CN107066640B - Variable configuration aircraft model iterative design method based on protection mapping - Google Patents

Variable configuration aircraft model iterative design method based on protection mapping Download PDF

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CN107066640B
CN107066640B CN201611026986.5A CN201611026986A CN107066640B CN 107066640 B CN107066640 B CN 107066640B CN 201611026986 A CN201611026986 A CN 201611026986A CN 107066640 B CN107066640 B CN 107066640B
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刘燕斌
张勇
陈柏屹
沈海东
陆宇平
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a variable configuration aircraft model iterative design method based on protection mapping, which mainly comprises the steps of describing an aircraft reference configuration, analyzing a matching relation between configuration change and flight conditions, finding an internal relation between the aircraft reference configuration and model dynamic characteristics, obtaining a simplified design model of an aircraft, analyzing model performance under the reference configuration and obtaining optimal model parameters, and realizing the optimal matching of the aircraft configuration and the operation conditions. The method is favorable for solving the problem of model parameter setting in the design of the variable configuration aircraft, under the complex flight condition, the feasible model parameters are obtained in a self-adaptive manner based on the protection mapping theory according to the expected open-loop and closed-loop performance requirements, the designed model is covered to the whole motion area through parameter switching, and meanwhile, the stability of the variable configuration aircraft can also be ensured.

Description

Variable configuration aircraft model iterative design method based on protection mapping
Technical Field
The invention relates to an aircraft model design method, in particular to a variable configuration aircraft model design method based on protection mapping, which applies a protection mapping theory to design a self-adaptive setting strategy of variable configuration aircraft model parameters to complete iterative design of a variable configuration aircraft model.
Background
The research of the variable configuration aircraft relates to a plurality of disciplines, is a collection of a plurality of leading-edge technologies, has strong prospective, strategic and initiative, and has great influence on future military development strategy, space technology, weapon system construction and even whole scientific and technical progress. The morphing aircraft flies faster, farther, and requires less sophisticated infrastructure than conventional aircraft payloads, and is prepared for re-flight.
The configuration changing technology can ensure that the aircraft has better flight efficiency in a large flight range, can improve flight performance, broaden flight envelope, replace the function of a conventional control surface and reduce the influence of resistance and elastic vibration. The research of the morphing aircraft needs to adopt a brand-new modeling theory, a pneumatic principle and a control method, because the research of the intelligent morphing technology relates to a plurality of fields and has the characteristic of multidisciplinary synthesis, the research of the morphing aircraft needs to integrate the content of the multidisciplinary into consideration, and particularly needs to integrate the model analysis and the iterative design, namely, the integrated comprehensive design concept is integrated in the research of the morphing aircraft.
In order to analyze the complex model characteristics of the deformable aircraft and ensure that the model still has good quality characteristics in the deformation process of the aircraft, the invention provides a model iterative design method based on a protection mapping theory. The method can obtain the optimal model parameters in the deformation process of the aircraft according to the given stability performance indexes, and the constructed model can cover the whole deformation process and the flight area through self-adaptive switching and iterative design, so that the actual application requirements of the variable configuration aircraft are met.
Disclosure of Invention
Technical problem
The invention provides a design method of a variable configuration aircraft model, which utilizes a protection mapping theory to construct constraint relations between model parameters and expected performance indexes under different flight conditions and geometric configurations, and realizes self-adaptive setting of the model parameters through iteration to ensure the overall stability of the variable configuration aircraft.
Technical scheme
In order to solve the above technical problem, the method of the present invention comprises the steps of:
the method comprises the following steps: the aircraft reference configuration is described in a parameterization mode, and the pressure P borne by a single surface element is obtained by adopting an engineering estimation theoryiThe aerodynamic forces and moments of the aircraft can be obtained by superposing the forces and moments on all surface elements
Figure GDA0003082771200000021
My＝∑Pi(dxinzi-dzinxi)Span,i (2)
Wherein d isi＝dxiib+dyijb+dzikbIs a distance vector, nxi、nyiAnd nziAre respectively the components of the vector of the external normal direction of a single surface element, so that the relationship between the aerodynamic force of the aircraft and the lift L, the resistance D and the attack angle alpha can be obtained
Figure GDA0003082771200000022
Further applying momentum theorem to estimate the thrust generated by the engine as FTAfter estimating the forces and moments, the following aircraft dynamics model is constructed:
Figure GDA0003082771200000023
wherein L is lift, D is drag, FTG is the thrust, G is the gravity, V is the velocity, alpha is the angle of attack, theta is the pitch angle, q is the pitch angle velocity, h is the height, m isvFor mass, MyFor pitching moment, IyIn order to be the moment of inertia,
Figure GDA0003082771200000031
in order to achieve a high degree of change,
Figure GDA0003082771200000032
in order to be able to accelerate the vehicle,
Figure GDA0003082771200000033
the rate of change of the angle of attack,
Figure GDA0003082771200000039
for the pitch angle rate to be,
Figure GDA0003082771200000035
is pitch angular acceleration;
step two: aiming at the aircraft dynamics model constructed in the first step, a polynomial fitting mode is adopted to obtain a proxy model of force and moment, deformation quantities are fused into the proxy model, and the internal characteristics of the dynamic characteristic change of the model are found by analyzing the matching relation between the configuration change and the flight condition;
the obtained aircraft force and moment surrogate model is expressed as a flight state vector X, and the nonlinear relation between a control input vector U and a configuration change parameter vector P is as follows:
Figure GDA0003082771200000036
wherein Q is dynamic pressure, kMIs the pitching moment coefficient; cL、CD、CM、CTRespectively, a lift coefficient, a drag coefficient, a pitching moment coefficient and a thrust coefficient, which are functions of a flight state vector X, a control input vector U and a configuration change parameter vector P;
further adopting a polynomial fitting mode to obtain a proxy model of force and moment, wherein the concrete process can be divided into four sub-steps of sample point design, model structure selection, model parameter identification and proxy model verification, and the obtained proxy model is in the form of
Figure GDA0003082771200000037
Where Ma is Mach number, betaL0Is a lift polynomial fitting coefficient, beta, related to XL1Is a lift polynomial fitting coefficient, beta, related to UD0As a coefficient of fitting to a resistance polynomial, betaD1Is a resistance polynomial fitting coefficient, beta, related to XD2Is a resistance polynomial fitting coefficient, beta, related to XUD3Is a resistance associated with UForce polynomial fitting coefficient, betaD4Is prepared by reacting with
Figure GDA0003082771200000038
Associated resistance polynomial fitting coefficient, betaM0Is a polynomial fitting coefficient of the pitching moment, betaM1For X-dependent pitching moment polynomial fitting coefficients, betaM2For the pitch moment polynomial fitting coefficient, beta, related to XUM3Is a polynomial fitting coefficient, beta, of the pitching moment related to UM4Is a resistance polynomial fitting coefficient, beta, related to MaT0Is a thrust polynomial fitting coefficient, betaT1Is a thrust polynomial fitting coefficient, beta, related to XT2Is a thrust polynomial fitting coefficient, beta, related to XUT3Fitting coefficients to the thrust polynomial associated with U, the coefficients being a function of the configuration change parameter vector P;
the deformation quantity is fused into the proxy model by the formula (6), the nonlinear model is subjected to approximate linearization processing, and a small-disturbance linearization equation is obtained
ΔX＝A(P,X)·ΔX+B(P,X)·ΔU (7)
A (P, X) and B (P, X) are states and input matrixes of the linear model, and the matching relation between configuration change and flight states can be analyzed by analyzing the pole-zero characteristics of the model, so that the inherent relation between the configuration change and the dynamic characteristics of the model is found;
step three: estimating a model performance boundary meeting expected indexes by applying a protection mapping theory aiming at a given configuration, finding out an overlapped part of model performances of two adjacent flight areas, obtaining an aircraft design model covering a motion area by adaptively changing operation conditions, and evaluating the model to obtain the model performance under the given configuration;
let Ω be the desired model performance index, vΩIs corresponding protection mapping, A (P, X) in the formula (7) is a polynomial matrix represented by a flight state vector X and a configuration change parameter vector P, the boundary of a model performance index omega is determined by a protection mapping theory and is substituted into A (P, X), and the protection mapping v can be knownΩ[A(P,X)]Is a multivariate polynomial; however, the device is not suitable for use in a kitchenThen finding the flight region associated with a given aircraft configuration P, making it in X ∈ [ X ]min,Xmax]The stability is kept in the range, and the main process is as follows:
(1) initialization: let m equal 1, Xm＝XminDesigning an initial aircraft configuration PmSuch that A (P)m,Xm) The characteristic values of all the parameters are positioned in a model performance index omega;
(2) determining a maximum stability interval: when P is equal to PmWhen, vΩ(A (P, X)) -0 is an equation relating to X only, all roots are found, and taken to be less than XmMaximum root ofX mAnd is greater than XmMinimum root of
Figure GDA0003082771200000041
A(X,Pm) Has a maximum stability interval of
Figure GDA0003082771200000042
(3) And (4) termination judgment: if it is not
Figure GDA0003082771200000043
The stable interval covers the whole parameter variation range, and the calculation is finished; otherwise make
Figure GDA0003082771200000051
Entering the next step;
(4) search for new aircraft configuration parameters Pm+1Assuming that the aircraft configuration parameter vector is NPAnd (3) dimension, starting from the first dimension, searching each dimension element one by one, and the specific process is as follows:
firstly, initializing: specifying search end precision epsilon and maximum number of searches omaxThe number of searches o is set to 1, Po＝PmSetting the current search dimension R to be 1;
② mixing Po+1All elements after the R-th dimension are fixed as PoCorresponding element value, i.e. Po+1(R+1,…,NP)＝Po(R+1,…,NP)；
Computing P when A is stableo+1Value of (R)A range of v at upper and lower boundaries of the rangeΩ[A(Xm,Po+1)]0 is greater than PoMinimal root of (R)
Figure GDA0003082771200000052
And is less than PoMaximum root of (R)P o+1,R；
④Po+1(R) is taken as the midpoint of the stability range
Figure GDA0003082771200000053
Judging the search completion for the second time: if R ≧ NPEnding the search for the second time and entering the sixth step; otherwise search for Po+1The next one-dimensional element, namely R is made to be R +1, and the second step is returned;
sixthly, judging the end of the search: if Po+1-Po||≤ε||1+PoI or o > omaxAfter the search is finished, entering the step (5); otherwise, if Po+1-Po||＞ε||1+PoAnd o is less than or equal to omaxMaking o equal to o +1 and R equal to 1, returning to the third step, and executing the processes of the third step and the sixth step again until the P is equal too+1-Po||≤ε||1+PoI or o > omaxEntering the step (5);
(5) let Pm+1＝Po+1If m is less than or equal to the given maximum value, returning to the step (2); once m is larger than a given maximum value, the iteration is ended, and the required configuration parameters are obtained;
giving initial aircraft configuration parameters and flight conditions, and obtaining a series of aircraft configuration parameters and corresponding stable intervals through the steps (1) to (5), wherein the aircraft configuration parameters can be switched according to scheduling variables in actual flight, and an analytic expression of the aircraft configuration parameters changing along with the scheduling variables can also be obtained through curve fitting;
step four: constructing an optimized performance index according to the task requirement of the aircraft, converting the comprehensive design problem of the geometric configuration and the stability into a coordination distribution problem of the configuration and the motion condition of the aircraft, and performing iterative optimization on the performance index by adopting an optimization algorithm based on protection mapping to obtain an optimal model parameter so as to realize the optimal matching of the configuration and the operation condition of the aircraft;
designing a performance index integrating configuration change and flight condition matching relationship
Figure GDA0003082771200000061
Wherein J is an objective function, G is a constraint, and Z (P, X) is an aircraft model dependent on configuration parameters and flight conditions;
giving detailed steps of the optimization strategy:
step 1: giving an initial value and initializing;
step 2: and (3) performing feasible solution on the approximate optimization problem, and obtaining feasible solution of the approximate optimization problem by using an optimization algorithm based on protection mapping, namely searching feasible solution P*So that
Figure GDA0003082771200000062
Step 3: determining a feasible solution P*If the feasible solution does not exist, executing Step 5;
step 4: will be feasible solution P*Bring into the design optimization problem, if satisfy the constraint, return to P*For designing an optimization feasible solution, obtaining an optimal matching value of the aircraft configuration and the operation condition, ending the search process, and if the constraint order P is not metc＝P*Continuing to execute the next step;
step 5: changing flight conditions, fine-dividing a flight area, and executing Step 3;
through the iterative optimization process, the optimal model parameters of the variable configuration aircraft can be obtained, and the optimal matching of the aircraft configuration and the operating conditions is realized.
In the method, in order to realize the self-adaptive setting of the model parameters of the variable configuration aircraft, a new model iterative design method is provided by adopting a protection mapping theory, firstly, a feasible region meeting the performance quality is constructed according to the performance indexes of an open loop and a closed loop of the model, then, the protection mapping theory is applied to obtain the model parameters of the variable configuration aircraft meeting the performance index requirements, and the model can cover the whole flight region by switching the parameters in an overlapped region, so that the self-adaptive adjustment of the model parameters is realized. More importantly, the change of flight conditions can be considered in the process of adjusting the model parameters of the variable configuration aircraft, and the change of the configuration of the aircraft can be integrated, so that the complex task requirement of the variable configuration aircraft is met.
In the method of the present invention, the steps and sub-steps thereof can be summarized as follows:
firstly, describing an aircraft reference configuration, extracting a parameterized characteristic shape, then acquiring the force and moment of the aircraft by adopting an engineering estimation theory, constructing an aircraft dynamic model by combining an imaginary work principle and a Michelsky equation, and quantifying a deformation action into a force increment form to be fused into a model equation.
Secondly, aiming at the constructed aircraft dynamics model, a proxy model of force and moment is obtained by adopting a polynomial fitting mode, deformation is fused into the proxy model, and the internal relation between the configuration change and the dynamic characteristics of the model is found by analyzing the matching relation between the configuration change and the flight conditions.
And thirdly, carrying out compromise analysis on the balance state and the dynamic characteristic by adopting a protection mapping theory aiming at the aircraft dynamic model, considering the influence of the aircraft operation condition and configuration change on the static balance characteristic and the dynamic characteristic, discussing the influence of the aircraft operation condition and configuration change on the flight stability, analyzing the internal relation between the aircraft operation condition and the dynamic characteristic of the model, and obtaining a simplified design model of the aircraft.
And then, a closed loop stable boundary meeting the performance index is estimated by applying a protection mapping theory aiming at the reference configuration, an overlapping part of the stable boundaries of two adjacent regions is found out, an aircraft design model covering a moving region is obtained by adaptively switching model parameters, the model is evaluated, and the model performance under the reference configuration is analyzed.
And finally, constructing an optimized performance index according to the task requirement of the aircraft, converting the comprehensive design problem of the geometric configuration and the stability into a coordination distribution problem of the configuration and the motion condition of the aircraft, and performing iterative optimization on the performance index by adopting an optimization algorithm based on protection mapping to obtain an optimal model parameter so as to realize the optimal matching of the configuration and the operation condition of the aircraft.
Advantageous effects
The method is beneficial to solving the problem of model parameter setting in the design of the variable configuration aircraft, and under the complex flight condition, the feasible model parameters are obtained in a self-adaptive manner based on the protection mapping theory according to the expected open-loop and closed-loop performance requirements, and the designed model is covered to the whole motion area through parameter switching, and meanwhile, the stability of the variable configuration aircraft can also be ensured. More importantly, the iterative design process of the model can complete the performance analysis of the open-loop and closed-loop systems of the aircraft, rapidly obtain a plurality of groups of model parameters meeting expected performance indexes, and can adaptively cover all flight ranges, thereby providing a good design tool for the practical application of the aircraft.
Drawings
FIG. 1 is a general design flow diagram of the present invention;
FIG. 2 is a flow chart of a dynamic model construction of a morphing aircraft;
FIG. 3 is a flowchart of an iterative design of a morphing aircraft;
fig. 4 is a detailed step diagram of the optimization strategy.
Detailed Description
The technical solution of the present invention will be specifically described below with reference to the accompanying drawings.
As shown in fig. 1, fig. 2 and fig. 3, the present embodiment is a method for iteratively designing a deformed aircraft model based on protection mapping, which includes the following steps:
the method comprises the following steps: the aircraft reference configuration is described in a parameterization mode, and the pressure P borne by a single surface element is obtained by adopting an engineering estimation theoryiThe aerodynamic forces and moments of the aircraft can be obtained by superposing the forces and moments on all surface elements
Figure GDA0003082771200000081
My＝∑Pi(dxinzi-dzinxi)Span,i (10)
Wherein d isi＝dxiib+dyijb+dzikbIs a distance vector, nxi、nyiAnd nziAre respectively the components of the vector of the external normal direction of a single surface element, so that the relationship between the aerodynamic force of the aircraft and the lift L, the resistance D and the attack angle alpha can be obtained
Figure GDA0003082771200000082
Further applying momentum theorem to estimate the thrust generated by the engine as FTAfter estimating the forces and moments, the following aircraft dynamics model is constructed:
Figure GDA0003082771200000083
wherein L is lift, D is drag, FTG is the thrust, G is the gravity, V is the velocity, alpha is the angle of attack, theta is the pitch angle, q is the pitch angle velocity, h is the height, m isvFor mass, MyFor pitching moment, IyIn order to be the moment of inertia,
Figure GDA0003082771200000084
in order to achieve a high degree of change,
Figure GDA0003082771200000085
in order to be able to accelerate the vehicle,
Figure GDA0003082771200000086
the rate of change of the angle of attack,
Figure GDA0003082771200000089
for the pitch angle rate to be,
Figure GDA0003082771200000088
is pitch angular acceleration;
step two: aiming at the aircraft dynamics model constructed in the first step, a polynomial fitting mode is adopted to obtain a proxy model of force and moment, deformation quantities are fused into the proxy model, and the internal characteristics of the dynamic characteristic change of the model are found by analyzing the matching relation between the configuration change and the flight condition;
the obtained aircraft force and moment surrogate model is expressed as a flight state vector X, and the nonlinear relation between a control input vector U and a configuration change parameter vector P is as follows:
Figure GDA0003082771200000091
wherein Q is dynamic pressure, kMIs the pitching moment coefficient; cL、CD、CM、CTRespectively, a lift coefficient, a drag coefficient, a pitching moment coefficient and a thrust coefficient, which are functions of a flight state vector X, a control input vector U and a configuration change parameter vector P;
further adopting a polynomial fitting mode to obtain a proxy model of force and moment, wherein the concrete process can be divided into four sub-steps of sample point design, model structure selection, model parameter identification and proxy model verification to obtain the target
The form of the proxy model is
Figure GDA0003082771200000092
Where Ma is Mach number, betaL0Is a lift polynomial fitting coefficient, beta, related to XL1Is a lift polynomial fitting coefficient, beta, related to UD0As a coefficient of fitting to a resistance polynomial, betaD1Is a resistance polynomial fitting coefficient, beta, related to XD2Is a resistance polynomial fitting coefficient, beta, related to XUD3Is a resistance polynomial fitting coefficient, beta, related to UD4Is prepared by reacting with
Figure GDA0003082771200000093
Associated resistance polynomial fitting coefficient, betaM0Is a polynomial fitting coefficient of the pitching moment, betaM1For X-dependent pitching moment polynomial fitting coefficients, betaM2For the pitch moment polynomial fitting coefficient, beta, related to XUM3Is a polynomial fitting coefficient, beta, of the pitching moment related to UM4Is a resistance polynomial fitting coefficient, beta, related to MaT0Is a thrust polynomial fitting coefficient, betaT1Is a thrust polynomial fitting coefficient, beta, related to XT2Is a thrust polynomial fitting coefficient, beta, related to XUT3Fitting coefficients to the thrust polynomial associated with U, the coefficients being a function of the configuration change parameter vector P;
the deformation quantity is fused into the proxy model by the formula (6), the nonlinear model is subjected to approximate linearization processing, and a small-disturbance linearization equation is obtained
ΔX＝A(P,X)·ΔX+B(P,X)·ΔU (15)
A (P, X) and B (P, X) are states and input matrixes of the linear model, and the matching relation between configuration change and flight states can be analyzed by analyzing the pole-zero characteristics of the model, so that the inherent relation between the configuration change and the dynamic characteristics of the model is found;
step three: estimating a model performance boundary meeting expected indexes by applying a protection mapping theory aiming at a given configuration, finding out an overlapped part of model performances of two adjacent flight areas, obtaining an aircraft design model covering a motion area by adaptively changing operation conditions, and evaluating the model to obtain the model performance under the given configuration;
let Ω be the desired model performance index, vΩIs corresponding protection mapping, A (P, X) in the formula (7) is a polynomial matrix represented by a flight state vector X and a configuration change parameter vector P, the boundary of a model performance index omega is determined by a protection mapping theory and is substituted into A (P, X), and the protection mapping v can be knownΩ[A(P,X)]Is a multivariate polynomial; then, the flight zone associated with a given aircraft configuration P is found to be X ∈ [ X ]min,Xmax]The stability is kept in the range, and the main process is as follows:
(1) initialization: let m equal 1, Xm＝XminDesigning an initial aircraft configuration PmSuch that A (P)m,Xm) The characteristic values of all the parameters are positioned in a model performance index omega;
(2) determining a maximum stability interval: when P is equal to PmWhen, vΩ(A (P, X)) -0 is an equation relating to X only, all roots are found, and taken to be less than XmMaximum root ofX mAnd is greater than XmMinimum root of
Figure GDA0003082771200000101
A(X,Pm) Has a maximum stability interval of
Figure GDA0003082771200000102
(3) And (4) termination judgment: if it is not
Figure GDA0003082771200000103
The stable interval covers the whole parameter variation range, and the calculation is finished; otherwise make
Figure GDA0003082771200000104
Entering the next step;
(4) search for new aircraft configuration parameters Pm+1Assuming that the aircraft configuration parameter vector is NPAnd (3) dimension, starting from the first dimension, searching each dimension element one by one, and the specific process is as follows:
firstly, initializing: specifying search end precision epsilon and maximum number of searches omaxThe number of searches o is set to 1, Po＝PmSetting the current search dimension R to be 1;
② mixing Po+1All elements after the R-th dimension are fixed as PoCorresponding element value, i.e. Po+1(R+1,…,NP)＝Po(R+1,…,NP)；
③ calculating A stabilityTiming Po+1(R) a value range, wherein the upper and lower boundaries of the range are respectively vΩ[A(Xm,Po+1)]0 is greater than PoMinimal root of (R)
Figure GDA0003082771200000111
And is less than PoMaximum root of (R)P o+1,R；
④Po+1(R) is taken as the midpoint of the stability range
Figure GDA0003082771200000112
Judging the search completion for the second time: if R ≧ NPEnding the search for the second time and entering the sixth step; otherwise search for Po+1The next one-dimensional element, namely R is made to be R +1, and the second step is returned;
sixthly, judging the end of the search: if Po+1-Po||≤ε||1+PoI or o > omaxAfter the search is finished, entering the step (5); otherwise, if Po+1-Po||＞ε||1+PoAnd o is less than or equal to omaxMaking o equal to o +1 and R equal to 1, returning to the third step, and executing the processes of the third step and the sixth step again until the P is equal too+1-Po||≤ε||1+PoI or o > omaxEntering the step (5);
(5) let Pm+1＝Po+1If m is less than or equal to the given maximum value, returning to the step (2); once m is larger than a given maximum value, the iteration is ended, and the required configuration parameters are obtained;
giving initial aircraft configuration parameters and flight conditions, and obtaining a series of aircraft configuration parameters and corresponding stable intervals through the steps (1) to (5), wherein the aircraft configuration parameters can be switched according to scheduling variables in actual flight, and an analytic expression of the aircraft configuration parameters changing along with the scheduling variables can also be obtained through curve fitting;
step four: constructing an optimized performance index according to the task requirement of the aircraft, converting the comprehensive design problem of the geometric configuration and the stability into a coordination distribution problem of the configuration and the motion condition of the aircraft, and performing iterative optimization on the performance index by adopting an optimization algorithm based on protection mapping to obtain an optimal model parameter so as to realize the optimal matching of the configuration and the operation condition of the aircraft;
designing a performance index integrating configuration change and flight condition matching relationship
Figure GDA0003082771200000113
Wherein J is an objective function, G is a constraint, and Z (P, X) is an aircraft model dependent on configuration parameters and flight conditions;
as shown in fig. 4, the detailed steps of the optimization strategy are given:
step 1: giving an initial value and initializing;
step 2: and (3) performing feasible solution on the approximate optimization problem, and obtaining feasible solution of the approximate optimization problem by using an optimization algorithm based on protection mapping, namely searching feasible solution P*So that
Figure GDA0003082771200000121
Step 3: determining a feasible solution P*If the feasible solution does not exist, executing Step 5;
step 4: will be feasible solution P*Bring into the design optimization problem, if satisfy the constraint, return to P*For designing an optimization feasible solution, obtaining an optimal matching value of the aircraft configuration and the operation condition, ending the search process, and if the constraint order P is not metc＝P*Continuing to execute the next step;
step 5: changing flight conditions, fine-dividing a flight area, and executing Step 3;
through the iterative optimization process, the optimal model parameters of the variable configuration aircraft can be obtained, and the optimal matching of the aircraft configuration and the operating conditions is realized.

Claims (1)

1. A variable configuration aircraft model iterative design method based on protection mapping is characterized by comprising the following steps:
the method comprises the following steps: the aircraft reference configuration is described in a parameterization mode, and the pressure P borne by a single surface element is obtained by adopting an engineering estimation theoryiThe forces and moments on all surface elements are superposed to obtain the aerodynamic force and moment of the aircraft
Figure FDA0003069291990000011
My＝∑Pi(dxinzi-dzinxi)Span,i (2)
Wherein d isi＝dxiib+dyijb+dzikbIs a distance vector, nxi、nyiAnd nziRespectively are the components of the vector of the external normal direction of a single surface element, and further the relationship between the aerodynamic force of the aircraft and the lift L, the resistance D and the attack angle alpha is obtained
Figure FDA0003069291990000012
Further applying momentum theorem to estimate the thrust generated by the engine as FTAfter estimating the forces and moments, the following aircraft dynamics model is constructed:
Figure FDA0003069291990000013
wherein L is lift, D is drag, FTG is the thrust, G is the gravity, V is the velocity, alpha is the angle of attack, theta is the pitch angle, q is the pitch angle velocity, h is the height, m isvFor mass, MyFor pitching moment, IyIn order to be the moment of inertia,
Figure FDA0003069291990000018
in order to achieve a high degree of change,
Figure FDA0003069291990000014
in order to be able to accelerate the vehicle,
Figure FDA0003069291990000015
the rate of change of the angle of attack,
Figure FDA0003069291990000016
for the pitch angle rate to be,
Figure FDA0003069291990000017
is pitch angular acceleration;
step two: aiming at the aircraft dynamics model constructed in the first step, a polynomial fitting mode is adopted to obtain a force and moment proxy model, deformation quantity is fused into the proxy model, and the internal characteristics of the dynamic characteristic change of the model are found by analyzing the matching relation between the configuration change and the flight condition;
the obtained aircraft force and moment surrogate model is expressed as a flight state vector X, and the nonlinear relation between a control input vector U and a configuration change parameter vector P is as follows:
Figure FDA0003069291990000021
wherein Q is dynamic pressure, kMIs the pitching moment coefficient; cL、CD、CM、CTRespectively, a lift coefficient, a drag coefficient, a pitching moment coefficient and a thrust coefficient, which are functions of a flight state vector X, a control input vector U and a configuration change parameter vector P;
further adopting a polynomial fitting mode to obtain a proxy model of force and moment, wherein the concrete process comprises four substeps of sample point design, model structure selection, model parameter identification and proxy model verification, and the obtained proxy model is in the form of
Figure FDA0003069291990000022
Where Ma is Mach number, betaL0Is a lift polynomial fitting coefficient, beta, related to XL1Is a lift polynomial fitting coefficient, beta, related to UD0As a coefficient of fitting to a resistance polynomial, betaD1Is a resistance polynomial fitting coefficient, beta, related to XD2Is a resistance polynomial fitting coefficient, beta, related to XUD3Is a resistance polynomial fitting coefficient, beta, related to UD4Is prepared by reacting with
Figure FDA0003069291990000023
Associated resistance polynomial fitting coefficient, betaM0Is a polynomial fitting coefficient of the pitching moment, betaM1For X-dependent pitching moment polynomial fitting coefficients, betaM2For the pitch moment polynomial fitting coefficient, beta, related to XUM3Is a polynomial fitting coefficient, beta, of the pitching moment related to UM4Is a resistance polynomial fitting coefficient, beta, related to MaT0Is a thrust polynomial fitting coefficient, betaT1Is a thrust polynomial fitting coefficient, beta, related to XT2Is a thrust polynomial fitting coefficient, beta, related to XUT3Fitting coefficients to the thrust polynomial associated with U, the coefficients being a function of the configuration change parameter vector P;
the deformation quantity is fused into the proxy model by the formula (6), the nonlinear model is subjected to approximate linearization processing, and a small-disturbance linearization equation is obtained
ΔX＝A(P,X)·ΔX+B(P,X)·ΔU (7)
A (P, X) and B (P, X) are states and input matrixes of the linear model, the matching relation between configuration change and flight states is analyzed by analyzing the pole-zero characteristics of the model, and the inherent relation between the configuration change and the dynamic characteristics of the model is found;
step three: estimating a model performance boundary meeting expected indexes by applying a protection mapping theory aiming at a given configuration, finding out an overlapped part of model performances of two adjacent flight areas, obtaining an aircraft design model covering a motion area by adaptively changing operation conditions, and evaluating the model to obtain the model performance under the given configuration;
let Ω be the desired model performance index, vΩIs corresponding protection mapping, A (P, X) in the formula (7) is a polynomial matrix represented by a flight state vector X and a configuration change parameter vector P, the boundary of a model performance index omega is determined by a protection mapping theory and is substituted into A (P, X), and the protection mapping v can be knownΩ[A(P,X)]Is a multivariate polynomial; then, the flight zone associated with a given aircraft configuration P is found to be X ∈ [ X ]min,Xmax]The stability is kept in the range, and the process is as follows:
(1) initialization: let m equal 1, Xm＝XminDesigning an initial aircraft configuration PmSuch that A (P)m,Xm) The characteristic values of all the parameters are positioned in a model performance index omega;
(2) determining a maximum stability interval: when P is equal to PmWhen, vΩ(A (P, X)) -0 is an equation relating to X only, all roots are found, and taken to be less than XmMaximum root ofX mAnd is greater than XmMinimum root of
Figure FDA0003069291990000031
A(X,Pm) Has a maximum stability interval of
Figure FDA0003069291990000032
(3) And (4) termination judgment: if it is not
Figure FDA0003069291990000033
The stable interval covers the whole parameter variation range, and the calculation is finished; otherwise make
Figure FDA0003069291990000034
Entering the next step;
(4) search for new aircraft configuration parameters Pm+1Assuming that the aircraft configuration parameter vector is NPDimension, starting from the first dimension, searching each one dimension by dimensionThe specific process of the vitamin elements is as follows:
firstly, initializing: specifying search end precision epsilon and maximum number of searches omaxThe number of searches o is set to 1, Po＝PmSetting the current search dimension R to be 1;
② mixing Po+1All elements after the R-th dimension are fixed as PoCorresponding element value, i.e. Po+1(R+1,…,NP)＝Po(R+1,…,NP)；
Computing P when A is stableo+1(R) a value range, wherein the upper and lower boundaries of the range are respectively vΩ[A(Xm,Po+1)]0 is greater than PoMinimal root of (R)
Figure FDA0003069291990000035
And is less than PoMaximum root of (R)P o+1,R；
④Po+1(R) is taken as the midpoint of the stability range
Figure FDA0003069291990000036
Judging the search completion for the second time: if R ≧ NPEnding the search for the second time and entering the sixth step; otherwise search for Po+1The next one-dimensional element, namely R is made to be R +1, and the second step is returned;
sixthly, judging the end of the search: if Po+1-Po||≤ε||1+PoI or o > omaxAfter the search is finished, entering the step (5); otherwise, if Po+1-Po||＞ε||1+PoAnd o is less than or equal to omaxMaking o equal to o +1 and R equal to 1, returning to the third step, and executing the processes of the third step and the sixth step again until the P is equal too+1-Po||≤ε||1+PoI or o > omaxEntering the step (5);
(5) let Pm+1＝Po+1If m is less than or equal to the given maximum value, returning to the step (2); once m is larger than a given maximum value, the iteration is ended, and the required configuration parameters are obtained;
giving initial aircraft configuration parameters and flight conditions, and obtaining a series of aircraft configuration parameters and corresponding stable intervals through the steps (1) to (5), wherein the aircraft configuration parameters are switched according to scheduling variables in actual flight, or obtaining an analytic expression of the aircraft configuration parameters changing along with the scheduling variables through curve fitting;
step four: constructing an optimized performance index according to the task requirement of the aircraft, converting the comprehensive design problem of the geometric configuration and the stability into a coordination distribution problem of the configuration and the motion condition of the aircraft, and performing iterative optimization on the performance index by adopting an optimization algorithm based on protection mapping to obtain an optimal model parameter so as to realize the optimal matching of the configuration and the operation condition of the aircraft;
designing a performance index integrating configuration change and flight condition matching relationship
Figure FDA0003069291990000041
Wherein J is an objective function, G is a constraint, and Z (P, X) is an aircraft model dependent on configuration parameters and flight conditions;
giving detailed steps of the optimization strategy:
step 1: giving an initial value and initializing;
step 2: and (3) performing feasible solution on the approximate optimization problem, and obtaining feasible solution of the approximate optimization problem by using an optimization algorithm based on protection mapping, namely searching feasible solution P*So that
Figure FDA0003069291990000042
Step 3: determining a feasible solution P*If the feasible solution does not exist, executing Step 5;
step 4: will be feasible solution P*Bring into the design optimization problem, if satisfy the constraint, return to P*For designing an optimization feasible solution, obtaining an optimal matching value of the aircraft configuration and the operation condition, ending the search process, and if the constraint order P is not metc＝P*Continuing to execute the next step;
step 5: changing flight conditions, fine-dividing a flight area, and executing Step 3;
through the iterative optimization process, the optimal model parameters of the variable configuration aircraft are obtained, and the optimal matching of the aircraft configuration and the operating conditions is realized.
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