Longitudinal aerodynamic parameter identification and calculation method in closed-loop state and large angle-of-attack range

Abstract

The invention belongs to the technical field of unmanned aerial vehicle aircraft design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method in a closed-loop state and a large angle-of-attack range, and the method comprises the steps: building a flight simulation model; performing aerodynamic characteristic simulation based on a flight simulation model to obtain an aerodynamic load value, comparing the aerodynamic load value with data tested in an actual flight experiment, and calculating and minimizing an error sum of squares; dividing the whole angle-of-attack range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, and carrying out model accuracy verification; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, and verifying the applicability of the flight simulation model. The method has universality and is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method in a large angle of attack range under a closed loop state. BACKGROUND

[0002] Aircraft aerodynamic characteristic parameters are important and indispensable bases for aircraft design, performance evaluation and simulator simulation. In actual engineering applications, accurate aerodynamic coefficients are usually obtained by numerical calculation, wind tunnel test and flight test. However, the results obtained by the first two methods often have certain deviations from the actual flight state due to the influence of experimental conditions or model simplification and other factors.

[0003] In comparison, flight test can directly reflect the aerodynamic characteristics of the aircraft in the actual flight environment, and therefore has more reference value. In particular, flight test under the closed loop control state of the unmanned aerial vehicle can not only obtain data closer to the actual flight state, but also provide reliable basis for subsequent aerodynamic parameter identification.

[0004] Therefore, the application proposes a longitudinal aerodynamic coefficient parameter identification method based on flight simulation and optimization calculation. SUMMARY

[0005] In order to solve the above problems, the application provides a longitudinal aerodynamic parameter identification calculation method in a large angle of attack range under a closed loop state, so as to solve the problem that accurate longitudinal aerodynamic characteristics cannot be accurately identified by simulation in the prior art.

[0006] The technical scheme of the application is: a longitudinal aerodynamic parameter identification calculation method in a large angle of attack range under a closed loop state, comprising:

[0007] Obtaining key parameters of longitudinal aerodynamic characteristics of the unmanned aerial vehicle, required data types and accuracy; establishing a flight simulation model; simultaneously setting an aerodynamic characteristic module and a flight control rate module in the flight simulation model;

[0008] Performing aerodynamic characteristic simulation based on the flight simulation model to obtain aerodynamic load values, comparing the aerodynamic load values with data tested in actual flight experiments, calculating error sum of squares and minimizing the error sum of squares;

[0009] Dividing the entire angle of attack range into multiple subintervals, obtaining optimal aerodynamic parameters of each subinterval based on the minimized error sum of squares, inputting the optimal aerodynamic parameters into the flight simulation model, and performing model accuracy verification, when the verification is passed, executing the next step;

[0010] The optimal aerodynamic parameters of the longitudinal aerodynamic model in the small angle of attack range are repeatedly obtained to obtain the longitudinal aerodynamic coefficient characteristics in the large angle of attack range; and the aerodynamic characteristic curves are drawn according to the optimal aerodynamic parameters to verify the applicability of the flight simulation model.

[0011] Preferably, the flight simulation model is established based on the dynamic equation of the unmanned aerial vehicle, and the dynamic equation of the unmanned aerial vehicle includes a six-degree-of-freedom motion equation, an aerodynamic characteristic model and a control input model.

[0012] Preferably, in the aerodynamic characteristic module, the key aerodynamic parameters identified are embedded into the aerodynamic force calculation formula, and the piecewise function method is used to describe the change law of the aerodynamic characteristics in different angle of attack ranges.

[0013] Preferably, in the flight control rate module, the control rate algorithm in the flight control system of the aircraft is embedded into the flight simulation model.

[0014] Preferably, when the aerodynamic characteristic simulation is performed based on the flight simulation model, the flight state of the unmanned aerial vehicle under the condition of a small angle of attack is simulated first, and specifically:

[0015] A series of flight angles of attack are first set, the corresponding dynamic response is calculated by giving an external excitation, and the history curve of the flight state parameters of the aircraft changing with time is recorded;

[0016] The control simulation conditions are consistent with the actual flight test conditions, including the flight speed and height parameters.

[0017] Preferably, in the calculation and minimization of the error sum of squares, the calculation formula of the error sum of squares is:

[0018] ;

[0019] Wherein, i is the flight state parameter serial number, n is the total number of flight state parameters required for calculating the SSE, f(t) is the flight state parameter change function with time, the subscript exp represents the flight test data, and sim represents the flight simulation data.

[0020] Preferably, the method for minimizing the error sum of squares is:

[0021] A genetic algorithm is used as an optimization tool;

[0022] The initial population size, the crossover probability and the mutation probability of the genetic algorithm are set, and the fitness function is defined as the reciprocal of the error sum of squares;

[0023] In each generation, the error sum of squares corresponding to each individual is calculated, and selection, crossover and mutation operations are performed according to the fitness value; the process is repeated until the convergence condition is met, and the minimized error sum of squares is obtained.

[0024] Preferably, the specific method for dividing the sub-intervals is:

[0025] Select the angle of attack at which the slope of the lift coefficient-angle of attack curve decreases beyond a set value; extract the angle of attack-time curve, lift coefficient-angle of attack scatter data, drag coefficient-angle of attack scatter data and pitch moment coefficient-angle of attack scatter data at different flight speeds from flight test data, obtain the drag coefficient nonlinear growth inflection point and the lift coefficient peak value and stall inflection point, obtain the sub-interval boundary, and divide the sub-intervals based on the sub-interval boundary.

[0026] Preferably, for each sub-interval, the flight state of the unmanned aerial vehicle in the small angle of attack range is simulated respectively, and the minimum error sum of squares of the simulation data and the test data in the actual flight test is calculated as the optimal aerodynamic parameter.

[0027] Preferably, when verifying the accuracy of the model, the optimal aerodynamic parameters of each region are integrated into a unified flight simulation model; a comparison threshold is set, and the simulation results and test data in different angle of attack ranges are compared, and when the difference is less than the comparison threshold, it is judged that the optimal aerodynamic parameter and the test data difference is within the set range.

[0028] Preferably, the collection requirements of flight test data include:

[0029] Flight state parameters: flight speed, height, attitude angle of the unmanned aerial vehicle in different angle of attack ranges;

[0030] Flight test conditions: clearly define the angle of attack range, flight speed range and environmental parameters of the flight test.

[0031] The longitudinal aerodynamic parameter identification and calculation method in the closed loop state of the large angle of attack range of the application has the following advantages:

[0032] The parameter identification is performed in a combined optimization manner, and the optimization method is a genetic algorithm, which has low requirements for the initial values of the longitudinal aerodynamic coefficients in the aerodynamic model module;

[0033] The change range of the angle of attack in the single flight simulation data and the flight test data should not be too large, so as to ensure that the longitudinal aerodynamic coefficients in the small angle of attack range change approximately linearly with the angle of attack;

[0034] The flight data under multiple different flight speeds (or flight angles of attack) is used for longitudinal aerodynamic coefficient parameter identification to obtain the longitudinal aerodynamic coefficient characteristics in the large angle of attack range;

[0035] The influence of the control law on the flight test during the flight process is considered, and the longitudinal aerodynamic coefficient parameter identification in the closed loop state is more accurate;

[0036] The longitudinal aerodynamic coefficient parameter identification is suitable for different types of unmanned aerial vehicles, improves the efficiency of parameter identification, and enhances the applicability of the model under complex flight conditions. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a whole algorithm flowchart of the present application;

[0038] Figure 2 is a graph of the change of the aircraft state parameters over time of the present application. DETAILED DESCRIPTION

[0039] To make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described in more detail below in combination with the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The described embodiments are part of the embodiments of the present application, not all of the embodiments. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. The embodiments of the present application will be described in detail below in combination with the drawings.

[0040] The first aspect of the present application provides a longitudinal aerodynamic parameter identification calculation method under a closed loop state and a large angle of attack range. The closed loop flight data of the unmanned aerial vehicle under different flight speeds or angles of attack is fully utilized. The complete flight simulation model including the control law is established to realize the accurate identification of the longitudinal aerodynamic characteristics under the large angle of attack range.

[0041] First, the main parameters of the longitudinal aerodynamic model and the parameters that should be included in the flight test data are determined, then the corresponding flight simulation model is established, and the control law is ensured to be consistent with the actual aircraft; the method of combined optimization is used for longitudinal aerodynamic model parameter identification calculation under small angle of attack range. First, the flight simulation data under a given longitudinal aerodynamic model is calculated (such as Figure 1 ), then the error sum of squares of the flight simulation data and the flight test data is calculated, finally the genetic algorithm is used to optimize the calculation according to the error sum of squares under different longitudinal aerodynamic model parameters, and the longitudinal aerodynamic model parameters under small angle of attack range are finally obtained; the longitudinal aerodynamic model parameter solving calculation under small angle of attack range is repeated several times to obtain the longitudinal aerodynamic coefficient characteristics under large angle of attack range.

[0042] As shown in Figures 1-2 , specifically comprising the following steps:

[0043] Step 1: Obtain the key parameters of the longitudinal aerodynamic characteristics of the UAV, the types and accuracy of data required for collection; establish a flight simulation model; and set up the aerodynamic characteristics module and flight control module in the flight simulation model.

[0044] In this application, the key parameters related to the longitudinal aerodynamic characteristics of the UAV need to be first identified. These parameters include but are not limited to lift coefficient, drag coefficient, pitch moment coefficient, and nonlinear aerodynamic characteristic parameters related to angle of attack.

[0045] The specific steps are as follows:

[0046] Step 1.1: Determine the main parameters of the longitudinal aerodynamic model:

[0047] (1) According to the flight characteristics of the UAV, consult relevant aerodynamics literature or manuals to determine the standard form of the longitudinal aerodynamic model and the key parameters required. For example, the lift coefficient is usually expressed as a function of the angle of attack α, i.e. (C L = C L0 + C Lα α), where C L0 is the lift coefficient at zero lift angle, and C Lα is the slope of the lift curve.

[0048] (2) For the actual flight characteristics of the UAV, further clarify the possible nonlinear terms or high-order effects (such as stall, airflow separation, etc.) in the model, and include them in the parameter list.

[0049] Step 1.2: Determine the collection requirements of flight test data:

[0050] In order to ensure the accuracy of subsequent parameter identification, a reasonable flight test scheme needs to be designed, and the types and accuracy of data required for collection need to be specified.

[0051] Specifically includes:

[0052] (1) Flight state parameters: flight speed, altitude, attitude angle (pitch angle, roll angle, yaw angle) of the UAV at different angles of attack.

[0053] (2) Flight test conditions: specify the angle of attack range (usually including small and large angle of attack regions), flight speed range, and environmental parameters (temperature, air pressure, etc.) of the flight test.

[0054] Step 1.3: Establish a flight simulation model

[0055] After obtaining the aforementioned parameters, a complete flight simulation model needs to be established based on the actual geometric characteristics, aerodynamic principles, and design requirements of the flight control system of the UAV. The construction of this model requires comprehensive consideration of the UAV's dynamic characteristics, aerodynamic characteristics, and control law characteristics, ensuring consistency between all components. The model should include the following:

[0056] (1) Dynamic equations of UAV: ​​including six-degree-of-freedom motion equations, aerodynamic characteristic model and control input model (such as thrust, control surface deflection angle, etc.).

[0057] (2) Aerodynamic characteristics module: The key aerodynamic parameters (such as lift coefficient, drag coefficient and pitching moment coefficient) are embedded into the aerodynamic calculation formula. The piecewise function method is used to describe the variation law of aerodynamic characteristics in different angles of attack.

[0058] (3) Flight control law module: To ensure the accuracy of the simulation results, this invention pays special attention to the implementation of the flight control law. The control law algorithm used in the actual flight control system is fully embedded into the simulation model to ensure that the control law characteristics of the simulation model under different angles of attack are consistent with the actual flight control law of the UAV.

[0059] Step 2: Perform aerodynamic characteristic simulation based on the flight simulation model to obtain aerodynamic load values, compare them with the data tested in the actual flight experiment, calculate the sum of squared errors and minimize them.

[0060] The specific steps are as follows:

[0061] Step 2.1: Calculate flight simulation data:

[0062] Based on the initially established simulation model, the flight state of the UAV under low angle of attack conditions is simulated. Specifically, this includes the following:

[0063] (1) Set a series of flight angle of attack (or flight speed) values, under a given external excitation (such as... Figure 1 The system calculates the corresponding dynamic response under the elevator command and records the history curve of the aircraft's flight state parameters changing over time.

[0064] (2) Ensure that the simulation conditions are consistent with the actual flight test conditions, including parameters such as flight speed and altitude, as well as flight control laws.

[0065] Step 2.2: Compare simulation data with experimental data

[0066] The simulated aerodynamic load values ​​are compared with the data measured in actual flight tests, and the sum of squared errors (SSE) is calculated. The specific formula is as follows:

[0067]

[0068] Where i is the sequence number of the flight state parameter, n is the total number of flight state parameters required to calculate SSE, f(t) is the function of the flight state parameter changing over time, the subscript exp represents flight test data, and sim represents flight simulation data.

[0069] Step 2.3: Optimize parameters to minimize the sum of squared errors

[0070] To improve the accuracy of the model, key parameters in the longitudinal aerodynamic model need to be optimized. The specific methods are as follows:

[0071] (1) Use a genetic algorithm (GA) as the optimization tool. This algorithm can find the global optimal solution in complex nonlinear problems by simulating natural selection and genetic mechanisms.

[0072] (2) Set parameters such as initial population size, crossover probability, and mutation probability for the genetic algorithm, and define the fitness function as the reciprocal of the sum of squared errors (SSE) (i.e., the higher the fitness, the better the model).

[0073] (3) Iterative optimization process: In each generation, the sum of squared errors corresponding to each individual (i.e., a set of aerodynamic parameters) is calculated, and selection, crossover, and mutation operations are performed based on the fitness value. This process is repeated until the convergence condition is met (such as the number of iterations reaching the upper limit or the fitness change being less than the threshold), and the minimized sum of squared errors is obtained.

[0074] Step 3: Divide the entire angle of attack range into multiple sub-intervals, obtain the optimal aerodynamic parameters for each sub-interval based on minimizing the sum of squared errors, input each optimal aerodynamic parameter into the flight simulation model, and verify the model accuracy. Once the verification is successful, proceed to the next step.

[0075] Specifically, it includes:

[0076] Step 3.1: Divide the angle of attack range:

[0077] Because the aerodynamic characteristics of UAVs can vary significantly across different angles of attack (e.g., linear characteristics at low angles of attack, while stall or airflow separation may occur at high angles of attack), it is necessary to divide the entire angle of attack range into multiple sub-intervals. The aerodynamic characteristics within each sub-interval can be described using different models.

[0078] Preferably, the specific method for dividing the sub-intervals is as follows:

[0079] Angles of attack are selected where the rate of descent of the lift coefficient-angle of attack curve exceeds a set value. Angle of attack-time curves, lift coefficient-angle of attack scatter plots, drag coefficient-angle of attack scatter plots, and pitch moment coefficient-angle of attack scatter plots are extracted from flight test data at different flight speeds. The inflection point of nonlinear growth of drag coefficient and the peak value and stall inflection point of lift coefficient are obtained to obtain the sub-interval boundary. Sub-intervals are then divided based on the sub-interval boundary.

[0080] Step 3.2: Repeatability parameter identification process:

[0081] For each angle-of-attack sub-region, repeat the simulation, comparison, and optimization process in step two to obtain the optimal aerodynamic parameters for that region. In particular, within the high angle-of-attack region, the following factors need to be considered:

[0082] (1) Introduction of nonlinear effects: For example, the relationship between the lift coefficient and the angle of attack may no longer be a simple linear relationship, but a stall inflection point or saturation phenomenon may occur.

[0083] (2) Multivariable optimization: Under high angle of attack, there may be coupling effects between aerodynamic parameters (such as the mutual influence between changes in lift and drag), and multiple parameters need to be optimized at the same time to obtain an accurate model.

[0084] Step 3.3: Verify the accuracy of the overall model:

[0085] After parameter identification for all sub-regions is completed, the optimal parameters for each region are integrated into a unified flight simulation model. A comparison threshold is set, and by comparing the simulation results with experimental data at different angles of attack, if the difference is less than the comparison threshold, it is determined that the difference between the optimal aerodynamic parameters and the experimental data is within the set range, and the overall accuracy and reliability of the model meet the requirements.

[0086] Step 4: Repeatedly obtain the optimal aerodynamic parameters of the longitudinal aerodynamic model within the small angle of attack range to obtain the longitudinal aerodynamic coefficient characteristics within the large angle of attack range; plot the aerodynamic characteristic curves based on each optimal aerodynamic parameter to verify the applicability of the flight simulation model.

[0087] Specifically, it includes:

[0088] Step 4.1: Plot the aerodynamic characteristic curves

[0089] Based on the optimized aerodynamic parameters, plot the lift coefficient, drag coefficient, and pitch moment coefficient curves of the UAV under different angles of attack. These curves should accurately reflect the aerodynamic characteristics of the UAV, especially the variation patterns in the small and large angles of attack regions.

[0090] Step 4.2: Verify the applicability of the model

[0091] By comparing simulation results with actual flight test data, the predictive accuracy of the model under different flight conditions is evaluated. In particular, under high angle-of-attack conditions, it is necessary to focus on the model's ability to describe nonlinear phenomena such as stall and airflow separation.

[0092] Step 4.3: Optimize the robustness of the model

[0093] If the model accuracy is found to be insufficient in certain areas, the parameters of the genetic algorithm can be further adjusted (such as increasing the population size or improving the crossover and mutation strategy) to improve the efficiency and accuracy of the optimization process.

[0094] Through the above steps, the aerodynamic characteristics modeling and optimization of UAVs can be systematically completed, and the longitudinal aerodynamic coefficient parameters under large angle of attack range in the closed-loop state of the UAV can be identified.

[0095] In summary, this application has the following advantages:

[0096] Parameter identification is performed using a combinatorial optimization approach, with the optimization method being a genetic algorithm. This approach has relatively low requirements for the initial values ​​of the longitudinal aerodynamic coefficients in the aerodynamic model module.

[0097] The range of angle of attack variation in single-flight simulation data and flight test data should not be too large, so as to ensure that the longitudinal aerodynamic coefficient changes approximately linearly with the angle of attack within a small angle of attack range;

[0098] Longitudinal aerodynamic coefficient parameters are identified from flight data under multiple different flight speeds (or angles of attack) to obtain longitudinal aerodynamic coefficient characteristics over a wide angle of attack range;

[0099] The influence of the control law on the flight test during flight was taken into account, and the longitudinal aerodynamic coefficient parameter identification under the corresponding closed-loop state was more accurate.

[0100] It is versatile and suitable for identifying longitudinal aerodynamic coefficient parameters of different types of UAVs, improving the efficiency of parameter identification and enhancing the applicability of the model under complex flight conditions.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying and calculating longitudinal aerodynamic parameters over a large angle-of-attack range in a closed-loop state, characterized in that, include: To obtain key parameters of the longitudinal aerodynamic characteristics of the UAV, the required data type and accuracy of the collected data; Establish a flight simulation model; and simultaneously set up an aerodynamic characteristics module and a flight control law module within the flight simulation model; Aerodynamic characteristics are simulated based on the flight simulation model to obtain aerodynamic load values, which are then compared with the data tested in actual flight experiments. The sum of squared errors is calculated and minimized. The entire angle of attack range is divided into multiple sub-intervals. The optimal aerodynamic parameters for each sub-interval are obtained based on minimizing the sum of squared errors. The optimal aerodynamic parameters are then input into the flight simulation model to verify the model's accuracy. Once the verification is successful, the next step is executed. The optimal aerodynamic parameters of the longitudinal aerodynamic model within the small angle of attack range are repeatedly obtained to obtain the longitudinal aerodynamic coefficient characteristics within the large angle of attack range; aerodynamic characteristic curves are plotted based on each optimal aerodynamic parameter to verify the applicability of the flight simulation model.

2. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 1, characterized in that, A flight simulation model is established based on the dynamic equations of the UAV, which include six-degree-of-freedom motion equations, aerodynamic characteristic model, and control input model.

3. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 2, characterized in that, In the aerodynamic characteristics module, the identified key aerodynamic parameters are embedded into the aerodynamic force calculation formula, and the piecewise function method is used to describe the aerodynamic characteristic variation law within different angles of attack.

4. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 2, characterized in that, In the flight control law module, all control law algorithms from the aircraft's flight control system are embedded into the flight simulation model.

5. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 1, characterized in that, When performing aerodynamic characteristic simulation based on a flight simulation model, the flight state of the UAV under low angle of attack conditions is first simulated, specifically: First, set a series of flight angle of attack values, calculate the corresponding dynamic response given external excitation, and record the history curve of the aircraft flight state parameters changing over time; The control simulation conditions are consistent with the actual flight test conditions, including flight speed and altitude parameters.

6. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 5, characterized in that, In calculating and minimizing the sum of squared errors, the formula for calculating the sum of squared errors is: ； Where i is the sequence number of the flight state parameter, n is the total number of flight state parameters required to calculate SSE, f(t) is the function of the flight state parameter changing over time, the subscript exp represents flight test data, and sim represents flight simulation data.

7. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 6, characterized in that, The method for minimizing the sum of squared errors is as follows: Use genetic algorithms as optimization tools; Set the initial population size, crossover probability, and mutation probability for the genetic algorithm; and define the fitness function as the reciprocal of the sum of squared errors. In each generation, the sum of squared errors for each individual is calculated, and selection, crossover, and mutation operations are performed based on the fitness value; this process is repeated until the convergence condition is met, and the minimized sum of squared errors is obtained.

8. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 1, characterized in that, The specific method for dividing subintervals is as follows: Angles of attack are selected where the rate of descent of the lift coefficient-angle of attack curve exceeds a set value. Angle of attack-time curves, lift coefficient-angle of attack scatter plots, drag coefficient-angle of attack scatter plots, and pitch moment coefficient-angle of attack scatter plots are extracted from flight test data at different flight speeds. The inflection point of nonlinear growth of drag coefficient and the peak value and stall inflection point of lift coefficient are obtained to obtain the sub-interval boundary. Sub-intervals are then divided based on the sub-interval boundary.

9. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 8, characterized in that, For each sub-interval, the flight state of the UAV in the small angle of attack range is simulated, and the minimum sum of squared errors between the simulation data and the test data in the actual flight test is calculated as the optimal aerodynamic parameters.

10. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 1, characterized in that, When verifying the accuracy of the model, the optimal aerodynamic parameters of each region are integrated into a unified flight simulation model. A comparison threshold is set, and the simulation results and experimental data are compared at different angles of attack. When the difference is less than the comparison threshold, it is determined that the difference between the optimal aerodynamic parameters and the experimental data is within the set range.

11. The method for identifying and calculating longitudinal aerodynamic parameters in a large angle-of-attack range under closed-loop conditions as described in claim 1, characterized in that, The requirements for collecting flight test data include: Flight status parameters: flight speed, altitude, and attitude angle of the UAV at different angles of attack; Flight test conditions: Define the angle of attack range, flight speed range, and environmental parameters for the flight test.

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