CN117610466A - Shell segment aerodynamic parameter identification method based on model prediction static programming algorithm - Google Patents
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Abstract
The invention belongs to the technical field of guided projectile pneumatic parameter identification, and particularly relates to a method for identifying projectile sectional pneumatic parameters based on a model prediction static programming algorithm. Compared with the prior art, the method is based on limited observation input of ballistic data, based on a Model Predictive Static Programming (MPSP) algorithm, the parameters to be identified are regarded as control amounts, aerodynamic parameters calculated by CFD or estimated by engineering are taken as initial values of the parameters to be identified, and the resistance coefficient and the lift coefficient are identified by taking external ballistic data such as position, speed and the like as observation. The identification method provided by the method has the characteristics of less input, high precision and quick calculation, and provides a new thought for the parameter identification of low-cost weapons such as shells, forced shells and the like.
Description
Technical Field
The invention belongs to the technical field of guided projectile pneumatic parameter identification, and particularly relates to a method for identifying projectile sectional pneumatic parameters based on a model prediction static programming algorithm.
Background
Common pneumatic parameter identification algorithms include differential solution engineering algorithms, least square methods, maximum likelihood estimation, kalman filtering and other traditional algorithms, neural networks, swarm algorithms and other intelligent optimization algorithms. The differential solving engineering algorithm obtains the acceleration information of the shell through differentiating the external trajectory data, and then solves and filters aerodynamic parameters according to a dynamics equation to obtain the identification values of the resistance coefficient and the lift coefficient. The Kalman filtering and intelligent optimization algorithm needs information such as projectile attitude angle, angular velocity and acceleration with certain precision requirement as observation when a model is built.
The differential solving engineering algorithm is simple and convenient, but the identification error (especially when the attack angle exists) is larger because the observation data is directly differentiated. The Kalman filtering and intelligent optimization algorithm needs more observation information and has certain requirements on precision. The high-precision attitude measuring device cannot be used on the shell due to the specificity of high overload, low cost and the like in the field of shot blasting, other high-precision observation information except for ballistic data cannot be provided, and the application of the device in the field of pneumatic parameter identification of the shell is limited.
Disclosure of Invention
First, the technical problem to be solved
The invention aims to solve the technical problems that: in recent years, with the continuous development of military science and technology, the conventional gun firing range has gradually failed to meet the combat demands. The remote development of shells is imperative. As one of the main means of the range extension of the shell, the pneumatic optimization design is particularly important. In the pneumatic optimization iterations, the pneumatic parameters are often calculated using Computational Fluid Dynamics (CFD). However, due to complex shapes such as a centering part, a wing groove, a belt and the like on the surface of the shell, errors exist in the CFD calculation result, and particularly when the flight Mach number of the shell spans the sub-span overtime, the calculation errors are larger. Therefore, the theoretical aerodynamic calculation result needs to be corrected by combining flight test data. The invention aims to extract the concerned aerodynamic parameters such as lift coefficient, drag coefficient and the like according to the external trajectory data.
(II) technical scheme
In order to solve the technical problems, the invention provides a shell segment aerodynamic parameter identification method based on a model prediction static programming algorithm, which comprises the following steps:
step 1: establishing an identification model;
step 2: pneumatic parameter identification based on a model prediction static programming algorithm;
step 3: and carrying out post-processing on the identification parameters to obtain a final identification result.
Wherein, the step 1 comprises the following steps:
step 11: giving an identification input;
step 12: establishing an identification dynamics model;
step 13: and (5) normalization treatment.
Wherein, in the step 11, an identification input is given; confirm four kinds of information as the identification input: (1) transmitting information including shot and program loading time; (2) outer ballistic information of projectile body position velocity; (3) projectile mass, projectile information of theoretical aerodynamic parameters; (4) actual weather information.
In the step 12, an identification dynamics model is established; only the mass center dynamics model of the projectile body is established because only the external trajectory data are provided; in addition, the shell shape is designed in an axisymmetric way, and the lift coefficient is equal to the lateral force coefficient, so that the identification model can be simplified into an elastomer centroid dynamics model in the following pitching plane:
in the formula (1), V, theta, x, y, q, S, c x ,c y M, g are respectively the velocity of the shell relative to the ground, the ballistic inclination angle, the x-direction position, the y-direction position, the actual dynamic pressure, the reference area, the resistance coefficient, the lift coefficient, the mass of the shell and the gravity acceleration,respectively representing missile acceleration, trajectory dip angle change rate, x-direction speed and y-direction speed;
step 13: normalizing; because of the difference in magnitude of the system variables, normalization processing is required to be carried out on the system variables before algorithm design is carried out; the treatment process is as follows:
in the formula (2), the subscript "min" represents the minimum value of the physical quantity in the identification step length, the subscript "max" represents the maximum value of the physical quantity in the identification step length, and the superscript "—" represents the quantity of the physical quantity after normalization treatment;
substituting the normalized variable in the formula (2) into the right side of the equal sign in the formula (1) to obtain a normalized longitudinal centroid motion model in the identification step p:
in the formula (3), the amino acid sequence of the compound,representing state variables constructed from normalized velocity, ballistic tilt angle, x-position, y-position; />And representing the normalized parameters to be identified.
In the step 2, pneumatic parameter identification of a static programming algorithm is predicted based on a model; the general principle of the algorithm is shown in figure 1. The method specifically comprises the following steps:
step 21: setting an identification step length;
step 22: setting an identification parameter initial value;
step 23: the method comprises the steps of taking theoretical aerodynamic parameters, external trajectory data and meteorological and ballistic parameters as algorithm inputs, determining initial values of parameters to be identified and initial values of ballistic states in each identification step length, carrying out numerical integration on a dynamics model, namely predicting, and generating initial sequences of each quantity to be identified and each ballistic state quantity;
step 24: calculating the terminal error of each identification step pNamely, the state variable X in the formula (3) p The absolute value of the difference between the predicted value and the measured value of the terminal moment in the identification step length is specifically:
in the formula (4), the amino acid sequence of the compound,indicating the terminal time t f Predicted state quantity of->Indicating the terminal time t f Is a true value; if the terminal error is->Exit if the accuracy is less than the specified accuracyThe algorithm carries out inverse normalization processing on the parameters to be identified obtained at the moment to obtain a parameter sequence to be identified; otherwise, go to step 25;
step 25: calculating matrix sensitive matrix in each identification step pThe calculation formula is as follows;
in the formula (5), the amino acid sequence of the compound,the Jacobian matrix of the normalized state quantity and the quantity to be identified is shown as a formula (5); h is the integral step length; i represents an identity matrix, and subscripts represent the dimensions of the matrix; />The number of nodes in the step length p is identified;
step 26: calculating the variation of the parameters to be identified in the identification step pAnd updating the parameter to be identified +.>The calculation formula is as follows:
in the formula (6), the amino acid sequence of the compound,is a weight matrix. />The parameters to be identified in the previous iteration period are obtained;
step 27: order theSubstituting the step (3) to predict the state, and continuing to perform iterative calculation.
In the step 21, the step length is set; because the flight Mach number span after the guided projectile/forced projectile is launched is larger, and the pneumatic appearance has obvious difference before and after rudder opening, attention is paid to the situation that parameters to be identified change stably in one step as much as possible when the step length is set, and the abrupt change is avoided.
In the step 22, an initial value of the identification parameter is set; providing c to be identified x ,c y Is provided by CFD numerical calculation results or engineering estimation results; and carrying out two-dimensional interpolation by using aerodynamic parameters at the time of actual flight Mach number and zero attack angle as an initial value.
In the step 3, if the recognition error in the recognition interval is dispersed greatly, the recognition parameters in each recognition step length are averaged to obtain the final recognition result at the middle time of the interval.
The method takes the parameter to be identified as a control quantity, takes the aerodynamic parameter calculated by CFD or estimated by engineering as an initial value of the parameter to be identified, and identifies the resistance coefficient and the lift coefficient by taking the external trajectory data such as position, speed and the like as observation.
The method has the characteristics of less input, high precision and quick calculation, and provides a new idea for the parameter identification of low-cost weapons of shells and forced shells.
(III) beneficial effects
Compared with the prior art, the method is based on limited observation input of ballistic data, based on a Model Predictive Static Programming (MPSP) algorithm, the parameters to be identified are regarded as control amounts, aerodynamic parameters calculated by CFD or estimated by engineering are taken as initial values of the parameters to be identified, and the resistance coefficient and the lift coefficient are identified by taking external ballistic data such as position, speed and the like as observation. The identification method provided by the method has the characteristics of less input, high precision and quick calculation, and provides a new thought for the parameter identification of low-cost weapons such as shells, forced shells and the like.
Drawings
Fig. 1 is a flow chart of the algorithm principle.
Fig. 2a and 2b are the results of identifying the drag coefficient and the lift coefficient.
Fig. 3a and 3b show the identification error and identification step size, the number of iterations per identification step size.
Detailed Description
To make the objects, contents and advantages of the present invention more apparent, the following detailed description of the present invention will be given with reference to the accompanying drawings and examples.
In order to solve the technical problems, the invention provides a shell segment aerodynamic parameter identification method based on a model prediction static programming algorithm, which comprises the following steps:
step 1: establishing an identification model;
step 2: pneumatic parameter identification based on a model prediction static programming algorithm;
step 3: and carrying out post-processing on the identification parameters to obtain a final identification result.
Wherein, the step 1 comprises the following steps:
step 11: giving an identification input;
step 12: establishing an identification dynamics model;
step 13: and (5) normalization treatment.
Wherein, in the step 11, an identification input is given; confirm four kinds of information as the identification input: (1) transmitting information including shot and program loading time; (2) outer ballistic information of projectile body position velocity; (3) projectile mass, projectile information of theoretical aerodynamic parameters; (4) actual weather information.
In the step 12, an identification dynamics model is established; only the mass center dynamics model of the projectile body is established because only the external trajectory data are provided; in addition, the shell shape is designed in an axisymmetric way, and the lift coefficient is equal to the lateral force coefficient, so that the identification model can be simplified into an elastomer centroid dynamics model in the following pitching plane:
in the formula (1), V, theta, x, y, q, S, c x ,c y M, g are respectively the velocity of the shell relative to the ground, the ballistic inclination angle, the x-direction position, the y-direction position, the actual dynamic pressure, the reference area, the resistance coefficient, the lift coefficient, the mass of the shell and the gravity acceleration,respectively representing missile acceleration, trajectory dip angle change rate, x-direction speed and y-direction speed;
step 13: normalizing; because of the difference in magnitude of the system variables, normalization processing is required to be carried out on the system variables before algorithm design is carried out; the treatment process is as follows:
in the formula (2), the subscript "min" represents the minimum value of the physical quantity in the identification step length, the subscript "max" represents the maximum value of the physical quantity in the identification step length, and the superscript "—" represents the quantity of the physical quantity after normalization treatment;
substituting the normalized variable in the formula (2) into the right side of the equal sign in the formula (1) to obtain a normalized longitudinal centroid motion model in the identification step p:
in the formula (3), the amino acid sequence of the compound,representing state variables constructed from normalized velocity, ballistic tilt angle, x-position, y-position; />Representing normalized waitAnd identifying parameters.
In the step 2, pneumatic parameter identification of a static programming algorithm is predicted based on a model; the general principle of the algorithm is shown in figure 1. The method specifically comprises the following steps:
step 21: setting an identification step length;
step 22: setting an identification parameter initial value;
step 23: the method comprises the steps of taking theoretical aerodynamic parameters, external trajectory data and meteorological and ballistic parameters as algorithm inputs, determining initial values of parameters to be identified and initial values of ballistic states in each identification step length, carrying out numerical integration on a dynamics model, namely predicting, and generating initial sequences of each quantity to be identified and each ballistic state quantity;
step 24: calculating the terminal error of each identification step pNamely, the state variable X in the formula (3) p The absolute value of the difference between the predicted value and the measured value of the terminal moment in the identification step length is specifically:
in the formula (4), the amino acid sequence of the compound,indicating the terminal time t f Predicted state quantity of->Indicating the terminal time t f Is a true value; if the terminal error is->If the accuracy is smaller than the specified accuracy, the algorithm is exited, and the parameter to be identified obtained at the moment is subjected to inverse normalization treatment to obtain a parameter sequence to be identified; otherwise, go to step 25;
step 25: calculating matrix sensitive matrix in each identification step pThe calculation formula is as follows;
in the formula (5), the amino acid sequence of the compound,the Jacobian matrix of the normalized state quantity and the quantity to be identified is shown as a formula (5); h is the integral step length; i represents an identity matrix, and subscripts represent the dimensions of the matrix; />The number of nodes in the step length p is identified;
step 26: calculating the variation of the parameters to be identified in the identification step pAnd updating the parameter to be identified +.>The calculation formula is as follows:
in the formula (6), the amino acid sequence of the compound,is a weight matrix. />The parameters to be identified in the previous iteration period are obtained;
step 27: order theSubstituting the step (3) to predict the state, and continuing to perform iterative calculation.
In the step 21, the step length is set; because the flight Mach number span after the guided projectile/forced projectile is launched is larger, and the pneumatic appearance has obvious difference before and after rudder opening, attention is paid to the situation that parameters to be identified change stably in one step as much as possible when the step length is set, and the abrupt change is avoided.
In the step 22, an initial value of the identification parameter is set; providing c to be identified x ,c y Is provided by CFD numerical calculation results or engineering estimation results; and carrying out two-dimensional interpolation by using aerodynamic parameters at the time of actual flight Mach number and zero attack angle as an initial value.
In the step 3, if the recognition error in the recognition interval is dispersed greatly, the recognition parameters in each recognition step length are averaged to obtain the final recognition result at the middle time of the interval.
The method takes the parameter to be identified as a control quantity, takes the aerodynamic parameter calculated by CFD or estimated by engineering as an initial value of the parameter to be identified, and identifies the resistance coefficient and the lift coefficient by taking the external trajectory data such as position, speed and the like as observation.
The method has the characteristics of less input, high precision and quick calculation, and provides a new idea for the parameter identification of low-cost weapons of shells and forced shells.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (10)
1. The method for identifying the aerodynamic parameters of the shell segments based on the model prediction static programming algorithm is characterized by comprising the following steps:
step 1: establishing an identification model;
step 2: pneumatic parameter identification based on a model prediction static programming algorithm;
step 3: and carrying out post-processing on the identification parameters to obtain a final identification result.
2. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm as claimed in claim 1, wherein said step 1 comprises:
step 11: giving an identification input;
step 12: establishing an identification dynamics model;
step 13: and (5) normalization treatment.
3. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm according to claim 2, wherein in step 11, an identification input is given; confirm four kinds of information as the identification input: (1) transmitting information including shot and program loading time; (2) outer ballistic information of projectile body position velocity; (3) projectile mass, projectile information of theoretical aerodynamic parameters; (4) actual weather information.
4. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm according to claim 3, wherein in step 12, an identification dynamics model is established; only the mass center dynamics model of the projectile body is established because only the external trajectory data are provided; in addition, the shell shape is designed in an axisymmetric way, and the lift coefficient is equal to the lateral force coefficient, so that the identification model can be simplified into an elastomer centroid dynamics model in the following pitching plane:
in the formula (1), V, theta, x, y, q, S, c x ,c y M, g are respectively the velocity of the shell relative to the ground, the ballistic inclination angle, the x-direction position, the y-direction position, the actual dynamic pressure, the reference area, the resistance coefficient, the lift coefficient, the mass of the shell and the gravity acceleration,respectively represent guidesBullet acceleration, ballistic dip rate of change, x-direction velocity, y-direction velocity;
step 13: normalizing; because of the difference in magnitude of the system variables, normalization processing is required to be carried out on the system variables before algorithm design is carried out; the treatment process is as follows:
in the formula (2), the subscript "min" represents the minimum value of the physical quantity in the identification step length, the subscript "max" represents the maximum value of the physical quantity in the identification step length, and the superscript "—" represents the quantity of the physical quantity after normalization treatment;
substituting the normalized variable in the formula (2) into the right side of the equal sign in the formula (1) to obtain a normalized longitudinal centroid motion model in the identification step p:
in the formula (3), the amino acid sequence of the compound,representing state variables constructed from normalized velocity, ballistic tilt angle, x-position, y-position; />And representing the normalized parameters to be identified.
5. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm according to claim 4, wherein in the step 2, the aerodynamic parameters of the static programming algorithm are identified based on the model predictive static programming algorithm; the method specifically comprises the following steps:
step 21: setting an identification step length;
step 22: setting an identification parameter initial value;
step 23: the method comprises the steps of taking theoretical aerodynamic parameters, external trajectory data and meteorological and ballistic parameters as algorithm inputs, determining initial values of parameters to be identified and initial values of ballistic states in each identification step length, carrying out numerical integration on a dynamics model, namely predicting, and generating initial sequences of each quantity to be identified and each ballistic state quantity;
step 24: calculating the terminal error of each identification step pNamely, the state variable X in the formula (3) p The absolute value of the difference between the predicted value and the measured value of the terminal moment in the identification step length is specifically:
in the formula (4), the amino acid sequence of the compound,indicating the terminal time t f Predicted state quantity of->Indicating the terminal time t f Is a measured state quantity of (a); if the terminal error is->If the accuracy is smaller than the specified accuracy, the algorithm is exited, and the parameter to be identified obtained at the moment is subjected to inverse normalization treatment to obtain a parameter sequence to be identified; otherwise, go to step 25;
step 25: calculating matrix sensitive matrix in each identification step pThe calculation formula is as follows;
in the formula (5), the amino acid sequence of the compound,the Jacobian matrix of the normalized state quantity and the quantity to be identified is shown as a formula (5); h is the integral step length; i represents an identity matrix, and subscripts represent the dimensions of the matrix; />The number of nodes in the step length p is identified;
step 26: calculating the variation of the parameters to be identified in the identification step pAnd updating the parameter to be identified +.>The calculation formula is as follows:
in the formula (6), the amino acid sequence of the compound,is a weight matrix. />The parameters to be identified in the previous iteration period are obtained;
step 27: order theSubstituting the step (3) to predict the state, and continuing to perform iterative calculation.
6. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm according to claim 5, wherein in step 21, step length is identified; because the flight Mach number span after the guided projectile/forced projectile is launched is larger, and the pneumatic appearance has obvious difference before and after rudder opening, attention is paid to the situation that parameters to be identified change stably in one step as much as possible when the step length is set, and the abrupt change is avoided.
7. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm as defined in claim 6, wherein in step 22, initial values of identification parameters are set; providing c to be identified x ,c y Is provided by CFD numerical calculation results or engineering estimation results; and carrying out two-dimensional interpolation by using aerodynamic parameters at the time of actual flight Mach number and zero attack angle as an initial value.
8. The method for identifying aerodynamic parameters of a shell segment based on a model predictive static programming algorithm as defined in claim 7, wherein in the step 3, if the identification error spread in the identification interval is large, the identification parameters in each identification step are averaged to obtain the final identification result at the middle time of the interval.
9. The method for identifying the shell segment aerodynamic parameters based on the model prediction static programming algorithm according to claim 8, wherein the method uses the parameters to be identified as control amounts, takes the aerodynamic parameters calculated by CFD or estimated by engineering as initial values of the parameters to be identified, and identifies the resistance coefficient and the lift coefficient by taking the external trajectory data such as position, speed and the like as observation.
10. The method for identifying the pneumatic parameters of the shell segments based on the model predictive static programming algorithm as set forth in claim 8, wherein the method has the characteristics of less input, high precision and quick calculation, and provides a new idea for identifying the parameters of low-cost weapons of shells and forced shells.
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