A longitudinal attitude dynamics modeling method for flapping-wing aircraft based on wind tunnel tests

Abstract

The present invention provides a longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests, including: simulating flight through wind tunnel tests to measure the experimental data of the longitudinal moment of the flapping-wing aircraft, calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data; dividing the longitudinal moment into a first moment, a second moment and a third moment based on the flapping motion and translational motion of the flapping-wing aircraft, and the synergistic effect of the flapping motion and translational motion; establishing a no-inflow model for the longitudinal moment of the flapping-wing aircraft according to the first moment, and determining the first model parameter according to the average longitudinal moment and flapping frequency; based on the no-inflow model, establishing an inflow model for the longitudinal moment of the flapping-wing aircraft by combining the second moment and the third moment, and determining the second model parameter according to the average longitudinal moment and flapping frequency. The present invention avoids flight tests with higher equipment requirements, reduces the difficulty of data acquisition, and improves the modeling accuracy based on wind tunnel test data.

Description

Technical Field

[0001] The present invention belongs to the field of aircraft modeling, and particularly relates to a longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests. Background Art

[0002] Bionic aircraft have become a research hotspot for various scientific research institutions and technology companies. A flapping-wing aircraft is a new type of aircraft inspired by biological flight, combining the advantages of fixed-wing and rotary-wing aircraft, with better maneuverability, higher aerodynamic efficiency, and excellent stealth capabilities, and can perform indoor and outdoor tasks.

[0003] Obtaining an accurate longitudinal attitude dynamics model of a flapping-wing aircraft is of great significance for the development of the control system, the design of the navigation system, and the flight simulation of a flapping-wing aircraft. At present, the mechanism research on flapping-wing flight is not yet mature. It is difficult to derive the dynamics model of a flapping-wing aircraft with strong coupling relationships theoretically, and the accuracy is not ideal. This poses a huge challenge to the research and development of flapping-wing aircraft, and it is of great significance to propose a highly available longitudinal dynamics modeling method for flapping-wing aircraft. Summary of the Invention

[0004] In order to solve the problems of difficult longitudinal attitude dynamics modeling and inaccurate models of flapping-wing aircraft in the prior art, the present invention proposes a longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests, including:

[0005] Simulating flight through wind tunnel tests, measuring the experimental data of the longitudinal moment of the flapping-wing aircraft, and calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data;

[0006] Based on the flapping motion and translational motion of the flapping-wing aircraft, and the synergistic effect of the flapping motion and translational motion, the longitudinal moment is divided into a first moment, a second moment, and a third moment;

[0007] Establishing a no-inflow model for the longitudinal moment of the flapping-wing aircraft according to the first moment, and determining the first model parameter according to the average longitudinal moment and flapping frequency;

[0008] Based on the no-inflow model, combining the second moment and the third moment to establish an inflow model for the longitudinal moment of the flapping-wing aircraft, and determining the second model parameter according to the average longitudinal moment and flapping frequency.

[0009] Optionally, the simulating flight through wind tunnel tests and measuring the experimental data of the longitudinal moment of the flapping-wing aircraft includes setting the experimental variables of the flapping-wing aircraft, and the experimental variables include flapping frequency, angle of attack, and inflow velocity;

[0010] Among them, the flapping frequency of the flapping-wing aircraft is set by controlling the PWM signal of the driving motor in the flapping-wing aircraft; the angle of attack of the flapping-wing aircraft is set by an angular displacement table; the oncoming flow velocity is set by controlling the flow velocity of the wind tunnel.

[0011] Optionally, calculating the average longitudinal moment and the flapping frequency of the flapping-wing aircraft based on the experimental data includes: performing an averaging process on the measured longitudinal moment, and the formula for the averaging process is:

[0012]

[0013] Among them, M y is the average longitudinal moment, M(i) is the longitudinal moment of each sampling, N is the total number of sampling points, N = Tfs, T is the total sampling time, and fs is the sampling frequency of the force sensor.

[0014] Optionally, calculating the average longitudinal moment and the flapping frequency of the flapping-wing aircraft based on the experimental data includes: performing a fast Fourier transform on the measured longitudinal moment to obtain a spectrum, and determining the flapping frequency of the flapping-wing aircraft from the peak position in the spectrum.

[0015] Optionally, the modeling method further includes: before calculating the average longitudinal moment and the flapping frequency of the flapping-wing aircraft based on the experimental data, performing a preprocessing operation on the experimental data, and the preprocessing operation includes denoising the experimental data based on low-pass filtering.

[0016] Optionally, the first moment is the self-moment generated by the periodic flapping of the flapping-wing aircraft, the second moment is the moment generated by the translational motion of the fixed wing, and the third moment is the moment generated by the synergistic effect of the flapping motion and the translational motion.

[0017] Optionally, establishing a no-oncoming-flow model for the longitudinal moment of the flapping-wing aircraft according to the first moment, and determining the first model parameter according to the average longitudinal moment and the flapping frequency includes:

[0018] Based on the characteristics of the moment and the flapping frequency during flapping-wing flight, expressing the no-oncoming-flow model as:

[0019] M1 = k1ω 2 ;

[0020] Among them, M1 represents the first moment, k1 is the linear coefficient of the first moment and the square of the flapping frequency, and ω is the flapping frequency; performing parameter identification and optimization on the first model parameter based on the least squares method, and the model for the parameter identification and optimization is:

[0021]

[0022] Among them, L1 is the difference between the longitudinal moment obtained from the experiment and the longitudinal moment calculated by the model, M y(i) is the longitudinal moment obtained from the i-th measurement in the wind tunnel test, ω(i) is the flapping frequency corresponding to the longitudinal moment obtained from the i-th measurement in the wind tunnel test, and T is the total number of experimental data.

[0023] Optionally, based on the no-inflow model, the second moment and the third moment are combined to establish an inflow model for the longitudinal moment of the flapping-wing aircraft, and the second model parameters are determined according to the average longitudinal moment and the flapping frequency, including:

[0024] Based on the hydrodynamic pressure formula, the second moment is expressed as:

[0025]

[0026] M2 represents the second moment, V is the inflow velocity, ρ is the air density, C m (α) is the pitching moment coefficient of the flapping-wing aircraft represented by the Fourier series, α is the stroke plane angle, k2, a n , b n are the second model parameters of the inflow model respectively, S is the area of the wing, and c is the average chord length of the wing;

[0027] Based on the synergistic effect of the flapping motion and the translational motion, the third moment is expressed as:

[0028] M3 = k3V 2 ω;

[0029] M3 represents the third moment, and k3 is the linear coefficient of the third moment with respect to the flapping frequency ω and the square of the inflow velocity V 2 ;

[0030] Combined with the no-inflow model, the first moment, the second moment, and the third moment are added as the inflow model;

[0031] Based on the least squares method, parameter identification and optimization are performed on the second model parameters of the inflow model, and the model for the parameter identification and optimization is

[0032] where L2 is the difference between the longitudinal moment obtained from the experiment minus the first moment and the longitudinal moment calculated by the model, M1(i) is the first moment calculated by the no-inflow model for the i-th time, M2(i) is the second moment calculated by the inflow model for the i-th time, and M3(i) is the third moment calculated by the inflow model for the i-th time.

[0033] Optionally, the modeling method further includes, before simulating flight through the wind tunnel test, performing a zeroing operation on the six-axis force balance used to measure the longitudinal moment.

[0034] The beneficial effects brought by the technical solution provided by the present invention are:

[0035] (1) The flight longitudinal moment data of the flapping-wing aircraft is obtained through wind tunnel tests, avoiding flight tests with higher equipment requirements and reducing the difficulty of data acquisition;

[0036] (2) Through the processing of experimental data, the influence of external interference on the experimental data is reduced, and the accuracy and availability of the data are improved;

[0037] (3) The data-driven modeling method avoids the complex means of theoretical modeling of the longitudinal moment of the flapping-wing aircraft and improves the modeling accuracy based on wind tunnel test data. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a schematic flow chart of a longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests proposed in an embodiment of the present invention;

[0040] Figure 2 It is a schematic diagram of a force measuring system for a flapping-wing aircraft based on wind tunnel tests provided in an embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of the force and reference plane of a flapping-wing aircraft provided in an embodiment of the present invention;

[0042] Figure 4 It is a schematic diagram of wing parameters provided in an embodiment of the present invention. Detailed Embodiments

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0044] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here.

[0045] It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the various processes do not imply the order of execution, and the order of execution of the various processes should be determined by their functions and internal logics, and should not constitute any limitation to the implementation processes of the embodiments of the present invention.

[0046] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0047] It should be understood that in the present invention, "a plurality of" means two or more. " / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "Including A, B, and C" and "including A, B, C" mean that all of A, B, and C are included. "Including A, B, or C" means including one of A, B, and C. "Including A, B, and / or C" means including any one or any two or all three of A, B, and C.

[0048] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A relatively", "A corresponding to B relatively", or "B corresponding to A relatively" means that B is associated with A, and B can be determined according to A. Determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information. The matching of A and B means that the similarity between A and B is greater than or equal to a preset threshold.

[0049] Depending on the context, as used herein, "if" can be interpreted as "when...", "while...", "in response to determining", or "in response to detecting".

[0050] The technical solutions of the present invention will be described in detail below with specific embodiments. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0051] Embodiment

[0052] As Figure 1 shown, this embodiment proposes a longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests, including:

[0053] S1: Simulate flight through wind tunnel tests, measure the experimental data of the longitudinal moment of the flapping-wing aircraft, and calculate the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data;

[0054] S2: Based on the flapping motion and translational motion of the flapping-wing aircraft, as well as the synergistic effect of the flapping motion and translational motion, divide the longitudinal moment into a first moment, a second moment, and a third moment;

[0055] S3: Establish a no-inflow model for the longitudinal moment of the flapping-wing aircraft according to the first moment, and determine the first model parameter according to the average longitudinal moment and flapping frequency;

[0056] S4: Based on the no-inflow model, combine the second moment and the third moment to establish an inflow model for the longitudinal moment of the flapping-wing aircraft, and determine the second model parameter according to the average longitudinal moment and flapping frequency.

[0057] In this embodiment, the above modeling method is implemented through Figure 2 the force measurement system of the flapping-wing aircraft shown, which specifically includes: a flapping-wing aircraft 1, a six-axis force balance 2, a fixed connector 3, an angular displacement table 4, a computer 5, an aircraft fixed bracket 6, and a wind tunnel 7. In this embodiment, the six-axis force balance 2 is connected to the center of gravity of the flapping-wing aircraft 1, and the six-axis force balance 2 and the flapping-wing aircraft 1 are fixed on the angular displacement table 4 by the fixed connector 3. The angular displacement table 4 is connected to the aircraft 6 fixed bracket, and the flapping-wing aircraft 1 communicates with the computer 5 through Bluetooth.

[0058] The main influencing factors of the longitudinal moment of the flapping-wing aircraft include the flapping frequency, flight angle of attack, and inflow velocity. Among them, by controlling the PWM signal of the drive motor in the flapping-wing aircraft 1, the flapping frequency of the flapping-wing aircraft 1 is set. Specifically, the computer 5 controls the input PWM signal of the drive motor through Bluetooth; the angle of attack of the flapping-wing aircraft 1 is set through the angular displacement table 4; the inflow velocity is set by controlling the flow rate of the wind tunnel 7. In this embodiment, the PWM duty cycle input to the drive motor is set from 50% to 100%, with an interval of 5%, the angle of attack of the flapping-wing aircraft is from 0° to 70°, with an interval of 10°, and the wind tunnel flow rate is from 0 to 6 m / s, with an interval of 1 m / s.

[0059] In this embodiment, the sampling frequency of the six-axis force balance 2 is 10000 hz, and each group of experiments conducts 10 seconds of data acquisition. Before simulating flight through wind tunnel tests, that is, before each time the wind tunnel is turned on and the flapping-wing aircraft is started, the six-axis force balance 2 used to measure the longitudinal moment needs to be zeroed to ensure the measurement accuracy of the six-axis force balance.

[0060] After completing the equipment preparation work of the force measurement system, the flapping-wing aircraft is first subjected to a wind tunnel test. Based on the principle of relativity of motion, the model or physical object of the aircraft is fixed in an artificial ground environment, and an artificial airflow is made to flow through it to simulate various complex flight states in the air, thereby obtaining experimental data, avoiding flight tests with higher equipment requirements, and reducing the difficulty of data acquisition.

[0061] For the convenience of subsequent modeling work, in this embodiment, the average longitudinal moment and flapping frequency of the flapping-wing aircraft are calculated based on the experimental data, including:

[0062] Perform an averaging process on the measured longitudinal moment, and the formula for the averaging process is:

[0063]

[0064] Among them, M y is the average longitudinal moment, M(i) is the longitudinal moment of each sampling, N is the total number of sampling points, N = Tfs, T is the total sampling time, and fs is the sampling frequency of the force measurement sensor.

[0065] Since it is not easy to accurately control the flapping frequency of the flapping-wing aircraft, according to the periodicity of the flapping motion, a fast Fourier transform is performed on the measured longitudinal moment to obtain a frequency spectrum, and the flapping frequency of the flapping-wing aircraft is determined by the peak position in the frequency spectrum.

[0066] In order to further improve the parameter identification accuracy of subsequent modeling, before calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data, a preprocessing operation is performed on the experimental data, and the preprocessing operation includes denoising the experimental data based on low-pass filtering. In this embodiment, low-pass filtering is used to isolate external vibrations and improve the accuracy of measurement data. Considering that the flapping frequency of the flapping-wing aircraft in this embodiment is about 8 hz to 25 hz, the cut-off frequency of the filter is set to 40 hz.

[0067] So far, the modeling preparation work is completed. In this embodiment, the longitudinal moment of the flapping-wing aircraft is split into a first moment M1, a second moment M2, and a third moment M3. The first moment is the self-moment generated by the periodic flapping of the flapping-wing aircraft, the second moment is the moment generated by the translational motion of the fixed wing, and the third moment is the moment generated by the synergistic effect of the flapping motion and the translational motion. Since the pitch angular velocity of the flapping-wing aircraft during flight is small, this embodiment does not consider the natural damping moment caused by the pitch angle motion. It can be seen that the longitudinal attitude moment of the flapping-wing aircraft can be decomposed into:

[0068] M y = M1 + M2 + M3.

[0069] In the case of no oncoming flow, the wind speed is not considered, then My is completely determined by the self - torque generated by periodic flapping, so M y can be directly expressed as M y = M1.

[0070] In this embodiment, referring to the characteristics of the torque and flapping frequency during insect flapping - wing flight, the torque and flapping frequency of the flapping - wing motion under wind - free conditions are quadratic - related. Therefore, the no - oncoming - flow model is expressed as:

[0071] M1 = k1ω 2 ;

[0072] where M1 represents the first torque, k1 is the linear coefficient of the first torque and the square of the flapping frequency, and ω is the flapping frequency. Thus, k1 is the first model parameter to be identified and optimized.

[0073] In this embodiment, based on the least - squares method, parameter identification and optimization of the first model parameter are carried out. The model of the parameter identification and optimization is:

[0074]

[0075] where L1 is the difference between the longitudinal torque obtained from the experiment and the longitudinal torque calculated by the model, M y (i) is the longitudinal torque measured in the i - th measurement of the wind - tunnel test, ω(i) is the flapping frequency corresponding to the longitudinal torque measured in the i - th measurement of the wind - tunnel test, and T is the total number of experimental data. According to the torque data obtained from the experiment, the above - mentioned model of parameter identification and optimization is solved to obtain the longitudinal - torque model parameter of the experimental prototype under no - oncoming - flow conditions.

[0076] After obtaining the longitudinal - dynamics model of the flapping - wing aircraft under no - oncoming - flow conditions in this embodiment, model - building and parameter identification of the longitudinal - dynamics model under oncoming - flow conditions are carried out. That is, based on the no - oncoming - flow model, combined with the second torque and the third torque, an oncoming - flow model of the longitudinal torque of the flapping - wing aircraft is established. The second model parameter is determined according to the average longitudinal torque and the flapping frequency, including: Based on the hydrodynamic - pressure formula, the second torque is expressed as:

[0077]

[0078] M2 represents the second torque, V is the oncoming - flow velocity, ρ is the air density. C m (α) is the pitching - moment coefficient of the flapping - wing aircraft represented by the Fourier series, α is the stroke - plane angle, k2, a n , b n are the second model parameters of the oncoming - flow model respectively, S is the area of the wing, and c is the average chord - span of the wing.

[0079] It should be noted that in this embodiment, the forces acting on the flapping-wing aircraft and the reference plane are as follows Figure 3 As shown, this embodiment uses the ZXY Euler coordinate system to describe the longitudinal attitude of the flapping-wing aircraft. Among them, V is the flight speed, θ is the pitch angle, β is the stroke angle, and α is the stroke plane angle between the flight speed and the stroke plane. In this embodiment, the stroke angle β at takeoff is defined as 0, then the stroke plane angle α can be expressed as:

[0080]

[0081] In this embodiment, considering the periodicity of the flapping motion, the pitch moment coefficient of the flapping-wing aircraft is represented by a Fourier series. And after fitting tests on the order of the Fourier series, the result can reach a relatively high fitting accuracy when the order is 1. Therefore, N = 1 is selected here.

[0082] In this embodiment, the parameters of the wing are as follows Figure 4 As shown, the calculation formula for the average chord length is:

[0083] W is the width of the wing, L is the length of the wing, and c(y) is the wing edge curve equation with respect to the xy coordinate axes.

[0084] Based on the synergistic effect of the flapping motion and the translational motion, the moment M3 generated by the synergy is constructed as a linear model of the flapping frequency ω and the square of the oncoming flow velocity V, that is, the third moment is expressed as:

[0085] M3 = k3V 2 ω;

[0086] M3 represents the third moment, and k3 is the linear coefficient of the third moment with respect to the flapping frequency ω and the square of the oncoming flow velocity V 2

[0087] Combined with the no-oncoming-flow model, the first moment, the second moment, and the third moment are added together as the oncoming-flow model, that is, M y = M1 + M2 + M3.

[0088] From the above modeling process, it can be seen that k2, k3, a n 、b n are all the second model parameters to be identified and optimized. In this embodiment, based on the least squares method, the second model parameters of the oncoming-flow model are identified and optimized, and the model of the parameter identification and optimization is

[0089] ​Wherein, L2 is the difference between the longitudinal moment obtained from the experiment minus the first moment and the longitudinal moment calculated by the model; M1(i) is the first moment calculated for the i-th time by the model without oncoming flow; M2(i) is the second moment calculated for the i-th time by the model with oncoming flow; and M3(i) is the third moment calculated for the i-th time by the model with oncoming flow.

[0090] According to the moment measurement data obtained from the wind tunnel test, solve the above model for parameter identification and optimization to obtain the longitudinal moment model parameters of the experimental prototype under the condition of oncoming flow.

[0091] The serial numbers in the above embodiments are only for description and do not represent the sequence in the assembly or use process of each component.

[0092] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A longitudinal attitude dynamics modeling method for flapping-wing aircraft based on wind tunnel tests, characterized in that, The described modeling method includes: Simulating flight through wind tunnel tests, measuring the experimental data of the longitudinal moment of the flapping-wing aircraft, and calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data; Based on the flapping motion and translational motion of the flapping-wing aircraft, as well as the synergistic effect of the flapping motion and translational motion, dividing the longitudinal moment into a first moment, a second moment, and a third moment; Establishing a no-inflow model for the longitudinal moment of the flapping-wing aircraft according to the first moment, and determining the first model parameter according to the average longitudinal moment and flapping frequency; Based on the no-inflow model, combining the second moment and the third moment to establish an inflow model for the longitudinal moment of the flapping-wing aircraft, and determining the second model parameter according to the average longitudinal moment and flapping frequency; The establishing a no-inflow model for the longitudinal moment of the flapping-wing aircraft according to the first moment, and determining the first model parameter according to the average longitudinal moment and flapping frequency includes: Based on the characteristics of the moment and flapping frequency during flapping flight, expressing the no-inflow model as: ； wherein, represents the first moment, is the linear coefficient of the first moment and the square of the flapping frequency, is the flapping frequency; Performing parameter identification and optimization on the first model parameter based on the least squares method, and the model of the parameter identification and optimization is: ； Among them, is the difference between the longitudinal moment obtained from the experiment and the longitudinal moment calculated by the model, is the longitudinal moment obtained from the i-th measurement in the wind tunnel test, is the flapping frequency corresponding to the longitudinal moment obtained from the i-th measurement in the wind tunnel test, is the total number of experimental data; The establishing an inflow model for the longitudinal moment of the flapping-wing aircraft based on the no-inflow model, combining the second moment and the third moment, and determining the second model parameter according to the average longitudinal moment and flapping frequency includes: Based on the hydrodynamic pressure formula, expressing the second moment as: ； represents the second moment, is the oncoming flow velocity, is the air density, is the pitching moment coefficient of the flapping-wing aircraft represented by the Fourier series, , is the stroke plane angle, and k2, an, bn are the second model parameters of the oncoming flow model respectively, is the area of the wing, is the mean chord length of the wing; Based on the synergistic effect of the flapping motion and translational motion, expressing the third moment as: ； represents the third moment, is the linear coefficient of the third moment with respect to the flapping frequency ω and the square of the oncoming flow velocity V2; Combining the no-inflow model, adding the first moment, the second moment, and the third moment as the inflow model; Performing parameter identification and optimization on the second model parameter of the inflow model based on the least squares method, and the model of the parameter identification and optimization is ； Among them, is the difference between the longitudinal moment obtained from the experiment after subtracting the first moment and the longitudinal moment calculated by the model. M1(i) is the first moment calculated for the i-th time by the model without oncoming flow, M2(i) is the second moment calculated for the i-th time by the oncoming flow model, and M3(i) is the third moment calculated for the i-th time by the oncoming flow model.

2. A longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests according to claim 1, characterized in that, The simulating flight through wind tunnel tests, measuring the experimental data of the longitudinal moment of the flapping-wing aircraft includes setting the experimental variables of the flapping-wing aircraft, and the experimental variables include flapping frequency, angle of attack, and inflow velocity; Among them, the flapping frequency of the flapping-wing aircraft is set by controlling the PWM signal of the driving motor in the flapping-wing aircraft; the angle of attack of the flapping-wing aircraft is set by an angular displacement table; the inflow velocity is set by controlling the flow velocity of the wind tunnel.

3. A longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests according to claim 1, characterized in that The calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data includes: Performing an averaging process on the measured longitudinal moment, and the formula for the averaging process is: ； Among them, is the average longitudinal moment, is the longitudinal moment of each sampling, is the total number of sampling points, , is the total sampling time, is the sampling frequency of the force sensor.

4. A longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests according to claim 1, characterized in that, The calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data includes: Performing a fast Fourier transform on the measured longitudinal moment to obtain a spectrum, and determining the flapping frequency of the flapping-wing aircraft from the peak position in the spectrum.

5. A longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests according to claim 1, characterized in that The modeling method further includes: before calculating the average longitudinal moment and flapping frequency of the flapping-wing aircraft based on the experimental data, performing a preprocessing operation on the experimental data, and the preprocessing operation includes denoising the experimental data based on low-pass filtering.

6. A longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests according to claim 1, characterized in that The calculation formula for the average chord length is: ； is the width of the wing is the length of the wing is the equation of the wing edge curve with respect to the y-axis 7. A longitudinal attitude dynamics modeling method for a flapping-wing aircraft based on wind tunnel tests according to claim 1, characterized in that The modeling method further includes, before simulating flight through wind tunnel tests, performing a zeroing operation on the six-axis force balance used to measure the longitudinal moment.

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