CN108256190A - Multiple target Aircraft Steering Engine optimum design method - Google Patents
Multiple target Aircraft Steering Engine optimum design method Download PDFInfo
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Abstract
The disclosure provides a kind of multiple target Aircraft Steering Engine optimum design method, includes the following steps：Step 1 determines the object function and design variable of Aircraft Steering Engine, establishes the structural parameters Model for Multi-Objective Optimization of Aircraft Steering Engine；Step 2 optimizes the structural parameters Model for Multi-Objective Optimization that step 1 obtains using particle cluster algorithm, obtains Pareto optimization disaggregation；Step 3 establishes Aircraft Steering Engine decision model using analytic hierarchy process (AHP).
Description
Technical field
This disclosure relates to a kind of Aircraft Steering Engine optimum design method more particularly to a kind of multiple target Aircraft Steering Engine optimization design
Method.
Background technology
Aircraft Steering Engine optimization design is a kind of multipole value, constrained non-linear poly-harmonic equation problem, is needed at the same time
Under the conditions of meeting national standard, user's requirement and particular constraints, demand makes what steering engine multinomial performance index was all optimal to set
Meter scheme.But since object function and constraints are all difficult to directly be showed with relational expression, and multiple targets are mutually made
About, it cannot get " a perfection solution " for all showing optimal in each target under normal conditions.Therefore, the processing of object function
And the construction of optimization algorithm influences the quality of Aircraft Steering Engine designing scheme, determines the quality of steering engine runnability.
Traditional optimization algorithm is based on classic extreme value theory and traditional random algorithm, in the practical application of steering engine optimization design
Certain achievement is obtained, but still there are some drawbacks.It such as can be micro- it is assumed that Dependence Problem gradient based on design variable
By force；Searching process is received initial solution and is restricted greatly, and optimal result easily converges on the local best points near initial value, ability of searching optimum
Difference.In addition, traditional optimization algorithm is mainly used for solving single-object problem, for multi-objective optimization question, mainly pass through
Single goal is translated into object function weighted array to optimize, and directly affects steering engine design result and runnability.
Last decade, with being constantly progressive for steering engine designing technique, engineering mathematics and modern intelligent algorithm are also constantly brought forth new ideas, this
Including genetic algorithm, simulated annealing, neural network and particle cluster algorithm etc., and in steering engine global optimization field
Application study to obtain significant results.Particle cluster algorithm is that a kind of new global optimization developed rapidly in recent years is calculated
Method.Since particle cluster algorithm is seldom to the limitation of optimization problem, object function and constraints are neither required can be micro-, also should not
Ask continuous, requiring nothing more than the problem can calculate.Its search process has directiveness, and search range is also empty throughout entire solution
Between, thus globally optimal solution can be efficiently found.Pareto theories are combined with particle cluster algorithm to solve multiple-objection optimization
The new approaches of problem, the shortcomings that overcoming traditional optimization algorithm, in neural network, machine learning, data mining, white adapt to control
System etc. is used widely.
Future aircraft develops to directions such as high speed, high-pressure trend, quick responses, and with air speed and other performance
Be continuously improved, the quiet dynamic load that aircraft rudder surface is born will bigger, deflection speed will faster, this requirement control rudder face it is accurate
The actuator of deflection is more powerful, power consumption is lower, and in the case where ensureing structural strength, quality is lighter.Therefore Aircraft Steering Engine conduct
The leading Aircraft Actuating system of future aircraft, its design and optimization have very important meaning.
Disclosure application multi-objective particle swarm algorithm (MOPSO) obtains the good non-dominated Optimality disaggregation of one group of diversity
It closes, the solution as multiple target Aircraft Steering Engine optimization problem.Application level analytic approach makees research object on this basis
For a system, decision is carried out according to the mode of thinking of decomposition, multilevel iudge, synthesis, obtains the optimization of multiple target Aircraft Steering Engine
Entire appraisement system is had levels, showed methodically by optimal solution, makes entire evaluation procedure very clear, clear and definite, has
The advantages that practicability, systematicness, terseness.
Invention content
The disclosure proposes a kind of Aircraft Steering Engine method for optimally designing parameters of multi-objective optimization algorithm, and this method mainly solves
Weight, power consumption minimize when steering engine designs, maximizing stiffness problem, ensure that Aircraft Steering Engine obtains optimum performance.
In order to achieve the above objectives, the technical solution that the disclosure uses for：It is proposed a kind of boat based on multi-objective optimization algorithm
Empty steering engine design method, this method is using steering engine weight, power consumption, rigidity as optimization aim, with length of connecting rod, connecting rod swing initial bit
Put, the discharge capacity of pressurized strut limited areal, pump is design variable, carry out multiple-objection optimization using multi-objective particle swarm algorithm, obtain
Pareto optimizes disaggregation, carries out decision finally by analytic hierarchy process (AHP), obtains the optimization design scheme of Aircraft Steering Engine.
The disclosure is achieved through the following technical solutions.
Multiple target Aircraft Steering Engine optimum design method, includes the following steps：
Step 1 determines the object function and design variable of Aircraft Steering Engine, establishes the structural parameters multiple target of Aircraft Steering Engine
Optimized model；
Step 2 optimizes the structural parameters Model for Multi-Objective Optimization that step 1 obtains using particle cluster algorithm, obtains
Obtain Pareto optimization disaggregation；
Step 3 establishes Aircraft Steering Engine decision model using analytic hierarchy process (AHP).
Further, the step 1 includes the following steps：
Step 101, Aircraft Steering Engine object function is determined：It is excellent as multiple target Aircraft Steering Engine to choose weight, energy consumption and rigidity
Change three object functions of design；
Step 102, the optimization design variable of Aircraft Steering Engine is determined：Determine target of the weight as Aircraft Steering Engine optimization design
Optimization design variable during function determines optimization design variable during object function of the energy consumption as Aircraft Steering Engine optimization design,
Determine optimization design variable during object function of the rigidity as Aircraft Steering Engine optimization design.
Wherein, for the whole installed power for improving aircraft, the weight of airborne Aircraft Steering Engine the big more limits Aircraft Steering Engine
Application, therefore quality become Aircraft Steering Engine performance an important indicator.Meanwhile another weight of energy consumption as Aircraft Steering Engine
Performance indicator is wanted, in the particularly efficient Aircraft Steering Engine design of Aircraft Steering Engine design, how to utilize existing technical equipment condition
Aircraft Steering Engine energy consumption is reduced as far as, is the technical issues of Aircraft Steering Engine design engineering staff must solve.At the same time, it is
Demand that Aircraft Steering Engine works in various conditions is catered to, improving rigidity also becomes the problem of Aircraft Steering Engine has to face.
Therefore, it when doing Aircraft Steering Engine optimization design, generally requires to optimize in multiple targets, the disclosure is with the weight of Aircraft Steering Engine
Amount, energy consumption, rigidity while as an optimization object function.Design variable is more, and design freedom is bigger, for the content of adjustment
More, the prioritization scheme obtained in this way also may be more preferable, but the scale optimized at this time becomes larger, and the difficulty of design is also corresponding
Increase, this can increase many difficulty, and greatly increase and calculate the time to the data processing of optimization process.Generally speaking, design becomes
The selection principle of amount is as follows：1) under the premise of design requirement is met, the number of design variable should be reduced to the greatest extent, mechanical optimization is set
Variable is generally not to be exceeded 5 in meter.2) it should select to be affected to object function, directly affect constraints and performance refers to
Target basic parameter is as design variable.Therefore, when object function and design requirement difference, design variable should also change therewith,
Best optimum results could be obtained.3) it should be one group of mutually independent basic variable to choose design variable.Their value
Range should be easier to determine.It is needed to choose different design variables according to different optimization aims.If using weight as
Optimization design target should then choose the structural parameters larger to the weight of Aircraft Steering Engine, such as length of connecting rod, the row of pump
Amount, motor torque constant etc..If with energy consumption design object as an optimization, should choose to the energy consumption of Aircraft Steering Engine compared with
Big structural parameters, such as length of connecting rod, connecting rod swing initial position, pressurized strut limited areal etc..If using rigidity as excellent
Change design object, then need to choose the EHA structural parameters larger to the stiffness effect of Aircraft Steering Engine, such as length of connecting rod, connecting rod
Swing initial position, pressurized strut limited areal etc..It is if final selected using the weight, energy consumption, rigidity of Aircraft Steering Engine as target
Using length of connecting rod, connecting rod swing initial position, pressurized strut limited areal, pump discharge capacity as design variable.
Further, in step 101, Weight Model is using the method that calculating all parts weight sums up respectively, meter
Formula is calculated to be shown below：mSteering engine=∑ mComponent；
Energy consumption model is expressed as：W=T × ω × t, sets requirement rudder face are transferred to final position from initial position, swing
30 ° of time t=0.5s, T and ω represents torque and the rotating speed of motor respectively in formula；
The method that rigidity model is summed up using all parts rigidity is calculated respectively, calculation formula are shown below：
Wherein KR1、KR2、KR3、KBRespectively hinge R1、R2、R3And the rigidity of base of steering gear, for constant value, KyFor steering engine liquid
Pressing spring rigidity, KrFor steering engine output rod rigidity, KLFor rocking arm rigidity；
It is using the weight, energy consumption and rigidity of steering engine as target, then final to select following 4 design variables in step 102：Wherein, X1, X2, X3, X4 represent the length of connecting rod of steering engine, connecting rod swing initial position, start respectively
The effective area of cylinder, the discharge capacity of pump.
Wherein,
Choose length of connecting rod L, connecting rod swing initial position θ, pressurized strut limited areal A, pump discharge capacity D be design variable,
Then in m particle position Vector Groups, the position of i-th particle in the 4th dimension space and velocity vector are represented as follows：
Xi={ xiL,xiθ,xiA,xiD(i=1,2 ..., m)
First particle is expressed as in the fast vector of 4 dimension spaces：
Vi={ viL,viθ,viA,viD(i=1,2 ..., m)
Up to the present optimum position that first particle is searched for is：
Pbest={ pbestL,pbestθ,pbestA,pbestD(i=1,2 ..., m)
The optimal location that entire population searches is：
Gbest={ gbestL,gbestθ,gbestA,gbestD(i=1,2 ..., m)
Particle can carry out the update of speed and position in t moment algorithm operational process according to following more new formula：
Vi(t+1)=Vi(t)+c1*r1*(Pbest-Xi(t))+c2*r2*(Gbest-Xi(t))
Xi(t+1)=Xi(t)+Vi(t+1)
Wherein c1And c2Referred to as Studying factors, r1And r2It is random number, value range is the random number between 0 to 1.From upper
The more new formula stated, which can be seen that, to be come, and the speed update item of each particle contains the velocity component of three parts.
First part is known as inertia portion, describes the velocity magnitude at particle current time for subsequent time particle speed
The influence of degree；Second part is known as itself study part, and describe particle is influenced by itself memory, is particle to mistake
Toward the summary at all moment, find last time particle to be reached the personal best particle point nearest from target, therefore,
It can be c1It is defined as " autognosis part ".Part III is known as team learning part, describes and considers group's particle
The experience of entire population is remembered in iterative process before, has reflected society's cooperation memory between entire group, has embodied each
Information sharing effect between particle by comparison, finds the desired positions that entire group's particle reached.It therefore, can be with
C2It is defined as " group cognition part ".
Further, the step 2 includes the following steps：
Step 201, population, arrange parameter and maximum iteration are initialized, dimensionality of particle is 4 (xL,xθ,xA,xD),
Population is 100；
Step 202, particle rapidity and particle position initial value are set, limit speed value range, the particle that goes beyond the scope is assigned
The opposite speed of particle is given, makes particle optimizing still in setting range；
Step 203, particle rapidity and particle position are substituted into the object function f of structureW(x),fP(x),fK(x) in, inferior horn
Mark W, P, K represent weight, energy consumption, rigidity respectively, acquire the fitness function value of each particle；
Step 204, according to Pareto dominance relations, non-dominant particle is selected, puts it into non-dominant concentration；
Step 205, according to multi-objective particle swarm algorithm more new formula, place is updated to particle rapidity and particle position
Reason；
Step 206, updated particle rapidity and particle position are substituted into object function fW(x),fP(x),fK(x) it in, obtains
To updated fitness function value, according to Pareto dominance relations, the particle with being stored in non-dominant collection is compared, deposits
Enter non-dominant value, delete by predominant value；
Step 207, by the non-dominant external concentration of concentration particle deposit, if the population of deposit is more than the maximum of external collection
Number is stored, using crowding distance method, extra relatively inferior solution is deleted, retains more excellent solution；
Step 208, descending processing is carried out to the particle that outside is concentrated；
Step 209, judge whether to have reached iterations, step 205 is rotated back into if not, continue to update iteration；If
Reach maximum iteration, then exported the particle of external concentration, optimize disaggregation as the Pareto of object function.
Wherein, adaptive value f (x) represents target function value.Choose weight fW(x), energy consumption fP(x), rigidity fK(x) it is target
Function.Mutually restricted by decision variable between each target, to the optimization of one of target must using other targets as cost,
The unit of each target is also inconsistent simultaneously, therefore, it is very difficult to objectively evaluate the superiority-inferiority of multiple target solution.Multi-objective optimization question
Solution is not unique, but there are an optimal solution set, element is known as Pareto optimal solutions in set.Multi-objective optimization question
In each solution correspond to an object vector so that its corresponding object vector be less than the corresponding target of Pareto optimal solutions to
Amount.Element in Pareto optimal solution sets is incomparable each other for all targets.
Multiple target Aircraft Steering Engine minimization problem can be described as:
In formulaFor area of feasible solution, E=f (x) | x ∈ RnIt is target solution vector space.
Further, the step 3 includes the following steps：
Step 301, Aircraft Steering Engine AHP Model is established；
Step 302, the judgment matrix compared two-by-two is constructed；
Step 303, whether test and judge matrix is consistency matrix, if it is, calculating power
Weight；If it is not, then the judgment matrix constructed to step 302 is adjusted.
Wherein, analytic hierarchy process (AHP) (Analytic Hierarchy Process, abbreviation AHP) be will be related to assignment decisions
Element resolve into the levels such as target, criterion, scheme, the multiple target that is combined of qualitative and quantitative analysis is carried out on basis herein
Method of decision analysis opinion.Its main feature is that the influence factor of the challenge of decision and its internal relation etc. is needed to go deep into
On the basis of analysis, the process mathematicization of decision is made using a small amount of quantitative data, so as to for multiple criteria, without apparent structure spy
Property complicated decision-making problems easy decision-making technique is provided.Aircraft Steering Engine mathematical optimization models are first had to steering engine evaluation problem
Methodization, stratification construct the structural model of a step analysis.Top is destination layer, usually needs to solve the problems, such as
Predeterminated target or rationality result；Middle layer is rule layer, contains all intermediate links taken to realize target, he can
To be made of several levels, for involved by target to be realized, consider criterion；The bottom is solution layer to realize that target may
The various measures taken, alternative etc..
The advantageous effect of the disclosure
(1) disclosure can simultaneously optimize multiple optimization aims, be conflicted with each other in multiple optimization aims,
Multiple optimization aims are effectively traded off to ask for comprehensive optimal solution.There is this method general applicability and the multiple target overall situation to receive simultaneously
Holding back property is suitble to steering engine optimization design application.
(2) the Pareto scheme collection of multiple target Aircraft Steering Engine optimization is obtained using particle swarm optimization algorithm, then passes through level
Analytic approach obtains best design.Designed scheme reduces steering engine weight, reduces steering engine energy consumption, increases steering engine rigidity, obtains
Steering engine optimum performance.
(3) decision problem of the multi-objective optimization design of power of Aircraft Steering Engine can be attributed to the range of operational research research, this public affairs
It opens application level analytic approach and calculates error minimum, the subjective judgement of policymaker and experience are imported into model, and be subject to quantification treatment,
There is practicability and validity in the complicated decision problem of processing.
Description of the drawings
Attached drawing shows the illustrative embodiments of the disclosure, and it is bright together for explaining the principle of the disclosure,
Which includes these attached drawings to provide further understanding of the disclosure, and attached drawing is included in the description and forms this
Part of specification.
Fig. 1 is the flow chart of the multiple target Aircraft Steering Engine optimum design method of disclosure specific embodiment.
Fig. 2 is the multi-objective particle swarm algorithm of the multiple target Aircraft Steering Engine optimum design method of disclosure specific embodiment
Flow chart.
Fig. 3 is that the analytic hierarchy process (AHP) of the multiple target Aircraft Steering Engine optimum design method of disclosure specific embodiment calculates stream
Cheng Tu.
Fig. 4 is the multiple target Aircraft Steering Engine optimum design method of disclosure specific embodiment based on analytic hierarchy process (AHP)
Aircraft Steering Engine assessment models.
Specific embodiment
The disclosure is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is only used for explaining related content rather than the restriction to the disclosure.It also should be noted that in order to just
It is illustrated only in description, attached drawing and the relevant part of the disclosure.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the disclosure can phase
Mutually combination.The disclosure is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
As shown in Figs 1-4, multiple target Aircraft Steering Engine optimum design method, includes the following steps：
Step 1 determines the object function and design variable of Aircraft Steering Engine, establishes the structural parameters multiple target of Aircraft Steering Engine
Optimized model；
Step 2 optimizes the structural parameters Model for Multi-Objective Optimization that step 1 obtains using particle cluster algorithm, obtains
Obtain Pareto optimization disaggregation；
Step 3 establishes Aircraft Steering Engine decision model using analytic hierarchy process (AHP).
The step 1 includes the following steps：
Step 101, Aircraft Steering Engine object function is determined：It is excellent as multiple target Aircraft Steering Engine to choose weight, energy consumption and rigidity
Change three object functions of design；
Step 102, the optimization design variable of Aircraft Steering Engine is determined：Determine target of the weight as Aircraft Steering Engine optimization design
Optimization design variable during function determines optimization design variable during object function of the energy consumption as Aircraft Steering Engine optimization design,
Determine optimization design variable during object function of the rigidity as Aircraft Steering Engine optimization design.
In step 101, Weight Model uses calculates the method that all parts weight sums up respectively, and calculation formula is as follows
Shown in formula：mSteering engine=∑ mComponent；
Energy consumption model is expressed as：W=T × ω × t, sets requirement rudder face are transferred to final position from initial position, swing
30 ° of time t=0.5s, T and ω represents torque and the rotating speed of motor respectively in formula；
The method that rigidity model is summed up using all parts rigidity is calculated respectively, calculation formula are shown below：
Wherein KR1、KR2、KR3、KBRespectively hinge R1、R2、R3And the rigidity of base of steering gear, for constant value, KyFor steering engine liquid
Pressing spring rigidity, KrFor steering engine output rod rigidity, KLFor rocking arm rigidity；
It is using the weight, energy consumption and rigidity of steering engine as target, then final to select following 4 design variables in step 102：Wherein, X1, X2, X3, X4 represent the length of connecting rod of steering engine, connecting rod swing initial position, start respectively
The effective area of cylinder, the discharge capacity of pump.
Then in m particle position Vector Groups, the position of i-th particle in the 4th dimension space and velocity vector are represented
It is as follows：
Xi={ xiL,xiθ,xiA,xiD(i=1,2 ..., m)
First particle is expressed as in the fast vector of 4 dimension spaces：
Vi={ viL,viθ,viA,viD(i=1,2 ..., m)
Up to the present optimum position that first particle is searched for is：
Pbest={ pbestL,pbestθ,pbestA,pbestD(i=1,2 ..., m)
The optimal location that entire population searches is：
Gbest={ gbestL,gbestθ,gbestA,gbestD(i=1,2 ..., m).
The step 2 includes the following steps,
Step 201, population, arrange parameter and maximum iteration are initialized, dimensionality of particle is 4 (xL,xθ,xA,xD),
Population is 100；
Step 202, particle rapidity and particle position initial value are set, limit speed value range, the particle that goes beyond the scope is assigned
The opposite speed of particle is given, makes particle optimizing still in setting range；
Step 203, particle rapidity and particle position are substituted into the object function f of structureW(x),fP(x),fK(x) it in, acquires
The fitness function value of each particle；
Step 204, according to Pareto dominance relations, non-dominant particle is selected, puts it into non-dominant concentration；
Step 205, according to multi-objective particle swarm algorithm more new formula：
Vi(t+1)=Vi(t)+c1*r1*(Pbest-Xi(t))+c2*r2*(Gbest-Xi(t))
Xi(t+1)=Xi(t)+Vi(t+1)
Processing is updated to particle rapidity and particle position；
Wherein c1And c2Referred to as Studying factors, r1And r2It is random number, value range is the random number between 0 to 1.
Step 206, updated particle rapidity and particle position are substituted into object function fW(x),fP(x),fK(x) it in, obtains
To updated fitness function value, according to Pareto dominance relations, the particle with being stored in non-dominant collection is compared, deposits
Enter non-dominant value, delete by predominant value；
Step 207, by the non-dominant external concentration of concentration particle deposit, if the population of deposit is more than the maximum of external collection
Number is stored, using crowding distance method, extra relatively inferior solution is deleted, retains more excellent solution；
Step 208, descending processing is carried out to the particle that outside is concentrated；
Step 209, judge whether to have reached iterations, step 205 is rotated back into if not, continue to update iteration；If
Reach maximum iteration, then exported the particle of external concentration, optimize disaggregation as the Pareto of object function.
The step 3 includes the following steps：
Step 301, Aircraft Steering Engine AHP Model is established；
Step 302, the judgment matrix compared two-by-two is constructed；
Step 303, whether test and judge matrix is consistency matrix, if it is, calculating power
Weight, if it is not, then the judgment matrix constructed to step 302 is adjusted.
In more detail,
Multiple target Aircraft Steering Engine optimum design method, as shown in Figure 1, specifically including following steps：
Step 1：It determines Aircraft Steering Engine object function, design variable and constraint, it is excellent to establish steering engine structural parameters multiple target
Change model.
For the whole installed power for improving aircraft, the weight of airborne Aircraft Steering Engine the big more limits answering for Aircraft Steering Engine
With, therefore quality becomes an important indicator of Aircraft Steering Engine performance.Meanwhile another importance of energy consumption as Aircraft Steering Engine
How energy index in the particularly efficient Aircraft Steering Engine design of Aircraft Steering Engine design, to the greatest extent may be used using existing technical equipment condition
Energy ground reduces Aircraft Steering Engine energy consumption, is that Aircraft Steering Engine design engineering staff must solve the problems, such as.At the same time, it navigates to cater to
The demand that empty steering engine works in various conditions improves the problem of rigidity also has to face as Aircraft Steering Engine.Therefore, it is doing
During Aircraft Steering Engine optimization design, generally require to optimize in multiple targets, the present invention with the weight of Aircraft Steering Engine, energy consumption,
Rigidity while as an optimization object function.
Step 101：Determine Aircraft Steering Engine object function；
For adapt to the integrated of aircraft, ultrahigh speed, high-power direction, traditional hydraulic actuation system certainly will will court
The direction for high-pressure trend, complicating is developed so that aircraft weight increases, then designs the high boat of low weight, less energy consumption, rigidity
Empty steering engine is of great significance.This patent chooses three mesh of weight, energy consumption, rigidity as multiple target Aircraft Steering Engine optimization design
Scalar functions.
The method that Weight Model is summed up using all parts weight is calculated respectively, calculation formula are shown below：mSteering engine
=∑ mComponent
Reduction motor output work(can be converted into, therefore energy consumption model can be expressed as by reducing the purpose of steering engine energy consumption：
W=T × ω × t
Sets requirement rudder face is transferred to final position from initial position, swings 30 ° of time t=0.5s.T and ω points in formula
Not Biao Shi motor torque and rotating speed.
The method that rigidity model is summed up using all parts rigidity is calculated respectively, calculation formula are shown below：
Wherein KR1、KR2、KR3、KBRespectively hinge R1、R2、R3And the rigidity of base of steering gear, constant value, K can be considered asyFor
Steering engine hydraulic spring stiffness, KrFor steering engine output rod rigidity, KLFor rocking arm rigidity.
Step 102：Determine Aircraft Steering Engine design variable；
It is needed to choose different design variables according to different optimization aims.If with weight design object as an optimization,
The steering engine structural parameters larger to the weight of steering engine should then be chosen, for example length of connecting rod, the discharge capacity of pump, motor torque are normal
Number etc..To obtain comparatively ideal optimization design effect, herein using these structure parameters as the optimization of Aircraft Steering Engine optimization design
Variable.In this way, during object function of the weight as steering engine optimization design, corresponding optimization design variable is length of connecting rod L, pump
Discharge capacity D, pressurized strut effective area A.
If with energy consumption design object as an optimization, need to choose the steering engine structure ginseng larger to the energy consumption of steering engine
Number, such as length of connecting rod, connecting rod swing initial position, pressurized strut limited areal etc..To obtain comparatively ideal optimization design effect,
The present invention is using these structure parameters as the optimized variable of steering engine optimization design.In this way, target of the energy consumption as EHA optimization designs
During function, corresponding optimization design variable be length of connecting rod L, the discharge capacity D of pump, pressurized strut effective area A, connecting rod swing initial bit
Put θ.
If with rigidity design object as an optimization, need to choose the steering engine structure ginseng larger to the stiffness effect of steering engine
Number, such as length of connecting rod, connecting rod swing initial position, pressurized strut limited areal etc..To obtain comparatively ideal optimization design effect,
Herein using these structure parameters as the optimized variable of steering engine optimization design.In this way, target of the rigidity as steering engine optimization design
During function, corresponding optimization design variable is length of connecting rod L, pressurized strut effective area A, connecting rod swing initial position θ.
If using the weight, energy consumption, rigidity of steering engine as target, final selected following 4 design variables：
Step 2：The multi-objective optimization question that step 1 obtains is optimized using particle cluster algorithm, obtains Pareto
Optimize disaggregation；
Multi-objective particle swarm algorithm is the optimizing algorithm carried out based on Pareto optimal solution sets mostly, using dominance relation structure
Make non-dominant collection, reuse dominance relation and compare particle quality, construct it is external collect, preserve population from starting to look for till now
All non-domination solutions arrived, guiding non-domination solution is rapidly close to the optimal forward positions of Pareto, multi-objective particle swarm algorithm flow such as Fig. 2
It is shown.
Step 201：Population is initialized, arrange parameter, maximum iteration, dimensionality of particle is 4 (xL,xθ,xA,xD), grain
Subnumber 100；
Step 202：Particle rapidity, position initial value are set, limit speed value range, the particle that goes beyond the scope assigns particle
Opposite speed makes particle optimizing still in setting range；
Step 203：Particle rapidity, position are brought into f in the object function of structureW(x),fP(x),fK(x), it acquires each
The fitness function value of particle；
Step 204：According to Pareto dominance relations, non-dominant particle is selected, puts it into non-dominant concentration；
Step 205：According to multi-objective particle swarm algorithm more new formula, processing is updated to particle rapidity and position；
Step 206：Bring speed after update and position into object function fW(x),fP(x),fK(x) it in, is fitted after obtaining update
Angle value is answered, according to Pareto dominance relations, the particle with being stored in non-dominant collection is compared, and is stored in non-dominant value, is deleted
By predominant value；
Step 207：By the non-dominant external collection of concentration particle deposit in, if the population of deposit is more than external collection most
Big storage number, using crowding distance method, deletes extra relatively inferior solution, retains more excellent solution；
Step 208：Particle is concentrated to carry out descending processing outside；
Step 209：Judge whether to have reached iterations, step 205 is rotated back into if not, continue to update iteration；If
Reach maximum iteration, then exported external concentration particle, optimize disaggregation as the Pareto of object function.
Step 3：Aircraft Steering Engine decision model is established using analytic hierarchy process (AHP)；
After obtaining Pareto optimization sets, how optimal-design method is obtained, this is a decision problem, and this patent makes
Decision is done with analytic hierarchy process (AHP), particular flow sheet is as indicated at 3
Step 301：Aircraft Steering Engine decision model is established using analytic hierarchy process (AHP).
Fig. 4 show the Aircraft Steering Engine decision model based on analytic hierarchy process (AHP), is Aircraft Steering Engine AHP model structure levels：
1st layer is destination layer, for the final Aircraft Steering Engine design preferably gone out；2nd layer is rule layer, and excellent evaluation side is selected for Aircraft Steering Engine
Face (criterion), including quality, energy consumption and rigidity；3rd layer is solution layer, for alternative each Aircraft Steering Engine design.
Step 302：Judgement Matricies
According to rule layer, the relationship between each index determines judgment matrix A
In formula, aijFor the opposite significance levels with target of index i and index j, value range 1-9, the bigger expression of numerical value
Relative importance is higher.Importance scale meaning table is as shown in table 1.
1 importance scale meaning table of table
By the significance level of more than statistical analysis 3 factors, judgment matrix is established according to table 1：
Step 303：The consistency check of judgment matrix
The computational methods of coincident indicator CI are as follows：
In formula, CI is the coincident indicator of judgment matrix；RI is the Aver-age Random Consistency Index of judgment matrix, specific
Value is referring to table 2；CR is the random consistency ratio of judgment matrix；λmaxFor characteristic root of a matrix maximum value；N is the rank of judgment matrix
Number.
The value of 2 Aver-age Random Consistency Index RI of table
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
By can be calculated λmax=3.08,Therefore this is sentenced
Disconnected matrix has satisfied consistency.If being unsatisfactory for consistency check, return to step 302 reconfigures judgment matrix.
Step 304：It is calculated by judgment matrix by comparison element for the relative weighting of the criterion
Continued products of the judgment matrix A per row element is calculated,Then
Seek MiN times root,By can be calculated
It willBy formulaIt is normalized, obtained normalized vector W is the weight system of each factor
Number, then
W=[0.066 0.149 0.785].
It will be understood by those of skill in the art that the above embodiment is used for the purpose of clearly demonstrating the disclosure, and simultaneously
Non- is that the scope of the present disclosure is defined.For those skilled in the art, may be used also on the basis of disclosed above
To make other variations or modification, and these variations or modification are still in the scope of the present disclosure.
Claims (5)
1. multiple target Aircraft Steering Engine optimum design method,
It is characterized by comprising the following steps：
Step 1 determines the object function and design variable of Aircraft Steering Engine, establishes the structural parameters multiple-objection optimization of Aircraft Steering Engine
Model；
Step 2 optimizes the structural parameters Model for Multi-Objective Optimization that step 1 obtains using particle cluster algorithm, obtains
Pareto optimizes disaggregation；
Step 3 establishes Aircraft Steering Engine decision model using analytic hierarchy process (AHP).
2. multiple target Aircraft Steering Engine optimum design method according to claim 1,
It is characterized in that, the step 1 includes the following steps：
Step 101, Aircraft Steering Engine object function is determined：Weight, energy consumption and rigidity is chosen to set as the optimization of multiple target Aircraft Steering Engine
Three object functions of meter；
Step 102, the optimization design variable of Aircraft Steering Engine is determined：Determine object function of the weight as Aircraft Steering Engine optimization design
When optimization design variable, determine optimization design variable during object function of the energy consumption as Aircraft Steering Engine optimization design, determine
Optimization design variable during object function of the rigidity as Aircraft Steering Engine optimization design.
3. multiple target Aircraft Steering Engine optimum design method according to claim 2,
It is characterized in that,
In step 101, Weight Model is using the method that calculating all parts weight sums up respectively, calculation formula such as following formula institute
Show：mSteering engine=∑ mComponent；
Energy consumption model is expressed as：W=T × ω × t, sets requirement rudder face are transferred to final position from initial position, swing 30 °
Time t=0.5s, T and ω represents torque and the rotating speed of motor respectively in formula；
The method that rigidity model is summed up using all parts rigidity is calculated respectively, calculation formula are shown below：
Wherein KR1、KR2、KR3、KBRespectively hinge R1、R2、R3And the rigidity of base of steering gear, for constant value, KyFor steering engine hydraulic pressure bullet
Spring rigidity, KrFor steering engine output rod rigidity, KLFor rocking arm rigidity；
It is using the weight, energy consumption and rigidity of steering engine as target, then final to select following 4 design variables in step 102：
Wherein, X1, X2, X3, X4 represent the length of connecting rod of steering engine, connecting rod swing initial position, make respectively
The effective area of dynamic cylinder, the discharge capacity of pump.
Then in m particle position Vector Groups, the position of i-th particle in the 4th dimension space and velocity vector are represented as follows：
Xi={ xiL,xiθ,xiA,xiD(i=1,2 ..., m)
First particle is expressed as in the fast vector of 4 dimension spaces：
Vi={ viL,viθ,viA,viD(i=1,2 ..., m)
Up to the present optimum position that first particle is searched for is：
Pbest={ pbestL,pbestθ,pbestA,pbestD(i=1,2 ..., m)
The optimal location that entire population searches is：
Gbest={ gbestL,gbestθ,gbestA,gbestD(i=1,2 ..., m).
4. multiple target Aircraft Steering Engine optimum design method according to claim 3,
It is characterized in that, the step 2 includes the following steps：
Step 201, population, arrange parameter and maximum iteration are initialized, dimensionality of particle is 4 (xL,xθ,xA,xD), population
It is 100；
Step 202, particle rapidity and particle position initial value are set, limit speed value range, the particle that goes beyond the scope assigns grain
The opposite speed of son, makes particle optimizing still in setting range；
Step 203, particle rapidity and particle position are substituted into the object function f of structureW(x),fP(x),fK(x) in, subscript W,
P, K represents weight, energy consumption, rigidity respectively, acquires the fitness function value of each particle；
Step 204, according to Pareto dominance relations, non-dominant particle is selected, puts it into non-dominant concentration；
Step 205, according to multi-objective particle swarm algorithm more new formula：
Vi(t+1)=Vi(t)+c1*r1*(Pbest-Xi(t))+c2*r2*(Gbest-Xi(t))
Xi(t+1)=Xi(t)+Vi(t+1)
Processing is updated to particle rapidity and particle position；
Wherein, c1And c2Referred to as Studying factors, r1And r2It is random number, value range is the random number between 0 to 1；
Step 206, updated particle rapidity and particle position are substituted into object function fW(x),fP(x),fK(x) it in, obtains more
Fitness function value after new, according to Pareto dominance relations, the particle with being stored in non-dominant collection is compared, and deposit is non-
Predominant value is deleted by predominant value；
Step 207, by the non-dominant external concentration of concentration particle deposit, if the population of deposit is more than the maximum storage of external collection
Number using crowding distance method, deletes extra relatively inferior solution, retains more excellent solution；
Step 208, descending processing is carried out to the particle that outside is concentrated；
Step 209, judge whether to have reached iterations, step 205 is rotated back into if not, continue to update iteration；If reach
Maximum iteration then exports the particle of external concentration, optimizes disaggregation as the Pareto of object function.
5. the multiple target Aircraft Steering Engine optimum design method according to claim 1 or 4,
It is characterized in that, the step 3 includes the following steps：
Step 301, Aircraft Steering Engine AHP Model is established；
Step 302, the judgment matrix compared two-by-two is constructed；
Step 303, whether test and judge matrix is consistency matrix, if it is, weight is calculated, if it is not, then to step 302
The judgment matrix of construction is adjusted.
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