change of a UAV equipped with a porous aerodynamic probe is calibrated in a wind tunnel to determine the discrete force data of the UAV under different working conditions, so as to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force of the UAV.

[0008] Environmental and pressure data during UAV flight are acquired using a porous aerodynamic probe and preset sensors, and the force characteristics of each UAV propeller are determined based on the environmental data, the pressure data, and the associated calibration model.

[0009] Based on the force characteristic data, attitude control commands are determined to adjust propeller parameters to counteract the additional forces caused by the wind field, thereby achieving attitude pre-adjustment.

[0010] In addition, the UAV wind-resistant attitude control method based on a porous aerodynamic probe according to the above embodiments of the present invention may also have the following additional technical features:

[0011] Furthermore, based on incoming flow and environmental information, the attitude changes of a UAV equipped with a porous aerodynamic probe are calibrated in a wind tunnel to determine the discrete force data of the UAV under different operating conditions. The steps to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force on the UAV include:

[0012] At different incoming Mach numbers, the incoming flow angle is adjusted with a preset step size, and the pressure data of the porous gas probe and the discrete data of the spanwise force and the discrete data of the transverse force of each propeller are collected simultaneously at different incoming flow angles.

[0013] The calibration curve is obtained by fitting discrete data using the least squares method. During the fitting process, an objective function is established to minimize the sum of squared residuals. The optimal fitting coefficients are then solved in matrix form to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force on the UAV.

[0014] The objective function is:

[0015]

[0016] The matrix is ​​in the following form:

[0017]

[0018]

[0019]

[0020]

[0021] in, For the first i Discrete values ​​of the lateral force of the UAV corresponding to each discrete data point. t Indicates lateral force. i For discrete data points, n The total number of discrete data points. for m The optimal fitting coefficients of the order-1 fitting polynomial Spread force for the i-th discrete data point m power term s For the orientation force identifier, To design the matrix, The fitting coefficient vector, This is a discrete data vector of lateral force.

[0022] Furthermore, the porous aerodynamic probe is a five-hole aerodynamic probe. The step of acquiring environmental and pressure data during UAV flight based on the porous aerodynamic probe and preset sensors, and determining the force characteristics of each UAV propeller according to the environmental data, the pressure data, and the associated calibration model, includes:

[0023] The target direction coefficient is determined based on the pressure data from the porous pneumatic probe using a preset direction coefficient formula.

[0024] Based on the target orientation coefficient and the associated calibration model, the spanwise and lateral forces of each UAV propeller are determined using a two-dimensional interpolation formula.

[0025] The formula for the preset direction coefficient is:

[0026]

[0027]

[0028] The two-dimensional interpolation formula is:

[0029]

[0030]

[0031] in, , , , and This represents the real-time pressure values ​​at each pressure measuring hole of the multi-hole pneumatic probe. This is the pitch direction coefficient. This is the yaw direction coefficient. For the axial force of drones, For the lateral force of the drone, , and These are the one-dimensional difference functions of spanwise force based on pitch direction coefficient, yaw direction coefficient, and incoming Mach number, respectively. , and These are the one-dimensional difference functions based on the pitch direction coefficient, yaw direction coefficient, and incoming Mach number corresponding to the lateral force.

[0032] Further, after determining the spanwise and lateral forces of each UAV propeller using a two-dimensional interpolation formula based on the target orientation coefficient and the associated calibration model, the process includes:

[0033] Based on the environmental data, the spanwise and lateral forces are corrected using a preset correction formula to determine the force characteristics of each propeller of the UAV.

[0034] The preset correction formula is as follows:

[0035]

[0036] in, The corrected force data, The original force data before correction. This is the temperature correction factor. This refers to the actual ambient temperature. For reference temperature, This is the altitude correction factor. This represents the actual altitude of the drone. For reference height.

[0037] Furthermore, the step of determining the attitude control command based on the force characteristic data includes:

[0038] Vector synthesis is performed on the spanwise and lateral forces within the force characteristic data to determine the vector parameters of the additional forces on the wind field.

[0039] The target spanwise counteracting force and the target lateral counteracting force are determined based on the vector parameters, the force characteristic data, and the current UAV flight mission, so as to adjust the propeller speed and control surface deflection according to the target spanwise counteracting force and the target lateral counteracting force.

[0040] Furthermore, prior to the step of calibrating the attitude changes of a UAV equipped with a porous aerodynamic probe in a wind tunnel based on incoming flow and environmental information, the following steps are included:

[0041] A digital twin model is constructed based on the 3D model of the UAV and the parameters of the porous aerodynamic probe for simulation.

[0042] Preliminary discrete force data are determined based on simulation, and preliminary key calibration points are selected through a pre-set clustering algorithm.

[0043] The key calibration points are measured and verified in a physical wind tunnel. Based on the feedback of the deviation between the simulation data and the measured data, the digital twin model is corrected. The target key calibration points are determined based on the corrected digital twin model and the preset clustering algorithm. The associated calibration model is then determined based on the target calibration points.

[0044] Furthermore, the porous pneumatic probe includes three-hole probes, five-hole probes, seven-hole probes, and thirteen-hole probes. The porous pneumatic probe consists of a probe and a support rod. The incoming air flows through the pressure measuring tube and enters the miniature pressure sensor to obtain the pressure value of the porous probe.

[0045] Another objective of this invention is to provide a wind-resistant attitude control device for a drone based on a porous aerodynamic probe, the device comprising: a drone, a porous aerodynamic probe disposed on the drone, a sensing device, and a flight control system;

[0046] In this process, real-time data is collected using the porous aerodynamic probe and sensing devices, so that the flight control system can adjust the attitude of the UAV based on the real-time data using the aforementioned UAV wind-resistant attitude control method based on the porous aerodynamic probe.

[0047] Another objective of this invention is to provide a wind-resistant attitude control system for unmanned aerial vehicles (UAVs) based on a porous aerodynamic probe, for implementing the aforementioned wind-resistant attitude control method for UAVs based on a porous aerodynamic probe, the system comprising:

[0048] The calibration module is used to calibrate the attitude changes of a UAV equipped with a porous aerodynamic probe in a wind tunnel based on incoming flow information and environmental information, to determine the discrete force data of the UAV under different operating conditions, and to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force of the UAV.

[0049] The force characteristic determination module is used to acquire environmental and pressure data during UAV flight based on a porous aerodynamic probe and a preset sensor, so as to determine the force characteristic data of each propeller of the UAV according to the environmental data, the pressure data and the associated calibration model.

[0050] The attitude adjustment module is used to determine attitude control commands based on the force characteristic data, so as to adjust the propeller parameters to counteract the additional force caused by the wind field and realize attitude pre-adjustment.

[0051] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe.

[0052] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe.

[0053] This invention utilizes a porous aerodynamic probe to sense wind field information in advance and combines it with a correlation calibration model to achieve precise force calculation. This attitude pre-adjustment mode fundamentally changes the passive response pattern of traditional wind-resistant technologies, significantly improving the flight stability and wind resistance of UAVs in complex wind environments. The method establishes a pressure-force correlation logic through wind tunnel calibration, enabling efficient determination of the UAV's force status based on real-time wind field pressure. This effectively solves the problems of insufficient accuracy and response efficiency in wind field sensing and force calculation in traditional technologies, avoiding the accumulation of attitude deviations. Simultaneously, the pre-intervention control logic eliminates the flight risks caused by control lag in traditional technologies, allowing the UAV to maintain a stable flight state under wind interference. This significantly improves the reliability and effectiveness of wind-resistant control, providing core technical support for the safe operation of UAVs in complex wind environments. Therefore, this invention solves the problem of the lack of a precise and rapid pre-adjustment method for UAV attitude control based on a porous aerodynamic probe using pre-sensing wind field information in existing technologies. Attached Figure Description

[0054] Figure 1 This is a flowchart of the UAV wind-resistant attitude control method based on a porous aerodynamic probe in the first embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the results of the UAV wind-resistant attitude control system based on a porous aerodynamic probe in the third embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of the electronic device in the fourth embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of the structure of the UAV wind-resistant attitude control device based on a porous aerodynamic probe in the second embodiment of the present invention;

[0058] Figure 5This is a schematic diagram of the UAV attitude calibration curve in the first embodiment of the present invention;

[0059] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0060] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0062] Example 1

[0063] Please see Figure 1 The figure shows a method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe in the first embodiment of the present invention. The method specifically includes steps S01-S03.

[0064] S01, based on incoming flow information and environmental information, calibrate the attitude change of a UAV equipped with a porous aerodynamic probe in a wind tunnel, determine the discrete force data of the UAV under different working conditions, and determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force on the UAV.

[0065] Specifically, at different incoming Mach numbers, the incoming flow angle is adjusted with a preset step size, and pressure data of the porous air probe and discrete data of spanwise and lateral forces of each propeller are collected simultaneously at different incoming flow angles. The calibration curve is obtained by fitting the discrete data using the least squares method, and an objective function for minimizing the sum of squared residuals is established during the fitting process. The optimal fitting coefficients are solved in matrix form to determine the correlation calibration model between the pressure measurement value of the porous air probe and the forces on the UAV.

[0066] The objective function is:

[0067]

[0068] The matrix is ​​in the following form:

[0069]

[0070]

[0071]

[0072]

[0073] in, For the first i Discrete values ​​of the lateral force of the UAV corresponding to each discrete data point. t Indicates lateral force. i For discrete data points, n The total number of discrete data points. for m The optimal fitting coefficients of the order-1 fitting polynomial Spread force for the i-th discrete data point m power term s For the orientation force identifier, To design the matrix, The fitting coefficient vector, This is a discrete data vector of lateral force.

[0074] Specifically, the purpose of multi-condition data acquisition in the wind tunnel is to obtain a benchmark correspondence between the pressure data of the porous aerodynamic probe and the actual forces experienced by the UAV, providing high-quality sample data for model construction. This involves covering all wind field conditions that the UAV may encounter during actual flight, simultaneously collecting discrete data on pressure, spanwise force, and lateral force, establishing input and output sample pairs, avoiding insufficient model adaptability due to single conditions, and thus obtaining a comprehensive discrete data set. This ensures that the subsequent model can adapt to a wide range of wind field conditions, laying a data foundation for accurate force calculation. Furthermore, the least squares method is used to fit and calibrate the model, transforming the discrete sample data into a continuous correlation model of pressure values ​​and forces. This enables rapid calculation from real-time pressure data to force data, eliminating random errors in the discrete data through residual sum of squares minimization optimization, obtaining a stable fitting coefficient vector, and constructing a directly callable mathematical model to replace the complex real-time measurement process. Finally, a high-precision correlation calibration model (goodness of fit R² > 0.999) is established, achieving rapid mapping from pressure data to spanwise and lateral forces, providing core algorithmic support for subsequent millisecond-level response.

[0075] Specifically, the porous pneumatic probe includes three-hole probes, five-hole probes, seven-hole probes, and thirteen-hole probes. The porous pneumatic probe consists of a probe and a support rod. The incoming air flows through the pressure measuring tube and enters the miniature pressure sensor to obtain the pressure value of the porous probe.

[0076] Additionally, prior to the step of calibrating the attitude changes of a UAV equipped with a porous aerodynamic probe in a wind tunnel based on incoming flow and environmental information, the following steps are included:

[0077] A digital twin model is constructed based on a 3D model of an unmanned aerial vehicle (UAV) and parameters of a porous aerodynamic probe for simulation. Preliminary discrete force data is determined based on the simulation, and preliminary key calibration points are selected using a pre-defined clustering algorithm. These key calibration points are then verified through field measurements in a physical wind tunnel. Based on the feedback from the deviation between the simulation and measured data, the digital twin model is corrected. Target key calibration points are then determined based on the corrected digital twin model and the pre-defined clustering algorithm, and the associated calibration model is determined based on these target calibration points. Specifically, the key calibration points for wind tunnel calibration are optimized through digital twin model simulation and correction, reducing the workload of physical wind tunnel measurements and improving the relevance and accuracy of the associated calibration model. Furthermore, key force conditions are predicted through digital twin simulation, high-value calibration points are selected, and deviations are verified through physical measurements, leading to reverse correction of the model. This achieves dual assurance of simulation optimization and field calibration, significantly reducing the number of wind tunnel calibration tests while improving the model's adaptability to key conditions, ensuring that the associated calibration model has the minimum solution error under core wind field conditions.

[0078] S02, acquire environmental and pressure data during UAV flight based on a porous aerodynamic probe and a preset sensor, and determine the force characteristics data of each propeller of the UAV based on the environmental data, the pressure data and the associated calibration model.

[0079] Specifically, the target orientation coefficient is determined based on the pressure data from the porous aerodynamic probe using a preset orientation coefficient formula; the spanwise and lateral forces of each UAV propeller are determined using a two-dimensional interpolation formula based on the target orientation coefficient and the associated calibration model.

[0080] The formula for the preset direction coefficient is:

[0081]

[0082]

[0083] The two-dimensional interpolation formula is:

[0084]

[0085]

[0086] in, , , , and This represents the real-time pressure values ​​at each pressure measuring hole of the multi-hole pneumatic probe. This is the pitch direction coefficient. This is the yaw direction coefficient. For the axial force of drones, For the lateral force of the drone, , and These are the one-dimensional difference functions of spanwise force based on pitch direction coefficient, yaw direction coefficient, and incoming Mach number, respectively. , and These are the one-dimensional difference functions based on the pitch direction coefficient, yaw direction coefficient, and incoming Mach number corresponding to the lateral force.

[0087] Specifically, the incoming flow direction features are extracted from the pressure data of the porous aerodynamic probe, providing a core basis for the directional dimension of subsequent force calculations. By calculating the pressure difference, the porous pressure signal is converted into quantified coefficients representing pitch and yaw directions, establishing a correlation between pressure distribution and incoming flow direction. This overcomes the limitation of traditional aerodynamic probes in sensing direction, thus accurately acquiring incoming flow direction information and providing key input parameters for two-dimensional interpolation, ensuring that subsequent force calculations match the directional characteristics of the actual wind field. Two-dimensional interpolation is used to solve for spanwise and lateral forces based on a correlation calibration model and directional coefficients, calculating real-time force data for each UAV propeller. This achieves the transformation from pressure and direction to specific forces, thereby calling the calibration curves for the corresponding operating conditions in the correlation calibration model. Interpolation algorithms fill the gaps in discrete data, quickly obtaining uniquely corresponding spanwise and lateral forces, avoiding calculation interruptions caused by missing data. Ultimately, real-time and accurate force data calculation is achieved, providing direct force basis for subsequent attitude control and ensuring that control commands can specifically counteract wind field forces. Figure 5 The figure shown is a schematic diagram of the attitude calibration curve of a certain type of UAV.

[0088] Furthermore, based on the environmental data, the spanwise and lateral forces are corrected using a preset correction formula to determine the force characteristics data of each propeller of the UAV.

[0089] The preset correction formula is:

[0090]

[0091] in, The corrected force data, The original force data before correction. This is a temperature correction factor. This refers to the actual ambient temperature. For reference temperature, This is the altitude correction factor. This represents the actual altitude of the drone. For reference altitude. Specifically, the stress data is corrected using environmental data to eliminate the impact of differences between the flight environment and the wind tunnel calibration reference environment on the stress data, improving the authenticity and reliability of the stress characteristic data. By combining preset correction formulas and based on real-time collected temperature and altitude data, the original stress data calculated by interpolation is dynamically compensated to adapt to the complex and ever-changing low-altitude environment. This makes the corrected stress characteristic data more consistent with actual flight conditions, avoiding inaccurate control commands caused by environmental deviations, and further improving the accuracy of wind resistance control.

[0092] S03, based on the force characteristic data, determine the attitude control command to adjust the propeller parameters to counteract the additional force caused by the wind field and achieve attitude pre-adjustment.

[0093] Specifically, the spanwise and lateral forces within the force characteristic data are vector-synthesized to determine the vector parameters of the additional forces exerted by the wind field. Based on these vector parameters, the force characteristic data, and the current UAV flight mission, the target spanwise and lateral counteracting forces are determined. The propeller speed and control surface deflection are then adjusted according to these target spanwise and lateral counteracting forces. Specifically, the purpose of vector synthesis of spanwise and lateral forces is to clarify the total additional forces exerted by the wind field on the UAV, avoiding force or directional deviations caused by relying solely on component force control. Through vector synthesis algorithms, the dispersed spanwise and lateral forces are integrated into unified total force parameters, providing a global basis for the force allocation of control commands. This ensures that control is comprehensive and non-redundant, accurately identifying the core characteristics of the total wind field interference, providing a clear target for subsequent targeted control, and avoiding the blindness of component force control. Furthermore, based on the total force parameters and flight mission requirements, a precise offsetting strategy is formulated to balance wind resistance and mission adaptability. The total force is decomposed into offsetting targets in two dimensions: spanwise and lateral. Control priorities are allocated in conjunction with the core mission requirements. For example, in logistics delivery scenarios, priority is given to ensuring directional stability, and in power line inspection scenarios, priority is given to ensuring altitude stability. This ensures that the control not only offsets wind field interference but also does not affect core operational indicators, thereby forming targeted offsetting force parameters. This provides clear standards for subsequent adjustments to propeller and control surface parameters, ensuring the scientific and practical nature of control commands.

[0094] In summary, the UAV wind-resistant attitude control method based on a porous aerodynamic probe in the above embodiments of the present invention, by sensing wind field information in advance through a porous aerodynamic probe and combining it with an associated calibration model to achieve accurate force calculation, completely changes the passive response pattern of traditional wind-resistant technology with an attitude pre-adjustment mode, significantly improving the flight stability and wind resistance of UAVs in complex wind environments. This method, through the pressure and force correlation logic established by wind tunnel calibration, efficiently determines the UAV's force status based on real-time wind field pressure, effectively solving the problems of insufficient accuracy and response efficiency in wind field sensing and force calculation of traditional technologies, avoiding the accumulation of attitude deviations. Simultaneously, by leveraging the pre-intervention control logic, it eliminates the flight risks caused by control lag in traditional technologies, allowing the UAV to maintain a stable flight state under wind field interference, greatly improving the reliability and effectiveness of wind-resistant control, and providing core technical support for the safe operation of UAVs in complex wind environments. Therefore, the present invention solves the problem of the lack of a precise and rapid UAV wind-resistant attitude control method based on a porous aerodynamic probe for pre-adjustment of UAV attitude based on early wind field sensing in the prior art.

[0095] Example 2

[0096] Please see Figure 4 The figure shown is a schematic diagram of the structure of the UAV wind-resistant attitude control device based on the porous aerodynamic probe proposed in the second embodiment of the present invention. The device includes: UAV 1, porous aerodynamic probe 2 installed on UAV 1, sensing device 3 and flight control system 4.

[0097] The process involves real-time data acquisition using the porous aerodynamic probe 2 and sensing device 3, enabling the flight control system 4 to adjust the UAV's attitude based on the aforementioned porous aerodynamic probe-based wind-resistant attitude control method. The porous aerodynamic probe 2 includes three-hole, five-hole, seven-hole, and thirteen-hole probes, each consisting of a probe and a support rod. Incoming airflow passes through a pressure measuring tube and enters a miniature pressure sensor to obtain the pressure values ​​across the porous structure. The small-sized porous aerodynamic probe employs an integrated design and high-precision 3D printing manufacturing.

[0098] Example 3

[0099] Please see Figure 2 The diagram shows a structural block diagram of the UAV wind-resistant attitude control system based on a porous aerodynamic probe proposed in the third embodiment of the present invention. This UAV wind-resistant attitude control system 200 based on a porous aerodynamic probe includes: a calibration module 21, a force characteristic determination module 22, and an attitude adjustment module 23, wherein:

[0100] The calibration module 21 is used to calibrate the attitude change of a UAV equipped with a porous aerodynamic probe in a wind tunnel based on incoming flow information and environmental information, to determine the discrete force data of the UAV under different working conditions, and to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force of the UAV.

[0101] The force characteristic determination module 22 is used to acquire environmental and pressure data during the flight of the UAV based on a porous aerodynamic probe and a preset sensor, so as to determine the force characteristic data of each propeller of the UAV according to the environmental data, the pressure data and the associated calibration model;

[0102] The attitude adjustment module 23 is used to determine attitude control commands based on the force characteristic data, so as to adjust the propeller parameters to counteract the additional force caused by the wind field and realize attitude pre-adjustment.

[0103] Example 4

[0104] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device according to the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the above-described method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe.

[0105] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0106] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0107] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0108] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe.

[0109] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0110] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0111] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0112] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0113] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for controlling the wind-resistant attitude of an unmanned aerial vehicle (UAV) based on a porous aerodynamic probe, characterized in that, The method includes: Based on incoming flow information and environmental information, the attitude change of a UAV equipped with a porous aerodynamic probe is calibrated in a wind tunnel to determine the discrete force data of the UAV under different working conditions, so as to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force of the UAV. Environmental and pressure data during UAV flight are acquired using a porous aerodynamic probe and preset sensors, and the force characteristics of each UAV propeller are determined based on the environmental data, the pressure data, and the associated calibration model. Based on the force characteristic data, attitude control commands are determined to adjust propeller parameters to counteract the additional forces caused by the wind field, thereby achieving attitude pre-adjustment. Based on incoming flow and environmental information, the attitude changes of a UAV equipped with a porous aerodynamic probe are calibrated in a wind tunnel to determine the discrete force data of the UAV under different operating conditions. The steps to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force on the UAV include: At different incoming Mach numbers, the incoming flow angle is adjusted with a preset step size, and the pressure data of the porous gas probe and the discrete data of the spanwise force and the discrete data of the transverse force of each propeller are collected simultaneously at different incoming flow angles. The calibration curve is obtained by fitting discrete data using the least squares method. During the fitting process, an objective function is established to minimize the sum of squared residuals. The optimal fitting coefficients are then solved in matrix form to determine the correlation calibration model between the pressure measurement value of the porous aerodynamic probe and the force on the UAV. The objective function is: The matrix is ​​in the following form: in, For the first i Discrete values ​​of the lateral force of the UAV corresponding to each discrete data point. t Indicates lateral force. i For discrete data points, n The total number of discrete data points. for m The optimal fitting coefficients of the order-1 fitting polynomial Spread force for the i-th discrete data point m power term s For the orientation force identifier, To design the matrix, The fitting coefficient vector, This represents a discrete data vector of lateral forces. The porous aerodynamic probe is a five-hole aerodynamic probe. The steps for acquiring environmental and pressure data during UAV flight based on the porous aerodynamic probe and preset sensors, and determining the force characteristics of each UAV propeller according to the environmental data, the pressure data, and the associated calibration model, include: The target direction coefficient is determined based on the pressure data from the porous pneumatic probe using a preset direction coefficient formula. Based on the target orientation coefficient and the associated calibration model, the spanwise and lateral forces of each UAV propeller are determined using a two-dimensional interpolation formula. The formula for the preset direction coefficient is: The two-dimensional interpolation formula is: in, , , , and This represents the real-time pressure values ​​at each pressure measuring hole of the multi-hole pneumatic probe. This is the pitch direction coefficient. This is the yaw direction coefficient. For the axial force of drones, For the lateral force of the drone, , and These are the one-dimensional difference functions of spanwise force based on pitch direction coefficient, yaw direction coefficient, and incoming Mach number, respectively. , and These are the one-dimensional difference functions based on the pitch direction coefficient, yaw direction coefficient, and incoming Mach number corresponding to the lateral force; The steps for determining attitude control commands based on the force characteristic data include: Vector synthesis is performed on the spanwise and lateral forces within the force characteristic data to determine the vector parameters of the additional forces on the wind field. The target spanwise counteracting force and the target lateral counteracting force are determined based on the vector parameters, the force characteristic data, and the current UAV flight mission, so as to adjust the propeller speed and control surface deflection according to the target spanwise counteracting force and the target lateral counteracting force.

2. The method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe according to claim 1, characterized in that, After determining the spanwise and lateral forces of each UAV propeller using a two-dimensional interpolation formula based on the target orientation coefficient and the associated calibration model, the following steps are included: Based on the environmental data, the spanwise and lateral forces are corrected using a preset correction formula to determine the force characteristics of each propeller of the UAV. The preset correction formula is as follows: in, The corrected force data, The original force data before correction. This is a temperature correction factor. This refers to the actual ambient temperature. For reference temperature, This is the altitude correction factor. This represents the actual altitude of the drone. For reference height.

3. The method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe according to claim 1, characterized in that, Prior to the step of calibrating the attitude changes of a UAV equipped with a porous aerodynamic probe in a wind tunnel based on incoming flow and environmental information, the following steps are included: A digital twin model is constructed based on the 3D model of the UAV and the parameters of the porous aerodynamic probe for simulation. Preliminary discrete force data are determined based on simulation, and preliminary key calibration points are selected through a pre-set clustering algorithm. The key calibration points are measured and verified in a physical wind tunnel. Based on the feedback of the deviation between the simulation data and the measured data, the digital twin model is corrected. The target key calibration points are determined based on the corrected digital twin model and the preset clustering algorithm. The associated calibration model is then determined based on the target key calibration points.

4. The method for controlling the wind-resistant attitude of a UAV based on a porous aerodynamic probe according to claim 1, characterized in that, The porous pneumatic probe includes a three-hole probe, a five-hole probe, a seven-hole probe, or a thirteen-hole probe. The porous pneumatic probe consists of a probe and a support rod. The incoming air flows through a pressure measuring tube and enters a miniature pressure sensor to obtain the pressure value of the porous probe.

5. A wind-resistant attitude control device for unmanned aerial vehicles based on a porous aerodynamic probe, characterized in that, The device includes: a drone, a porous aerodynamic probe mounted on the drone, a sensing device, and a flight control system; The method involves real-time data acquisition using the porous aerodynamic probe and sensing device, enabling the flight control system to adjust the attitude of the UAV based on the real-time data using the UAV wind-resistant attitude control method based on any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the UAV wind-resistant attitude control method based on a porous aerodynamic probe as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the UAV wind-resistant attitude control method based on a porous aerodynamic probe as described in any one of claims 1-4.

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