CN108256180A - Unmanned plane model verification method based on multiple dimensioned Gauss feature error fit - Google Patents
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Abstract
The present invention relates to aerospace fields, perform mission requirements and controller design demand to combine unmanned plane, conclusion is estimated in the verification of quantitative model.And by means of computer virtual simulation, simulating, verifying is carried out to institute's extracting method of the present invention on emulation platform.For this purpose, the technical solution adopted by the present invention is, based on the unmanned plane model verification method of multiple dimensioned Gauss feature error fit, including following four step：1. asking for data basis error to be verified, pedestal error after treatment will reflect practical flight and the control information that model of a syndrome to be tested exports in time-domain；2. multiple dimensioned Gauss feature error fit, using the morphological feature of multiple Gaussian function characterization control information；3. scale fitting function matches, start with from the angle of morphological feature, match practical flight data and model of a syndrome output data to be tested one by one；4. definition verification evaluation function, quantitative verification criterion.Present invention is mainly applied to aerospace occasions.
Description
Technical field
The present invention relates to aerospace fields, relate generally to the model validation problem of unmanned plane mathematical model, specifically, relate to
And multiple unmanned plane mathematical models are when comparing, the feature extraction of output data Multiscale Morphological and quantitative analysis.
Background technology
The grade of UAV Intelligent control is higher and higher, and the complexity of task is also higher and higher, and control system is wanted
Ask also increasingly stringenter.The basis that mathematical model is designed as automatic control algorithm is demonstrate,proved in controller design and system stability
It is bright etc. to have played very big effect.With the development of aircraft flight technology, the diversification of aerial mission demand, flight speed
The promotion of degree and the raising of flight maneuver, the design of aircraft become increasingly complex, and structure design tends to diversification.Unmanned plane is empty
Coupling between pneumatic power system, flight control system, engine system, elastic construction system is also more and more apparent.
During establishing unmanned plane mathematical model, the modeling method of use is broadly divided into Experimental Modeling and mechanism is built
Mould method.Experimental Modeling is experimental flight data to be taken to carry out mathematical modeling by the way of practical flight experiment.Mechanism is built
The method of mould is that the action rule of unmanned plane is obtained by the way of illation of mechanism.It can not be kept away during founding mathematical models
That exempts from has simplified and approximate process, and the obtained model accuracy of different modeling methods is different, and complexity is also not quite similar.
Controller design personnel need to use mathematical modulo when designing controller architecture and proving closed-loop system stability when control performances
Type.The excessively poor mathematical model of precision will cause theoretically outstanding control algolithm, and effect is undesirable in practical applications.Precision
Often structure is increasingly complex for high mathematical model, and nonlinear characteristic is stronger, and the mathematical model that structure is excessively complicated, in controller
Functional control algolithm can be caused to be difficult to apply during design, and the control performance of closed-loop system difficult of proof.Therefore, for nothing
The evaluation of man-machine digital's model needs to consider the characteristics such as precision and complexity.In order to which controller design personnel is given to provide more
Suitable mathematical model, it is desirable to be able to the model verification method of objective evaluation unmanned plane mathematical model quality.
Existing model verification method is started with mostly from the difference for comparing practical flight data and mathematical model output, emphasis
The size of error is investigated, the smaller mathematical model of error, it is better to show.However in modeling process, inevitably
It will appear model simplification, such as ignore specific physics law, the approximation to function during analytical form mathematical model is obtained.Nothing
The output of man-machine digital's model is difficult to fit like a glove with practical flight data, the optimal mathematical modulo that single requirement error minimum obtains
Type may be the extremely complex mathematical model of mathematical expression.Excessively complicated mathematical model will give controller design and ground
Simulation work brings problem and unnecessary resource consumption.
In order to solve this problem, the present invention pays close attention to how to evaluate the appropriate unmanned plane mathematical model of complexity.When
Excessively complicated mathematical model can not be in use, model user needs one kind that can reasonably evaluate mathematical model complexity is
No appropriate model verification method.Existing model verification method is the output to unmanned plane mathematical model in time-domain mostly
It is evaluated, because the output of unmanned plane is the function of time, which can be influenced by reference instruction.Reference instruction is ground
The flight path signal that dough figurine person or high-grade intelligent body are provided according to unmanned plane during flying task.This flight path signal is with very
Strong task feature, step signal during such as some particular point in time, such as the track change signal in some period.It carries
The reference signal of task feature can lead to the unmanned plane mathematical model output signal again with task feature, this allows for nobody
The output of machine mathematical model morphologically has certain feature.Unmanned plane mathematical modulo is investigated in the error out of time-domain merely
The quality of type, it is difficult to embody this morphological feature.The present invention from the morphological feature angle of unmanned plane mathematical model output data into
Row analysis verifies thinking using multiple dimensioned model, gives the side that model user is multiple dimensioned, multiple degrees of freedom evaluates mathematical model
Method.Using the method for multiple Gauss feature error fit, the morphological feature of unmanned plane output is extracted on multiple dimensioned with
And quantitative analysis, provide suitable model verification conclusion.
By the retrieval to the prior art, similar invention is not found.Especially for the model of unmanned plane mathematical model
Verification method lacks the method for focused data morphological feature.This technology can provide new for the verification of unmanned plane mathematical model
Approach, more preferably complicated unmanned plane mathematical model carry out model verification work, are carried for high Intelligent unattended machine control algorithm design person
For service.
Invention content
In order to overcome the deficiencies of the prior art, the present invention is directed to propose the model assessed suitable for Unmanned Aerial Vehicle Data morphological feature
Verification method performs mission requirements and controller design demand with reference to unmanned plane, and conclusion is estimated in the verification of quantitative model.And by
In computer virtual simulation, simulating, verifying has been carried out to institute's extracting method of the present invention on emulation platform.For this purpose, what the present invention used
Technical solution is, based on the unmanned plane model verification method of multiple dimensioned Gauss feature error fit, including following four step：①
Data basis error to be verified is asked for, pedestal error after treatment will reflect practical flight and to be verified in time-domain
The control information of model output；2. multiple dimensioned Gauss feature error fit, using the shape of multiple Gaussian function characterization control information
State feature；3. scale fitting function matches, start with from the angle of morphological feature, match practical flight data and mould to be verified one by one
Type output data；4. definition verification evaluation function, quantitative verification criterion.
Specifically：
1. step, asks for data basis error to be verified
Standard unmanned plane during flying data are r (t), and t is the time.Flying quality sampling number is Tr, unmanned plane number to be verified
Model data is yi(t), model output data sampling number is, numbers of the i for unmanned plane mathematical model to be verified, standard
Unmanned plane during flying data basis error isUnmanned plane mathematical model basis to be verified to be verified is accidentally
Difference is：
Step 2., multiple dimensioned Gauss feature error fit
Using the multiple Gaussian function of following formula to margin of error erIt is fitted
WhereinTo be fitted dimensional information, value range is 1~Tr。To be fitted scaleUnder jth item fitting term system
Number, different scales, which defines, will obtain different fitting results, and the different model of correspondence is verified conclusion by different fitting results,For Gaussian function, expression-form is：
WhereinFor the central value of each fit term Gaussian function,Width for each fit term Gaussian function.Using
The multiple Gaussian function of following formula is to the margin of errorIt is fitted
WhereinTo be fitted dimensional information, value range is To be fitted scaleUnder jth item fitting term system
Number, different scales, which defines, will obtain different fitting results, and the different model of correspondence is verified conclusion by different fitting results,For Gaussian function, expression-form is：
WhereinFor the central value of each fit term Gaussian function,Width for each fit term Gaussian function.
3., scale fitting function matches step
For each unmanned plane mathematical model to be verified, by pedestal error ask for and multiple dimensioned Gauss feature error
Two steps are fitted, two groups of data will be obtained and verified to model, scaleUnder, standard unmanned plane during flying data fitting parameter groupAnd scaleUnder, unmanned plane mathematical model data to be verified
Fitting parameter groupEach fitting parameter group represents multiple high
The superposition of this function.In order to be fitted function matching, dimensional information takesGaussian function represents data to be verified
Morphological feature, to multiple Gaussian functions in two fitting parameter groups according to fitting coefficient, central value and width information progress
Match, Gaussian function most similar in two groups of data is corresponded, if cost function is
With cost function φiThe minimum target of value, carries out minimum matching operation, establishes in two fitting parameter groups
Each elements AjWith BjOne-to-one relationship, obtain corresponding treated fitting parameter group
4. step, verifies evaluation function
For the fitting parameter group after matchingCarry out verification evaluation function ψiCalculating, calculation formula is as follows：
The value of the function puies forward the quantitative verification criterion of model verification method as the present invention.Evaluation function value is got over
Low, then model of a syndrome to be tested and Standard Flight Data are closer, and opinion scale can according to demand be chosen according to model user, comment
Valency scale value is higher, and the form precision of verification is higher.
The features of the present invention and advantageous effect are：
Quantitative model verification is carried out in the comparison work that the present invention can be directed to multiple verification models, for each nothing to be verified
Man-machine digital's model provides a quantitative model verification result.This method is not limited by model structure and modeling method
System, can be verified with match exponents height, strong nonlinearity, strong coupling feature mathematical model.And the verification method can make with model
The concrete application environment of user carries out rescaling, and scale value can be taken from 1 to fully sampled points.Can using precision evaluation as
Preferentially, small scale is selected to carry out high-precision model verification, selects precision optimal models.Can also be using fitness-for-service assessment it is preferential,
Large scale is selected to carry out morphological feature model verification, selects the optimal models that morphological feature is moderate, and model structure is suitble to.
Social benefit and economic benefit：Modeling work and high Intelligent unattended machine of the present invention to unmanned plane mathematical model
The raising of control algolithm has highly important promotion meaning.The present invention can provide effective unmanned plane model verification method,
Appreciation gist can be provided for modeling work person, device projector screening in order to control is suitble to the complexity of controller design appropriate
Unmanned plane mathematical model.It is effective especially for model structure is complicated, control performance requires high UAV system exploitation to have
Impetus.Be high-intelligentization, high performance control method uses work service in novel UAV system, and shortening is ground
The period is sent out, a large amount of actual flying test consumption is saved, reduces unmanned plane development cost, UAV Intelligentization is pushed to realize process.
Description of the drawings：
1 unmanned plane model of attached drawing verifies system construction drawing.
Unmanned plane model verification method flow chart of the attached drawing 2 based on multiple dimensioned Gauss feature error fit.
Unmanned plane model verification method software of the attached drawing 3 based on multiple dimensioned Gauss feature error fit realizes surface chart.
Specific embodiment
The validation problem of present invention research unmanned plane mathematical model, pays close attention to multiple unmanned plane mathematical models under comparison
Quantitative verification problem.Consider model structure complexity, while consider the mould under nonlinear characteristic, coupled characteristic, high-order characteristic
Type validation problem.The morphological feature of mathematical model to be verified is analyzed, provides multiple dimensioned model verification criterion.
Unmanned plane model verification method proposed by the present invention based on multiple dimensioned Gauss feature error fit, it is intended to explore nothing
The new demand proposed during man-machine high intelligent algorithm exploitation to complex mathematical model, the model verification method established have important reason
By meaning and actual application prospect.Multiple dimensioned freely setting will be believed to the analysis in terms of controller developer's model performance
Breath promotes application process of the advanced control algorithm in complicated UAV system, and the foundation for mathematical model provides verification work
Support in terms of work.UAV Intelligentization can be promoted to develop, shorten the R＆D cycle of novel UAV system, both rapidly and efficiently
It reduces expenses again, there is good application prospect and economic value.
The present invention is directed to overcome the deficiencies in the prior art, are integrated as mainly studying with theoretical method and Virtual Simulation
Means for unmanned plane mathematical model validation problem, propose to be suitable for the model authentication of Unmanned Aerial Vehicle Data morphological feature assessment
Method performs mission requirements and controller design demand, quantitative model verification assessment result with reference to unmanned plane.And by means of meter
Calculation machine virtual emulation has carried out simulating, verifying on emulation platform to institute's extracting method of the present invention.
The unmanned plane group of planes coordinated control system performance estimating method that analysis is disturbed based on multiple dimensioned wind is walked including following four
Suddenly：1. ask for data basis error to be verified, pedestal error after treatment will reflect in time-domain practical flight and
The control information of model of a syndrome output to be tested.2. multiple dimensioned Gauss feature error fit, using multiple Gaussian function characterization error letter
The morphological feature of breath.3. scale fitting function matches, start with from the angle of morphological feature, match practical flight data one by one with treating
Verify model output data.4. definition verification evaluation function, quantitative verification criterion, the size of the value directly corresponds to be verified
The superior and inferior evaluating of model.
With reference to attached drawing, the invention will be further described.
Referring to Fig. 1, unmanned plane model is verified in structure, Standard Flight Data and multiple unmanned plane mathematical models to be verified
Data will receive identical reference instruction signal.The reference instruction signal is the expectation of flight position and flight attitude at any time
Value, aerial mission, features of terrain, environmental characteristic according to unmanned plane etc. is provided by earth station or advanced control unit.In phase
Under same reference instruction and the effect of identical control structure, the flying quality in the record-setting flight task period, to Standard Flight
Data carry out sampling the reference data as system to be verified.Simultaneously respectively to unmanned plane mathematical model output data to be verified into
Row sampling, the data set as verification model performance quality.
It is the specific implementation flow chart of this algorithm referring to Fig. 2, the specific steps are：
1. step, asks for data basis error to be verified
Standard unmanned plane during flying data are r (t), and t is the time.Flying quality sampling number is Tr, unmanned plane number to be verified
Model data is yi(t), model output data sampling number isNumbers of the i for unmanned plane mathematical model to be verified, standard
Unmanned plane during flying data basis error isUnmanned plane mathematical model basis to be verified to be verified is accidentally
Difference is：
Step 2., multiple dimensioned Gauss feature error fit
Using the multiple Gaussian function of following formula to margin of error erIt is fitted
WhereinTo be fitted dimensional information, value range is 1~Tr。To be fitted scaleUnder jth item fitting term system
Number, different scales, which defines, will obtain different fitting results, and the different model of correspondence is verified conclusion by different fitting results,For Gaussian function, expression-form is：
WhereinFor the central value of each fit term Gaussian function,Width for each fit term Gaussian function.Using
The multiple Gaussian function of following formula is to the margin of errorIt is fitted
WhereinTo be fitted dimensional information, value range is To be fitted scaleUnder jth item fitting term system
Number, different scales, which defines, will obtain different fitting results, and the different model of correspondence is verified conclusion by different fitting results,For Gaussian function, expression-form is：
WhereinFor the central value of each fit term Gaussian function,Width for each fit term Gaussian function.
3., scale fitting function matches step
For each unmanned plane mathematical model to be verified, by pedestal error ask for and multiple dimensioned Gauss feature error
Two steps are fitted, two groups of data will be obtained and verified to model, scaleUnder, standard unmanned plane during flying data fitting parameter groupAnd scaleUnder, unmanned plane mathematical model data to be verified
Fitting parameter groupEach fitting parameter group represents multiple high
The superposition of this function.In order to be fitted function matching, dimensional information takesGaussian function represents data to be verified
Morphological feature, to multiple Gaussian functions in two fitting parameter groups according to fitting coefficient, central value and width information progress
Match, Gaussian function most similar in two groups of data is corresponded, if cost function is
With cost function φiThe minimum target of value, carries out minimum matching operation, establishes in two fitting parameter groups
Each elements AjWith BjOne-to-one relationship, obtain corresponding treated fitting parameter group
4. step, verifies evaluation function
For the fitting parameter group after matchingCarry out verification evaluation function ψiCalculating, calculation formula is as follows：
The value of the function puies forward the quantitative verification criterion of model verification method as the present invention.Evaluation function value is got over
Low, then model of a syndrome to be tested and Standard Flight Data are closer, and opinion scale can according to demand be chosen according to model user, comment
Valency scale value is higher, and the form precision of verification is higher.
Referring to Fig. 3, the unmanned plane model verification method human-computer interaction master control based on multiple dimensioned Gauss feature error fit is soft
Part interface is developed using Matlab engine techniques.Six functional areas are included in interface：Unmanned plane mathematical model area, verification ginseng
Number setting area, closed-Loop Analysis area, verification result area, real-time flight data area and output data area.Unmanned plane mathematical model area work(
It can include importing, simulation times setting, the setting of practical flight data and operation of Simulink programs of mathematical model to be verified etc.
Function.The function is substantially carried out acquisition and the preparation of data, has reserved mathematical model introducting interface to be verified, can access more
A mathematical model to be verified.Mathematical model to be verified will carry out verification analysis with data mode, therefore the invention will not be by
The influence of the specific analytical form of mathematical model and modeling method, it is applied widely.Certificate parameter setting area's function includes verification ruler
The selection function of degree.Verification scale is selected as the important open parameter of verification, can carry out oneself setting, the change of scale will
Directly affect verification conclusion.Closed-Loop Analysis area function includes reference instruction desired value set-up function, matching and stability analysis
Function.The functions such as verification result area function includes Data Matching test, fitting result is shown, pedestal error is shown.The functional areas
The figure displaying function of data in verification process is given, the displaying of these analysis result figures will provide detailed model error
Shape information provides comprehensive unmanned plane mathematical model information to be verified, and can be fitted according to these results for model user
When adjustment dimensional information.Real-time flight area function includes real-time flight data import feature.When output data area function is included in
Between display data result in domain.
Claims (2)
1. a kind of unmanned plane model verification method based on multiple dimensioned Gauss feature error fit, it is characterized in that, including following four
A step：
1. ask for data basis error to be verified, pedestal error after treatment will reflect in time-domain practical flight and
The control information of model of a syndrome to be tested；2. multiple dimensioned Gauss feature error fit, using multiple Gaussian function characterization control information
Morphological feature；3. scale fitting function match, start with from the angle of morphological feature, one by one match practical flight data with it is to be verified
Model output data；4. definition verification evaluation function, quantitative verification criterion.
2. the unmanned plane model verification method as described in claim 1 based on multiple dimensioned Gauss feature error fit, feature
It is, specifically：
1. step, asks for data basis error to be verified
Standard unmanned plane during flying data are r (t), and t is the time, and flying quality sampling number is Tr, unmanned plane mathematical model to be verified
Data are yi(t), model output data sampling number isNumbers of the i for unmanned plane mathematical model to be verified, standard unmanned plane
Flying quality pedestal error isUnmanned plane mathematical model pedestal error to be verified to be verified is：
Step 2., multiple dimensioned Gauss feature error fit
Using the multiple Gaussian function of following formula to margin of error erIt is fitted
WhereinTo be fitted dimensional information, value range is 1~Tr,To be fitted scaleUnder jth item fitting term coefficient, no
Same scale, which defines, will obtain different fitting results, and the different model of correspondence is verified conclusion by different fitting results,For Gaussian function, expression-form is：
WhereinFor the central value of each fit term Gaussian function,Width for each fit term Gaussian function.Using following formula
Multiple Gaussian function is to the margin of errorIt is fitted
WhereinTo be fitted dimensional information, value range is To be fitted scaleUnder jth item fitting term coefficient, no
Same scale, which defines, will obtain different fitting results, and the different model of correspondence is verified conclusion by different fitting results,For Gaussian function, expression-form is：
WhereinFor the central value of each fit term Gaussian function,Width for each fit term Gaussian function.
3., scale fitting function matches step
For each unmanned plane mathematical model to be verified, by pedestal error ask for and multiple dimensioned Gauss feature error fit
Two steps will obtain two groups of data and be verified to model, scaleUnder, standard unmanned plane during flying data fitting parameter groupAnd scaleUnder, unmanned plane mathematical model data to be verified
Fitting parameter groupEach fitting parameter group represents multiple high
The superposition of this function.In order to be fitted function matching, dimensional information takesGaussian function represents data to be verified
Morphological feature, to multiple Gaussian functions in two fitting parameter groups according to fitting coefficient, central value and width information progress
Match, Gaussian function most similar in two groups of data is corresponded, if cost function is
With cost function φiThe minimum target of value, carries out minimum matching operation, establishes each in two fitting parameter groups
Elements AjWith BjOne-to-one relationship, obtain corresponding treated fitting parameter group
4. step, verifies evaluation function
For the fitting parameter group after matchingCarry out verification evaluation function ψiCalculating, calculation formula is as follows：
The value of the function puies forward the quantitative verification criterion of model verification method as the present invention, and evaluation function value is lower, then
Model of a syndrome to be tested and Standard Flight Data are closer, and opinion scale can according to demand be chosen according to model user, evaluate ruler
Degree value is higher, and the form precision of verification is higher.
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