CN111475940B - Flexible baseline dynamic prediction method based on fiber bragg grating sensor and wing mode - Google Patents
Flexible baseline dynamic prediction method based on fiber bragg grating sensor and wing mode Download PDFInfo
- Publication number
- CN111475940B CN111475940B CN202010257888.2A CN202010257888A CN111475940B CN 111475940 B CN111475940 B CN 111475940B CN 202010257888 A CN202010257888 A CN 202010257888A CN 111475940 B CN111475940 B CN 111475940B
- Authority
- CN
- China
- Prior art keywords
- baseline
- wing
- strain
- model
- flexible
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 239000000835 fiber Substances 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000006073 displacement reaction Methods 0.000 claims abstract description 45
- 238000012546 transfer Methods 0.000 claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 24
- 239000011159 matrix material Substances 0.000 claims description 46
- 238000004088 simulation Methods 0.000 claims description 14
- 230000009471 action Effects 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000005484 gravity Effects 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 6
- 238000003491 array Methods 0.000 claims description 5
- 238000005311 autocorrelation function Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 3
- 230000004044 response Effects 0.000 abstract description 3
- 238000011160 research Methods 0.000 abstract description 2
- 230000001419 dependent effect Effects 0.000 abstract 1
- 230000002349 favourable effect Effects 0.000 abstract 1
- 230000008569 process Effects 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 3
- 238000005452 bending Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 239000011541 reaction mixture Substances 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/12—Simultaneous equations, e.g. systems of linear equations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Operations Research (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Optical Transform (AREA)
Abstract
The invention discloses a flexible baseline dynamic prediction method based on fiber bragg grating sensors and wing modes, which comprises the steps of calculating a structure displacement and a baseline quantity between sub-nodes by utilizing a mode superposition principle based on structure strain response, and predicting the baseline quantity change condition at the future moment by using a baseline dynamic model established based on real-time flexible baseline data; the flexible base line is predicted in advance by reducing the calculated amount by using a modal method and dynamically modeling to solve the problem of time delay of real-time transfer alignment caused by time consumed by calculation from actual measurement dependent variable to base line amount, and the method is favorable for realizing the real-time transfer alignment of the high-precision pose of the sub-node. The method can be used for remarkably improving the real-time performance of transfer alignment and making up the defects of the conventional baseline solution and estimation method research.
Description
Technical Field
The invention belongs to the field of navigation, and discloses a flexible baseline dynamic prediction method based on a fiber grating sensor and a wing mode.
Background
In recent years, airborne distributed pos (position and Orientation system) has been widely used in the fields of aviation, national defense and military, especially in the field of high-precision earth observation of multi-task imaging loads, due to its characteristics of being capable of realizing multi-node measurement, high pose precision and the like. As a technology for auxiliary alignment of the pose of the sub-node by the distributed main node, transfer alignment is a key technology of the distributed POS, however, the transfer alignment technology needs to be further researched in both aspects of flexible lever arm conditions and real-time dynamic alignment at present. On one hand, in the flexible baseline measurement method, the existing method for obtaining displacement through fitting based on the strain of the fiber grating sensor has the defects of poor rapidity caused by relatively large calculated amount and unsuitability for dynamic measurement; on the other hand, the conversion calculation of the strain quantity measured by the fiber grating sensor to the baseline quantity needs time, and in the actual flight process, the transfer alignment between the main node and the sub-node and the strain measurement of the current wing by the sensor are carried out simultaneously, so that the baseline quantity converted at the current measurement time of the sensor cannot be used in the transfer alignment process at the current time in real time, and the problem of asynchronous time is caused.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to make up the defects of dynamic measurement research of the existing airborne distributed POS position and orientation measurement method under the condition of a flexible baseline, a flexible baseline dynamic prediction method based on a fiber bragg grating sensor and a wing mode is provided, and the time asynchronism between real-time transmission alignment and flexible baseline value measurement is compensated by using the dynamic prediction method while the flexible baseline is rapidly measured, so that the position and orientation accuracy of the distributed system sub-nodes is improved.
The technical scheme adopted by the invention is as follows: a flexible baseline dynamic prediction method based on a fiber bragg grating sensor and a wing mode comprises the following steps:
firstly, measuring the strain of the wing when the wing is dynamically deformed by using fiber bragg grating sensors symmetrically adhered to measuring points on the upper and lower surfaces of the wing;
step two, solving a modal transfer matrix of wing strain to displacement by using wing modal information extracted by the wing simulation model, and solving transverse and axial displacement and flexible baseline quantity by using the modal transfer matrix and the measuring point strain when the dynamic deformation of the wing is measured in the step one;
step three, determining the order and the dynamic parameters of the flexible baseline model according to the solved flexible baseline quantity to obtain a dynamic model; and then, carrying out advanced prediction estimation on the flexible baseline quantity according to the dynamic model, and realizing prediction output of the flexible baseline under dynamic deformation.
The second step comprises the following steps:
(1) firstly, a wing simulation model is constructed by using simulation software under the condition that the structural parameters of the wing are known, and an axial strain modal matrix [ psi ] extracted from the wing simulation modelu]Axial displacement mode matrix
And transverse displacement mode matrix
Deriving an axial transfer matrix DSTuAnd a transverse transfer matrix DSTw：
(2) Then, taking the deformation state of the wing under the action of the self gravity as an initial state, and utilizing the strain measured by the fiber bragg grating sensor to calculate the axial displacement and the transverse displacement under the action of stress:
wherein the content of the first and second substances,
the strain values at N measuring points measured by the fiber bragg grating sensor are represented, and dy and dz are axial and transverse displacement of the stressed deformation of the wing relative to the initial gravity state respectively;
(3) and finally, calculating and solving the flexible baseline quantity by means of a coordinate conversion principle according to the axial displacement and the transverse displacement obtained by solving.
In the third step, the method comprises the following steps:
(1) firstly, selecting the unit array length predicted by the flexible baseline quantity according to the strain demodulation frequency of the fiber bragg grating, and dividing a baseline quantity sequence converted from the actual measurement strain quantity into a plurality of baseline quantity unit arrays containing the same number of baseline values;
(2) determining the model order by using an AIC (advanced information center) criterion according to the baseline quantity unit array;
(3) identifying parameters of the flexible baseline model by using the baseline quantity unit number converted from the measured strain quantity to obtain a flexible baseline model;
(4) carrying out multiple recursion predictions on the baseline quantity by using a flexible baseline model to obtain a prediction array consisting of baseline prediction values;
(5) selecting the next group of baseline quantity unit arrays converted from the measured strain quantity in the step (1) backwards, and repeating the steps (3) and (4), namely realizing the flexible baseline dynamic model of which the model parameters change along with the measured value;
(6) and finally, realizing the prediction output of the flexible base line under the dynamic deformation by the flexible base line dynamic model.
In the step (3), the method for identifying the flexible baseline model parameters is as follows:
(1) first, the baseline values to be predicted at time n are expressed as a combination of baseline values converted from the measured strain at the previous p times as follows:
wherein the content of the first and second substances,
is the predicted value of x (n), and x (n) is the measured value at time n, i.e., the current value x (n) is predicted from the measured values at p past times x (n), where aiIs a model parameter;
(2) then, introducing x (n) an autocorrelation function over the length of the array of baseline quantities converted from the measured strain as:
wherein, N is the length of a unit array of baseline quantity converted from the actual measurement strain for identifying the model parameters, N is more than or equal to 0 and less than or equal to N-1, and the autocorrelation function sequence and the model parameter sequence based on the array are written into the following matrix equation form:
(3) finally, solving the matrix equation by using a Levinson-Durbin recursion algorithm to obtain a model parameter a1,a2,a3,..,apThe end result is the sum of the squared errors
To a minimum.
The more detailed technical scheme of the invention is as follows:
1. and measuring the strain of the measuring point when the wing is dynamically deformed according to the fiber bragg grating sensors symmetrically adhered to the measuring points on the upper surface and the lower surface of the wing.
On the grating layout, the measuring points which need to be arranged are obtained by the sensor according to the base line in a mode of symmetrically installing the measuring points on the upper surface and the lower surface of the same position of the wing to isolate the influences of temperature and other factors as shown in figure 1. Assuming that the beam is acted by a tensile force F along the x-axis and a rotation moment M along the z-axis, the i-th sensitive induction of the fiber bragg grating sensor on the beam is changed into:
in the formula:
respectively the wire strain caused by the x-axial tension F,
respectively, the linear strain caused by the axial bending of j (x, y, z) caused by the rotational moment M. Aiming at the wing structure, under the action of an x-axis tension F and a z-axis rotation moment M, in the formula (1),
from this, the line strain at the ith point of the Y-axis bend can be derived from equation (1):
in the formula (2), the reaction mixture is,
the line strain at the ith point for Y-axis bending,
and (3) obtaining the strain quantity of the wing when the wing is dynamically deformed by using the formula (2) for the line strain of the ith point of the wing, which is obtained by the fiber bragg grating sensor in real time.
2. And solving a modal transfer matrix from wing strain to displacement according to wing modal information extracted by the wing simulation model, and solving displacement and flexible baseline quantity by using the modal transfer matrix and a measuring point strain quantity when the wing is dynamically deformed in real measurement.
The mode superposition principle shows that the structural deformation can be expressed as the product of a displacement mode matrix and a mode generalized coordinate. As can be seen from the definition of the strain mode, the strain of the structure under load can also be represented by the linear combination of the strain modes of each order, and the linear combination coefficient is the same as that of the displacement, i.e. the generalized mode matrix is the same.
Where { D } is the displacement value to be estimated, { S } is the strain value measured with the sensor, [ phi ]d]And [ psis]Respectively displacement and strain mode matrixes, q is a mode coordinate, N is a strain measurement point number, and N is a mode order.
After obtaining the strain mode matrix and the actually measured strain value, when N is larger than or equal to N, the following can be obtained:
under the conditions that the modal coordinate q corresponding to the first n-order strain mode is obtained and the first n-order displacement mode is known, the deformation is calculated as follows:
wherein note:
is a strain to displacement transfer matrix.
The method comprises the steps of calculating the displacement of the structure based on the modal superposition principle of structural strain response, reflecting modal parameters of the inherent vibration characteristics of the structure, and sensitively reflecting the influence caused by the local rigidity change of the structure, constructing a simulation model of the wing structure by using simulation software under the condition that the structural parameters of the wing are known, wherein the simulation model consists of grid points, selecting corresponding grids at positions of sub-nodes, extracting displacement modes and strain modes, and obtaining a modal transfer matrix of the wing according to a formula (6).
And then, the strain quantity measured by the fiber grating sensor can be directly converted into displacement quantity by utilizing the previous n-order mode transfer matrix, so that the solution of the fiber grating to the displacement is realized.
Further, the wing is stressed to generate axial deformation (Y direction) and transverse deformation (Z direction), as shown in the side view geometrical diagram of the wing in fig. 2, and the length of the base line is the linear distance between A, B. R 'represents A' (x) of the wing after deformation under the condition of self gravityA',yA',zA')，B'(xB',yB',zB') The base length between the two points; r 'represents A' (x) after the wing is deformed under stressA″,yA″,zA″)，B″(xB″,yB″,zB″) The base length between the two points.
Axial strain mode matrix [ psi ] extracted from wing simulation modelu]Axial displacement mode matrix
And transverse displacement mode matrix
The axial transfer matrix DST can be obtained from equation (6)uAnd a transverse transfer matrix DSTw：
Taking the deformation state of the wing under the action of the gravity as an initial state, and solving the relative displacement change under the action of stress by using the actually measured strain of the fiber grating sensor:
wherein the content of the first and second substances,
the strain values at N measuring points measured by the fiber bragg grating sensor are shown, dy and dz are axial and transverse displacement of the stressed deformation of the wing relative to the initial gravity state respectively,
the strain amount at the measuring point i when the strain is stressed and deformed,
is the strain at the measuring point i under the action of gravity.
Finally, the coordinates of the point A 'and the point B' after the wing deformation can be obtained by means of the coordinate conversion principle as follows:
the base length is calculated as:
from this, the flexible baseline quantity converted from the strain quantity was calculated.
3. Determining the order and the dynamic parameters of a flexible baseline model according to the wing mode and the measured flexible baseline quantity obtained by the fiber bragg grating sensor to obtain a dynamic model; and performing advanced prediction estimation on the flexible baseline quantity according to the dynamic model.
Because the solving process for obtaining the flexible baseline quantity between the sub-nodes on the wing based on the fiber bragg grating sensor and the modal method needs time, at the current sampling moment of the fiber bragg grating sensor, the pose of the sub-nodes cannot be compensated in real time through transmission alignment by the solved baseline, and time delay exists between the two steps. The delay can be eliminated by using the measured flexible baseline data to perform dynamic modeling and then predicting the baseline quantity in advance.
Firstly, a baseline quantity sequence converted from an actual measurement strain quantity is regarded as a continuous equal-length array, and the unit array length for predicting the baseline quantity is selected according to the fiber bragg grating strain demodulation frequency. The demodulation frequency of the fiber bragg grating is generally set to be 40Hz, if the unit array length predicted in advance is 20, the unit array length is predicted forwards for 0.5s, the range from 2Hz to 10Hz is changed according to the wing jitter frequency, the predicted unit array length is limited by the demodulation frequency, and the length of the predicted array can be properly prolonged under the condition that the demodulation frequency is higher.
The baseline population converted from measured strain quantities is then used to determine the model order using the AIC criterion, i.e. such that:
AIC＝Nlnσ2+2p (12)
reaches a minimum value, where N is the data volume, p is the model order, σ2Is the variance value of the model built.
Further, the baseline value to be predicted at time n is expressed as a combination of baseline values converted from the measured strain at the previous p times as follows:
wherein the content of the first and second substances,
is the predicted value of x (n), i.e. the current value x (n) is predicted from x (n) past p values. In the formula aiAre the model parameters.
The predicted quadratic error is:
to minimize E, each coefficient aiShould satisfy E to aiHas a partial derivative of 0, i.e.:
in conjunction with equation (14), a predicted standard set of equations is obtained:
the formula (16) is an equation set containing p unknowns, and each coefficient a can be obtained by solving the equation set1,a2,a3,..,ap. The minimum mean square error can be obtained using equations (14) and (16):
an autocorrelation method is adopted for solving the p-element equation system of the formula (16). Introducing x (n) an autocorrelation function over the length of the array of baseline quantities converted from the measured strain as:
wherein N is the unit array length of the baseline quantity converted from the measured strain for model parameter identification, and N is more than or equal to 0 and less than or equal to N-1.
Then equation (16) can be written as:
written in the form of a matrix equation:
solving the model parameter a of the matrix equation by using a Levinson-Durbin recursion algorithm1,a2,a3,..,apThe end result is an error
And reaches the minimum.
And finally, dynamically solving the model parameters and dynamically outputting the predicted values. The number of baseline values obtained by actual measurement strain conversion for model parameter identification is set to be N, the length of a unit predicted value array is set to be N, the N value influences the rapidity of the whole model parameter identification, adjustment can be carried out according to programmed running time of the patent algorithm in practical application, and specific model recursion modes and dynamic prediction output are shown in a specific implementation mode 3.
The invention has the beneficial effects that:
(1) the invention relates to a flexible baseline dynamic prediction method based on a fiber bragg grating sensor and a wing mode, which comprises the steps of obtaining strain quantities of all sub-nodes of a wing by utilizing the fiber bragg grating sensor which is symmetrically adhered to the wing up and down, calculating structural displacement and baseline quantities among the sub-nodes by utilizing a mode superposition principle based on structural strain response, and predicting the change condition of the baseline quantities at the future moment by utilizing a dynamic model established based on real-time data of a flexible baseline. In the process of modeling by utilizing actually measured baseline data, parameters of the model are continuously updated by continuously updated actually measured data, and dynamic advanced prediction of the baseline data is realized by combining a recursion algorithm. The predicted flexible baseline data can be used for transmitting an alignment technology in real time to improve the pose measurement precision of the sub-nodes, and further improve the distributed multi-task load imaging precision.
(2) The flexible baseline quantity solving method based on the wing modes adopts a mode superposition method to obtain the deformation quantity only by means of the product calculation of the discrete strain quantity measured by the fiber grating sensor and the mode transfer matrix, and the mode transfer matrix is obtained by the wing model constructed according to the simulation of wing structure parameters and can be determined before the aircraft executes a flight task, so that the modal method has the advantages of reduced calculated quantity and shorter time consumption.
(3) The flexible baseline prediction algorithm based on the actual measurement baseline value recursion model utilizes the actual measurement baseline data to construct the dynamic model and predict the baseline quantity in advance at the future moment, can compensate the time delay effect caused by the conversion of the actual measurement strain quantity to the baseline quantity, is applied to the real-time transmission alignment process of the child nodes, and compared with the traditional statistical model or pure mechanical model based on a large amount of empirical data, the modeling depends on the real-time baseline data, the model parameters change along with the change in real time, and finally the dynamic prediction of the flexible baseline can be realized.
Drawings
FIG. 1 is a schematic diagram of a fiber grating sensor measuring deformation of an airfoil according to a fiber grating sensor and a flexible baseline dynamic prediction method of an airfoil modal;
FIG. 2 is a side view geometry of a wing deflection based on a fiber grating sensor and a method for flexible baseline dynamic prediction of wing mode;
FIG. 3 is a data processing flow chart of a method for dynamically predicting a flexible baseline based on a fiber grating sensor and a wing mode according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
As shown in fig. 3, a flexible baseline dynamic prediction method based on a fiber grating sensor and a wing mode includes the following steps:
1. and measuring strain of a measuring point when the wing is dynamically deformed according to the fiber bragg grating sensors symmetrically adhered to the measuring points on the upper surface and the lower surface of the wing.
In actual operation, the steps are as follows:
(1) allowing the wings to stand still for 5min in a straight state, and obtaining the wavelength of each measuring point of the fiber bragg grating as the initial wavelength reference;
(2) according to the basic principle of measuring strain of the fiber bragg grating sensor, the time-varying wavelength at the measuring point of the fiber bragg grating on the upper surface and the lower surface when the wing shakes is converted into the time-varying strain quantity at the measuring point of the upper surface and the lower surface, namely the time-varying strain quantity in the formula (1)
And
(3) method for determining axial strain of wing during flutter by using formula (2)
2. And solving a modal transfer matrix from wing strain to displacement according to wing modal information extracted by the wing simulation model, and solving displacement and flexible baseline quantity by using the modal transfer matrix and a measuring point strain quantity when the wing is dynamically deformed in real measurement.
In actual operation, the steps are as follows:
(1) simulating a simulation model conforming to the wing real object according to the structural data of the wing;
(2) the wing simulation model consists of a certain number of grids, the serial numbers corresponding to the coordinates of all grid points are obtained, and the grid point serial numbers corresponding to the measuring points are screened out;
(3) Forming a matrix by the screened serial numbers, extracting the first 9-order modal information of the grid points corresponding to each serial number, including axial strain, axial displacement and transverse displacement information, and forming an axial strain modal matrix [ psiu]Axial displacement mode matrix
And transverse displacement mode matrix
(4) Obtaining an axial transfer matrix DST according to equations (7) and (8)uAnd a transverse transfer matrix DSTw；
(5) Time-varying axial displacement data and transverse displacement data are solved according to the formula (9) by utilizing the FBGs actual measurement strain;
(6) the combination of equations (9-11) yields time-varying flexible baseline data.
3. Determining the order and the dynamic parameters of a flexible baseline model according to the wing mode and the measured flexible baseline quantity obtained by the fiber bragg grating sensor to obtain a dynamic model; and then carrying out advanced prediction estimation on the flexible baseline quantity according to the dynamic model.
In actual operation, the steps are as follows:
(1) and setting the length of the identification array of the unit parameter as N, the length of the prediction array as N and the model order as p in the array recursive prediction process. Obtaining the change trend of the AIC value about the model order by using a first group of unit parameter identification arrays obtained by actual measurement strain conversion according to a formula (12), and taking the model order corresponding to the AIC extreme point;
(2) taking X0Solving for initial measured strain quantity a base line quantity matrix with length N, namely X0＝[x1 x2 ... xN]Identifying model parameters according to the model parameter identification algorithm;
(3) selecting [ x ]N+1-p xN+2-p ... xN]Estimating a first baseline quantity predicted value
I.e. the measured baseline value sequenceThe estimated value listed at the N +1 th sampling point, and will be
Adding the original matrix to form a new matrix
Estimating a second predicted value
(4) By the recursion, the first group of base line quantity predicted value matrixes are obtained by solving for n times
And judging whether the number of the newly added measured data reaches n. If the number of the newly added data reaches n, X0Translating the sampling points backwards by N to obtain a second group of baseline quantity matrixes X with the length of N data converted from the measured strain quantity1＝[xn+1 xn+2 ... xn+N]；
(5) Repeating (2), (3) and (4) to obtain a second group of base line quantity predicted value matrixes
(6) By analogy, a dynamic flexible baseline model with model parameters continuously updated along with the updating of the actually measured flexible baseline data is obtained, and nT can be predicted in advancesFlexible baseline value (T) of length of timesThe demodulation time interval of the fiber bragg grating sensor) to compensate the time delay from the measured strain quantity to the flexible baseline quantity calculation in the real-time sub-node transmission alignment process.
The adjustable parameters of the dynamic model are data length for model parameter identification and single group predicted data length, and can be adjusted by combining the demodulation frequency and wing jitter frequency of the fiber bragg grating in practical application so as to achieve the prediction effect closer to an actual measurement value.
Claims (1)
1. A flexible baseline dynamic prediction method based on a fiber bragg grating sensor and a wing mode is characterized by comprising the following steps:
firstly, measuring the strain of the wing when the wing is dynamically deformed by using fiber bragg grating sensors symmetrically adhered to measuring points on the upper and lower surfaces of the wing;
step two, solving a modal transfer matrix of wing strain to displacement by using wing modal information extracted by the wing simulation model, and solving transverse and axial displacement and flexible baseline quantity by using the modal transfer matrix and the measuring point strain when the dynamic deformation of the wing is measured in the step one;
step three, determining the order and the dynamic parameters of the flexible baseline model according to the solved flexible baseline quantity to obtain a dynamic model; then, performing advanced prediction estimation on the flexible baseline quantity according to the dynamic model to realize prediction output of the flexible baseline under dynamic deformation;
the second step comprises the following steps:
(1) firstly, a wing simulation model is constructed by using simulation software under the condition that the structural parameters of the wing are known, and an axial strain modal matrix [ psi ] extracted from the wing simulation modelu]Axial displacement mode matrix
And transverse displacement mode matrix
Deriving an axial transfer matrix DSTuAnd a transverse transfer matrix DSTw：
(2) Then, taking the deformation state of the wing under the action of the self gravity as an initial state, and utilizing the strain measured by the fiber bragg grating sensor to calculate the axial displacement and the transverse displacement under the action of stress:
wherein the content of the first and second substances,
the strain values at N measuring points measured by the fiber bragg grating sensor are represented, and dy and dz are axial and transverse displacement of the stressed deformation of the wing relative to the initial gravity state respectively;
(3) finally, calculating and solving flexible baseline quantity by means of a coordinate conversion principle according to the axial displacement and the transverse displacement obtained by solving;
in the third step, the method comprises the following steps:
(1) firstly, selecting unit array length predicted by flexible baseline quantity according to fiber bragg grating strain demodulation frequency, and dividing a baseline quantity sequence converted from measured strain quantity into a plurality of baseline quantity unit arrays containing the same number of baseline values;
(2) determining the model order by using an AIC (advanced information center) criterion according to the baseline quantity unit array;
(3) identifying parameters of the flexible baseline model by using the baseline quantity unit number converted from the measured strain quantity to obtain a flexible baseline model;
(4) carrying out multiple recursion predictions on the baseline quantity by using a flexible baseline model to obtain a prediction array consisting of baseline prediction values;
(5) selecting a next group of baseline quantity unit arrays converted from the actual measurement strain quantity in the step (1) backwards, and repeating the steps (3) and (4), namely realizing a flexible baseline dynamic model with model parameters changing along with the actual measurement value;
(6) finally, the flexible baseline dynamic model realizes the prediction output of the flexible baseline under dynamic deformation;
in the step (3) of the third step, the method for identifying the flexible baseline model parameters comprises the following steps:
(1) first, the baseline values to be predicted at time n are expressed as a combination of baseline values converted from the measured strain at the previous p times as follows:
wherein the content of the first and second substances,
is the predicted value of x (n), and x (n) is the measured value at time n, i.e., the current value x (n) is predicted from the measured values at p past times x (n), where aiIs a model parameter;
(2) then, introducing x (n) an autocorrelation function over the length of the array of baseline quantities converted from the measured strain as:
wherein, N is the length of a baseline quantity unit array converted from the actual measurement strain for identifying the model parameters, N is more than or equal to 0 and less than or equal to N-1, and the autocorrelation function sequence and the model parameter sequence based on the array are written into the following matrix equation form:
(3) finally, solving the matrix equation by using a Levinson-Durbin recursion algorithm to obtain a model parameter a1,a2,a3,..,apThe end result being an error
To a minimum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010257888.2A CN111475940B (en) | 2020-04-03 | 2020-04-03 | Flexible baseline dynamic prediction method based on fiber bragg grating sensor and wing mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010257888.2A CN111475940B (en) | 2020-04-03 | 2020-04-03 | Flexible baseline dynamic prediction method based on fiber bragg grating sensor and wing mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111475940A CN111475940A (en) | 2020-07-31 |
| CN111475940B true CN111475940B (en) | 2022-07-12 |
Family
ID=71750507
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010257888.2A Expired - Fee Related CN111475940B (en) | 2020-04-03 | 2020-04-03 | Flexible baseline dynamic prediction method based on fiber bragg grating sensor and wing mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111475940B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112525191B (en) * | 2021-02-08 | 2021-06-08 | 北京航空航天大学 | Airborne distributed POS transfer alignment method based on relative strapdown calculation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8909040B1 (en) * | 2013-02-05 | 2014-12-09 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Method and apparatus of multiplexing and acquiring data from multiple optical fibers using a single data channel of an optical frequency-domain reflectometry (OFDR) system |
| CN107727097A (en) * | 2017-09-18 | 2018-02-23 | 北京航空航天大学 | Information fusion method and device based on airborne distributed location attitude measurement system |
| CN108398130A (en) * | 2018-02-22 | 2018-08-14 | 北京航空航天大学 | Flexural deformations measure the distributed POS Transfer Alignments modeling method and device of network |
| CN108413887A (en) * | 2018-02-22 | 2018-08-17 | 北京航空航天大学 | Fiber grating assists wing deformation measurement method, device and the platform of distribution POS |
| CN108801166A (en) * | 2018-05-29 | 2018-11-13 | 北京航空航天大学 | Fiber grating wing distortion measurement modeling based on cantilever beam theory and scaling method |
-
2020
- 2020-04-03 CN CN202010257888.2A patent/CN111475940B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8909040B1 (en) * | 2013-02-05 | 2014-12-09 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Method and apparatus of multiplexing and acquiring data from multiple optical fibers using a single data channel of an optical frequency-domain reflectometry (OFDR) system |
| CN107727097A (en) * | 2017-09-18 | 2018-02-23 | 北京航空航天大学 | Information fusion method and device based on airborne distributed location attitude measurement system |
| CN108398130A (en) * | 2018-02-22 | 2018-08-14 | 北京航空航天大学 | Flexural deformations measure the distributed POS Transfer Alignments modeling method and device of network |
| CN108413887A (en) * | 2018-02-22 | 2018-08-17 | 北京航空航天大学 | Fiber grating assists wing deformation measurement method, device and the platform of distribution POS |
| CN108801166A (en) * | 2018-05-29 | 2018-11-13 | 北京航空航天大学 | Fiber grating wing distortion measurement modeling based on cantilever beam theory and scaling method |
Non-Patent Citations (1)
| Title |
|---|
| 一种无封装FBG应变传感器标定方法;李慧鹏等;《半导体光电》;20181215(第06期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111475940A (en) | 2020-07-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108120451A (en) | Based on silicon micro accerometer temperature-compensation method, the system for improving PSO optimization neural networks | |
| CN108121856B (en) | Dynamic stability analysis method for full-flight-domain aircraft | |
| CN104048676B (en) | MEMS (Micro Electro Mechanical System) gyroscope random error compensating method based on improved particle filter | |
| CN111368466B (en) | Mechanical vibration prediction method based on frequency response function parameter correction | |
| CN111638034B (en) | Strain balance temperature gradient error compensation method and system based on deep learning | |
| CN110631792A (en) | Seismic hybrid test model updating method based on convolutional neural network | |
| CN111475940B (en) | Flexible baseline dynamic prediction method based on fiber bragg grating sensor and wing mode | |
| CN115687983A (en) | Bridge health state monitoring method and system and electronic equipment | |
| CN112696319A (en) | Wind turbine model-based control and estimation with accurate online models | |
| CN113947035A (en) | Data heaven-earth correlation method for transition of hypersonic velocity boundary layer | |
| CN103885867A (en) | Online evaluation method of performance of analog circuit | |
| CN116090191A (en) | Simulation method and system of offshore wind turbine under comprehensive airflow factors | |
| CN113722860B (en) | Transient thermodynamic state online evaluation method, device and medium based on reduced order model | |
| CN109446557B (en) | Random pneumatic elastic system stability analysis method based on probability density evolution | |
| CN107977730A (en) | A kind of wind measurement method of multisensor Data Fusion technology | |
| CN117035001A (en) | Conversion force sensor temperature compensation method based on Kriging interpolation | |
| CN116628854A (en) | Wing section aerodynamic characteristic prediction method, system, electronic equipment and storage medium | |
| CN114417681B (en) | Two-dimensional structure deformation monitoring method and device based on dynamic decision and neural network | |
| Gupta et al. | Dynamic programming approach to load estimation using optimal sensor placement and model reduction | |
| CN115114985A (en) | Sensor system distributed fusion method based on set theory | |
| CN106338254B (en) | The quick monitoring and forecasting systems of underground engineering construction and method based on 3D laser scanning | |
| CN109753018B (en) | Error compensation system and dynamic compensation method based on cloud intelligence | |
| CN113408040A (en) | Analog data correction method and system in civil engineering | |
| CN106225704B (en) | A kind of adaptive location choosing method for FBG structure detection | |
| CN108225205A (en) | A kind of barrel structure deformation calculation method measured based on grating strain and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220712 |