Fixed wing and propeller combined vertical take-off and landing unmanned aerial vehicle control method and unmanned aerial vehicle

Abstract

The invention discloses a fixed wing and propeller combined vertical take-off and landing unmanned aerial vehicle control method and an unmanned aerial vehicle, and relates to the field of airplanes, in the method, a pitching control torque value is calculated based on a first rotating speed value, a second rotating speed value, a third rotating speed value and a fourth rotating speed value corresponding to four propellers of the unmanned aerial vehicle and a torque calculation formula; converting the pitching control moment value into an equivalent rudder deflection angle value according to an equivalent conversion formula; according to the ratio of the equivalent rudder deflection angle value to the current actual rudder deflection angle value of the pneumatic control surface of the unmanned aerial vehicle, a propeller rotating speed control increment value and a rudder deflection angle control increment value of the pneumatic control surface are calculated; superposing the propeller rotating speed control increment value to the current rotating speed values of the four propellers to obtain a propeller control instruction; superposing the rudder deflection angle control increment value of the pneumatic control surface to the current actual rudder deflection angle value of the pneumatic control surface to obtain a control surface control instruction; and simultaneously executing the propeller control instruction and the control surface control instruction. The control method and device are used for improving the control precision of the composite wing unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of aircraft, and particularly relates to a control method for a vertical take-off and landing unmanned aerial vehicle (UAV) with a fixed wing and a propeller combination and the UAV. BACKGROUND

[0002] The UAVs can be classified into multi-rotor UAVs, fixed-wing UAVs and compound-wing UAVs according to structural forms. The fixed-wing UAVs have the advantages of high flight speed and long endurance time, but require a long take-off and landing distance and have high requirements for take-off and landing sites. The multi-rotor UAVs can take off and land vertically, have low requirements for take-off and landing sites, but have the problems of short endurance time and poor load capacity. These limitations make it difficult for the UAVs with a single structural form to meet the requirements of high maneuverability tasks such as anti-UAVs.

[0003] In order to solve the above problems, a compound-wing UAV (VTOL) has been developed. The UAV adopts two kinds of actuators, i.e., multi-rotors and aerodynamic rudders. The multi-rotors are mainly used for control in the vertical take-off and landing stage, and the aerodynamic rudders are mainly used for attitude control in the cruising stage when the speed increases to a certain degree. This design enables the UAV to realize vertical take-off and landing and obtain good cruising performance.

[0004] However, the existing VTOLs use the two kinds of actuators as independent control means, which makes it difficult to accurately grasp the influence of the two kinds of actuators on attitude changes in the process of attitude control, thereby reducing the accuracy of UAV control. SUMMARY

[0005] The application provides a control method for a vertical take-off and landing unmanned aerial vehicle (UAV) with a fixed wing and a propeller combination and the UAV, and is used for improving the accuracy of control of the compound-wing UAV.

[0006] In a first aspect, the application provides a control method for a vertical take-off and landing unmanned aerial vehicle (UAV) with a fixed wing and a propeller combination. The control method comprises the following steps: calculating a pitch control moment value generated by four propellers of the UAV based on a first rotation speed value, a second rotation speed value, a third rotation speed value and a fourth rotation speed value corresponding to the four propellers and a moment calculation formula; converting the pitch control moment value into an equivalent rudder deflection angle value according to an equivalent conversion formula; calculating a propeller rotation speed control increment value and an aerodynamic rudder deflection angle control increment value according to a ratio of the equivalent rudder deflection angle value to a current actual rudder deflection angle value of the aerodynamic rudder of the UAV; adding the propeller rotation speed control increment value to current rotation speed values of the four propellers to obtain propeller control instructions; and adding the aerodynamic rudder deflection angle control increment value to the current actual rudder deflection angle value of the aerodynamic rudder to obtain rudder control instructions; Simultaneously execute propeller control commands and rudder control commands.

[0007] By adopting the above technical solution, the control increment is calculated based on the ratio of the equivalent rudder deflection angle to the actual rudder deflection angle, thus coordinating propeller speed control and rudder deflection angle control. When executing control commands, the propeller speed and rudder deflection angle are adjusted synchronously, avoiding response lag or over-adjustment that might occur if attitude control is solely reliant on the propeller or rudder alone. This collaborative control method utilizes the advantages of propeller thrust torque and rudder aerodynamic torque, improving the accuracy of control over the compound-wing UAV, achieving a combination of propeller control system and rudder control system, and enhancing the stability and controllability of the UAV during vertical takeoff and landing.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the torque calculation formula is as follows: ; In the above formula, This is the pitch control torque value. This is the vertical distance from the propeller to the drone's center of gravity. These are the force coefficients of the four propellers. , , and These are the first speed value, the second speed value, the third speed value, and the fourth speed value, respectively.

[0009] By adopting the above technical solution, considering the vertical distance from the propeller to the center of gravity of the UAV, the propeller force coefficient, and the rotational speed of each propeller, a more accurate mathematical relationship between the propeller speed and the pitch control torque is established, reflecting the differentiated influence of propellers at different positions on the pitch control torque, thereby improving the accuracy of UAV attitude control.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the equivalent conversion formula is: ; In the above formula, This is the equivalent rudder deflection angle value. This is the pitch control torque value. This represents the dynamic pressure value of the drone in its current flight state. The characteristic area of ​​the drone, The characteristic length of the drone, This is the derivative of the aerodynamic rudder deflection moment of the UAV.

[0011] By adopting the above technical solution and introducing parameters such as dynamic pressure, characteristic area, characteristic length, and derivative of aerodynamic rudder deflection moment, a quantitative relationship between pitch control torque and equivalent rudder deflection angle was established. The current flight state of the UAV was considered, ensuring the conversion results could adapt to control requirements under different flight conditions. The use of the equivalent conversion formula eliminated the heterogeneity between the propeller control system and the rudder control system, enabling coordinated control of the two systems under the same dimensions. This improved the compatibility of the control systems and allowed propeller control and rudder control to work together better, thereby enhancing the control performance of the UAV.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before calculating the pitch control torque value generated by the four propellers based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula, the method further includes: When the pitch rate of the UAV exceeds the preset transition flight rate threshold, acquire pressure data on the fixed wing surface and propeller wake velocity data. Determine the drone's wake velocity vector based on propeller wake velocity data; The angle between the wake velocity vector and the local incoming flow velocity vector is defined as the interference angle; the product of the interference angle and the local incoming flow velocity is defined as the interference intensity. When the pressure coefficient difference between adjacent detection points is greater than the preset separation judgment threshold, flow separation is determined to have occurred. Calculate the thrust correction factor based on the region where flow separation occurs and the corresponding disturbance intensity; The output thrust of the propeller at the corresponding position is adjusted according to the thrust correction coefficient to correct the rotational speed value, and the first, second, third and fourth rotational speed values ​​and torque calculation formulas corresponding to the four propellers of the UAV are executed to calculate the pitch control torque value generated by the four propellers. When the pitch angular velocity of the UAV is not greater than the preset transition flight angular velocity threshold, the step of calculating the pitch control torque value generated by the four propellers is executed based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula.

[0013] By adopting the above technical solution, a judgment mechanism based on pitch angular velocity values ​​was introduced. Combined with fixed-wing surface pressure data and propeller wake velocity data, flow separation phenomena were monitored and processed in real time. The impact of the propeller wake on the aerodynamic characteristics of the fixed-wing was quantified by calculating the interference angle and intensity. When flow separation was detected, the propeller output thrust was adjusted using a thrust correction coefficient to compensate for the aerodynamic performance loss caused by flow separation. During the transition flight phase, a correlation model between the propeller wake and the aerodynamic characteristics of the fixed-wing was established, enabling the control system to adjust the propeller thrust output accordingly. This improved the flight stability of the UAV in complex aerodynamic environments and enhanced the control reliability during the transition flight phase.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, a thrust correction factor is calculated based on the region where flow separation occurs and the corresponding disturbance intensity, specifically including: The surface of the fixed wing is divided into front detection area, middle detection area and rear detection area at equal intervals along the chord length; Based on the detection results where the pressure coefficient difference between adjacent detection points is greater than the preset separation judgment threshold, it is determined whether flow separation exists in each detection area; The product of the interference angle at the corresponding location in each detection area and the local incoming flow velocity is taken as the interference intensity value; The ratio of the interference intensity value of the detection area where flow separation exists to the preset reference interference intensity value is used as the thrust correction coefficient for the detection area where flow separation exists. The thrust correction factor for the detection region where no flow separation occurs is set to 1.

[0015] By adopting the above technical solution, the fixed wing surface is divided into three detection zones along the chord direction: front, middle, and rear. The presence of flow separation in each zone is determined based on the pressure coefficient difference between adjacent detection points. The thrust correction coefficient for each zone is calculated by combining the interference intensity value obtained by multiplying the interference angle at the corresponding location in each zone with the local incoming flow velocity, resulting in more accurate thrust correction. When flow separation exists in the detection zone, the ratio of the interference intensity value of that zone to a preset reference interference intensity value is used as the thrust correction coefficient, accurately reflecting the degree of influence of flow separation on thrust. When no flow separation exists in the detection zone, the thrust correction coefficient is set to 1, indicating that no thrust correction is needed in that zone. This allows for more targeted thrust adjustments based on the flow characteristics at different locations on the fixed wing surface, improving the accuracy of thrust correction and thus enhancing the flight stability of the UAV during transitional flight.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, before determining the drone's wake velocity vector based on propeller wake velocity data, the method further includes: Obtain the turning angular velocity and flight speed values ​​of the drone; When the turning angular velocity value is greater than the preset turning angular velocity threshold, the quotient of the square of the turning angular velocity value and the flight speed value is determined as the centrifugal acceleration value. The arctangent of the centrifugal acceleration value and the gravitational acceleration value are determined as the airflow deflection angle value; The direction of airflow deviation is determined by the turning direction of the drone; Adjust the distribution of the detection area on the fixed wing surface according to the airflow deflection angle value along the direction of airflow deviation.

[0017] By employing the above technical solution, the turning angular velocity and flight speed of the UAV are obtained. When the turning angular velocity exceeds a preset threshold, the centrifugal acceleration is calculated based on the quotient of the square of the turning angular velocity and the flight speed. The airflow deflection angle is then determined by the arctangent of the centrifugal acceleration and the gravitational acceleration. The airflow offset direction is then determined based on the UAV's turning direction. Finally, the distribution of the detection area on the fixed wing surface is adjusted along the airflow offset direction according to the airflow deflection angle. This method of dynamically adjusting the distribution of the detection area based on turning motion parameters allows the arrangement of the detection area to adjust accordingly with changes in airflow direction during the UAV's turn, ensuring that the detection area is always at a critical position affected by the actual airflow. This results in more accurate pressure data and flow separation information, improving the accuracy of thrust correction during turning flight.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, adjusting the distribution position of the detection area on the fixed wing surface according to the airflow deflection angle value along the airflow offset direction specifically includes: The sine value of the airflow deflection angle is determined as the offset coefficient of the detection area; Multiply the chord length of the fixed wing surface by the detection area offset coefficient to obtain the lateral offset distance value of the detection area; The front, middle, and rear detection areas on the fixed wing surface are shifted by the lateral offset distance value of the detection area along the airflow offset direction.

[0019] By employing the above technical solution, the sine value of the airflow deflection angle is used as the offset coefficient of the detection area. This coefficient is multiplied by the chord length of the fixed wing surface to obtain the lateral offset distance of the detection area. Then, the three detection areas are shifted by the corresponding distance along the airflow offset direction. This approach considers the nonlinear relationship between the airflow deflection angle and the actual required offset distance, ensuring that the offset of the detection area matches the actual degree of airflow deflection. Because a sine value is used as the offset coefficient, a smaller offset distance is obtained at small deflection angles, while a larger offset distance is provided at large deflection angles. This nonlinear offset distance calculation method is more consistent with aerodynamic characteristics, improving the rationality of the detection area position adjustment and thus ensuring the accuracy of pressure data acquisition.

[0020] In a second aspect, embodiments of this application provide a drone, which includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the drone to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a drone, cause the drone to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product that, when run on a drone, causes the drone to perform the method described in any possible implementation of the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application provides a control method for a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) combining a fixed-wing and propeller configuration. The control increment is calculated based on the ratio of the equivalent rudder deflection angle to the actual rudder deflection angle, enabling coordinated control of propeller speed and rudder deflection angle. When executing control commands, the propeller speed and rudder deflection angle are adjusted synchronously, avoiding response lag or over-adjustment that might occur if attitude control relies solely on the propeller or rudder. This coordinated control method utilizes the advantages of propeller thrust torque and rudder aerodynamic torque, improving the accuracy of control over the compound-wing UAV, combining the propeller control system and the rudder control system, and enhancing the stability and controllability of the UAV during VTOL.

[0024] 2. This application provides a control method for a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) combining a fixed-wing and a propeller. It introduces a judgment mechanism based on pitch angular velocity values, combining fixed-wing surface pressure data and propeller wake velocity data to monitor and process flow separation phenomena in real time. By calculating the interference angle and intensity, the influence of the propeller wake on the aerodynamic characteristics of the fixed-wing is quantified. When flow separation is detected, the propeller output thrust is adjusted using a thrust correction coefficient to compensate for the aerodynamic performance loss caused by flow separation. During the transition flight phase, a correlation model between the propeller wake and the aerodynamic characteristics of the fixed-wing is established, enabling the control system to adjust the propeller thrust output accordingly, improving the flight stability of the UAV in complex aerodynamic environments and enhancing the control reliability during the transition flight phase.

[0025] 3. This application provides a control method for a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) combining a fixed-wing and propeller configuration. The method acquires the UAV's turning angular velocity and flight speed. When the turning angular velocity exceeds a preset threshold, centrifugal acceleration is calculated based on the quotient of the square of the turning angular velocity and the flight speed. The airflow deflection angle is then determined using the arctangent of the centrifugal acceleration and gravitational acceleration. The airflow offset direction is then determined based on the UAV's turning direction. Finally, the distribution of the detection area on the fixed-wing surface is adjusted along the airflow offset direction according to the airflow deflection angle. This method of dynamically adjusting the detection area distribution based on turning motion parameters allows the detection area to be adjusted accordingly with changes in airflow direction during the UAV's turn, ensuring that the detection area is always at a critical position affected by the actual airflow. This results in more accurate pressure data and flow separation information, improving the accuracy of thrust correction during turning flight. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of a vertical take-off and landing unmanned aerial vehicle (UAV) combining a fixed wing and a propeller, provided in an embodiment of this application.

[0027] Figure 2 This is a flowchart illustrating a control method for a vertical take-off and landing unmanned aerial vehicle (UAV) combining a fixed wing and a propeller, as described in an embodiment of this application.

[0028] Figure 3 This is a flowchart illustrating a control method that combines the effects of propeller wake and the flow separation characteristics of a fixed wing in an embodiment of this application.

[0029] Figure 4 This is a flowchart illustrating a method for dynamically adjusting the detection area considering the influence of turning motion in an embodiment of this application.

[0030] Figure 5This is a schematic diagram of the physical device structure for controlling a vertical take-off and landing unmanned aerial vehicle (UAV) that combines a fixed wing and a propeller, as provided in an embodiment of this application.

[0031] Explanation of reference numerals in the attached figures: 1. Guiding head; 2. Missile body; 3. Servo mechanism (including rudder blades); 4. Propeller; 5. Fixed wing. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] Currently, drones are classified according to their structural form into multicopter drones, fixed-wing drones, and compound-wing drones. Compound-wing drones are also known as VTOL (Vertical Take-off and Landing) drones, which will be referred to as VTOL below.

[0035] The advantages of fixed-wing UAVs are high flight speed and long endurance, while the disadvantages are long takeoff and landing distances and high requirements for takeoff and landing sites. The disadvantage of folding fixed-wing UAVs is that they require the assistance of a carrier aircraft or launch device for takeoff, and have high requirements for launch hardware.

[0036] Multi-rotor drones have the advantage of being able to take off and land vertically and have low requirements for takeoff and landing sites, but they have short flight time and poor payload capacity.

[0037] VTOLs possess two types of actuators: the propellers of a multi-rotor UAV and the aerodynamic control surfaces of a fixed-wing UAV. This allows them to achieve both vertical takeoff and landing (VTOL) like a multi-rotor UAV and forward flight like a fixed-wing UAV. This application provides a control method for a VTOL that combines a fixed-wing rotor and a propeller.

[0038] First, combine Figure 1This application describes a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) combining a fixed wing and a propeller, including a seeker head 1, a missile body 2, a servo motor (including rudder blades) 3, a propeller 4, and a fixed wing 5. During vertical takeoff, the propeller 4 rotates to provide lift for the UAV. A quadcopter control strategy is employed to control the UAV's pitch angle from 90° in the vertical position to 0°, while the four propellers simultaneously accelerate, increasing the UAV's airspeed. During this process, the aerodynamic control surfaces are preset to elevators that tilt the UAV up, and these control surfaces assist the quadcopter control in rapidly changing the UAV's pitch attitude.

[0039] When the drone reaches the predetermined cruising altitude, control the rotation speed of propeller 4 and the deflection of the servo blades of servo 3 to adjust the drone's attitude to the cruising state. If the drone needs to hover in the air, control the rotation speed of propeller 4 and the deflection of the servo blades of servo 3 to adjust the drone's attitude, and the drone can hover according to the control requirements; at the end of the cruise phase, control the rotation speed of propeller 4 and the deflection of servo blades 3 to adjust the drone's landing state.

[0040] The following example is used in conjunction with Figure 2 The present application describes a control method for a vertical take-off and landing unmanned aerial vehicle (UAV) combining a fixed wing and a propeller, as described in the embodiments of this application: Please see Figure 2 This is a flowchart illustrating a control method for a vertical take-off and landing unmanned aerial vehicle (UAV) combining a fixed wing and a propeller, as described in an embodiment of this application.

[0041] S101. Based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula, calculate the pitch control torque value generated by the four propellers. Based on the first, second, third, and fourth rotational speeds of the drone's four propellers and the torque calculation formula, the pitch control torque generated by the four propellers is calculated. The torque calculation formula is as follows: ; In the above formula, This is the pitch control torque value. This is the vertical distance from the propeller to the drone's center of gravity. These are the force coefficients of the four propellers. , , and These are the first speed value, the second speed value, the third speed value, and the fourth speed value, respectively.

[0042] By adopting the above technical solution, considering the vertical distance from the propeller to the center of gravity of the UAV, the propeller force coefficient, and the rotational speed of each propeller, a more accurate mathematical relationship between the propeller speed and the pitch control torque is established, reflecting the differentiated influence of propellers at different positions on the pitch control torque, thereby improving the accuracy of UAV attitude control.

[0043] Taking the pitch channel as an example, the general mathematical model is: ; in It's an angle of attack; It is the propulsion generated by the propeller that propels the aircraft forward; It is the pitch angular velocity; For the quality of the drone; For drone speed; It is the aerodynamic normal force; It is the acceleration due to gravity; The trajectory inclination angle; It is the moment of inertia; It is the aerodynamic pitching moment; It is the pitching moment generated by the multi-rotor; This is a normal overload.

[0044] Under the assumption of a small angle , And under the following flight characteristics at this time: ; Further simplification yields: ; At this time, the drone's flight speed is between 30 and 80 m / s, although This factor cannot be ignored, but it can be balanced by generating lift in an equilibrium state. After canceling this out, the dynamic process control is considered with the equilibrium state as the initial state. Furthermore, expressing the aerodynamic forces and moments as aerodynamic coefficients, the mathematical model of the pitch channel can be obtained as follows: ; The pitch control torque generated by the multi-rotor is the data obtained by calculating the torque using the torque calculation formula.

[0045] The four propellers on the drone are arranged in a specific configuration, with each propeller having independent speed control capabilities. The required control torque is generated by adjusting the speed differences between the different propellers. (First speed value) The second speed value corresponds to the current rotational speed of the first propeller. The third speed value corresponds to the current rotational speed of the second propeller. The fourth speed value corresponds to the current rotational speed of the third propeller. This corresponds to the current rotational speed of the fourth propeller. The parameters in the torque calculation formula... This represents the vertical distance from the propeller to the center of gravity of the drone, and the value of this distance is determined based on the specific structural design of the drone. This represents the force coefficient for the four propellers. This coefficient reflects the proportional relationship between the square of the propeller rotational speed and the thrust generated. Its value depends on physical parameters such as the propeller blade design, diameter, and pitch. Pitch control torque value. This represents the torque generated around the drone's horizontal axis by the combined action of the four propellers, used to control the drone's pitch attitude. The specific model, size, material, and other parameters of the propellers can be selected according to the drone's design requirements and are not limited here. The deflection angle of the control surface represents the angle of direct force control or the angle of control surface deflection that generates normal force (lift direction). This refers to the pitch control surface deflection angle.

[0046] The specific methods for implementing this step include: The first method involves directly measuring the rotational speed using sensors. A rotational speed sensor, such as a Hall effect sensor or photoelectric encoder, is installed on each propeller motor of the UAV to monitor the propeller speed in real time. The sensor transmits the collected rotational speed signal to the flight control computer via a data bus. The flight control computer filters the received rotational speed data to remove noise interference and obtain an accurate rotational speed value. Then, the four rotational speed values ​​are substituted into the torque calculation formula, and the pitch control torque value is calculated using a floating-point unit. The second method involves obtaining the rotational speed value through feedback from the motor controller. When controlling the brushless motor, the UAV's electronic speed controller (ESC) can estimate the motor speed using back EMF detection or current detection methods. The ESC feeds back the estimated rotational speed value to the flight control computer via a PWM signal or a digital communication protocol (such as the CAN bus). After receiving the rotational speed feedback value, the flight control computer calculates the actual propeller speed value based on the pre-calibrated transmission ratio between the motor speed and the propeller speed, and then performs torque calculation.

[0047] S102. Convert the pitch control torque value into an equivalent rudder deflection angle value according to the equivalent conversion formula. The UAV converts the pitch control torque value into an equivalent control deflection angle value using an equivalent conversion formula, where the equivalent conversion formula is: ; In the above formula, This is the equivalent rudder deflection angle value. This is the pitch control torque value. This represents the dynamic pressure value of the drone in its current flight state. The characteristic area of ​​the drone, The characteristic length of the drone, This is the derivative of the aerodynamic rudder deflection moment of the UAV.

[0048] Equivalent rudder deflection value This represents the deflection angle of the control surfaces required to generate the same pitch control torque as a propeller if only aerodynamic control surfaces are used. The dynamic pressure value in the equivalent conversion formula... This reflects the aerodynamic pressure of the drone under its current flight conditions, and its calculation method is as follows: =0.5 pV 2 ,in p At the current altitude, the air density V The flight speed of the drone. Feature area. The reference area is usually chosen as the wing area of ​​the UAV; for fixed-wing UAVs, it is generally the projected area of ​​the wing. Characteristic length The average aerodynamic chord length or fuselage length is typically chosen, with the specific choice depending on the UAV's aerodynamic design reference frame. Aerodynamic rudder deflection moment derivative. This represents the rate of change of the dimensionless pitching moment coefficient per unit rudder deflection angle. This parameter is obtained through wind tunnel testing or computational fluid dynamics (CFD) simulation. The dynamic pressure value can be obtained through pitot tube measurements, atmospheric data computer calculations, or other airspeed measurement equipment; no specific method is specified here.

[0049] The specific methods for implementing this step include: The first method involves real-time calculation and equivalent conversion of dynamic pressure. The UAV measures total pressure and static pressure using a pitot tube and a static pressure orifice to calculate the dynamic pressure value. Simultaneously, the current flight altitude is obtained through the GPS / INS integrated navigation system, and the air density at that altitude is determined based on a standard atmospheric model or measured atmospheric data. The flight control computer then reads data from a stored aerodynamic database, pre-obtained through wind tunnel testing or CFD simulation. The value may vary with flight state parameters such as angle of attack and Mach number, therefore interpolation is required to obtain the accurate value under the current state. Substituting all parameters into the equivalent transformation formula, the equivalent rudder deflection angle is calculated. The second implementation method is a rapid transformation based on a lookup table. During the ground preparation phase, the UAV pre-calculates the dynamic pressure and aerodynamic derivatives under different flight states (combinations of speed, altitude, and angle of attack) according to the expected flight envelope range, establishing a multidimensional lookup table. During flight, based on the current flight state parameters, the required parameters are quickly obtained from the lookup table using multidimensional linear interpolation or spline interpolation methods, and then the equivalent transformation calculation is performed. This method reduces real-time computation and improves response speed.

[0050] S103. Based on the ratio of the equivalent rudder deflection angle value to the current actual rudder deflection angle value of the UAV's aerodynamic control surface, calculate the propeller speed control increment value and the aerodynamic control surface rudder deflection angle control increment value. The UAV calculates the propeller speed control increment and the aerodynamic control surface deflection angle control increment based on the ratio of the equivalent deflection angle to the actual deflection angle of the UAV's aerodynamic control surfaces. The core of this step is establishing a coordinated allocation relationship between the propeller control system and the aerodynamic control surface control system. The equivalent deflection angle represents the angle of deflection of the control torque generated by the propeller, which is equivalent to the deflection angle of the control surface. The actual deflection angle is the current physical deflection angle of the aerodynamic control surface. By calculating the ratio of the two, the relative magnitudes of the current propeller control contribution and the control surface control contribution can be assessed, thereby determining how to allocate the control increment. The propeller speed control increment refers to the amount of speed change that needs to be increased or decreased based on the current speed, used to adjust the control torque generated by the propeller. The aerodynamic control surface deflection angle control increment refers to the additional angle of deflection required by the control surface. The allocation strategy for the control increment can adopt a fixed ratio allocation, dynamic weight allocation, or an optimization-based allocation method; no specific limitation is made here.

[0051] The first feasible approach is based on proportional control increment calculation. The UAV first calculates the ratio k= between the equivalent rudder deflection angle and the actual rudder deflection angle. / δ_actual, where δ_actual is the current actual deflection angle of the control surface. Based on the preset allocation coefficient α (0 < α < 1), the propeller control weight w_p = α × k / (1 + k), and the control surface control weight w_s = 1 / (1 + k). When the total pitch control command increment Δu is received, the propeller speed control increment is calculated using the reverse torque formula: Δω = sqrt(Δu × w_p / (dc_T)). This increment is evenly distributed among the four propellers; the first two propellers have their increment reduced, and the last two have their increment increased. The aerodynamic control surface deflection angle control increment Δδ = Δu × w_s. The second implementation method is intelligent allocation based on fuzzy logic. The UAV establishes a fuzzy controller, with input variables including the ratio of the equivalent deflection angle to the actual deflection angle, current flight speed, altitude, etc. A fuzzy rule base is defined; if the ratio is large and the speed is low, the propeller control weight is increased. Through fuzzy inference and defuzzification processes, the control weights of the propeller and control surfaces are obtained. Calculate the respective control increments based on the weights and total control requirements.

[0052] S104. The propeller speed control increment value is superimposed on the current speed value of the four propellers to obtain the propeller control command; the aerodynamic control surface deflection angle control increment value is superimposed on the current actual deflection angle value of the aerodynamic control surface to obtain the control surface command. The UAV generates propeller control commands by superimposing the incremental values ​​of propeller speed control onto the current speed values ​​of the four propellers. Simultaneously, it generates control commands by superimposing the incremental values ​​of aerodynamic control surface deflection angle control onto the current actual deflection angle of the aerodynamic control surfaces. The propeller control commands are the target speed values ​​sent to each propeller motor controller, obtained through superposition: ω_cmd_i = ω_current_i + Δω_i, where i represents the i-th propeller, ω_current_i is the current speed, and Δω_i is the corresponding control increment. The control surface control commands are the target deflection angles sent to the servo actuators: δ_cmd = δ_current + Δδ. This superposition ensures the continuity of control and avoids abrupt changes in control values. When generating control commands, the physical limitations of the actuators, such as the maximum propeller speed limit and the maximum deflection angle limit of the control surfaces, need to be considered. The update frequency of the control commands can be set according to system requirements, generally between 50Hz and 200Hz, and is not limited here.

[0053] The specific methods for implementing this step include: The first method involves direct superposition and amplitude limiting. The UAV flight control computer obtains the real-time rotational speeds of the four propellers and the actual deflection angles of the control surfaces. For propeller control, based on pitch control requirements, the control commands for the first two propellers are ω_cmd_1 = ω_current_1 - Δω, ω_cmd_2 = ω_current_2 - Δω; the control commands for the latter two propellers are ω_cmd_3 = ω_current_3 + Δω, ω_cmd_4 = ω_current_4 + Δω. An amplitude limiting check is performed to ensure all rotational speed commands are within the range [ω_min, ω_max]. For control surface control, δ_cmd = δ_current + Δδ is calculated and limited to the range [-δ_max, +δ_max]. The second method involves command generation considering dynamic compensation. When superimposing control increments, the UAV introduces dynamic characteristic compensation for the actuators. For propellers, considering the motor's rotational inertia and response delay, a feedforward compensation strategy is adopted, adding an acceleration compensation term to the control increments. For the control surface, considering the bandwidth limitations and nonlinear characteristics of the servo motor, more accurate control commands are generated through inverse model compensation.

[0054] A potential new technical problem encountered during this step is saturation caused by control commands exceeding the physical limitations of the actuators. To address this issue, the UAV can implement an anti-saturation integral strategy and a control redistribution mechanism. When a propeller speed command is detected to have reached its upper or lower limit, the incremental control torque that the propeller cannot provide is calculated, and this incremental torque is redistributed to unsaturated propellers or the control contribution of the control surfaces is increased. In practice, a saturation evaluation function is established, and the control allocation weights are dynamically adjusted based on the distance of each actuator from the saturation boundary, prioritizing the use of actuators with larger control margins, thereby ensuring that the overall control effect is not affected by the saturation of a single actuator.

[0055] S105, Simultaneously execute propeller control commands and rudder control commands.

[0056] The drone simultaneously executes propeller control commands and control surface commands. Simultaneous execution means that both types of control commands are sent to their respective actuators within the same control cycle, achieving coordinated control. Propeller control commands drive the brushless motor via an electronic speed controller (ESC) to adjust the propeller speed; control surface commands control the servo motor via a servo driver to adjust the control surface deflection angle. During execution, it is crucial to ensure the synchronization and real-time nature of the commands to avoid control coupling problems caused by inconsistent execution timing.

[0057] The specific methods for implementing this step include: The first method is synchronous execution based on a real-time operating system. The UAV flight control system uses a real-time operating system (RTOS) to create high-priority control tasks. At the beginning of each control cycle, the control task simultaneously sends PWM signals or digital commands to the ESC to control the propeller speed and sends PWM signals to the servo controller to control the control surface deflection. Hardware timers ensure the accuracy of the control cycle, and DMA transfer reduces CPU usage and guarantees the real-time nature of command transmission. The second method is asynchronous compensated execution based on timestamps. Considering the differences in response characteristics of different actuators, the UAV attaches timestamp information to each control command. Based on the pre-calibrated response delay of the actuators, slower-responding control commands are sent earlier to align the actual responses of different actuators in time.

[0058] A potential new technical problem encountered when performing this step is the degradation of transient control performance due to the mismatch in response speeds between the propeller and the control surfaces. To address this issue, a dynamic response coordinator can be introduced into the UAV. This coordinator pre-filters control commands based on the frequency response characteristics of the actuators. By designing a matched filter, the fast-response propeller control commands are appropriately delayed and smoothed, achieving better coordination with the slow-response control surfaces in the time domain. This reduces transient oscillations caused by asynchronous responses and improves overall control quality.

[0059] In the above embodiments, the control increment is calculated based on the ratio of the equivalent rudder deflection angle to the actual rudder deflection angle, enabling coordinated control of propeller speed and rudder deflection angle. When executing control commands, the propeller speed and rudder deflection angle are adjusted synchronously, avoiding response lag or over-adjustment that might occur if attitude control relies solely on the propeller or rudder. This coordinated control method utilizes the advantages of propeller thrust torque and rudder aerodynamic torque, improving the accuracy of control over the compound-wing UAV, achieving a combination of propeller control system and rudder control system, and enhancing the stability and controllability of the UAV during vertical takeoff and landing.

[0060] The above embodiments describe a method for coordinated propeller and control surface control based on equivalent control surface deflection angles. However, in practical applications, UAVs experience complex aerodynamic environments during vertical takeoff and landing, especially during the transition flight phase, where propeller wake significantly impacts the aerodynamic characteristics of the fixed wing. To further improve control accuracy, this application also provides a control method that combines the effects of propeller wake and the flow separation characteristics of the fixed wing. This method dynamically corrects the propeller thrust by real-time monitoring of the pressure distribution on the fixed wing surface and the propeller wake characteristics, thereby better adapting to the aerodynamic environment changes during the transition flight phase.

[0061] The following is combined with Figure 3 The present application describes a control method that combines the effects of propeller wake and the flow separation characteristics of fixed-wing aircraft. Please see Figure 3 This is a flowchart illustrating a control method that combines the effects of propeller wake and the flow separation characteristics of a fixed wing in an embodiment of this application.

[0062] S201. When the pitch rate of the UAV is greater than the preset transition flight rate threshold, acquire the pressure data of the fixed wing surface and the propeller wake velocity data. When the UAV's pitch rate exceeds a preset transition flight angular velocity threshold, the UAV acquires pressure data from the fixed-wing surface and propeller wake velocity data. The pitch rate refers to the angular velocity of the UAV's rotation around its horizontal axis, measured in real-time by an onboard gyroscope or inertial measurement unit (IMU). The preset transition flight angular velocity threshold is a key parameter for determining whether the UAV is in the transition phase from vertical takeoff and landing to horizontal flight. This threshold is determined based on the UAV's aerodynamic characteristics and flight envelope, with a typical range of 5-15 degrees per second. The pressure data from the fixed-wing surface refers to the static pressure values ​​collected by pressure sensors distributed on the upper and lower surfaces of the wing. This data reflects the airflow state and load distribution on the wing surface. The propeller wake velocity data refers to the velocity vector information of the airflow behind the propeller, including axial, radial, and circumferential velocity components. The arrangement density of the pressure sensors, the sampling frequency, and the specific model of the wake velocity measurement equipment can be selected according to the UAV's design requirements and are not limited here.

[0063] Unmanned aerial vehicles (UAVs) can acquire pressure data by embedding a miniature pressure sensor array on the surface of a fixed wing. The sensor array is arranged at predetermined intervals along the wingspan and chord, with each sensor connected to a data acquisition unit via a high-speed data bus. The data acquisition unit synchronously samples multiple pressure signals at a sampling frequency of at least 100Hz, performing analog-to-digital conversion and preliminary filtering. For propeller wake velocity data, a five-hole probe or hot-wire anemometer is mounted at an appropriate location behind the propeller to measure the three-dimensional velocity components of the wake. The measuring device is fixed to the fuselage by a support structure to ensure it does not interfere with the normal operation of the propeller. UAVs can also employ a non-contact measurement method based on particle image velocimetry (PIV) technology. Tracer particles are released behind the propeller, and a high-speed camera captures images of the particle motion. Image processing algorithms are used to calculate the wake velocity field. Simultaneously, a pressure sensor film based on MEMS technology is attached to the wing surface. This flexible sensor can adapt to the curved shape of the wing, providing high spatial resolution pressure distribution measurements.

[0064] S202. Determine the UAV's wake velocity vector based on the propeller wake velocity data; The UAV determines its wake velocity vector based on propeller wake velocity data. The wake velocity vector is a three-dimensional vector containing three components of the induced velocity generated by the propeller in the body coordinate system. Determining this vector involves processing and synthesizing the measured discrete velocity data to form a velocity vector that characterizes the overall wake characteristics. The magnitude of the wake velocity vector reflects the strength of the propeller thrust, while its direction indicates the main flow direction of the wake. When determining the wake velocity vector, the influence of factors such as the propeller's rotation direction, number of blades, and rotational speed on the wake characteristics must be considered. The velocity data processing methods can include arithmetic mean, weighted average, or other statistical methods; no specific method is limited here.

[0065] Unmanned aerial vehicles (UAVs) can determine the wake velocity vector using the spatial averaging method. The measurement area behind the propeller is divided into multiple sub-regions, and velocity measurement points are placed within each sub-region. The velocity data at each measurement point are transformed from the measurement coordinate system to the aircraft coordinate system. The arithmetic mean of the velocities at all measurement points within each sub-region is calculated to obtain the average velocity vector for that sub-region. Different weighting coefficients are assigned to each sub-region based on its distance from the propeller center and the intensity of the wake influence; a weighted average is then used to obtain the overall wake velocity vector. Alternatively, UAVs can employ analytical calculation methods based on momentum theory. Based on the propeller's thrust coefficient, torque coefficient, and operating parameters, the average induced velocity on the propeller disk is calculated using momentum theory formulas. Combining the propeller's advance ratio and incoming flow conditions, the axial and tangential components of the wake velocity are obtained through iterative solutions. The calculation results are compared and corrected with partial measurement data to improve the accuracy of velocity vector determination.

[0066] S203. The angle between the wake velocity vector and the local incoming flow velocity vector is determined as the interference angle; the product of the interference angle and the local incoming flow velocity is determined as the interference intensity. The UAV defines the disturbance angle as the angle between its wake velocity vector and the local incoming flow velocity vector, and the disturbance intensity as the product of the disturbance angle and the local incoming flow velocity. The local incoming flow velocity vector refers to the velocity of the free flow unaffected by the propeller at a specific location on the fixed wing surface; this velocity is determined by the UAV's flight speed and the local angle of attack. The disturbance angle reflects the degree to which the propeller wake alters the original airflow direction; a larger value indicates a more significant impact of the wake on the fixed wing's aerodynamic characteristics. The disturbance intensity is a comprehensive parameter that considers both the change in flow direction (disturbance angle) and the magnitude of the flow velocity (local incoming flow velocity), used to quantify the degree of disturbance of the local flow field by the propeller wake on the fixed wing. The disturbance angle is calculated using a vector angle formula, with a value ranging from 0 to 180 degrees. The local incoming flow velocity can be obtained through pitot tube measurement, GPS velocity estimation, or other methods, which are not limited here.

[0067] Drones can directly calculate interference parameters using vector operations. First, the wake velocity vector V_wake and the local incoming flow velocity vector V_local are represented in the same coordinate system. The angle between the two vectors is then calculated using the vector dot product formula: = arccos((V_wake · V_local) / (|V_wake | |V_local |)) V_local | The interference angle is obtained. The magnitude of the local incoming flow velocity is also considered. | I_dist = 0 x |V_local | The interference intensity was calculated from the dynamic pressure measured by the pitot tube system. 0 ,in Cp = (p - p_inf) / (0.5 p V 2)The unit is radians. To improve calculation accuracy, the velocity vector is low-pass filtered to remove high-frequency noise. The UAV can also use lookup table interpolation to quickly determine interference parameters. During the ground testing phase, a database of interference angles and intensities under different flight conditions is established through wind tunnel testing or CFD simulation. The database input parameters include flight speed, angle of attack, propeller speed, etc., and the output is the corresponding interference parameters. During flight, based on the current state parameters, the interference angle and intensity values ​​are quickly retrieved through multidimensional interpolation.

[0068] S204. When the pressure coefficient difference between adjacent detection points is greater than the preset separation judgment threshold, flow separation is determined to have occurred. When the pressure coefficient difference between adjacent detection points exceeds a preset separation threshold, the drone determines that flow separation has occurred. The pressure coefficient is a dimensionless parameter, defined as follows: p_inf ,in p For measuring static pressure, p For far-field static pressure, |Cp_i+1 - Cp_i | air density, V The incoming flow velocity is denoted as . Adjacent detection points refer to pressure measurement points arranged adjacent to each other along the flow direction or spanwise on the fixed airfoil surface. The pressure coefficient difference reflects the pressure gradient at adjacent locations. When this difference exceeds the preset separation threshold, it indicates a sharp change in the local flow field, which is a typical characteristic of flow separation. The preset separation threshold is determined based on airfoil characteristics and Reynolds number, with a typical range of 0.3-0.8. Flow separation can also be determined in conjunction with other parameters such as surface friction coefficient and velocity distribution, but these are not limited here.

[0069] Unmanned aerial vehicles (UAVs) can detect flow separation through real-time pressure coefficient calculation and differential comparison. Static pressure values ​​at each measuring point are acquired from a pressure sensor array, while dynamic pressure values ​​are acquired from the airspeed system. The pressure coefficient at each measuring point is calculated, forming a pressure coefficient distribution map. Differential calculations are performed on the pressure coefficients of adjacent measuring points to obtain the pressure coefficient gradient. When the pressure coefficient difference between adjacent measuring points... k_i = I_dist_i / I_ref When a preset threshold is exceeded, the location is marked as potentially indicating flow separation. To avoid false positives, a continuous multi-point judgment strategy is adopted; flow separation is only confirmed when three or more consecutive adjacent point pairs exceed the threshold. Drones can also employ machine learning-based flow separation identification methods. This involves collecting a large amount of pressure distribution data containing both separated and non-separated flow states, extracting statistical features of the pressure coefficient distribution, such as mean, variance, and kurtosis. A Support Vector Machine (SVM) or Random Forest classifier is trained to establish a mapping relationship between pressure features and flow states. After real-time pressure data acquisition, feature vectors are extracted and input into the classifier to directly determine whether flow separation exists.

[0070] S205. Calculate the thrust correction factor based on the region where flow separation occurs and the corresponding disturbance intensity; The UAV calculates the thrust correction coefficient based on the area where flow separation occurs and the corresponding interference intensity. Specifically, this includes: dividing the fixed wing surface into front, middle, and rear detection areas at equal intervals along the chord length; determining whether flow separation exists in each detection area based on the detection result that the pressure coefficient difference between adjacent detection points is greater than a preset separation judgment threshold; using the product of the interference angle at the corresponding position of each detection area and the local incoming flow velocity as the interference intensity value; using the ratio of the interference intensity value of the detection area with flow separation to the preset reference interference intensity value as the thrust correction coefficient for the detection area with flow separation; and setting the thrust correction coefficient for the detection area without flow separation to 1.

[0071] The UAV calculates the thrust correction coefficient based on the region where flow separation occurs and the corresponding disturbance intensity. The UAV divides the fixed wing surface into three equally spaced detection zones along the chord length: a front detection zone, a middle detection zone, and a rear detection zone. This division facilitates locating the specific position of flow separation and enabling targeted control adjustments. The front detection zone typically corresponds to the vicinity of the airfoil's leading edge, the middle detection zone to the vicinity of the maximum thickness, and the rear detection zone to the vicinity of the airfoil's trailing edge. The presence of flow separation in each detection zone is determined based on the pressure coefficient difference. The thrust correction coefficient is a dimensionless coefficient used to adjust the propeller's output thrust; its value reflects the degree of thrust adjustment required to compensate for lift loss caused by flow separation. A preset baseline disturbance intensity value is a typical disturbance intensity under standard flight conditions without flow separation, serving as a reference for calculating the thrust correction coefficient. The number of detection zones and the specific values ​​of the baseline disturbance intensity value can be adjusted according to the UAV's design parameters and are not limited here.

[0072] The UAV can determine the thrust correction factor through zonal evaluation and ratio calculation. For each detection area, the number of measurement points exceeding the separation judgment threshold within that area is counted, and the severity index of flow separation is calculated. The interference intensity value at the center of each area is obtained as the representative interference intensity for that area. For areas where flow separation exists, the thrust correction factor is calculated. I_dist_i ,in I_ref For the first i Interference intensity in each region = C_T p n 2 DA preset baseline interference intensity value is used. To avoid over-correction, the calculated correction coefficient is limited, typically to the range of [0.8, 1.5]. For areas where no flow separation is detected, the thrust correction coefficient is directly set to 1.0. The UAV can also employ a fuzzy logic-based thrust correction coefficient calculation method. A fuzzy control rule base is established, with input variables including the degree of flow separation and interference intensity, and the output being the thrust correction coefficient. Linguistic variables such as "slight separation," "moderate separation," "severe separation," "low interference," "medium interference," and "high interference" are defined. The thrust correction requirement for each area is obtained through fuzzy inference, and the specific correction coefficient value is obtained after defuzzification.

[0073] A potential technical challenge in this step is the uneven thrust distribution caused by significant differences in thrust correction coefficients across different regions. To address this, the UAV can introduce spatial smoothing of the thrust correction coefficients. The calculated thrust correction coefficients for each region are spatially filtered, and a Gaussian kernel function is used to weighted average the correction coefficients of adjacent regions. The smoothing intensity is adaptively adjusted based on the spatial gradient of the correction coefficients; a larger gradient results in stronger smoothing. This approach preserves the necessary thrust adjustments for flow separation regions while avoiding abrupt changes in thrust distribution, thus maintaining the overall consistency of thrust output.

[0074] S206. Adjust the output thrust of the propeller at the corresponding position according to the thrust correction coefficient to correct the rotational speed value, and execute the steps of calculating the pitch control torque value generated by the four propellers based on the first rotational speed value, second rotational speed value, third rotational speed value and fourth rotational speed value corresponding to the four propellers of the UAV and the torque calculation formula. The UAV adjusts the output thrust of the propellers at corresponding positions based on the thrust correction coefficient to correct the rotational speed. It then executes a step to calculate the pitch control torque generated by the four propellers based on the first, second, third, and fourth rotational speeds and the torque calculation formula. The purpose of thrust correction is to compensate for the aerodynamic performance degradation caused by flow separation by adjusting the propeller speed to change the thrust output. Since thrust is proportional to the square of the rotational speed, the rotational speed correction value is calculated using the square root of the thrust correction coefficient. The corrected rotational speed must be within the safe operating range of the propellers to avoid overspeeding or stalling. After completing the rotational speed correction, the corrected rotational speed value is substituted into the torque calculation formula to recalculate the pitch control torque, achieving compensatory control against the effects of flow separation.

[0075] Drones can be precisely adjusted using the thrust-speed mapping relationship. This is based on the propeller's thrust coefficient curve. T C_T 4 ,in target = k_thrust x T_current For thrust coefficient,n For rotational speed, D For the diameter, the target thrust is [value missing]. T_ k_thrust ,in new = n_current x sqrt(k_thrust) This is the thrust correction factor. Solving for the corrected rotational speed yields the result. n_ Delta T = T_current x (k_thrust - 1) Perform a limit check on the rotational speed correction values ​​to ensure they are within the range of [n_min, n_max]. Substitute the four corrected rotational speed values ​​into the pitch moment calculation formula to obtain the control moment considering flow separation compensation. The UAV can also use an incremental correction method. Calculate the thrust correction amount. Delta n Convert thrust correction into speed increment Delta n = n_current x (k_thrust - 1) / 2 By using Taylor expansion approximation, when the correction amount is small, Figure 4 Figure 4 The corrected speed value is obtained by adding the speed increment to the current speed. This method is simple to calculate and suitable for real-time control applications.

[0076] S207. When the pitch angular velocity value of the UAV is not greater than the preset transition flight angular velocity threshold, the step of calculating the pitch control torque value generated by the four propellers based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula is executed.

[0077] When the UAV's pitch rate is not greater than a preset transition flight rate threshold, the UAV performs the step of calculating the pitch control torque generated by the four propellers based on the first, second, third, and fourth rotational speeds corresponding to the four propellers and the torque calculation formula. This indicates that the UAV is in a relatively stable flight state, with minimal interference from the propeller wake to the fixed wing, eliminating the need for complex flow separation detection and thrust correction. Directly using the current propeller rotational speeds for torque calculation simplifies the control process and reduces the computational burden.

[0078] In the above embodiments, a judgment mechanism based on pitch angular velocity values ​​is introduced, combining fixed-wing surface pressure data and propeller wake velocity data to monitor and process flow separation phenomena in real time. By calculating the interference angle and interference intensity, the influence of propeller wake on the aerodynamic characteristics of the fixed-wing is quantified. When flow separation is detected, the propeller output thrust is adjusted using a thrust correction coefficient to compensate for the aerodynamic performance loss caused by flow separation. During the transition flight phase, a correlation model between propeller wake and fixed-wing aerodynamic characteristics is established, enabling the control system to adjust propeller thrust output in a targeted manner, improving the flight stability of the UAV in complex aerodynamic environments and enhancing the control reliability during the transition flight phase.

[0079] The above embodiments describe a control method based on pitch angular velocity threshold judgment combined with flow separation detection. In actual flight, in addition to vertical takeoff and landing and transitional flight, UAVs also need to perform complex maneuvers such as turns. The centrifugal force generated during turns causes the airflow direction to deflect, thus affecting the pressure distribution and flow separation characteristics of the fixed wing surface. To accurately capture the aerodynamic characteristic changes during turns, this application also provides a method for dynamically adjusting the detection area considering the influence of turning motion. This method adjusts the position of the detection area on the fixed wing surface in real time by analyzing the motion parameters during the turn.

[0080] The following is combined with a_c = 0 2 / V The present application describes a method for dynamically adjusting the detection area considering the influence of turning motion in its embodiments: Please see 0 This is a flowchart illustrating a method for dynamically adjusting the detection area considering the influence of turning motion in an embodiment of this application.

[0081] S301. Obtain the turning angular velocity and flight speed values ​​of the UAV; The UAV acquires turn rate and flight speed values. Turn rate refers to the angular velocity of the UAV's rotation around its vertical axis (yaw axis), characterizing the speed at which the UAV performs a turn maneuver, typically measured in degrees per second (° / s) or radians per second. This parameter is obtained in real-time through an onboard gyroscope, inertial measurement unit (IMU), or angular rate sensor, reflecting the rate at which the UAV changes its heading in the horizontal plane. Flight speed refers to the UAV's speed relative to the air, i.e., airspeed, measured through a pitot tube, airspeed sensor, or GPS / INS integrated navigation system, measured in meters per second (m / s) or kilometers per hour (km / h). During turn-based flight, these two parameters together determine the magnitude of the centrifugal force experienced by the UAV, thus affecting the airflow characteristics on the wing surface. The measurement accuracy and sampling frequency of turn rate, as well as the measurement method for flight speed, can be selected according to the specific configuration of the UAV and mission requirements, and are not limited here.

[0082] Drones can directly measure turning angular velocity using high-precision MEMS gyroscopes. The gyroscope is mounted near the drone's center of gravity, and its output signal undergoes analog-to-digital conversion and digital filtering to remove high-frequency noise and vibration interference. A Kalman filter algorithm is used to fuse gyroscope data with data from other attitude sensors, improving the accuracy and stability of angular velocity measurement. For flight speed, a Pitot-Pyrrhometry system is installed at the nose of the drone to measure the difference between total pressure and static pressure, calculating the indicated airspeed using Bernoulli's equation. Simultaneously, temperature and altitude sensor data are used to correct the indicated airspeed to vacuum speed. Drones can also employ a GPS / INS tightly coupled speed measurement method. The ground speed vector is obtained via a GPS receiver, and combined with attitude information and wind speed estimates provided by the INS, the airspeed vector is calculated. When GPS signals are interfered with, the short-term accuracy of the INS maintains the continuity of speed measurement. Turning angular velocity is measured using a fiber optic gyroscope or laser gyroscope within the INS. These high-precision gyroscopes have lower drift rates and higher bandwidth, enabling accurate capture of rapid turning motions.

[0083] S302. When the turning angular velocity value is greater than the preset turning angular velocity threshold, the quotient of the square of the turning angular velocity value and the flight speed value is determined as the centrifugal acceleration value. When the turning angular velocity value exceeds a preset turning angular velocity threshold, the UAV determines the centrifugal acceleration value as the quotient of the square of the turning angular velocity value and the flight speed value. The preset turning angular velocity threshold is the standard for determining whether the UAV is in a significant turning state. This threshold is determined based on the UAV's structural strength, aerodynamic characteristics, and control system response capability, with a typical range of 3-10 degrees / second. The calculation of the centrifugal acceleration value is based on the physical principles of circular motion, and the formula is as follows: 0_rad = 0_deg x pi / 180 ,in 0_deg The turning angular velocity (radians per second). V The speed is measured in meters per second (m / s). This centrifugal acceleration characterizes the centripetal force effect experienced by the UAV during a turn, directly affecting the apparent direction and magnitude of gravity on the wing surface. The unit of centrifugal acceleration is m / s², and its magnitude determines the degree of airflow deflection. The specific value of the turning angular velocity threshold can be adjusted according to different flight stages and mission requirements, and is not limited here.

[0084] The drone can determine centrifugal acceleration through real-time calculation and threshold judgment. First, the turning angular velocity is converted from degrees / second to radians / second. > 0_threshold Determine if the turning angular velocity exceeds the threshold. R = V / 0_rad a_c = V 2 / R = V x 0_rad = 0_rad 2 x V Then perform centrifugal acceleration calculation. Calculate the turning radius. 0_flow = arctan(a_c / g) ,in V The current flight speed. According to the centripetal acceleration formula... a_cThe centrifugal acceleration value is obtained. To avoid division by zero errors, the centrifugal acceleration is set to zero when the flight speed is below the minimum value (e.g., 5 m / s). The UAV can also employ a fast calculation method based on a lookup table. A two-dimensional lookup table for turning angular velocity and flight speed is pre-established, storing the corresponding centrifugal acceleration values. The resolution of the lookup table is set according to the control accuracy requirements, typically with a grid interval of 1 degree / s for angular velocity and 2 m / s for speed. After acquiring the current parameters in real time, the centrifugal acceleration value is quickly obtained from the lookup table through bilinear interpolation, reducing the computational load of real-time calculations.

[0085] S303. The arctangent of the centrifugal acceleration value and the gravitational acceleration value is determined as the airflow deflection angle value; The drone uses the arctangent of centrifugal acceleration and gravitational acceleration as the airflow deflection angle. The airflow deflection angle characterizes the angle by which the apparent direction of gravity deflects relative to the actual direction of gravity due to centrifugal force during a turn. This angle is determined by the formula... a_c The calculation yielded, where atio = a_c / 0_flow_rad = arctan(ratio) This is the centrifugal acceleration value. g This represents gravitational acceleration (approximately 9.81 m / s²). Physically, this angle reflects the change in the relationship between the direction of the incoming airflow and the wing chord line when the UAV turns. The unit of the airflow deflection angle is radians or degrees, and its magnitude directly affects the asymmetry of pressure distribution on the fixed wing surface. In the extreme case, when centrifugal acceleration equals gravitational acceleration, the airflow deflection angle reaches 45 degrees. The arctangent function can be calculated using standard mathematical library functions or lookup tables; no specific method is specified here.

[0086] The UAV can determine the airflow deflection angle using a direct calculation method. Obtain the centrifugal acceleration value calculated in step S302. 0_flow_deg = 0_flow_rad x 180 / pi and the pre-stored gravitational acceleration constant g =9.80665 m / s². Calculate the acceleration ratio r. ratio g This ratio represents the magnitude of centrifugal acceleration relative to gravitational acceleration. The deflection angle is calculated using the arctangent function: 0_flow_rad = pi / 2 - 1 / ratio a_c / g This yields the angle value in radians. Convert radians to degrees: 0_flow = a_c / g - (a_c / g) 3 / 3 + (a_c / g) This facilitates subsequent processing and display. To improve numerical stability, when Figure 5 When the value is >10, set directly. Figure 5 Figure 5 This avoids the accuracy loss of the arctangent function when it reaches large values. UAVs can also employ approximate calculation methods based on Taylor series expansions. When the centrifugal acceleration is relatively small ( ​ <0.5), use Taylor expansion: ​​ 5 / 5 This approximation method has sufficient accuracy within a small angle range and is computationally more efficient. For large angles, we switch to the precise arctangent function calculation, achieving full coverage through piecewise functions.

[0087] S304. The direction of airflow deviation is determined by the turning direction of the drone; The UAV determines the airflow deviation direction by turning. Turning direction refers to the direction of the UAV's rotation around its vertical axis, and can be either a left turn or a right turn. When the UAV turns left, the turning angular velocity is positive (according to the right-hand rule), and the airflow deviation direction points to the right side of the wing; when turning right, the turning angular velocity is negative, and the airflow deviation direction points to the left side of the wing. This deviation is due to the centrifugal force generated by the turn causing a tilt in the apparent direction of gravity, resulting in a lateral component of the relative airflow direction on the wing surface. Determining the airflow deviation direction provides a directional reference for subsequent lateral adjustments to the detection area. The turning direction can be determined based on the sign of the angular velocity, control surface deflection commands, or pilot input; no specific criteria are specified here.

[0088] The UAV can determine the direction of airflow deviation by analyzing the sign of the angular velocity. It reads the yaw angular velocity value ω_yaw output from the gyroscope, which includes magnitude and sign information. According to the coordinate system definition, if ω_yaw > 0, it is determined to be a left turn, and the airflow deviation direction is set to +Y (pointing to the right wing); if ω_yaw < 0, it is determined to be a right turn, and the airflow deviation direction is set to -Y (pointing to the left wing). The deviation direction is represented as a unit vector: direction = [0, 1, 0] for a left turn, and direction = [0, -1, 0] for a right turn. To avoid jitter near the zero point, a dead zone is set; when |ω_yaw| < 0.5 degrees / second, it is considered that there is no turn, and the deviation direction is set to zero vector. The UAV can also use a direction determination method based on control surface commands. It monitors the control commands of the rudder and ailerons; when the rudder deflects to the left (positive value) or the aileron generates a left roll moment, it is determined to be a left turn intention. Combined with the current roll angle information, the consistency of the turning direction is confirmed. This method can predict the turning direction in advance and start adjusting the detection area before the angular velocity changes significantly, thereby improving the system's response speed.

[0089] S305. The sine value of the airflow deflection angle is determined as the offset coefficient of the detection area. The UAV determines the sine value of the airflow deflection angle as the detection area offset coefficient. The detection area offset coefficient is a dimensionless parameter used to quantify the relative distance the detection area needs to move laterally. This coefficient is calculated using the formula k_offset=sin(θ_flow), where θ_flow is the airflow deflection angle determined in step S303. Geometrically, the sine value reflects the projection component of the deflection angle in the lateral direction. When the airflow deflection angle is 0 degrees, the offset coefficient is 0, indicating no adjustment is needed; when the deflection angle reaches 90 degrees, the offset coefficient is 1, indicating the maximum lateral offset. This sine function-based mapping ensures a non-linear but monotonically increasing relationship between the offset and the deflection angle. The sine function can be calculated using mathematical library functions, lookup tables, or polynomial approximation; no limitation is made here.

[0090] The offset coefficient can be determined by standard trigonometric functions. Obtain the airflow deflection angle value θ_flow (in radians) from step S303. Call the sine function from the mathematical library: k_offset = sin(θ_flow). Check the range of the calculation result to ensure k_offset ∈ [0, 1]. Considering non-ideal factors in practical applications, a correction factor is introduced: k_offset_corrected = k_offset × correction_factor, where correction_factor is calibrated based on flight test data, with a typical value of 0.8-1.0. Store the offset coefficient and pass it to subsequent steps. Alternatively, the UAV can use a fast calculation method based on piecewise linear approximation. Divide the interval [0, π / 2] into multiple sub-intervals, and approximate the sine function with a linear function within each sub-interval. For example, in the interval [0, π / 6], sin(θ) ≈ θ; in the interval [π / 6, π / 3], sin(θ) ≈ 0.5 + 0.866 × (θ - π / 6). By determining the range of the angle, the corresponding linear formula is selected to quickly calculate the offset coefficient, with the calculation error controlled within 2%.

[0091] S306. Multiply the chord length of the fixed wing surface by the detection area offset coefficient to obtain the lateral offset distance value of the detection area; The UAV multiplies the chord length of the fixed-wing surface by the detection area offset coefficient to obtain the lateral offset distance of the detection area. The chord length of the fixed-wing surface refers to the straight-line distance from the leading edge to the trailing edge of the wing. For variable-chord wings, the average aerodynamic chord length is usually taken as a reference value. The formula for calculating the lateral offset distance of the detection area is d_offset = c × k_offset, where c is the chord length (meters) and k_offset is the offset coefficient determined in step S305. This distance value represents the actual physical distance that the detection area needs to move in the spanwise direction (perpendicular to the chord direction). Lateral offset allows the detection area to follow the airflow deflection generated by turning and remain within the main area of ​​airflow influence. The measurement position of the chord length, the averaging method, and the unit of the offset distance can be selected according to specific application requirements and are not limited here.

[0092] The UAV can calculate the offset distance using direct multiplication. The local chord length value c_local at the current detection position is read from the wing geometry database. If the wing has a variable chord length design, interpolation is performed based on the spanwise position: c_local = c_root × (1-η) + c_tip × η, where η is the dimensionless spanwise position, and c_root and c_tip are the root and tip chord lengths, respectively. The offset coefficient k_offset from step S305 is obtained, and the offset distance is calculated: d_offset = c_local × k_offset. The calculation results are checked for reasonableness to ensure that the offset distance does not exceed 10% of the wing's half-span. The offset distance value is converted into a specific coordinate increment in the sensor coordinate system. Alternatively, the UAV can use a unified calculation method based on a reference chord length. The average aerodynamic chord length MAC is used as the reference value: MAC = (2 / 3) × c_root × (1 + λ + λ²) / (1 + λ), where λ is the tip-to-root ratio. The same reference chord length is used to calculate the offset distance for all detection areas, simplifying the calculation process. To compensate for local chord length differences, a position correction coefficient is introduced: d_offset_local = d_offset_ref × (c_local / MAC)^0.5. This method ensures that the detection area offsets at different spanwise positions are consistent.

[0093] S307. The front, middle and rear detection areas on the fixed wing surface are shifted by the lateral offset distance value of the detection area along the airflow offset direction.

[0094] The UAV translates the front, middle, and rear detection areas on the fixed wing surface by a distance equal to the lateral offset of the detection area along the airflow offset direction. The front detection area is located near the leading edge of the wing (approximately 5%-25% chord length), the middle detection area is located near the maximum thickness (approximately 30%-50% chord length), and the rear detection area is located near the trailing edge (approximately 70%-90% chord length). The translation operation involves moving the center position of each detection area a specified distance along the spanwise direction (airflow offset direction) while maintaining the shape and size of the detection area. This dynamic adjustment ensures that pressure detection always covers critical flow areas during turns. The translated detection areas retain the original sensor density and sampling strategy. The specific range of the detection areas, the number of sensors, and the translation implementation method can be flexibly adjusted according to the system design and are not limited here.

[0095] The UAV can achieve translation of the detection area through coordinate transformation. A local coordinate system is established on the wing surface, with the x-axis along the chord and the y-axis along the spanwise direction. The original center coordinates of each detection area are read: P_front=[x_f, y_f], P_mid=[x_m, y_m], P_rear=[x_r, y_r]. Based on the airflow offset direction (±Y direction) determined in step S304 and the offset distance d_offset in step S306, the new center coordinates are calculated: P_front_new=[x_f, y_f±d_offset], P_mid_new=[x_m, y_m±d_offset], P_rear_new=[x_r, y_r±d_offset]. The scanning sequence of the sensor acquisition system is updated, prioritizing the reading of sensor data within the translated detection area. For areas outside the sensor coverage, pressure values ​​are estimated using a spatial interpolation algorithm. The UAV can also employ a software implementation method based on a virtual detection area. While keeping the physical sensor positions unchanged, a virtual detection area is defined at the data processing layer. By using coordinate mapping, sampling points within the virtual detection area are mapped to the nearest physical sensor. Using bilinear interpolation or Kriging interpolation, the pressure values ​​at the virtual sampling points are calculated based on measurements from surrounding sensors. This method eliminates the need to move hardware, allowing for flexible adjustment of the detection area through software algorithms, thus offering greater adaptability.

[0096] A potential new technical challenge in this step is the reduced resolution in some areas after the detection region has been translated, due to insufficient sensor density. To address this, the UAV can employ an adaptive sampling density adjustment strategy. This involves analyzing the sensor distribution density within each detection region after translation to identify sparse areas. A super-resolution reconstruction algorithm is then applied to these sparse areas, utilizing compressed sensing theory and the sparsity and continuity constraints of pressure distribution to reconstruct a high-resolution pressure field from a limited number of measurement points. Specifically, a sparse representation basis function for the pressure distribution (such as a wavelet basis or Fourier basis) is constructed, and the reconstruction problem is solved by minimizing the L1 norm. This method can maintain measurement accuracy and spatial resolution after the detection region has been translated, even with a limited number of sensors.

[0097] In the above embodiments, the turning angular velocity and flight speed of the UAV are acquired. When the turning angular velocity exceeds a preset threshold, the centrifugal acceleration is calculated based on the quotient of the square of the turning angular velocity and the flight speed. The airflow deflection angle is then determined by the arctangent of the centrifugal acceleration and the gravitational acceleration. The airflow offset direction is then determined based on the UAV's turning direction. Finally, the distribution of the detection area on the fixed wing surface is adjusted along the airflow offset direction according to the airflow deflection angle. This method of dynamically adjusting the distribution of the detection area based on turning motion parameters allows the arrangement of the detection area to be adjusted accordingly with changes in airflow direction during the UAV's turn. This ensures that the detection area is always at a critical position affected by the actual airflow, thereby obtaining more accurate pressure data and flow separation information, and improving the accuracy of thrust correction during turning flight.

[0098] The drone in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. ​ This is a schematic diagram of the physical device structure for controlling a vertical take-off and landing unmanned aerial vehicle (UAV) that combines a fixed wing and a propeller, as provided in an embodiment of this application.

[0099] It should be noted that, ​ The structure of the drone shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0100] like ​As shown, the drone includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from storage portion 408 into Random Access Memory (RAM) 403. The RAM 403 also stores various programs and data required for drone operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0101] The following components are connected to I / O interface 405: input section 406 including a camera, infrared sensor, etc.; output section 407 including a liquid crystal display (LCD) and speakers, etc.; storage section 408 including a hard disk, etc.; and communication section 409 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0102] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the various functions defined in the present invention.

[0103] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electric, magnetic, optical, electromagnetic, infrared, or semiconductor drone, device, or apparatus, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of drones, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based drone performing the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the drone described in the above embodiments; or it may exist independently and not assembled into the drone. The storage medium carries one or more computer programs that, when executed by a processor of a drone, cause the drone to implement the methods provided in the above embodiments.

[0106] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0107] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0108] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0109] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A control method for a vertical takeoff and landing unmanned aerial vehicle (UAV) combining a fixed-wing and a propeller, characterized in that, include: Based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula, the pitch control torque value generated by the four propellers is calculated. The pitch control torque value is converted into an equivalent rudder deflection value according to the equivalent conversion formula. Based on the ratio of the equivalent rudder deflection angle value to the current actual rudder deflection angle value of the UAV's aerodynamic control surface, the propeller speed control increment value and the aerodynamic control surface rudder deflection angle control increment value are calculated. The propeller speed control increment value is superimposed on the current speed value of the four propellers to obtain the propeller control command; The control increment value of the aerodynamic control surface deflection angle is superimposed on the current actual deflection angle value of the aerodynamic control surface to obtain the control surface command; Simultaneously execute the propeller control command and the rudder control command.

2. The method according to claim 1, characterized in that, The formula for calculating the torque is: ； In the above formula, the The pitch control torque value is the value of the pitch control torque. The vertical distance from the propeller to the center of gravity of the drone is... The force coefficients of the four propellers, the The above The above and the aforementioned These are the first rotational speed value, the second rotational speed value, the third rotational speed value, and the fourth rotational speed value, respectively.

3. The method according to claim 1, characterized in that, The equivalent conversion formula is: ； In the above formula, the The equivalent rudder deflection angle value, the The pitch control torque value is the value of the pitch control torque. The dynamic pressure value of the drone in its current flight state is the value of the drone's dynamic pressure. The characteristic area of ​​the drone, the The characteristic length of the drone, the Let be the derivative of the aerodynamic rudder deflection moment of the UAV.

4. The method according to claim 1, characterized in that, Before calculating the pitch control torque value generated by the four propellers based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula, the method further includes: When the pitch rate of the UAV is greater than the preset transition flight rate threshold, the pressure data of the fixed wing surface and the propeller wake velocity data are acquired. The wake velocity vector of the UAV is determined based on the propeller wake velocity data; The angle between the wake velocity vector and the local incoming flow velocity vector is defined as the interference angle; the product of the interference angle and the local incoming flow velocity is defined as the interference intensity. When the pressure coefficient difference between adjacent detection points is greater than the preset separation judgment threshold, flow separation is determined to have occurred. Calculate the thrust correction factor based on the region where the flow separation occurs and the corresponding disturbance intensity; The output thrust of the propeller at the corresponding position is adjusted according to the thrust correction coefficient to correct the rotational speed value, and the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula are executed to calculate the pitch control torque value generated by the four propellers. When the pitch angular velocity value of the UAV is not greater than the preset transition flight angular velocity threshold, the step of calculating the pitch control torque value generated by the four propellers is executed based on the first, second, third, and fourth rotational speed values ​​corresponding to the four propellers of the UAV and the torque calculation formula.

5. The method according to claim 4, characterized in that, The calculation of the thrust correction coefficient based on the region where the flow separation occurs and the corresponding disturbance intensity specifically includes: The surface of the fixed wing is divided into a front detection area, a middle detection area, and a rear detection area at equal intervals along the chord length direction. Based on the detection results where the pressure coefficient difference between adjacent detection points is greater than the preset separation judgment threshold, it is determined whether flow separation exists in each detection area; The product of the interference angle at the corresponding position of each detection area and the local incoming flow velocity is used as the interference intensity value; The ratio of the interference intensity value of the detection region where the flow separation exists to the preset reference interference intensity value is used as the thrust correction coefficient for the detection region where the flow separation exists. The thrust correction factor for the detection region where the flow separation does not exist is set to 1.

6. The method according to claim 4, characterized in that, Before determining the wake velocity vector of the UAV based on the propeller wake velocity data, the method further includes: Obtain the turning angular velocity and flight speed values ​​of the drone; When the turning angular velocity value is greater than the preset turning angular velocity threshold, the quotient of the square of the turning angular velocity value and the flight speed value is determined as the centrifugal acceleration value; The arctangent of the centrifugal acceleration value and the gravitational acceleration value is determined as the airflow deflection angle value; The turning direction of the drone is used as the airflow deflection direction; The detection area distribution position on the fixed wing surface is adjusted according to the airflow deflection angle value along the airflow offset direction.

7. The method according to claim 6, characterized in that, The step of adjusting the distribution position of the detection area on the fixed wing surface according to the airflow deflection angle value along the airflow offset direction specifically includes: The sine value of the airflow deflection angle is determined as the detection area offset coefficient; Multiply the chord length of the fixed wing surface by the detection area offset coefficient to obtain the lateral offset distance value of the detection area; The front, middle, and rear detection areas on the fixed wing surface are shifted by the lateral offset distance value of the detection area along the airflow offset direction.

8. A drone, characterized in that, The drone includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the UAV to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the drone, the drone causes the drone to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the drone, it causes the drone to perform the method as described in any one of claims 1-7.

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