Fixed-wing aircraft robust element flight aerodynamic modeling and prediction method and medium

Abstract

The invention relates to a fixed-wing aircraft robust element flight aerodynamic modeling and prediction method and a medium, and the method comprises the steps: obtaining discrete aerodynamic data sets under different state conditions, and building an aerodynamic common basis function of a fixed-wing aircraft; for disturbance of real aerodynamic force and real aerodynamic torque relative to the nominal aerodynamic model, on the basis of the aerodynamic common basis function, augmenting the common basis function with robustness to obtain an augmented common basis function; and based on the augmented general basis function, approaching the aerodynamic force and the aerodynamic moment of the fixed-wing aircraft to obtain a robust element flight aerodynamic model. Compared with the prior art, a good foundation is laid for effectively identifying the influence of wind and accurately predicting the aerodynamic force on line, so that the robust element flight aerodynamic model has better migration capability and engineering applicability, and the accuracy of predicting the aerodynamic force and the aerodynamic moment of the fixed-wing aircraft under the strong wind condition is improved.

Description

Technical Field

[0001] The invention relates to the technical field of aerodynamic force prediction of aviation aircraft, and in particular to a method and medium for modeling and predicting robust element flight aerodynamic force of fixed-wing aircraft. Background Art

[0002] Wind-resistant flight is the key and difficulty of all-weather autonomous flight of current fixed-wing UAVs. It is precisely because of the limitations of environmental interference factors such as wind that fixed-wing UAVs are often called "good weather aircraft". In order to achieve precise control of UAV maneuvering flight and improve the safety and reliability of UAV flight in windy environments, it is necessary to accurately predict the aerodynamic force (moment) of UAVs under wind interference conditions in real time. Due to cost considerations, fixed-wing UAVs usually do not use overly expensive precision airflow sensor equipment, so it is difficult to obtain accurate angle of attack and sideslip angle information in real time. The traditional aerodynamic model that uses physical variables such as angle of attack and sideslip angle to describe the change law of the aerodynamic force (moment) of the aircraft in the airflow coordinate system is not conducive to the online migration application of UAV flight. Therefore, the development of algorithms, especially advanced algorithms, for the real-time and accurate prediction of the aerodynamic force (moment) of fixed-wing UAVs is very urgent and necessary.

[0003] For the online aerodynamic modeling and prediction of aircraft under strong wind disturbance, scholars first proposed the "neural-fly" method for rotorcraft, using deep meta-learning of the Generative AdversativeNets (GAN) architecture to train the common basis function neural network model, and established a common aerodynamic basis function model for rotorcraft under different wind conditions, thereby realizing real-time aerodynamic modeling and migration application under unknown wind conditions. Currently, scholars have used deep meta-learning methods to establish a common basis function neural network model of aircraft aerodynamics and aerodynamic torque under different wind conditions for fixed-wing aircraft, considering more variables such as aircraft angular velocity and rudder deflection angle, which can better predict the aerodynamics and aerodynamic torque under wind disturbance conditions. In order to solve the problem that the common basis function model of black box neural network in "neural flight" is difficult to train efficiently, fully test and reliably verify in actual application, scholars further used multivariate function variable decomposition technology to develop a fixed-wing aircraft meta-flight aerodynamic modeling method considering wind interference, and obtained an aerodynamic common basis function model of aircraft aerodynamic force and aerodynamic moment that is determined by the characteristics of the fixed-wing aircraft itself and is universal under different wind conditions, which can better achieve accurate prediction of aircraft aerodynamic force and aerodynamic moment under wind disturbance conditions. However, due to the influence of factors such as engine jet and propeller slipstream in actual flight, there may be differences between the actual aerodynamic force and actual aerodynamic moment of the aircraft and the results obtained through CFD numerical calculation and wind tunnel test. The fixed-wing aircraft aerodynamic common basis function obtained by the above method still has the problem of low reliability in online migration application in actual application. Summary of the invention

[0004] The technical problem to be solved by the present application is to provide a method for robust flight aerodynamic modeling and prediction of fixed-wing aircraft, which has the characteristic of improving the accuracy of online prediction of aerodynamic force and aerodynamic moment of fixed-wing aircraft under strong wind conditions.

[0005] In a first aspect, an embodiment provides a method for robust element flight aerodynamic modeling of a fixed-wing aircraft, comprising: Obtain discrete aerodynamic data sets under different state conditions and establish the aerodynamic commonality basis functions of fixed-wing aircraft; Aiming at the disturbance of the real aerodynamic force and the real aerodynamic moment relative to the nominal aerodynamic model, based on the aerodynamic common basis function, the common basis function with robustness is augmented to obtain an augmented common basis function; Based on the augmented common basis function, the aerodynamic force and aerodynamic moment of the fixed-wing aircraft are approximated to obtain a robust element flight aerodynamic model.

[0006] In a second aspect, an embodiment provides a method for predicting robust element-flight aerodynamic forces of a fixed-wing aircraft, comprising: Determine the wind disturbance effect coefficient; Obtain the motion variables of the fixed-wing aircraft relative to the earth, including flight altitude, ground speed vector, Euler attitude angle, angular velocity and rudder angle; Inputting the motion variables and wind disturbance coefficients into a robust element flight aerodynamic model to predict aerodynamic forces and aerodynamic moments; The robust element flight aerodynamic model is modeled based on the fixed-wing aircraft robust element flight aerodynamic modeling method described in any one of the above embodiments.

[0007] In a third aspect, an embodiment provides a computer-readable storage medium, in which a program is stored, and the program can be loaded by a processor and execute the fixed-wing aircraft robust element flight aerodynamic modeling method and / or the fixed-wing aircraft robust element flight aerodynamic prediction method described in any of the above embodiments.

[0008] The beneficial effects of the present invention are: The augmented aerodynamic common basis functions constructed in the robust element flight aerodynamic modeling can achieve accurate approximation of the aerodynamic force and aerodynamic moment of fixed-wing aircraft under the disturbance of the aerodynamic model. This lays a good foundation for effectively identifying the influence of wind and accurately predicting aerodynamics online, making the robust element flight aerodynamic model have better migration capability and engineering applicability, thereby improving the accuracy of the prediction of aerodynamic force and aerodynamic moment of fixed-wing aircraft under strong wind conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of a flow chart of a method for robust element flight aerodynamic modeling of a fixed-wing aircraft according to an embodiment of the present application; Figure 2 This application Figure 1 A method flow diagram of an embodiment of step S10; Figure 3 It is a schematic diagram of a flow chart of a method for predicting aerodynamic force of a fixed-wing aircraft in robust unit flight according to an embodiment of the present application; Figure 4 It is a schematic diagram of the pitch moment prediction error of the robust element flight aerodynamic model under the discrete state situation of one embodiment of the present application; Figure 5 It is a schematic diagram of the pitch moment prediction result of the robust element flight aerodynamic model under the continuous state situation of one embodiment of the present application; Figure 6 It is a schematic diagram of the pitch moment prediction error of the robust element flight aerodynamic model under continuous state conditions of an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0011] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0012] The serial numbers assigned to the components in this article, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0013] To facilitate the description of the inventive concept of the present application, the aerodynamic force prediction technology of fixed-wing aircraft is briefly described below.

[0014] The coordinate systems related to the fixed-wing aircraft flight aerodynamic modeling include the aircraft body coordinate system, airflow coordinate system and ground coordinate system. b Fixed to the aircraft, origin o b Located at the center of mass of the aircraft, o b x b The axis is in the plane of symmetry of the aircraft and points to the nose, o b y b The axis is perpendicular to the aircraft's symmetry plane and points to the right of the fuselage. b z b The axis points downwards from the fuselage in the plane of symmetry of the aircraft. a Fixed to the aircraft, origin o a Located at the center of mass of the vehicle, o a x a Axis coincides with airspeed, o a z a The axis lies in the symmetry plane of the aircraft and is a x aThe axis is vertical and points downwards from the fuselage, o a y a Axis perpendicular to o a x a z a Plane, direction is determined by the right-hand rule. Ground coordinate system S g Fixed to the ground, origin o g At a point on the ground, o g x g The axis points in a certain direction in the horizontal plane, o g z g The axis is perpendicular to the horizontal plane and points to the center of the earth, o g y g The axis is determined by the right-hand rule.

[0015] Among the currently developed technologies, two main methods have been developed in the aerodynamic modeling of fixed-wing aircraft based on aerodynamic common basis functions considering wind interference. One is the "neural flight" method that uses neural networks to model common basis functions and obtains aerodynamic common basis function models of aircraft aerodynamic forces and aerodynamic moments under different wind conditions through deep meta-learning methods; the other is the fixed-wing aircraft meta-flight aerodynamic modeling method considering wind interference, which performs multivariate variable decomposition on the traditional aerodynamic model and obtains aerodynamic common basis function models of aircraft aerodynamic forces and aerodynamic moments that are determined by the characteristics of the fixed-wing aircraft itself and are common under different wind conditions. Both methods construct aerodynamic common basis functions based on discrete state aerodynamic (moment) data sets obtained through CFD numerical calculations, wind tunnel tests, etc., laying a good foundation for the online prediction of aerodynamic forces and aerodynamic moments of fixed-wing aircraft under strong wind conditions.

[0016] However, the applicant discovered during the study that due to the influence of factors such as engine jet and propeller slipstream in actual flight, there may be differences between the actual aerodynamic force and actual aerodynamic moment acting on the aircraft and the results obtained through CFD numerical calculations and wind tunnel tests, which in turn affects the reliability of the common basis function models obtained by the above two methods in actual online migration applications.

[0017] In view of this, the present application provides a method and medium for robust meta-flight aerodynamic modeling and prediction of fixed-wing aircraft. For aerodynamic models disturbed within a certain range, the developed augmented common basis function model is used to effectively approximate and fit the aerodynamic forces and aerodynamic moments of the aircraft, thereby enhancing the robustness of the "neural flight" aerodynamic model and the meta-flight aerodynamic model, thereby improving the accuracy of online prediction of aircraft aerodynamic forces and aerodynamic moments under strong wind conditions, and laying a good foundation for the migration application of online prediction of aerodynamic forces and aerodynamic moments in fixed-wing aircraft flight in real wind fields.

[0018] A method for robust flight aerodynamic modeling of a fixed-wing aircraft provided in one embodiment of the present application is provided in Figure 1 ,include: Step S10, obtaining discrete aerodynamic data sets under different state conditions, and establishing aerodynamic common basis functions of the fixed-wing aircraft.

[0019] In one embodiment, establishing the aerodynamic commonality basis function of the fixed-wing aircraft may include two methods, including: Method 1: Using discrete aerodynamic data sets under different state conditions, a traditional aerodynamic model of a fixed-wing aircraft expressed in the body coordinate system is established, and the aerodynamic force and aerodynamic moment in the aerodynamic model are Taylor expanded for the wind speed vector expressed in the ground coordinate system. The approximation accuracy of the Taylor expansion expressions of different orders to the original function is compared, and the aerodynamic force expansion order and aerodynamic moment expansion order that meet the preset accuracy requirements are obtained, thereby determining the common basis functions of the aerodynamic force and aerodynamic moment components.

[0020] Method 2: Using discrete aerodynamic data sets under different state conditions, based on different wind conditions, the discrete aerodynamic data sets are expanded to obtain multiple labeled data sets. Based on deep meta-learning, the aerodynamic common basis functions are modeled and trained using a neural network model, thereby obtaining a common basis function neural network model of aerodynamic force and aerodynamic moment components.

[0021] In one embodiment, please refer to Figure 2 , step S10 may include: Step S101, obtaining discrete aerodynamic data sets under different state conditions.

[0022] Step S102, establishing an aerodynamic force coefficient and aerodynamic moment coefficient model of the fixed-wing aircraft based on discrete aerodynamic data sets under different state conditions.

[0023] In one embodiment, step S102 includes: , , in, , and They respectively represent the axial force coefficient, lateral force coefficient and normal force coefficient of the aircraft, that is, the aerodynamic coefficient of the aircraft. , and They respectively represent the rolling moment coefficient, pitch moment coefficient and yaw moment coefficient of the aircraft, that is, the aerodynamic moment coefficient of the aircraft. represents the aircraft's angle of attack, represents the measured slip angle of the aircraft, It represents the Mach number of the aircraft; , and They represent the deflection angles of the aircraft's elevator, aileron, and rudder respectively; , and They represent the static rolling moment coefficient, static pitching moment coefficient and static yaw moment coefficient of the aircraft respectively; p, q and r represent the rolling angular velocity, pitching angular velocity and yaw angular velocity respectively; , , , and represent respectively the rolling moment dynamic derivative coefficient related to the rolling angular velocity, the rolling moment dynamic derivative coefficient related to the yaw angular velocity, the pitching moment dynamic derivative coefficient related to the pitch angular velocity, the yaw moment dynamic derivative coefficient related to the rolling angular velocity and the yaw moment dynamic derivative coefficient related to the yaw angular velocity; and They represent the lateral reference length and longitudinal reference length of the aircraft respectively; V represents the velocity amplitude of the aircraft.

[0024] Step S103, establishing a traditional aerodynamic model of the fixed-wing aircraft based on the aerodynamic force coefficient and aerodynamic moment coefficient model.

[0025] In one embodiment, step S103 includes: , in, , and They respectively represent the axial force, lateral force and normal force of the aircraft, that is, the aerodynamic force of the aircraft. , and They respectively represent the rolling moment, pitch moment and yaw moment of the aircraft, that is, the aerodynamic moment of the aircraft. Q Indicates dynamic pressure, , represents the air density, which is related to the height of the aircraft and is a function of the aircraft height. S represents the reference area of ​​the aircraft, represents the diagonalization operator, represents the 3×3 identity matrix, Represents a 3×3 matrix of zeros.

[0026] Step S104, based on the traditional aerodynamic model, obtaining the aerodynamic common basis functions of the fixed-wing aircraft.

[0027] In one embodiment, based on the above-mentioned traditional aerodynamic model, the aerodynamic force and aerodynamic moment in the aerodynamic model are calculated with respect to the wind speed vector represented in the ground coordinate system. Taylor expansion is performed, and the approximation accuracy of Taylor expansion expressions of different orders to the original function is compared to obtain the aerodynamic force expansion order and aerodynamic moment expansion order that meet the preset accuracy requirements, thereby determining the common basis functions of the aerodynamic force and aerodynamic moment components.

[0028] in, represents the wind speed vector expressed in the ground coordinate system, , and It represents the velocity components of the wind speed vector in the x, y, and z directions of the ground coordinate system, and T represents transpose.

[0029] In one embodiment, the aerodynamic commonality basis function vector includes: , Where x represents the motion variable of the aircraft relative to the earth, The aerodynamic commonality basis function vector representing the i-th aerodynamic force and aerodynamic moment component, represents the jth aerodynamic commonality basis function of the ith component, 1≤j≤N i , N i represents the number of aerodynamic commonality basis functions of the i-th aerodynamic force and aerodynamic moment component, .

[0030] So that the aerodynamic model of the modeled fame satisfies .

[0031] in, , represents the pseudo dynamic pressure, , represents the velocity vector of the aircraft relative to the earth in the ground coordinate system, represents the vector modulus operation, represents the standard atmospheric density corresponding to sea level, , and It represents the velocity components of the ground speed vector of the aircraft in the x, y, and z directions of the ground coordinate system. represents the wind disturbance coefficient function related only to the wind speed variable expressed in the ground coordinate system, ; represents the common basis function matrix related only to the motion variable x of the aircraft relative to the earth, , represents the common basis function vector about the axial force, represents the common basis function vector about the lateral force, represents the common basis function vector about the normal force, represents the common basis function vector about the rolling moment, represents the common basis function vector about the pitching moment, Represents the common basis function vector about the yaw moment. , h represents the altitude of the aircraft, represents the Euler attitude angle, , , and They represent the roll angle, pitch angle, and yaw angle respectively. represents the angular velocity, , represents the rudder deflection angle, .

[0032] Step S20 , for the disturbance of the real aerodynamic force and the real aerodynamic moment relative to the nominal aerodynamic model, based on the aerodynamic common basis function, the common basis function with robustness is augmented to obtain an augmented common basis function.

[0033] The following takes any component of the aerodynamic force and aerodynamic moment of a fixed-wing aircraft in actual flight as an example to illustrate the method of obtaining the augmented common basis function.

[0034] In one embodiment, the pitch moment is taken as an example for description.

[0035] Assume that the pitch moment of a fixed-wing aircraft in actual flight is: , in, Indicates the actual pitch moment of the aircraft. represents the true pitching moment coefficient, or , , , and represents the constant perturbation coefficient, represents the nominal pitching moment coefficient.

[0036] Those skilled in the art can understand that the relationship between the real pitch moment coefficient and the nominal pitch moment coefficient is not limited to the above two situations, and there may be other situations.

[0037] exist In the case of , the actual pitch moment of the aircraft can be further written as: , in, , , .

[0038] Based on the nominal aerodynamic model and the common basis function of the pitch moment obtained previously, we have .

[0039] Therefore, the actual pitch aerodynamic moment of a fixed-wing aircraft can be expressed as: .

[0040] Then the augmented common basis function about the pitching moment is obtained as follows: .

[0041] Where x represents the motion variable of the aircraft relative to the earth, represents the augmented common basis function vector about the pitching moment, represents the common basis function vector about the pitching moment, , represents the standard atmospheric density corresponding to sea level, , and It represents the velocity components of the ground speed vector of the aircraft in the x, y, and z directions of the ground coordinate system. represents the pseudo dynamic pressure, , It represents the ground velocity vector of the aircraft relative to the earth in the ground coordinate system. represents a vector modulus operation, and T represents a transpose operation.

[0042] exist In the case of , the actual pitch aerodynamic moment of the fixed-wing aircraft can be expressed as: .

[0043] Then the augmented common basis function about the pitching moment is obtained as: .

[0044] Step S30, based on the augmented common basis function, the aerodynamic force and aerodynamic moment of the fixed-wing aircraft are approximated to obtain a robust element flight aerodynamic model.

[0045] In one embodiment, step S30 includes: , in, , , and They represent the dimensionless axial force coefficient, lateral force coefficient and normal force coefficient of the aircraft respectively, , and They represent the dimensionless rolling moment coefficient, pitching moment coefficient and yaw moment coefficient of the aircraft respectively; represents the wind disturbance coefficient function that needs to be determined online, ; represents the augmented common basis function matrix related only to the motion variable x of the aircraft relative to the ground, ; represents the wind speed vector expressed in the ground coordinate system, represents the augmented common basis function vector about the axial force, represents the augmented common basis function vector about the lateral force, represents the augmented common basis function vector about the normal force, represents the augmented common basis function vector about the rolling moment, represents the augmented common basis function vector about the yaw moment.

[0046] The aerodynamic common basis functions constructed in the robust element flight aerodynamic modeling can achieve accurate approximation of the aerodynamic force and aerodynamic moment of the fixed-wing aircraft under the disturbance of the aerodynamic model, which creates good conditions for effectively identifying the influence of wind and accurately predicting aerodynamics online, making the robust element flight aerodynamic model have better migration ability and engineering applicability.

[0047] Based on the robust element flight aerodynamic model obtained above, online real-time prediction of aerodynamic force and aerodynamic moment of fixed-wing aircraft can be realized.

[0048] The method for robust meta-flight aerodynamic modeling of fixed-wing aircraft based on any of the above-mentioned embodiments can effectively approximate and fit the aerodynamic force and aerodynamic moment of the aircraft for the aerodynamic model disturbed within a certain range by utilizing the developed augmented common basis function model, thereby improving the robustness of the "neural flight" aerodynamic model and the meta-flight aerodynamic model, and obtaining a more robust meta-flight aerodynamic model, thereby improving the accuracy of online prediction of aerodynamic force and aerodynamic moment of the aircraft under strong wind conditions, and laying a good foundation for the migration application of online prediction of aerodynamic force and aerodynamic moment in fixed-wing aircraft flight in real wind fields.

[0049] In one embodiment of the present application, a method for predicting robust flight aerodynamic forces of a fixed-wing aircraft is provided, comprising: Step S100, determining the wind disturbance effect coefficient.

[0050] In one embodiment, determining the wind disturbance effect coefficient includes any one of the following methods: Method 1: numerically differentiate the velocity and angular velocity variables of the aircraft, use the aircraft dynamics equation to solve the aerodynamic force and aerodynamic moment of the aircraft, and use the solved aerodynamic force and aerodynamic moment as observations. Based on the robust element flight aerodynamic model, the least squares method is used to determine the wind disturbance coefficient; Method 2: Taking the speed and angular velocity variables of the aircraft as observations, based on the robust element flight aerodynamic model and the aircraft motion dynamics equation, the wind disturbance coefficient is regarded as a state variable, and an extended Kalman filter is constructed to determine the wind disturbance coefficient.

[0051] Step S200, obtaining the motion variables of the fixed-wing aircraft relative to the earth, the motion variables including flight altitude, ground speed vector, Euler attitude angle, angular velocity and rudder deflection angle.

[0052] Step S300: input the motion variables and wind disturbance coefficients into the robust element flight aerodynamic model to predict aerodynamic forces and aerodynamic moments. The robust element flight aerodynamic model is modeled based on the fixed-wing aircraft robust element flight aerodynamic modeling method in any of the above embodiments.

[0053] To illustrate the effect of the prediction, the robust element flight aerodynamic modeling of a fixed-wing UAV is carried out and verified below.

[0054] Since traditional aerodynamic modeling is an existing technology, this paper models the traditional aerodynamic torque model of a certain public fixed-wing aircraft, assuming that the actual aerodynamic torque coefficient , , and the nominal aerodynamic moment coefficient , , There is a constant + proportional relationship between them, specifically , , .

[0055] In the robust meta-flight aerodynamic modeling, the meta-flight aerodynamic modeling technology is first used to establish the common basis function of computational aerodynamics, and the aircraft altitude is obtained. , ground speed vector , Euler attitude angle , angular velocity , rudder deflection angle is the common basis function of the fixed-wing aircraft aerodynamic moment of the input (uniformly represented by variable x). In order to balance the approximation accuracy and generalization performance, the common basis function vector of the aerodynamic moment , , are all taken as 20 dimensions. Further, the given Expand to , and In the equation, we get the augmented common basis function vector with a dimension of 25 , and During the flight, the augmented common basis function is used as the basis, combined with the wind disturbance coefficient determined by identification means, to construct a robust element flight aerodynamic model of fixed-wing aircraft, and the aerodynamic torque of the fixed-wing aircraft is predicted.

[0056] The developed robust element flight aerodynamic model is verified, considering two cases, one is the discrete state case and the other is the continuous state case.

[0057] For the discrete state case, the aerodynamic torque prediction of the randomly given aircraft state is considered. The wind speed in the environment is set to 20m / s, and the direction vector in the ground coordinate system is , the aircraft altitude is h = 900m, the Euler attitude angle is randomly given, and the ground velocity vector of the aircraft relative to the earth in the body coordinate system is given as , , It obeys the uniform random distribution in the interval [-5,5]m / s. The ground speed of the aircraft in the ground coordinate system is determined by the Euler attitude angle conversion calculation. The angular velocity of the three axes of the aircraft obeys the uniform distribution in the interval [-1,1]rad / s. The elevator deflection angle 、Rudder deflection angle , Aileron deflection angle It obeys the uniform distribution of [-25,25]deg. Based on the least squares method, the aerodynamic moment measurement data of the first 500 states are taken as the identification data to determine the wind disturbance coefficient, and then the aerodynamic moment of the last 1500 states is predicted. There is noise interference at the level of 40% of the true value in the measurement data.

[0058] Please refer to Figure 4 , gives the prediction error of the aircraft pitch aerodynamic moment. represents a discrete number, It represents the pitch moment error, where the identification section is the error between the measured pitch moment and the true pitch moment, and the prediction section is the error between the predicted pitch moment and the true pitch moment. It can be seen from the figure that the predicted value based on the robust element flight aerodynamic model greatly reduces the measurement error, and the result is closer to the true value.

[0059] For the continuous state case, the aerodynamic torque prediction of the ideal forced pitch motion of the aircraft is considered. The wind speed in the environment is set to 20m / s, and the direction is given in the ground coordinate system as , the aircraft's altitude is h=900m, and the pitch attitude angle is The rolling angle and yaw angle are 0 degrees. The corresponding three-axis angular velocity is determined by the change of attitude angle. The velocity of the aircraft relative to the earth in the ground coordinate system is given as m / s, the elevator control law is deg, the rudder deflection angle is =10deg, the aileron deflection angle is =0deg, the data sampling time interval is Based on the least squares method, the aerodynamic moment measurement data of the first 10 seconds are taken as the identification data to determine the wind disturbance coefficient, and then the aerodynamic moment data of the last 40 seconds are predicted. There is noise interference at the level of 40% of the true value in the measurement data.

[0060] Please refer to Figure 5 and Figure 6 , Figure 5 The prediction results of the pitch aerodynamic moment are given. Figure 6 The prediction error of the pitch aerodynamic moment is given in the figure. Indicates time, It represents the pitch moment error, where the identification section is the error between the measured pitch moment and the true pitch moment, and the prediction section is the error between the predicted pitch moment and the true pitch moment. The calculation results show that the predicted value is consistent with the true value, which illustrates the effectiveness of the robust element flight aerodynamic model.

[0061] In one embodiment of the present application, a computer-readable storage medium is provided, on which a program is stored. The stored program includes a method for robust element flight aerodynamic modeling of a fixed-wing aircraft and / or a method for robust element flight aerodynamic prediction of a fixed-wing aircraft in any of the above-mentioned embodiments that can be loaded by a processor and processed.

[0062] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0063] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A robust flight aerodynamic modeling method for fixed-wing aircraft, It is characterized in that include: Obtain discrete aerodynamic data sets under different state conditions and establish the aerodynamic commonality basis functions of fixed-wing aircraft; Aiming at the disturbance of the real aerodynamic force and the real aerodynamic moment relative to the nominal aerodynamic model, based on the aerodynamic common basis function, the common basis function with robustness is augmented to obtain an augmented common basis function; Based on the augmented common basis function, the aerodynamic force and aerodynamic moment of the fixed-wing aircraft are approximated to obtain a robust element flight aerodynamic model.

2. The method for robust flight aerodynamic modeling of a fixed-wing aircraft according to claim 1, It is characterized in that The method of obtaining discrete aerodynamic data sets under different state conditions and establishing aerodynamic common basis functions of fixed-wing aircraft includes any of the following methods: Method 1: Using discrete aerodynamic data sets under different state conditions, a traditional aerodynamic model of a fixed-wing aircraft expressed in a body coordinate system is established, and the aerodynamic force and aerodynamic moment in the aerodynamic model are Taylor expanded for the wind speed vector expressed in the ground coordinate system. The approximation accuracy of the Taylor expansion expressions of different orders to the original function is compared, and the aerodynamic force expansion order and aerodynamic moment expansion order that meet the preset accuracy requirements are obtained, thereby determining the common basis functions of the aerodynamic force and aerodynamic moment components; Method 2: Using discrete aerodynamic data sets under different state conditions, based on different wind conditions, the discrete aerodynamic data sets are expanded to obtain multiple labeled data sets. Based on deep meta-learning, a neural network model is used to model and train the aerodynamic common basis functions, thereby obtaining a common basis function neural network model of aerodynamic force and aerodynamic moment components.

3. The method for robust flight aerodynamic modeling of a fixed-wing aircraft according to claim 1, It is characterized in that The disturbance of the real aerodynamic force and the real aerodynamic moment relative to the nominal aerodynamic model is based on the aerodynamic common basis function, and the common basis function with robustness is augmented to obtain the augmented common basis function, including: The method of obtaining the augmented common basis function for the disturbance of aerodynamic force and aerodynamic moment of a fixed-wing aircraft in actual flight relative to the nominal aerodynamic model includes: Assume that the pitch moment of the fixed-wing aircraft in actual flight is: ， in, represents the actual pitch moment of the aircraft, Q represents the dynamic pressure, , represents the air density, V represents the velocity amplitude of the aircraft, Indicates the longitudinal reference length of the aircraft; represents the true pitching moment coefficient, or , , , and represents the constant perturbation coefficient, represents the nominal pitch moment coefficient, q represents the roll angular velocity, represents the elevator deflection angle of the aircraft; then the augmented common basis function about the pitch moment is obtained, including: or, , Where x represents the motion variable of the aircraft relative to the earth, represents the augmented common basis function vector about the pitching moment, represents the common basis function vector about the pitching moment, , represents the standard atmospheric density corresponding to sea level, , and It represents the velocity components of the ground speed vector of the aircraft in the x, y, and z directions of the ground coordinate system. represents the pseudo dynamic pressure, , represents the ground speed vector of the aircraft in the ground coordinate system, represents a vector modulus operation, and T represents a transpose operation.

4. The method for robust flight aerodynamic modeling of a fixed-wing aircraft according to claim 3, It is characterized in that The method of approximating the aerodynamic force and aerodynamic moment of the fixed-wing aircraft based on the augmented common basis function to obtain a robust element flight aerodynamic model includes: ， in, , and They represent the axial force, lateral force and normal force of the aircraft respectively; , and They represent the rolling moment, pitching moment and yaw moment of the aircraft respectively; S represents the reference area of ​​the aircraft, , represents the diagonalization operator, Indicates the lateral reference length of the aircraft; , and They represent the dimensionless axial force coefficient, lateral force coefficient and normal force coefficient of the aircraft respectively, , and They represent the dimensionless rolling moment coefficient, pitching moment coefficient and yaw moment coefficient of the aircraft respectively; represents the wind disturbance coefficient function, ; represents the augmented common basis function matrix related only to the motion variable x of the aircraft relative to the ground, ; represents the wind speed vector expressed in the ground coordinate system, represents the augmented common basis function vector about the axial force, represents the augmented common basis function vector about the lateral force, represents the augmented common basis function vector about the normal force, represents the augmented common basis function vector about the rolling moment, represents the augmented common basis function vector about the yaw moment.

5. A robust method for predicting flight aerodynamic forces of fixed-wing aircraft. It is characterized in that include: Determine the wind disturbance effect coefficient; Obtain the motion variables of the fixed-wing aircraft relative to the earth, including flight altitude, ground speed vector, Euler attitude angle, angular velocity and rudder angle; Inputting the motion variables and wind disturbance coefficients into a robust element flight aerodynamic model to predict aerodynamic forces and aerodynamic moments; The robust element flight aerodynamic model is modeled based on the fixed-wing aircraft robust element flight aerodynamic modeling method described in any one of claims 1 to 4.

6. The method for predicting the robust flight aerodynamic force of a fixed-wing aircraft according to claim 5, It is characterized in that The determination of the wind disturbance effect coefficient includes any one of the following methods: Method 1: numerically differentiate the velocity and angular velocity variables of the aircraft, use the aircraft dynamics equation to solve the aerodynamic force and aerodynamic moment of the aircraft, and use the solved aerodynamic force and aerodynamic moment as observations. Based on the robust element flight aerodynamic model, the least squares method is used to determine the wind disturbance coefficient; Method 2: Taking the speed and angular velocity variables of the aircraft as observations, based on the robust element flight aerodynamic model and the aircraft motion dynamics equation, the wind disturbance coefficient is regarded as a state variable, and an extended Kalman filter is constructed to determine the wind disturbance coefficient.

7. A computer-readable storage medium, It is characterized in that The medium stores a program, which can be loaded by a processor and execute the fixed-wing aircraft robust element flight aerodynamic modeling method as described in any one of claims 1 to 4 and / or the fixed-wing aircraft robust element flight aerodynamic prediction method as described in any one of claims 5 to 6.

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