CN108595756B - Method and device for estimating flight interference of large envelope - Google Patents

Method and device for estimating flight interference of large envelope Download PDF

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CN108595756B
CN108595756B CN201810233100.7A CN201810233100A CN108595756B CN 108595756 B CN108595756 B CN 108595756B CN 201810233100 A CN201810233100 A CN 201810233100A CN 108595756 B CN108595756 B CN 108595756B
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范国梁
袁如意
刘振
刘朝阳
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Abstract

The invention belongs to the technical field of automatic control, and particularly relates to a method and a device for estimating flight interference of a large envelope curve. The method aims to solve the problem that the analysis method of the rigid aircraft in the prior art cannot be applied to the flight dynamics characteristics of the elastic aircraft. The invention provides a large envelope flight interference estimation method, which comprises the steps of respectively obtaining an input signal and an output signal of a large envelope aircraft, carrying out fast Fourier transform signal analysis on the input signal and the output signal, and carrying out division operation on the signals obtained by analysis to obtain a baud graph of the output signal to the input signal; extracting a sub-region image in the baud graph; and inputting the sub-region image of the bode diagram into a convolution neural network model for identification to obtain the category of the sub-region image of the bode diagram so as to complete the flight interference estimation of the large envelope curve. The method can form a vibration mode off-line learning mechanism and an on-line robust flexible identification mechanism of a deep learning mechanism, and realizes on-line measurement and calculation of elastic characteristic parameters.

Description

Method and device for estimating flight interference of large envelope
Technical Field
The invention belongs to the technical field of automatic control, and particularly relates to a method and a device for estimating flight interference of a large envelope curve.
Background
With the continuous expansion of mission range of the aircraft, the flight envelope line of the aircraft is larger and larger. Generally, an aircraft flies in the atmosphere (within 2 ten thousand meters) and in the near space at the edge of the atmosphere (within 2 to 10 ten thousand meters), the flight speed range of the aircraft is between high subsonic speed and high supersonic speed above mach 5, the flight speed change is large, the dynamic characteristic change of the aircraft is also large, and therefore in the flying process of the aircraft, the performance parameters of the aircraft, such as structural elasticity and the like, cannot be ignored, and the design of a flight control law has a great influence on the performance parameters of the aircraft. The control law design method of the aircraft aims to meet the requirement that the aircraft can guarantee the flight quality of the aircraft at different typical working points in an envelope and the aircraft can reach ideal performance indexes in both a time domain and a frequency domain.
As the structural modal frequency and the rigid modal frequency of the aircraft are closer to each other, the influence of the elastic effect on the flight dynamics of the elastic aircraft is more obvious, particularly, the stability control characteristics become more complex and severe, and the analysis method of the rigid aircraft cannot be used for research, so that a flight dynamics model capable of containing multidisciplinary coupling is urgently required to be established for the elastic aircraft, and a targeted control law is designed for the elastic characteristics.
Therefore, how to provide a solution for identifying the characteristic parameters of the large envelope flight control law for the elastic interference is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In order to solve the above problems in the prior art, that is, to solve the problem that the analysis method of the rigid aircraft in the prior art cannot be applied to the flight dynamics characteristics of the elastic aircraft, the invention provides a method for estimating flight interference of a large envelope, comprising the following steps:
respectively acquiring an input signal and an output signal of a large-envelope aircraft, and performing Fast Fourier Transform (FFT) signal analysis on the input signal and the output signal to respectively obtain a first FFT signal and a second FFT signal;
performing a division operation on the first FFT signal and the second FFT signal to obtain a bode plot of the output signal versus the input signal;
extracting an image with resonance characteristics in the bode graph as a sub-region image of the bode graph;
and inputting the sub-region image of the bode diagram into a preset convolution neural network model for identification to obtain the category of the sub-region image of the bode diagram so as to complete the estimation of the flight interference of the large envelope curve.
In a preferred technical solution of the above method, the category of the sub-region image of the bode map includes a flat region, a resonance region, and a descent region.
In a preferred embodiment of the above method, the method further comprises: and performing self-adaptive elastic filtering based on the elastic conduction spatial characteristic fusion.
In a preferred embodiment of the foregoing method, the adaptive elastic filtering method includes:
acquiring elastic fluctuation data of the large envelope aircraft through a vibration sensor and recording the spatial position of the vibration sensor;
carrying out speed correction processing on the elastic fluctuation data;
according to the space distribution difference of the sensor array of the large envelope aircraft, solving time domain and frequency domain information of the elastic fluctuation data by adopting a high-dimensional joint spectrum analysis technology;
and establishing a spatial distribution map of a fluctuation curve equation for flight control feedback and stability analysis of the large envelope aircraft according to the elastic fluctuation data, the spatial distribution difference of the sensor array and the time domain and frequency domain information of the elastic fluctuation data.
In a preferred technical scheme of the method, the input signal is an elevator of the large envelope aircraft, and the output signal is a track inclination angle, a height, an attack angle and a pitch angle rate of the large envelope aircraft.
The invention also provides a storage device in which a plurality of programs are stored, said programs being adapted to be loaded by a processor and to carry out the method of estimating a flight disturbance of a large envelope line as described above.
The invention also provides a processing device, which comprises a processor and a storage device; a processor adapted to execute various programs; a storage device adapted to store a plurality of programs; the program is adapted to be loaded by a processor and to perform the method of estimating a flight disturbance of a large envelope line as described above.
Compared with the closest prior art, the invention provides a large envelope flight interference estimation method, which comprises the steps of respectively obtaining an input signal and an output signal of a large envelope aircraft, and carrying out Fast Fourier Transform (FFT) signal analysis on the input signal and the output signal to respectively obtain a first FFT signal and a second FFT signal; performing division operation on the first FFT signal and the second FFT signal to obtain a baud graph of the output signal to the input signal; extracting an image with resonance characteristics in the bode graph as a sub-region image of the bode graph; and inputting the sub-region image of the bode diagram into a preset convolution neural network model for identification to obtain the category of the sub-region image of the bode diagram so as to complete the estimation of the flight interference of the large envelope curve.
The technical scheme at least has the following beneficial effects:
1. the method is characterized in that a closed-loop online robust inversion mechanism of elastic characteristic parameters is designed aiming at elastic interference of a large envelope aircraft, identifiability of closed-loop identification and a robust identification method under data interference are given, and a vibration mode offline learning mechanism and an online robust flexible identification mechanism of a deep learning mechanism are formed;
2. the invention provides a self-adaptive elastic filtering method for the fusion of elastic conduction space characteristics of a large envelope aircraft, and provides an implementation method for the online measurement and calculation of elastic characteristic parameters.
Drawings
FIG. 1 is a graph of the amplitude-frequency characteristics of a control input to a control output according to one embodiment of the present invention;
FIG. 2 is a graph of amplitude-frequency characteristics of a linearized model according to one embodiment of the present invention;
FIG. 3 is a graph of the amplitude-frequency response of the output signal of a large envelope aircraft according to one embodiment of the invention;
FIG. 4 is a graph of amplitude-frequency response of an input signal versus elastic modal output according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating feature extraction based on elastic fluctuation of deep learning according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a method for estimating a flight interference of a large envelope curve according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a coordinate system definition and stress analysis of a node of an elastic vibration sensor and an elastic beam according to an embodiment of the present invention;
fig. 8 is a schematic diagram of adaptive elastic filtering based on elastic conductive spatial characteristic fusion according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
The invention adopts a calculation method of a robust inversion mechanism of flight elastic characteristic parameters of the large envelope, and analyzes key influence factors of closed-loop cross coupling among uncertainty, rigid body mode, elastic mode and a control system on the dynamics modeling and cross coupling characteristics of uncertainty, elasticity and control of a large envelope aircraft; a closed-loop online robust inversion mechanism of elastic characteristic parameters is designed, identifiability of closed-loop identification and a robust identification method under data interference are provided, a vibration mode offline learning mechanism and an online robust flexible identification mechanism of a deep learning mechanism are formed, and the method is a novel data-driven elastic characteristic parameter inversion mechanism and a calculation implementation method based on elastic characteristic mode extraction.
Meanwhile, an adaptive elastic filtering mechanism based on the fusion of elastic conduction spatial characteristics is formed, the spatial characteristics of the elastic fluctuation conduction mechanism are given, and the time-space distribution of the fluctuation characteristics of different positions of the aircraft is realized; giving a fluctuation characteristic space sampling mode of distributed fluctuation conduction and redundancy characteristics of fluctuation data, constructing a data fusion mechanism, and realizing a calculation method for enhancing and isolating elastic characteristic parameters; a sensing mechanism and a spatial configuration mechanism of fluctuation characteristic measurement are formed, a mathematical expression and a calculation mechanism of elastic fluctuation space estimation facing vibration isolation are given, and an elastic filtering method based on adaptive elastic decoupling and spatial data fusion under the condition of multi-axis elastic characteristic parameter inversion is formed.
Modeling the dynamics of the large envelope aircraft elastomer aircraft, the model can be represented by the following formula:
Figure BDA0001603115520000041
Figure BDA0001603115520000042
Figure BDA0001603115520000043
Figure BDA0001603115520000044
Figure BDA0001603115520000045
Figure BDA0001603115520000046
the model comprises 11 flight states, 5 rigid body states V, gamma, h, α, Q respectively representing speed, track inclination angle, altitude, attack angle and pitch angle rate, and 6 elastomer states
Figure BDA0001603115520000047
Respectively representing the first three elastic modes and their differentials. Wherein, ω isiNatural frequency of elastic mode, ξiM, g, I as damping ratioyyL, D, T, M, N representing mass, gravitational acceleration, and moment of inertia about the Y axis, respectivelyiRespectively representing lift force, resistance force, thrust force, pitching moment and generalized elastic force. The model has a complex nonlinear relation with the flight state and the control input, and can be represented by a high-precision fitting model, which is specifically represented by the following formula:
Figure BDA0001603115520000051
wherein the content of the first and second substances,
Figure BDA00016031155200000613
represents an elastic mode vector of ═ 2 [, ]c,e]TThe vector of the rudder deflection angle is represented,c,erespectively showing the rudder deflection angles of the canard and the elevator,
Figure BDA0001603115520000053
represents the dynamic pressure, ρ is the atmospheric density.
The aerodynamic coefficient of a large envelope aircraft can be shown in equation (8):
Figure BDA0001603115520000054
wherein, CT,φThe thrust coefficient of thrust versus roll angle,
Figure BDA0001603115520000055
are respectively provided withIs CT,φDerivatives of the angle of attack of order 3, 2, 1 and offset values; cTIs the thrust coefficient of thrust versus angle of attack,
Figure BDA0001603115520000056
are respectively CTDerivatives of the angle of attack of order 3, 2, 1 and offset values;
CLcoefficient of lift, angle of attack
Figure BDA0001603115520000057
(elastic mode vector), andc,e]T(rudder deflection angle vector) of the rudder angle of the vehicle,c,erespectively showing the rudder deflection angles of the canard and the elevator,
Figure BDA0001603115520000058
as the lift coefficient of lift to angle of attack,
Figure BDA0001603115520000059
as a pair of lifting forceseThe coefficient of lift force of (a) is,
Figure BDA00016031155200000510
as a pair of lifting forcescThe coefficient of lift force of (a) is,
Figure BDA00016031155200000511
the lift coefficient is zero, and the lift coefficient is zero,
Figure BDA00016031155200000512
the lift coefficient is the lift force versus elastic modal vector η.
CDIs coefficient of resistance, angle of attack
Figure BDA00016031155200000513
(elastic mode vector), andc,e]T(rudder deflection angle vector) of the rudder angle of the vehicle,c,erespectively showing the rudder deflection angles of the canard and the elevator,
Figure BDA0001603115520000061
for resistance to attackThe square of the angle and the coefficient of resistance at the angle of attack,
Figure BDA0001603115520000062
is a resistance paireSum of squareseThe coefficient of resistance of (a) is,
Figure BDA0001603115520000063
is a resistance paircSum of squarescThe coefficient of resistance of (a) is,
Figure BDA0001603115520000064
the resistance coefficient of the material is zero, and the material is,
Figure BDA0001603115520000065
is the drag coefficient of drag versus elastic mode vector η.
CMIs the coefficient of pitching moment and the angle of attack
Figure BDA0001603115520000066
(elastic mode vector), andc,e]T(rudder deflection angle vector) of the rudder angle of the vehicle,c,erespectively showing the rudder deflection angles of the canard and the elevator,
Figure BDA0001603115520000067
the coefficient of the pitching moment coefficient is the square of the pitching moment to the attack angle and the attack angle,
Figure BDA0001603115520000068
as a pitching moment paireThe coefficient of the pitching moment of (a),
Figure BDA0001603115520000069
as a pitching moment paircThe coefficient of the pitching moment of (a),
Figure BDA00016031155200000610
the pitch moment coefficient is zero, and the pitch moment coefficient is zero,
Figure BDA00016031155200000611
pitch moment system for pitch moment coefficient versus elastic modal vector ηAnd (4) counting.
L,D,T,M,NiRespectively representing lift force, resistance force, thrust force, pitching moment and generalized elastic force.
Figure BDA00016031155200000612
The thrust T, the lift L, the drag D and the pitching moment M are paired
Figure BDA00016031155200000613
Thrust coefficient, lift coefficient, drag coefficient, and pitching moment coefficient of (elastic modal vector).
Figure BDA00016031155200000614
Is a generalized elastic force pair
Figure BDA00016031155200000615
The generalized elastic force coefficient (elastic modal vector).
The canard wing of the large envelope aircraft is connected to the elevator by a hinge to eliminate the non-minimum phase characteristic, so the control input to be designed for the aircraft may be u ═ ce,φ]TAnd phi is the engine fuel equivalence ratio. The control output may be y ═ V, h]TI.e. control speed and altitude.
The aircraft mathematical model is a typical multivariable, strong nonlinear and strong coupling model, and due to the fact that the aircraft is high in flying speed, large in flying envelope, complex in mechanism such as scramjet combustion and the like, and lack of sufficient flight test data support, the control model has serious uncertainty and belongs to a typical system with complex structure uncertainty. While uncertainty has a significant impact on model properties. Taking the case of reentry flight, the reentry process is accompanied by rapid velocity and altitude changes, resulting in dynamic pressure
Figure BDA00016031155200000616
The fast time-varying and uncertain effect of dynamic pressure variations have a significant influence on the flight characteristics.
As shown in FIG. 1, FIG. 1 is a graph of amplitude-frequency characteristics of control input to control output, and a large envelope aircraft is a multidisciplinary intersection product, and complex coupling effects exist among the structure, the propulsion system, the aeroelastic effect, the thermal effect and the control.
From a control point of view, the main coupling effect is represented by the coupling between the engine and the coupling between the rigid-elastic modes. Due to the adoption of the scramjet engine to obtain oxygen materials from air, the overall structure of the large envelope aircraft adopts a high-degree engine-body integrated design.
The front end of the body of the large envelope aircraft is often used as a precompression system of the air inlet flow of an engine, and the rear body is used as an expansion system of the wake flow, so that the posture of the body directly influences the working condition of the engine; in turn, because the engine is often installed at the lower part of the body, the thrust line does not pass through the center of mass, so the thrust generates extra moment, and the shear flow of the rear body also generates extra lift force.
From the expression of the thrust T, it can be seen that the engine thrust T is significantly influenced by the attack angle α in both the cantilever beam model and the free beam model, and therefore, the high-performance attack angle control is important for the normal operation of the scramjet engine, and shows the coupling between the aerodynamic effect and the engine operating condition.
At the same time, from pitching moment
Figure BDA0001603115520000073
The expression shows that the thrust T can generate additional pitching moment with the acting arm zTTherefore, the longitudinal pitching motion is controlled by the fuel equivalence ratio in addition to the elevator, and the cross coupling of the control is shown.
Due to the adoption of the fuselage made of a light material with a large slenderness ratio, the elastic effect of the hypersonic aircraft is more obvious than that of a common aircraft. The elastic deformation of the body will directly change the characteristics of the shock waves of the attachment on the body, on one hand, the aerodynamic characteristics are directly influenced, on the other hand, the air intake condition of the engine is also changed, and therefore the flight characteristics are obviously influenced. The rigid body-elastic coupling effect of the free beam model is taken as an example for analysis:
as known from the pneumatic model, the rigid body-elastic cross coupling in the model is embodied in the expression of force and moment, and the coupling parameter set for defining the elastic mode to the rigid body dynamic is as follows
Figure BDA0001603115520000071
j is T, L, D, M, and the coupling parameter set for defining rigid body mode to elastic dynamic is
Figure BDA0001603115520000072
i is 1,2, 3. The frequency characteristic of the linearized model is analyzed, and the amplitude-frequency characteristic is shown in fig. 2. The long period mode of slight oscillation can be seen in FIG. 2, corresponding to a frequency of 0.0485rad/s identified at "P". The response of velocity V is not significantly affected by the elastic coupling, but the response of height h exhibits a significant elastic coupling characteristic, denoted as "F1”、“F2”、“F3"frequency of site identification and first three-order elastic mode η1、η2、η3The natural frequencies of the frequency bands are approximate, and are respectively 21.17 rad/s, 53.92rad/s and 109.1 rad/s.
When the coupling parameters are uncertain, there is a cross-coupling effect, as shown in FIG. 3, FIG. 3 is a slaveeTo the amplitude-frequency response diagram of γ, h, α, q, first consider the parameter set Θ1: respectively to theta1The parameters in the method are subjected to uncertainties of-100% (no elastic coupling), 0% (nominal elastic coupling) and + 100% (increased elastic coupling), the corresponding characteristic roots of the linearized model under three conditions are shown in the table, the coupling coefficient of the elastic mode to the rigid mode obviously influences the position of the open loop pole of the model, and the change proportion of the characteristic roots corresponding to the long-period mode and the height mode is larger than that of the short-period mode, so that the conclusion of the coupling strength degree between different rigid modes and the elastic mode can be obtained: the high-frequency wave-splitting mode is a long-period mode, a high-period mode and a short-period mode from strong to weak.
Table 1: theta1System open loop characteristics with parameters having different uncertainty degreesRoot of Chinese wampee
Figure BDA0001603115520000081
To further confirm this conclusion, the frequency analysis method was first used to examine the coupling of elastic to rigid modes:
under the condition of + 100% parameter uncertainty, a track inclination angle gamma (corresponding to a long-period mode), a height h (corresponding to a height mode), an attack angle α (corresponding to a short-period mode) and a pitch angle rate q (corresponding to a short-period mode) are respectively selected as single output quantities, an elevator deflection angle is selected as single input quantity, the model is linearized, then the linearized model is subjected to spectrum characteristic analysis to obtain amplitude-frequency characteristics, as shown in fig. 2, it can be seen from fig. 2 that response characteristics of gamma and h oscillate at frequencies corresponding to elastic modes, and coupling influence on response characteristics of short-period modes (α and q) is much smaller.
Conversely, the coupling effect of rigid body modes on elastic modes was examined:
let Θ be2The parameters in the step (A) have-100% uncertainty, and the first three-order elastic modal generalized coordinate value η is respectively selected1、η2、η3For single output, the elevator deflection angle is also selected as single input, and after the model is linearized, the amplitude-frequency characteristic is obtained, as shown in fig. 3, where fig. 3 iseTo η1、η2、η3Each amplitude-frequency response curve corresponds to two peak points, one of which is at the natural frequency of the elastic mode itself, and the other of which points to a common frequency value (identified by a circle in the figure), namely about 0.0415rad/s, which is very similar to the frequency of the long-period mode.
In conjunction with fig. 3 and 4, it can be concluded that: for an elastic body of a free beam model, the coupling effect between the elastic mode and the long-period mode in the rigid body mode is most obvious, and the strength of cross coupling is clarified between the elastic mode and the height mode and between the elastic mode and the short-period mode, so that the elastic mode can be actively inhibited in a targeted manner during the design of a controller.
By combining the problem to be solved by the invention and the practical application scene, the deep learning is suitable for processing the off-line elastic vibration simulation big data with noise, and the method intensively reflects three big trends of the current machine learning algorithm:
1. reducing model bias with a more complex model;
2. improving the accuracy of statistical estimation by using big data;
3. and solving the large-scale optimization problem by using an extensible gradient descent algorithm.
Deep learning is an end-to-end machine learning system, and can be used for processing a two-dimensional space structure in an image by convolution and processing a time sequence structure in data such as elastic fluctuation and the like by a recurrent neural network. Deep learning can increase the scale of training data under the condition of complex model structure; adding various a priori knowledge about the data structure to the new model structure; end-to-end learning can forego intermediate steps based on artificial rules.
As shown in fig. 5, fig. 5 exemplarily shows a feature extraction diagram of elastic fluctuation based on deep learning. In the embodiment of the invention, elastic fluctuation based on deep learning is adopted for feature extraction, wherein in the aspect of resonance point detection, a scanning window or candidate window method is adopted, the scanning window method shares features between adjacent windows, an image with a large area can be rapidly scanned, the candidate window method can efficiently identify in an image candidate area, and change of an object aspect ratio is more flexibly processed, so that high intersection ratio coverage rate is obtained.
Specifically, as shown in fig. 6, fig. 6 exemplarily shows a flow schematic diagram of a method for estimating flight interference of a large envelope, where the method for inverting elastic feature parameters based on deep learning includes the following steps:
step S11: respectively acquiring an input signal and an output signal of the large-envelope aircraft, and performing Fast Fourier Transform (FFT) signal analysis on the input signal and the output signal to respectively obtain a first FFT signal and a second FFT signal;
acquiring an input signal, the input signal may include: lift ruddereThe output signals can include track inclination angle gamma (corresponding to a long-period mode), height h (corresponding to a height mode), attack angle α (corresponding to a short-period mode) and pitch angle rate q (corresponding to a short-period mode), and FFT (Fast Fourier transform) signal analysis is respectively carried out on the track inclination angle gamma (corresponding to a long-period mode), the height h (corresponding to a height mode), the attack angle α (corresponding to a short-period mode) and the pitch angle rate q (corresponding to a short-period mode), and the signals of the elevating rudder are analyzedeAnd performing FFT signal analysis, namely performing FFT signal analysis on the input signal to obtain a result as a first FFT signal, performing FFT signal analysis on the output signal to obtain a result as a second FFT signal, dividing the FFT signals to obtain a Baud diagram of a track inclination angle gamma, a height h, an attack angle α and a pitch angle rate q to a corresponding transfer function of the elevator, and uniformly setting the frequency range to be 0.001-1000 hz., wherein the Baud diagrams are used as input images for deep learning neural network identification.
Step S12: extracting an image with resonance characteristics in the bode graph as a sub-region image of the bode graph;
and (3) sliding the bode graph from the frequency range of 0.001-1000hz in sequence by taking 1.5 octaves as a window on the bode graph, and extracting an image of the bode graph as a sub-region of the bode graph characteristic. This step is mainly used to extract the area of the distortion characteristic of the bode plot with the resonance characteristic.
Step S13: inputting the sub-region image of the bode diagram into a preset convolution neural network model for identification to obtain the category of the sub-region image of the bode diagram so as to complete the estimation of the flight interference of the large envelope curve;
and sending the extracted subregion image into a convolutional neural network for identification. The convolution templates of the convolutional neural network are bode image images of a flat area, a resonance area and a descent area respectively.
Step S14: and (5) classifying the regions.
The sub-regions of the bode plot are classified as flat region Fat, Resonance region Resonance, down region down.
After the inversion of the elastic characteristic parameters is completed based on deep learning, the definition and stress analysis of the elastic vibration sensor node and the elastic beam coordinate system are carried out.
However, because the elastic aircraft has more complex dynamic characteristics than the rigid aircraft, and the data of the currently disclosed elastic aircraft model are less, the research on the control of the elastic body at the present stage is mainly focused on the longitudinal motion of the elastic body, at this time, in order to avoid adding lateral maneuver in the flight task, all the lateral states are assumed to be 0, for the cantilever beam model, the first-order elastic mode of a front body (subscript f) and a rear body (subscript a) is considered, and the longitudinal motion equation is as follows:
Figure BDA0001603115520000101
Figure BDA0001603115520000102
Figure BDA0001603115520000103
Figure BDA0001603115520000104
Figure BDA0001603115520000105
Figure BDA0001603115520000106
Figure BDA0001603115520000107
wherein, ηi(i ═ f, a) represents the generalized elastic coordinate value of the first order mode shape,
Figure BDA0001603115520000111
is a primary vibration of the first orderType-dependent coupling coefficient, NiFor elastic force in general, omegaiAs elastic natural frequency, ξiIs an elastic damping coefficient, constant
Figure BDA0001603115520000112
Other symbol definitions are the same as rigid body models. The pitch angle rate equation in the model is added with elastic coupling, and the elastic equation is also subjected to pitch moment
Figure BDA0001603115520000114
Thereby exhibiting a rigid-elastic dynamic coupling relationship.
The self-adaptive elastic filtering method based on the fusion of the elastic conduction spatial characteristics specifically comprises the following steps:
step S21: sampling by adopting sensors, and recording the corresponding spatial position of each sensor;
due to the strong periodic characteristics of the elastic fluctuation, the response of the elastic fluctuation on the fuselage of the airplane can also show periodicity, such as the spatial bending and twisting positions of the equivalent beam, so that a vibration fluctuation sensor is required to be mounted on the airplane for sampling to acquire data of the sensor, wherein the sensor adopts the vibration fluctuation sensor shown in fig. 7 for sampling.
Step S22: speed correction processing of data;
in practical application, the flying speed is changed, and then the periodic data of the vibration fluctuation can be processed later only after being subjected to speed correction, and the specific method can be that the data acquired by the triaxial accelerometer is divided by the dynamic pressure
Figure BDA0001603115520000113
Step S23: spectral analysis of the fluctuation signal;
the typical wave motion signal is subjected to spectrum analysis technology (such as spectrum analysis, principal component analysis, autocorrelation analysis and phase time trajectory difference PTPD method), so that a periodic signal of vibration can be extracted; by utilizing the spatial distribution difference of the sensor array (inertial device, gyroscope and accelerometer), and applying a high-dimensional combined spectrum analysis technology (such as time-space evolution correlation analysis and cross spectrum analysis: a linear array BORGMAN method/a polygonal array ESTEVA method), the time domain and frequency domain parameter information of the vibration fluctuation can be obtained.
Step S24: and establishing a spatial distribution diagram of the wave curve equation in the projectile body.
The elastic characteristic parameters estimated in real time, and the spatial distribution parameters and the real-time sensor parameters of the sensor array in fig. 6 can establish the spatial distribution of a wave curve equation in a projectile body, so that the actual attitude parameters, angular rate parameters and vibration isolation calculation of an accelerometer can be performed according to data fusion of multiple sensor points, and further the attitude parameters, angular rate parameters and acceleration parameters with greatly attenuated wave characteristics are obtained, and the parameters and the upper limit of the wave amplitude thereof are used for analyzing flight control feedback and stability.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The invention also provides a storage device in which a plurality of programs are stored, said programs being adapted to be loaded by a processor and to perform the method of estimating flight disturbances of a large envelope as described above.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and the related descriptions of the storage device according to the embodiment of the present invention may refer to the corresponding process in the foregoing embodiment of the method for estimating flight interference with large envelope, and have the same beneficial effects as the foregoing method for estimating flight interference with large envelope, and are not described herein again.
A processing apparatus comprising a processor, a storage device; a processor adapted to execute various programs; a storage device adapted to store a plurality of programs; the program is adapted to be loaded by a processor and to perform the method of estimating flight disturbances of a large envelope as described above.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and the related descriptions of the processing apparatus according to the embodiment of the present invention may refer to the corresponding process in the foregoing large envelope flight interference estimation method embodiment, and have the same beneficial effects as the above large envelope flight interference estimation method, and are not described herein again.
Those of skill in the art will appreciate that the method steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of electronic hardware and software. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (6)

1. A method for estimating large envelope flight interference, comprising:
respectively acquiring an input signal and an output signal of a large-envelope aircraft, and performing Fast Fourier Transform (FFT) signal analysis on the input signal and the output signal to respectively obtain a first FFT signal and a second FFT signal;
performing a division operation on the first FFT signal and the second FFT signal to obtain a bode plot of the output signal versus the input signal;
extracting an image with resonance characteristics in the bode graph as a sub-region image of the bode graph;
inputting the sub-region image of the bode diagram into a preset convolution neural network model for identification to obtain the category of the sub-region image of the bode diagram so as to complete the estimation of the flight interference of the large envelope;
the input signal is a rudder deflection angle of an elevator of the large envelope aircraft, and the output signal is a track inclination angle, a height, an attack angle and a pitch angle rate of the large envelope aircraft;
the preset convolution templates of the convolution neural network model are bode image images of a flat area, a resonance area and a descent area respectively.
2. The method of claim 1, wherein the categories of sub-region images of the bode plot include a flat region, a resonance region, and a dip region.
3. The method of claim 1, further comprising: and performing self-adaptive elastic filtering based on the elastic conduction spatial characteristic fusion.
4. The method of claim 3, wherein the method of adaptive elastic filtering comprises:
acquiring elastic fluctuation data of the large envelope aircraft through a vibration sensor and recording the spatial position of the vibration sensor;
carrying out speed correction processing on the elastic fluctuation data;
according to the space distribution difference of the sensor array of the large envelope aircraft, solving time domain and frequency domain information of the elastic fluctuation data by adopting a high-dimensional joint spectrum analysis technology;
and establishing a spatial distribution map of a fluctuation curve equation for flight control feedback and stability analysis of the large envelope aircraft according to the elastic fluctuation data, the spatial distribution difference of the sensor array and the time domain and frequency domain information of the elastic fluctuation data.
5. A memory device having stored therein a plurality of programs, characterized in that said programs are adapted to be loaded by a processor and to carry out the method of large envelope flight interference estimation as claimed in any one of claims 1 to 4.
6. A processing apparatus comprising a processor, a storage device; a processor adapted to execute various programs; a storage device adapted to store a plurality of programs; characterized in that the program is adapted to be loaded by a processor and to carry out the method of large envelope flight interference estimation according to any of claims 1-4.
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