Method, device and medium for online estimation of aircraft aerodynamic parameters based on thrust deviation correction

Abstract

The present invention provides an online estimation method, device and medium for aircraft aerodynamic parameters based on thrust deviation correction, and relates to the technical field of online identification of aircraft parameters. The method comprises: using an extended Kalman filter to iteratively predict the augmented state of the aircraft system step by step; using a forgotten recursive least squares estimator to estimate the axial aerodynamic force and normal aerodynamic force coefficients and the thrust deviation proportional coefficient within a given time window, and then calculating the aerodynamic lift, drag coefficients and the corrected thrust estimation results; judging the convergence of the thrust deviation proportional coefficient estimation results; if it has not converged, aerodynamic derivative estimation is carried out based on the uncorrected thrust and lift and drag aerodynamic coefficients; if it has converged, the corrected thrust and the corrected lift and drag aerodynamic coefficients are brought into the general recursive least squares estimator to carry out aerodynamic derivative estimation. The online estimation method proposed by the present invention can effectively improve the accuracy of longitudinal aerodynamic derivative estimation under thrust measurement deviation.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft parameter online identification, and in particular to an aircraft aerodynamic parameter online estimation method, device and medium based on thrust deviation correction. Background Art

[0002] During the flight of an aircraft, online estimation of aerodynamic parameters refers to gradually obtaining the aircraft's aerodynamic characteristic parameters in real time from flight measurement data through methods such as filtering or recursion. It is an important means to obtain the aerodynamic characteristics of an aircraft in a real environment. Real-time acquisition of aerodynamic parameters is of great significance for flight safety and fault adaptation.

[0003] In the study of online parameter estimation methods, estimation accuracy has always been the focus of attention, among which the accuracy of measurement data is an important factor affecting accuracy. In the actual flight process of an aircraft, it is generally powered flight, and thrust needs to be measured as a system input, but engine thrust is usually difficult to measure directly and can often only be estimated using some engineering methods. Therefore, there is an inevitable deviation between the thrust measurement data and the true value, especially when the aircraft makes maneuvers. The credibility of the estimated thrust data is usually low, which also brings difficulties to the identification of aerodynamic characteristic parameters of powered flight data, which will greatly affect the accuracy and credibility of the aerodynamic parameter identification results.

[0004] Thrust measurement deviation directly affects the flight state of the aircraft and brings uncertainty to the flight state model. Unlike factors such as wind field and atmospheric environment disturbance, the impact of thrust measurement deviation belongs to the category of deterministic influence. Therefore, some random variable processing methods are not suitable for thrust measurement deviation processing. At present, evasive methods are mostly used to deal with thrust deviation in aerodynamic parameter identification, such as using data under unpowered flight, or only identifying aerodynamic parameters in the direction not affected by thrust. However, for some aircraft whose engines are not shut down and axial aerodynamic parameters, the aerodynamic parameters obtained by identification must include the influence of thrust measurement deviation. If the deviation is large, the identification result will be unreliable.

[0005] Therefore, it is urgent to develop an online identification method that can simultaneously estimate thrust deviation and aerodynamic parameters for the online estimation of aerodynamic parameters under the influence of thrust deterministic deviation, so as to solve the accuracy problem caused by inaccurate thrust measurement in the long-term aerodynamic parameter identification. Summary of the invention

[0006] The present invention aims to solve at least one of the above-mentioned technical problems existing in the prior art.

[0007] To this end, a first aspect of the present invention provides an online estimation method for aircraft aerodynamic parameters based on thrust deviation correction.

[0008] A second aspect of the present invention provides a computer device.

[0009] A third aspect of the present invention provides a computer-readable storage medium.

[0010] The present invention provides an online estimation method for aircraft aerodynamic parameters based on thrust deviation correction, comprising: S1, initializing the parameters of the extended Kalman filter, the forgotten recursive least squares estimator and the general recursive least squares estimator; S2, using the extended Kalman filter, i The augmented state prediction of the aircraft system i +1 step augmented state of the aircraft system; wherein the state variables in the extended Kalman filter include the aerodynamic coefficients of the aircraft; S3, using a forgotten recursive least squares estimator to estimate the axial aerodynamic coefficient, the normal aerodynamic coefficient, and the thrust deviation proportional coefficient within a given time window and including the i+1th step according to the thrust measurement data and the overload measurement data; S4. Using i The state estimation result of step +1 obtains the coordinate transformation matrix, which transforms the axial aerodynamic coefficient and the normal aerodynamic coefficient in the body coordinate into the drag coefficient and the lift coefficient as the corrected aerodynamic coefficient; S5. Calculate a corrected thrust estimation result according to the estimated thrust deviation proportional coefficient; S6. judging the convergence of the thrust deviation proportional coefficient estimation result within a given data window; S7. If the thrust deviation proportional coefficient estimation result has not converged, RLS estimation is performed based on the uncorrected thrust and aerodynamic force coefficient to obtain the aerodynamic derivative estimation result; if the thrust deviation proportional coefficient estimation result has converged, the corrected thrust, corrected lift coefficient and drag coefficient are used from the convergence moment to be substituted into the general recursive least squares estimator to obtain the aerodynamic derivative estimation result.

[0011] The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to the above technical solution of the present invention may also have the following additional technical features: In the above technical solution, the parameters of the initialization extended Kalman filter, the forgotten recursive least squares estimator and the general recursive least squares estimator include: Set the initial values, initial covariance matrix, noise covariance matrix, window length, and forgetting factor size for the extended Kalman filter, forgotten recursive least squares estimator, and general recursive least squares estimator.

[0012] In the above technical solution, the aerodynamic coefficient of the aircraft includes six-component aerodynamic coefficients.

[0013] In the above technical solution, step S3 includes: The data window length is N w And including i The thrust measurement data and overload measurement data of step +1 are input into the forgotten recursive least squares estimator to calculate the axial aerodynamic coefficient, the normal aerodynamic coefficient and the thrust deviation proportional coefficient, wherein the overload measurement data includes the measurement value of the axial acceleration and the normal acceleration of the aircraft, and the calculation method includes:

[0014]

[0015] in, Indicates dynamic pressure; Indicates the characteristic area of ​​the aircraft; Indicates thrust measurement data; represents the axial aerodynamic coefficient; It represents the thrust deviation proportionality coefficient; Indicates the mass of the aircraft; It represents the measured value of the axial linear acceleration of the aircraft; represents the normal aerodynamic coefficient; Represents the measured value of the normal acceleration of the aircraft.

[0016] In the above technical solution, in step S4, the i The method for obtaining the coordinate transformation matrix from the state estimation result of step +1 includes calculating the coordinate transformation matrix using the estimated data of the angle of attack and the sideslip angle.

[0017] In the above technical solution, the method of calculating the corrected thrust estimation result according to the estimated thrust deviation proportional coefficient in step S5 includes:

[0018] in, represents the corrected thrust estimation result.

[0019] In the above technical solution, the method for judging the convergence of the thrust deviation proportional coefficient estimation result within a given data window in step S6 includes: For each thrust deviation proportional coefficient estimation result reaching a given data window length, the mean and standard deviation of the data segment are calculated; Determine whether the absolute deviation of the current mean value relative to the mean value of the previous data segment and the current standard deviation exceed the convergence judgment threshold. If both are less than the threshold value, it is considered that the thrust deviation proportional coefficient estimation result at the current moment has converged, that is, the thrust estimation result has converged; otherwise, it has not converged.

[0020] In the above technical solution, in step S7, during the RLS estimation process based on the uncorrected thrust and aerodynamic coefficients, the thrust and aerodynamic coefficients output by the extended Kalman filter are input into a general recursive least squares estimator to obtain an aerodynamic derivative estimation result.

[0021] A computer device provided by the present invention includes a processor and a memory, wherein a computer program is stored in the memory. When the computer program is loaded and executed by the processor, an online estimation method for aerodynamic parameters of an aircraft based on thrust deviation correction as described in any one of the above technical solutions is implemented.

[0022] The present invention provides a computer-readable storage medium storing a program, which, when loaded by a processor, implements an online estimation method for aerodynamic parameters of an aircraft based on thrust deviation correction as described in any one of the above technical solutions.

[0023] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are: The present invention is based on the idea of ​​multi-source measurement data fusion. It utilizes the extended Kalman filter, the forgotten recursive least squares estimator and the general recursive least squares estimator to jointly realize the online estimation of aerodynamic parameters based on thrust deviation correction, and designs a convergence judgment link for the thrust estimation results obtained recursively. The overall algorithm framework can run on an airborne computing platform to realize online processing of measurement data.

[0024] The aerodynamic parameter online estimation algorithm based on thrust deviation correction established by the present invention is based on system input and output measurement data, overload measurement data and thrust measurement data, and can simultaneously perform online estimation of the aircraft state, thrust deviation proportional coefficient, aerodynamic force coefficient and aerodynamic derivative.

[0025] The online estimation method proposed in the present invention can effectively improve the estimation accuracy of aerodynamic derivatives under thrust measurement deviations. For the same aircraft level flight simulation example, this method can reduce the relative root mean square error of the drag stability derivative identification results under four different thrust measurement deviations by about 40%-55% compared with the uncorrected case.

[0026] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 is a flow chart of an online estimation method of aircraft aerodynamic parameters based on thrust deviation correction according to an embodiment of the present invention; Figure 2 It is a comparative schematic diagram of correcting the thrust estimation results of the aircraft using the disclosed method in four deviation situations; Figure 3 It is a schematic diagram comparing the uncorrected and corrected aerodynamic derivative identification results when the thrust deviation proportional coefficient is 0.5; Figure 4 It is a schematic diagram comparing the uncorrected and corrected aerodynamic derivative identification results when the thrust deviation proportional coefficient is 0.8; Figure 5 It is a schematic diagram comparing the uncorrected and corrected aerodynamic derivative identification results when the thrust deviation proportional coefficient is 1.2; Figure 6 Schematic diagram comparing the uncorrected and corrected aerodynamic derivative identification results when the thrust deviation proportional coefficient is 1.5. DETAILED DESCRIPTION

[0028] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0030] Refer to the following Figures 1 to 6 The following describes an online estimation method of aircraft aerodynamic parameters based on thrust deviation correction according to some embodiments of the present invention.

[0031] Some embodiments of the present application provide an online estimation method for aircraft aerodynamic parameters based on thrust deviation correction.

[0032] like Figure 1 As shown, the first embodiment of the present invention proposes an online estimation method for aircraft aerodynamic parameters based on thrust deviation correction, including steps S1 to S8.

[0033] S1. Initialize the parameters of the extended Kalman filter, the forgotten recursive least squares estimator, and the general recursive least squares estimator.

[0034] It should be noted that the extended Kalman filter (EKF), the forgotten recursive least squares estimator (FRLS) and the general recursive least squares estimator (RLS) are all common algorithms in aircraft parameter identification. EKF can transform the parameter estimation problem into a state estimation problem. According to the actual flight situation of the aircraft, a continuous estimation model is adopted, and real-time effect is achieved through discrete time measurement and discrete filtering algorithm. RLS is one of the commonly used identification algorithms in system identification, which is suitable for identification of multi-input and multi-output linear motion models; it updates parameter estimates by recursion and is suitable for real-time system parameter identification. FRLS combines the advantages of the RLS algorithm and can effectively identify the time-varying characteristics of system parameters; by introducing the forgetting factor, the algorithm gives decreasing weights to past data, thereby adapting to the time-varying characteristics of parameters. The specific algorithm content will not be repeated here.

[0035] In some embodiments, the initializing the parameters of the extended Kalman filter, the forgotten recursive least squares estimator and the general recursive least squares estimator includes: Set the initial values, initial covariance matrix, noise covariance matrix, window length, and forgetting factor size for the extended Kalman filter, forgotten recursive least squares estimator, and general recursive least squares estimator.

[0036] S2, using the extended Kalman filter, i The augmented state prediction of the aircraft system i +1 step of vehicle system augmentation state.

[0037] exist Figure 1 In the embodiment shown, the system input variable u, the system output measurement variable y and the thrust measurement value F are input into the extended Kalman filter to obtain the system state variable x, the aerodynamic drag coefficient and the aerodynamic lift coefficient .

[0038] System state variables usually include position information and speed information of the aircraft, etc. In the present disclosure, the aerodynamic coefficients of the aircraft are also used as state variables in the extended Kalman filter; in a specific embodiment, the aerodynamic coefficients of the aircraft include six-component aerodynamic coefficients, and the six-component aerodynamic coefficients usually include lift coefficient, drag coefficient, pitch moment coefficient, roll moment coefficient, yaw moment coefficient and lateral force coefficient. The present disclosure mainly uses the lift coefficient and the drag coefficient as examples for explanation; that is, in the present disclosure, the six-component aerodynamic coefficients are used as an extension of the state variables through step S2, and the EKF filter is used to calculate the estimated value and covariance matrix of the i+1th step based on the estimated value and covariance matrix of the i-th step.

[0039] S3. Use the forgotten recursive least squares estimator to estimate the axial aerodynamic coefficient, normal aerodynamic coefficient and thrust deviation proportional coefficient based on the thrust measurement data and the overload measurement data.

[0040] like Figure 1 As shown, the system state variable x output by the extended Kalman filter, the thrust measurement value F and the overload measurement data are all input into the forgotten recursive least squares estimator FRLS.

[0041] Specifically, step S3 includes: The data window length is N w And including i The thrust measurement data and overload measurement data of step +1 are input into the forgotten recursive least squares estimator to calculate the estimated axial aerodynamic coefficient, normal aerodynamic coefficient and thrust deviation proportional coefficient, wherein the overload measurement data includes the measurement value of the axial acceleration and the normal acceleration of the aircraft, and the calculation method includes:

[0042]

[0043] in, Indicates dynamic pressure; Indicates the characteristic area of ​​the aircraft; Indicates thrust measurement data; represents the axial aerodynamic coefficient; It represents the thrust deviation proportionality coefficient; Indicates the mass of the aircraft; It represents the measured value of the axial linear acceleration of the aircraft; represents the normal aerodynamic coefficient; Represents the measured value of the normal acceleration of the aircraft.

[0044] According to the above calculation method, we can calculate the i +1 step state estimation result, that is, i +1 step axial aerodynamic coefficient, normal aerodynamic coefficient, and thrust deviation proportionality factor.

[0045] S4. Using i The state estimation result of the +1 step obtains the coordinate transformation matrix, which converts the axial aerodynamic force and normal aerodynamic force coefficients in the body coordinates into drag and lift coefficients as the corrected aerodynamic coefficients.

[0046] Specifically, the axial aerodynamic force coefficient and the normal aerodynamic force coefficient obtained in step S3 are parameters in the body coordinate system, and they need to be converted into the velocity coordinate system, the axial aerodynamic force coefficient is converted into the drag coefficient, and the normal aerodynamic force coefficient is converted into the lift coefficient.

[0047] In a specific embodiment, step S4 uses i The method of obtaining the coordinate transformation matrix from the state estimation result of step +1 includes calculating the coordinate transformation matrix using the estimated data of the angle of attack and the sideslip angle. Converted into the corrected drag coefficient through the coordinate transformation matrix , the normal aerodynamic coefficient Converted into the corrected lift coefficient through the coordinate transformation matrix .

[0048] S5. Calculate a corrected thrust estimation result according to the estimated thrust deviation proportional coefficient.

[0049] The method for calculating the corrected thrust estimation result according to the estimated thrust deviation proportional coefficient in step S5 includes:

[0050] in, represents the corrected thrust estimation result.

[0051] S6. Determine the convergence of the thrust deviation proportional coefficient estimation results within a given data window.

[0052] In some embodiments, the method of judging the convergence of the thrust deviation proportional coefficient estimation result within a given data window in step S6 includes: For each thrust deviation proportional coefficient estimation result reaching a given data window length, the mean and standard deviation of the data segment are calculated; Determine whether the absolute deviation of the current mean value relative to the mean value of the previous data segment and the current standard deviation exceed the convergence judgment threshold. If both are less than the threshold value, it is considered that the thrust deviation proportional coefficient estimation result at the current moment has converged, that is, the thrust estimation result has converged; otherwise, it has not converged.

[0053] S7. If the thrust deviation proportional coefficient estimation result has not converged, RLS estimation is performed based on the uncorrected thrust and aerodynamic force coefficient to obtain the aerodynamic derivative estimation result; if the thrust deviation proportional coefficient estimation result has converged, the corrected thrust, corrected drag coefficient and lift coefficient are used from the convergence moment to be substituted into the general recursive least squares estimator to obtain the aerodynamic derivative estimation result.

[0054] Specifically, in step S7, during the RLS estimation process based on the uncorrected thrust and aerodynamic coefficients, the thrust and aerodynamic coefficients output by the extended Kalman filter are directly input into a general recursive least squares estimator to obtain an aerodynamic derivative estimation result.

[0055] S8. Repeat steps S1-S7 until all flight data processing is completed or until all sampled data filtering processing is completed.

[0056] In a specific embodiment, the longitudinal flight data identification of a certain civil aircraft standard model aircraft in level flight is taken as an example to illustrate the specific implementation method of the present disclosure. The aircraft flying speed is 0.2 Mach and the flight altitude is 100m. Considering the influence of measurement noise, a single-cycle "3211" small disturbance signal with an amplitude of 3 degrees is added to the trim elevator deflection input between the simulation time of 0.5s and 2.5s to stimulate the dynamic response of the system and improve the accuracy of aircraft parameter identification. The flight simulation is carried out to obtain the system input and output measurement data as the data input of the online estimation method, in which it is assumed that the thrust measurement value is relative to the actual situation. The thrust deviation proportional coefficient They are 0.5, 0.8, 1.2 and 1.5 times respectively.

[0057] The specific process is as follows: Initialization: The initial state estimate of the EKF filter is set by the flight data at the beginning of the simulation. The initial aerodynamic coefficients and derivatives to be estimated are both 0, and the initial value of the measurement noise covariance matrix is ​​10. -4 I, I represent the unit matrix; the estimation window length of the RLS estimator is set to 100 data points, the estimation window of the FRLS estimator is set to 1000, the convergence judgment window length is 500, the forgetting factor is taken as 0.99, the parameter estimation convergence threshold is taken as 0.1, the initial covariance matrix of the state variables, aerodynamic coefficients and thrust deviation proportional coefficient is taken as the unit matrix, and the initial covariance matrix of the longitudinal aerodynamic derivative is taken as a diagonal matrix with diagonal elements of [1, 1e-2, 1e-2, 1, 1].

[0058] State estimation: Use the EKF filter to predict the system augmented state at step i+1 from the system augmented state at step i.

[0059] Thrust deviation proportional coefficient and aerodynamic coefficient estimation: Based on the calculation method in step S3 and the FRLS estimator, the i+2- N w The state estimation results of the i+1 step, the thrust and overload measurement data are used to estimate the axial aerodynamic coefficient, the normal aerodynamic coefficient and the thrust deviation proportional coefficient, and the angle of attack and sideslip angle estimation data are used to obtain the coordinate transformation matrix. The corresponding drag coefficient and lift coefficient are obtained through the coordinate transformation matrix, and then the aerodynamic and thrust coefficient correction results of the i+1 step are obtained.

[0060] Convergence judgment: For each thrust deviation proportional coefficient reaching a given window length The estimation result counts the mean and standard deviation of the data segment, and determines whether the absolute deviation of the current mean relative to the mean of the previous data segment and the current standard deviation exceed the convergence judgment threshold. If both are less than the threshold value, it is considered that the current moment The estimated result has converged, otherwise it has not converged.

[0061] Aerodynamic derivative estimation: If If the estimation result has converged, then i+2- N w The corrected thrust, corrected drag coefficient and lift coefficient estimated in step i+1 are substituted into the RLS estimator to estimate the aerodynamic derivatives of step i+1; if If the estimated results do not converge, the uncorrected thrust F and aerodynamic drag coefficient are used. and the aerodynamic lift coefficient Estimate the aerodynamic derivatives at step i+1.

[0062] For this flight data identification example, the above steps are used to calculate the thrust estimation results of the aircraft under four deviation conditions. Figure 2 As shown in Figure 2, it is found that the thrust variation curve obtained by FRLS estimation can converge to the reference value for simulation, that is, the trim thrust. Figures 3 to 6 As shown in the figure, under different thrust deviation proportional coefficients, the aerodynamic drag stability derivative estimated by the thrust and aerodynamic coefficient correction results can gradually approach the reference value; the root mean square error of the identification results with and without correction relative to the reference value is calculated respectively, and it is found that the identification accuracy of the drag stability derivative is improved by about 40%~55% for the four deviation conditions.

[0063] Some other embodiments of the present invention provide a computer device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is loaded and executed by the processor, the online estimation method of aircraft aerodynamic parameters based on thrust deviation correction as described in any of the above embodiments is implemented.

[0064] Still other embodiments of the present invention provide a computer-readable storage medium storing a program, which, when loaded by a processor, implements the method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction as described in any of the above embodiments.

[0065] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0066] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction, characterized in that: include: S1, initializing the parameters of the extended Kalman filter, the forgotten recursive least squares estimator and the general recursive least squares estimator; S2, using the extended Kalman filter, i The augmented state prediction of the aircraft system i +1 step augmented state of the aircraft system; wherein the state variables in the extended Kalman filter include the aerodynamic coefficients of the aircraft; S3, using a forgotten recursive least squares estimator to estimate the axial aerodynamic coefficient, the normal aerodynamic coefficient, and the thrust deviation proportional coefficient within a given time window and including the i+1th step according to the thrust measurement data and the overload measurement data; S4. Using i The state estimation result of step +1 obtains the coordinate transformation matrix, which converts the axial aerodynamic coefficient and normal aerodynamic coefficient in body coordinates into drag coefficient and lift coefficient as the corrected aerodynamic coefficient of step i+1; S5. Calculate the corrected thrust estimation result of step i+1 according to the estimated thrust deviation proportional coefficient; S6. judging the convergence of the thrust deviation proportional coefficient estimation result within a given data window; S7. If the estimation result of the thrust deviation proportional coefficient has not converged, a general recursive least squares estimation is performed based on the uncorrected thrust and aerodynamic force coefficient to obtain the aerodynamic derivative estimation result of the i+1th step; if the estimation result of the thrust deviation proportional coefficient has converged, the corrected thrust, the corrected lift coefficient and the drag coefficient are introduced into the general recursive least squares estimator from the moment of convergence to obtain the aerodynamic derivative estimation result of the i+1th step.

2. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 1, characterized in that: The parameters of the initialization extended Kalman filter, the forgotten recursive least squares estimator and the general recursive least squares estimator include: Set the initial values, initial covariance matrix, noise covariance matrix, window length, and forgetting factor size for the extended Kalman filter, forgotten recursive least squares estimator, and general recursive least squares estimator.

3. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 1, characterized in that: The aerodynamic coefficients of the aircraft include six-component aerodynamic coefficients.

4. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 1, characterized in that: Step S3 includes: The data window length is N w And including i The thrust measurement data and overload measurement data of step +1 are input into the forgotten recursive least squares estimator to calculate the axial aerodynamic coefficient, the normal aerodynamic coefficient and the thrust deviation proportional coefficient, wherein the overload measurement data includes the measurement value of the axial acceleration and the normal acceleration of the aircraft, and the calculation method includes: in, Indicates dynamic pressure; Indicates the characteristic area of ​​the aircraft; Indicates thrust measurement data; represents the axial aerodynamic coefficient; It represents the thrust deviation proportionality coefficient; Indicates the mass of the aircraft; It represents the measured value of the axial linear acceleration of the aircraft; represents the normal aerodynamic coefficient; Represents the measured value of the normal acceleration of the aircraft.

5. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 1, characterized in that: In step S4, the i The method for obtaining the coordinate transformation matrix from the state estimation result of step +1 includes calculating the coordinate transformation matrix using the estimated data of the angle of attack and the sideslip angle.

6. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 4, characterized in that: The method for calculating the corrected thrust estimation result according to the estimated thrust deviation proportional coefficient in step S5 includes: in, represents the corrected thrust estimation result.

7. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 1, characterized in that: The method for judging the convergence of the thrust deviation proportional coefficient estimation result within a given data window in step S6 includes: For each thrust deviation proportional coefficient estimation result reaching a given data window length, the mean and standard deviation of the data segment are calculated; Determine whether the absolute deviation of the current mean value relative to the mean value of the previous data segment and the current standard deviation exceed the convergence judgment threshold. If both are less than the threshold value, it is considered that the thrust deviation proportional coefficient estimation result at the current moment has converged, that is, the thrust estimation result has converged; otherwise, it has not converged.

8. The method for online estimation of aircraft aerodynamic parameters based on thrust deviation correction according to claim 1, characterized in that: In step S7, in the general recursive least squares estimation process based on the uncorrected thrust and aerodynamic force coefficients, the thrust and aerodynamic force coefficients output by the extended Kalman filter are input into the general recursive least squares estimator to obtain the aerodynamic derivative estimation result.

9. A computer device, characterized in that: The invention comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded and executed by the processor, an online estimation method for aerodynamic parameters of an aircraft based on thrust deviation correction as claimed in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: A program is stored, and when the program is loaded by a processor, an online estimation method of aircraft aerodynamic parameters based on thrust deviation correction as described in any one of claims 1 to 8 is implemented.

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