CN105022881B - A kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization - Google Patents

A kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization Download PDF

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CN105022881B
CN105022881B CN201510434862.XA CN201510434862A CN105022881B CN 105022881 B CN105022881 B CN 105022881B CN 201510434862 A CN201510434862 A CN 201510434862A CN 105022881 B CN105022881 B CN 105022881B
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段海滨
李俊男
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Beihang University
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Abstract

The present invention is a kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization, and implementation step is：Step 1：Build carrier-borne aircraft and stern flow Simulink emulation modules；Step 2：Initialize dove colony optimization algorithm parameter；Step 3：Design cost function；Step 4：Guidance law parameter to be optimized is set in Simulink；Step 5：Optimizing is carried out using the map compass operator of dove colony optimization algorithm；Step 6：Optimizing is carried out using the terrestrial reference operator of dove colony optimization algorithm；Step 7：Store results are simultaneously verified.This method can effectively reduce the work difficulty for flying control designer, and improve robustness of the carrier landing guidance rule to stern flow interference.

Description

A kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization
Technical field
The present invention is a kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization, belongs to carrier-borne aircraft technology Field.
Background technology
Carrier-borne aircraft is the important force in national defence, the important role of performer in naval of various countries.Carrier-borne aircraft technology is each The focus of state's research, embodies a national defense technology strength, and domestic and international many scholars are to the related every skill of carrier-borne aircraft Art carried out in-depth study.Ship-board aircraft and land airplane have very big difference, and marine particular surroundings to carrier-borne aircraft Warship brings huge challenge, and carrier-borne aircraft needs to land in type aircraft carrier deck, and type aircraft carrier deck limited length, it is impossible to is carrier-borne Machine provides bigger ground run distance, therefore carrier-borne aircraft must successfully hook subtracting together to realize in four check cables in landing The purpose of speed.Aircraft carrier can at sea be caused its deck that moving for six degree of freedom occurs by the effect of wave, if be added without Compensation can cause the deviation of the tactile warship point of carrier-borne aircraft.In addition, a factor far-reaching to carrier landing is exactly aircraft carrier wake flow Influence, aircraft carrier advances across the sea, or when blowing air over deck, to producing interference in air flow behind aircraft carrier, wake flow can make Obtain carrier-borne aircraft and deviate given glide path, impacted safely against warship.
Manually warship can be influenceed by weather conditions and human factor, it is impossible to ensure under severe conditions warship peace Entirely, therefore there is autonomous landing on the ship technology, it is possible to achieve the round-the-clock autonomous landing on the ship of carrier-borne aircraft.Guidance law optimization design is autonomous The important part of warship technology.When designing carrier landing guidance law, it is necessary to be optimized to guidance law so that warship and lead Drawing rule strengthens the robustness of wake radiation, improves tracking accuracy of the carrier-borne aircraft to glide path, reach safety the purpose of warship. The different phase of warship can use different guidance law parameters according to real needs, and these parameters are difficult using the method for artificial tune ginseng The effect being optimal, great work load can be also brought to designer, therefore develop a kind of automatic optimization method and enter Row guidance law parameter adjustment is very necessary technology, can not only reduce the live load of designer, can also improve and is System performance.
Swarm intelligence is an important branch of bionic intelligence, people by the observation to nature biotechnology colony, by The inspiration of biocenose behavior in nature, general increase, its behavior pattern is depicted with the mode of mathematics on this basis Come.On the basis of Swarm Intelligent Model, there has been proposed the concept of Swarm Intelligent Algorithm, with the behavior mould of biocenose Formula carrys out solving-optimizing problem.Dove group algorithm (Pigeon Inspired Optimization, PIO) is that Haibin Duan exist A kind of new heuristic colony intelligence optimized algorithm proposed in 2014, the algorithm are inspired by pigeon group behavior, according to Pigeon is during target is found, successively according to the behavioral characteristic of magnetic field and terrestrial reference as instruction, it is established that map compass with Two kinds of algorithm mechanism of terrestrial reference.
Dove group can carry out Primary Location, then according to terrestrial reference during destination is found with initial reference to the sun and magnetic field Be accurately positioned, according to this characteristic, dove group's algorithm proposes two kinds of corresponding operators, respectively map compass operator and Terrestrial reference mechanism, to simulate this characteristic of dove group, and both operators are combined into solution optimization problem.
(1) map compass operator
In map and compass operator, dove group advances with the guide mode of compass according to the map, in D dimension spaces, i-th The positional information X of pigeoniWith velocity information ViPer a generation, once, specific replacement criteria is shown below for renewal：
Vi(t)=Vi(t-1)·e-Rt+rand·(Xg-Xi(t-1)) (1)
Xi(t)=Xi(t-1)+Vi(t) (2)
In formula, R is map and the compass factor, rand be one from 0 to 1 between a random number randomly generating, XgIt is Global optimum position under current iteration number, is obtained by the positional information of more all pigeons.Map compass operator shows Be intended to as shown in Figure 1, the pigeon of rightmost is the pigeon for possessing global optimum's positional information in figure, thin arrow represent pigeon it Preceding velocity, block arrow represented under the machining function, the adjustment direction vector of pigeon speed, and two velocities are superimposed Result afterwards is exactly the velocity of current pigeon.
(2) terrestrial reference operator
Because pigeon is finding the later stage of destination, what is relied primarily on is terrestrial reference to carry out the guiding of target, is this basis Its behavioral trait proposes terrestrial reference operator.The operator provides that dove group's number per a generation halves, in order to arrive at faster, Remaining pigeon directly flies to destination.Specific replacement criteria is shown below：
Xi(t)=Xi(t-1)+rand·(Xc(t)-Xi(t-1)) (5)
In above formula, NpFor the number of dove group, fitness is the cost function of pigeon positional information, in order to try to achieve cost Functional minimum value, f can be takenminAs object function, XcIt is the weighting place-centric of dove group.The schematic diagram of terrestrial reference operator is such as Shown in accompanying drawing 2, pigeon outside circle departs from dove group, and the pigeon of center is the destination of remaining pigeon, remaining dove group Drawn close to purpose center rapidly.The overall flow figure of dove group's algorithm is as shown in Figure 3.
The content of the invention
1st, goal of the invention：
The present invention proposes a kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization, and the purpose is to carry Intelligent parameter setting method is restrained for a kind of carrier landing guidance, to reduce the work difficulty of designer, and improves guidance law to warship The robustness of wake radiation.
This method builds typical stern flow interference using carrier-borne aircraft Simulink control simulation models in Controlling model Module, the error of carrier-borne aircraft closed-loop system given glide path of deviation under stern flow interference is obtained by emulation, in this error base Optimization problem object function is built on plinth, the guidance law parameter value of optimization is solved using dove colony optimization algorithm.
2nd, technical scheme：
The features such as present invention is strong using colony intelligence optimized algorithm ability of searching optimum, and application is wide, exploitation one kind are based on dove The carrier-borne aircraft autonomous landing on the ship guidance law Optimization Design of colony optimization algorithm is as follows the step of this method：
Step 1：Build carrier-borne aircraft and stern flow Simulink emulation modules
Stern flow model in this method considers following four component：Air turbulence, the disturbance of stable state aircraft carrier wake flow, cycle Property aircraft carrier wake flow disturbance and randomness aircraft carrier wake flow disturbance.
U1And W1Respectively horizontal air turbulent flow component and vertical air turbulence component.Using unit white noise through overmolding Filter filtering obtains, and their spatial power spectrum density is shown below：
U2And W2Respectively horizontal wake flow steady-state component and vertical wake flow steady-state component.They can pass through piece-wise linearization Method obtain, the concrete shape of its curve is as shown in Figure 4.
U3And W3Respectively horizontal wake flow periodic component and vertical wake flow periodic component, what they can be calculated by formula Method obtains, and its specific formula for calculation is shown below：
Wherein,
In formula, ωsFor pitch frequency, θsFor pitch amplitude, VwodFor deck wind, V=10m/s carrier-borne aircraft velocities of approach, X For aircraft from warship with a distance from, P is random phase.
U4And W4Respectively horizontal wake flow random component and vertical wake flow random component.Using unit white noise through overmolding Filter filtering obtains, and its calculation formula is shown below：
Wherein, rand is random number, and σ (x) and τ (x) are the coefficients with distance dependent, and its shape provides in figure 5.
Carrier-borne aircraft model and its inner ring automatic pilot used are provided by specific design requirement.
Step 2：Initialize dove colony optimization algorithm parameter
(1) Optimal Parameters dimension D is initialized
The parameter optimized in this method is the parameter in carrier-borne aircraft autonomous landing on the ship guidance law, can be according to the form of guidance law It is different and change.
(2) population quantity M is initialized
The population quantity M of colony intelligence optimized algorithm influences very big on effect of optimization.The selection of general population quantity is optimization 3-5 times of problem dimension.
(3) attenuation coefficient R is initialized
Attenuation coefficient R is applied in the map compass operator of dove group's algorithm, and it influences the decay speed of particle itself speed.
(4) population position and speed are initialized
The position upper limit P for not setting colony in search spacemaxWith position lower limit Pmin, and speed limit VmaxAnd speed Lower limit Vmin.An initial position x is initialized to each particle in populationiWith initial speed Vi。
(5) algorithmic algebra is set
Dove colony optimization algorithm has two operators, is map compass operator and terrestrial reference operator respectively, is needed before algorithm computing point Not She Ding the operation of two algorithms maximum algebraically NC1And NC2。
Step 3：Design cost function
Being set in for cost function is very crucial in the optimization of carrier landing guidance rule, and its setting directly affects effect of optimization. In this method, the target of optimization guidance law parameter is to make carrier-borne aircraft under the stern flow interference during warship deviate to specify downslide The displacement in road is minimum, and controlled quentity controlled variable input is as small as possible.Therefore the cost function being defined as follows：
Wherein, hcFor specify glide path highly instruct, h for carrier-borne aircraft highly, θcFor the angle of pitch to inner ring automatic pilot Instruction, θcssFor stable state when the angle of pitch instruction, w1And w2For weight, t1And t2The period being concerned about for designer.
Step 4：Guidance law parameter to be optimized is set in Simulink
Parameter in optimized algorithm is passed into Simulink models, is loaded into the initialization function of Simulink modules With the .mat files of guidance law parameter value, the guidance law defined is write in guidance law parametric gain module in Simulink Parametric variable name.
Step 5：Optimizing is carried out using PIO map compass operator
Using the group position and speed of initialization, global optimum position is chosen according to initial individual cost function value Xg.According to formula Vi(t)=Vi(t-1)·e-Rt+rand·(Xg-Xi(t-1)) (1) and formula Xi(t)=Xi(t-1)+Vi(t) (2) the speed V of each individual of formula renewal iniWith position xi, the cost function value of newly-generated particle is calculated, if new particle Cost function value it is lower than the cost function value of global optimum position, then newly-generated particle position is defined as the new overall situation Optimal location Xg.Optimizing is carried out using map compass operator repeatedly, until operation algebraically is more than map compass operator maximum algebraically NC1When stop.
Step 6：Optimizing is carried out using PIO terrestrial references operator
By the use of map compass operator optimizing result as terrestrial reference operator initial population, according to formula Xc(t)=∑ Xi (t)·fitness(Xi(t))/∑fitness(Xi(t)) (4) and formula Xi(t)=Xi(t-1)+rand·(Xc(t)-Xi(t- 1)) the speed V of each individual of formula renewal in (5)iWith position xi, the cost function value of newly-generated particle is calculated, if new grain The cost function value of son is lower than the cost function value of global optimum position, then newly-generated particle position is defined as new complete Office optimal location Xg.According to formula Np(t)=Np(t-1)/2 (3) calculate the Population of new population, are calculated according to formula (3) As a result cost function larger portion individual in colony is given up, preferably colony enters as colony is retained in selection current group Row next round optimizing, optimizing is carried out using terrestrial reference operator repeatedly, until operation algebraically is more than terrestrial reference operator maximum algebraically NC2When stop Only.
Step 7：Store results are simultaneously verified
The result of terrestrial reference arithmetic operators optimization is considered as final guidance law optimum results, and this result is stored in into .mat files In, the .mat files are called in Simulink modules, are emulated using the guidance law parameter of optimization, observation carrier-borne aircraft is in tail Drain off and disturb the lower tracking accuracy to specifying glide path, assess the result of guidance law optimization.If being unsatisfied with to optimum results, can adjust The cost function used during whole optimization, restarting algorithm optimizes, until obtaining satisfied optimum results.
3rd, advantage and effect：
The present invention proposes a kind of carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization, and the purpose is to carry Intelligent parameter setting method is restrained for a kind of carrier landing guidance.This method effectively reduces the work difficulty of design of control law personnel, and And improve the robustness that guidance law is disturbed stern flow.It is different to the performance requirement of guidance law under different warship environment, By changing the cost function of optimization problem, this method can rapidly design the guidance law for meeting to require according to real needs Parameter, mitigate designer's work load.
Brief description of the drawings
Fig. 1 dove colony optimization algorithm map compass operator schematic diagrames.
Fig. 2 dove colony optimization algorithm terrestrial reference operator schematic diagrames.
Fig. 3 dove colony optimization algorithm overall flow figures.
The horizontal wake flow steady-state component schematic diagrames of Fig. 4 a.
The vertical wake flow steady-state component schematic diagrames of Fig. 4 b.
Fig. 5 a σ (x) are with warship distance change curve synoptic diagram.
Fig. 5 b τ (x) are with warship distance change curve synoptic diagram.
Fig. 6 carrier landings control the general frame.
The horizontal wake radiation schematic diagram of Fig. 7 a synthesis.
The vertical wake radiation schematic diagram of Fig. 7 b synthesis.
Fig. 8 cost function evolution curve synoptic diagrams.
Glide path deviation schematic diagram under the interference of Fig. 9 stern flows.
Figure 10 the angle of attack response schematic diagram during warship.
Figure 11 the speed responsive schematic diagram during warship.
Figure 12 the pitching angular response schematic diagram during warship.
Figure 13 automatic pilot instruction input schematic diagram during warship.Figure 14 FB(flow block)s of the present invention.
Label and symbol description are as follows in figure：
Nc --- optimized algorithm iterations
N --- it is unsatisfactory for condition (no)
Y --- meet condition (YES)
θc--- the automatic pilot angle of pitch instructs
δe--- elevator instructs
T --- throttle thrust command
herror--- height error
α --- the angle of attack
V --- speed
θ --- the angle of pitch
Embodiment
See Fig. 1-Figure 14, this hair is verified below by a specific carrier-borne aircraft autonomous landing on the ship guidance law Optimal Example The validity of bright proposed design method, the stern flow interference used in this example derive from American army mark MIL28785C to The flow perturbation model gone out, the carrier-borne aircraft model used are F-18 longitudinal directions nonlinear model, and the inner ring automatic pilot used is Angle of pitch instruction type, approach power compensation system is used simultaneously during carrier-borne aircraft is longitudinally controlled.Carrier-borne aircraft speed in this example For 70m/s, warship is about completed before and after 84s.Experimental calculation machine is configured to i5-4210M processors, 2.60Ghz dominant frequency, in 4G Deposit, software is MATLAB 2013b versions.
See Figure 14, the specific implementation step of this example is as follows：
Step 1：Build carrier-borne aircraft and stern flow Simulink emulation modules
Stern flow model in this example considers four components：Air turbulence, the disturbance of stable state aircraft carrier wake flow, cycle Property aircraft carrier wake flow disturbance and randomness aircraft carrier wake flow disturbance.
U1And W1Respectively horizontal air turbulent flow component and vertical air turbulence component.Using unit white noise through overmolding Filter filtering obtains, and their spatial power spectrum density is shown below：
U2And W2Respectively horizontal wake flow steady-state component and vertical wake flow steady-state component.They can pass through piece-wise linearization Method obtain, the concrete shape of its curve is as shown in Figure 4.
U3And W3Respectively horizontal wake flow periodic component and vertical wake flow periodic component, what they can be calculated by formula Method obtains, and its specific formula for calculation is shown below：
Wherein,
In formula, ωs=0.7rad/s is pitch frequency, θs=1.414rad is pitch amplitude, Vwod=10m/s is deck Wind, V=10m/s are carrier-borne aircraft velocity of approach, X be aircraft from warship with a distance from, its scope is that [- 5000,0] m, P is random phase, its Value is between [0,2 π].
U4And W4Respectively horizontal wake flow random component and vertical wake flow random component.Using unit white noise through overmolding Filter filtering obtains, and its calculation formula is shown below：
Wherein, rand is random number, and σ (x) and τ (x) are the coefficients with distance dependent, and its shape provides in figure 5.Warship Carrier aircraft warship control the general frame as shown in Figure 6, and the stern flow interference of the synthesis used in emulation is as shown in Figure 7.
Step 2：Initialize dove colony optimization algorithm parameter
(1) Optimal Parameters dimension D is initialized
The parameter optimized in this example is the parameter in carrier-borne aircraft autonomous landing on the ship guidance law, and the guidance law of acquiescence is PID-DD (ratio, integration, differential, second-order differential) form, it is therefore desirable to which the parameter of optimization is 4, setting Optimal Parameters dimension D=4.
(2) population quantity M is initialized
The population quantity M of colony intelligence optimized algorithm influences very big on effect of optimization.The selection of general population quantity is optimization 3-5 times of problem dimension.The default value of population quantity is 15 in this example.
(3) attenuation coefficient R is initialized
Attenuation coefficient R is applied in the map compass operator of dove group's algorithm, and it influences the decay speed of particle itself speed. Attenuation coefficient R default value is 0.1 in this example.
(4) population position and speed are initialized
The position upper limit P for not setting colony in search spacemax=[3,3,5,5] and position lower limit Pmin=[0,0,0,0], And speed limit Vmax=0.2 (Pmax-Pmin) and lower velocity limit Vmin=0.2 (Pmin-Pmax).To each particle in population Initialize an initial position xiWith initial speed Vi。
(5) algorithmic algebra is set
Dove colony optimization algorithm has two operators, is map compass operator and terrestrial reference operator respectively, is needed before algorithm computing point Not She Ding the operation of two algorithms maximum algebraically NC1=15 and NC2=20.
Step 3：Design cost function
Being set in for cost function is very crucial in the optimization of carrier landing guidance rule, and its setting directly affects effect of optimization. In this method, the target of optimization guidance law parameter is to make carrier-borne aircraft under the stern flow interference during warship deviate to specify downslide The displacement in road is minimum, and controlled quentity controlled variable input is as small as possible.Therefore the cost function being defined as follows：
Wherein, hcHighly being instructed for specified glide path, it is the constant straight line of a slope, initial value 305.8m, Slope is -4.2706m/s.H be carrier-borne aircraft height, θcTo be instructed to the angle of pitch of inner ring automatic pilot, θcss=-3.2deg For stable state when the angle of pitch instruction, w1=1 and w2=0.2 is weight coefficient, t1=20s and t2=83s be designer be concerned about when Between section.
Step 4：Guidance law parameter to be optimized is set in Simulink
Parameter in optimized algorithm is passed into Simulink models, is loaded into the initialization function of Simulink modules With " Kopt.mat " file of guidance law parameter value, write what is defined in guidance law parametric gain module in Simulink Guidance law parametric variable Kopt (1), Kopt (2), Kopt (3), Kopt (4), this four variables correspond to ratio, integration, micro- respectively Divide the gain with second-order differential.
Step 5：Optimizing is carried out using PIO map compass operator
Using the group position and speed of initialization, global optimum position is chosen according to initial individual cost function value Xg.The speed V of each individual of renewaliWith position xi, the cost function value of newly-generated particle is calculated, if the cost letter of new particle The cost function value of numeric ratio global optimum position is lower, then newly-generated particle position is defined as new global optimum position Xg.Optimizing is carried out using map compass operator repeatedly, until operation algebraically is more than map compass operator maximum algebraically NC1When stop.
Step 6：Optimizing is carried out using PIO terrestrial references operator
The initial population of terrestrial reference operator, the speed V of each individual of renewal are used as by the use of the result of map compass operator optimizingi With position xi, the cost function value of newly-generated particle is calculated, if the cost function value of new particle is than the generation of global optimum position Valency functional value is lower, then newly-generated particle position is defined as new global optimum position Xg.Novel species is calculated according to formula (3) The Population of group, the result calculated according to formula (3) give up cost function larger portion individual in colony, and selection is current Preferably colony carries out next round optimizing as colony is retained in colony, carries out optimizing using terrestrial reference operator repeatedly, until operation Algebraically is more than terrestrial reference operator maximum algebraically NC2When stop.
Step 7：Store results are simultaneously verified
The result of terrestrial reference arithmetic operators optimization is considered as final guidance law optimum results, and the result of this suboptimization is Kopt (1) =2.156, Kopt (2)=0.403, Kopt (3)=2.974, Kopt (4)=3.185.This result is stored in " Kopt.mat " In file, called in Simulink modules and be somebody's turn to do " Kopt.mat " file, emulated using the guidance law parameter of optimization, observed Carrier-borne aircraft, to the tracking accuracy of specified glide path, assesses the result that guidance law optimizes under wake radiation.The cost of optimization process As shown in Figure 8, the error of glide-slope deviation as shown in Figure 9, angle of attack response during warship, speed to function evolution curve Respectively as shown in accompanying drawing 10- accompanying drawings 12, inner ring angle of pitch instruction input is as shown in figure 13 for response and pitching angular response.If to optimization As a result it is unsatisfied with, cost function that can be to be used during adjusting and optimizing, restarting algorithm optimizes, until obtaining the excellent of satisfaction Change result.
By above-mentioned optimization process, one group of guidance law parameter to stern flow with higher robustness can be obtained, is optimized The evolution curve of middle cost function as shown in Figure 8, warship simulation result figure as shown in accompanying drawing 9-13, can be with from simulation result Find out, the displacement of carrier-borne aircraft glide-slope deviation during warship is smaller, and systematic entirety can be satisfactory.

Claims (1)

  1. A kind of 1. carrier-borne aircraft autonomous landing on the ship Guidance Law Design method based on dove group optimization, it is characterised in that：This method specifically walks It is rapid as follows：
    Step 1：Build carrier-borne aircraft and stern flow Simulink emulation modules
    Stern flow model considers following four component：Air turbulence, the disturbance of stable state aircraft carrier wake flow, the disturbance of periodicity aircraft carrier wake flow Disturbed with randomness aircraft carrier wake flow；
    U1And W1Respectively horizontal air turbulent flow component and vertical air turbulence component, molding filtration is passed through using unit white noise Device filters to obtain, and their spatial power spectrum density is shown below：
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;Phi;</mi> <msub> <mi>U</mi> <mn>1</mn> </msub> </msub> <mo>=</mo> <mfrac> <mn>5.663</mn> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mn>30.48</mn> <mi>&amp;Omega;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;Phi;</mi> <msub> <mi>W</mi> <mn>1</mn> </msub> </msub> <mo>=</mo> <mfrac> <mn>2.0275</mn> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mn>30.48</mn> <mi>&amp;Omega;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
    U2And W2Respectively horizontal wake flow steady-state component and vertical wake flow steady-state component, they are obtained by the method for piece-wise linearization Arrive；
    U3And W3Respectively horizontal wake flow periodic component and vertical wake flow periodic component, the method that they are calculated by formula obtain, Its specific formula for calculation is shown below：
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>U</mi> <mn>3</mn> </msub> <mo>=</mo> <msub> <mi>&amp;theta;</mi> <mi>s</mi> </msub> <msub> <mi>V</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>2.22</mn> <mo>+</mo> <mn>0.0009</mn> <mi>X</mi> <mo>)</mo> </mrow> <mi>C</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>W</mi> <mn>3</mn> </msub> <mo>=</mo> <msub> <mi>&amp;theta;</mi> <mi>s</mi> </msub> <msub> <mi>V</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mn>4.98</mn> <mo>+</mo> <mn>0.0018</mn> <mi>X</mi> <mo>)</mo> </mrow> <mi>C</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
    Wherein,
    <mrow> <mi>C</mi> <mo>=</mo> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mo>{</mo> <msub> <mi>&amp;omega;</mi> <mi>s</mi> </msub> <mo>&amp;lsqb;</mo> <mi>t</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mfrac> <mrow> <mi>V</mi> <mo>-</mo> <msub> <mi>V</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>d</mi> </mrow> </msub> </mrow> <mrow> <mn>0.85</mn> <msub> <mi>V</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>d</mi> </mrow> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mi>X</mi> <mrow> <mn>0.85</mn> <msub> <mi>V</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>d</mi> </mrow> </msub> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mo>+</mo> <mi>P</mi> <mo>}</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
    In formula, ωsFor pitch frequency, θsFor pitch amplitude, VwodFor deck wind, V=10m/s carrier-borne aircraft velocities of approach, X is winged Machine from warship with a distance from, P is random phase；
    U4And W4Respectively horizontal wake flow random component and vertical wake flow random component, molding filtration is passed through using unit white noise Device filters to obtain, and its calculation formula is shown below：
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>U</mi> <mn>4</mn> </msub> <mo>=</mo> <mo>&amp;lsqb;</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&amp;rsqb;</mo> <mo>&amp;CenterDot;</mo> <mo>&amp;lsqb;</mo> <mfrac> <mi>s</mi> <mrow> <mi>s</mi> <mo>+</mo> <mn>0.1</mn> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mi>sin</mi> <mrow> <mo>(</mo> <mn>10</mn> <mi>&amp;pi;</mi> <mi>t</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <msqrt> <mrow> <mn>2</mn> <mi>&amp;tau;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </msqrt> </mrow> <mrow> <mi>&amp;tau;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>s</mi> <mo>+</mo> <mn>1</mn> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>V</mi> <mn>4</mn> </msub> <mo>=</mo> <msub> <mi>W</mi> <mn>4</mn> </msub> <mo>=</mo> <mo>&amp;lsqb;</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&amp;rsqb;</mo> <mo>&amp;CenterDot;</mo> <mo>&amp;lsqb;</mo> <mfrac> <mi>s</mi> <mrow> <mi>s</mi> <mo>+</mo> <mn>0.1</mn> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mi>sin</mi> <mrow> <mo>(</mo> <mn>10</mn> <mi>&amp;pi;</mi> <mi>t</mi> <mo>)</mo> </mrow> <mfrac> <mrow> <mn>0.035</mn> <msub> <mi>V</mi> <mrow> <mi>w</mi> <mi>o</mi> <mi>d</mi> </mrow> </msub> <msqrt> <mn>6.66</mn> </msqrt> </mrow> <mrow> <mn>3.33</mn> <mi>s</mi> <mo>+</mo> <mn>1</mn> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, rand is random number, and σ (x) and τ (x) are the coefficient with distance dependent, carrier-borne aircraft model and its inner ring used Automatic pilot is provided by specific design requirement；
    Step 2：Initialize dove colony optimization algorithm parameter
    (1) Optimal Parameters dimension D is initialized
    The parameter of optimization is the parameter in carrier-borne aircraft autonomous landing on the ship guidance law, is changed according to the form of guidance law difference；
    (2) population quantity M is initialized
    The population quantity M of colony intelligence optimized algorithm is very big on effect of optimization influence, and the selection of general population quantity is optimization problem 3-5 times of dimension；
    (3) attenuation coefficient R is initialized
    Attenuation coefficient R is applied in the map compass operator of dove group's algorithm, and it influences the decay speed of particle itself speed；
    (4) population position and speed are initialized
    In the position upper limit P of search space setting colonymaxWith position lower limit Pmin, and speed limit VmaxAnd lower velocity limit Vmin；To each individual position x in populationiWith speed ViAll assign initial value；
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>P</mi> <mi>min</mi> </msub> <mo>+</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>max</mi> </msub> <mo>-</mo> <msub> <mi>P</mi> <mi>min</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>V</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>V</mi> <mi>min</mi> </msub> <mo>+</mo> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>V</mi> <mi>max</mi> </msub> <mo>-</mo> <msub> <mi>V</mi> <mi>min</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    (5) algorithmic algebra is set
    Dove colony optimization algorithm has two operators, is map compass operator and terrestrial reference operator respectively, needs to set respectively before algorithm computing The maximum algebraically NC of fixed two algorithms operation1And NC2；
    Step 3：Design cost function
    Being set in for cost function is very crucial in the optimization of carrier landing guidance rule, and its setting directly affects effect of optimization；Optimization The target of guidance law parameter is to make carrier-borne aircraft under the stern flow interference during warship deviate to specify the displacement of glide path minimum, And controlled quentity controlled variable inputs cost function that is as small as possible, therefore being defined as follows：
    <mrow> <mi>cos</mi> <mi> </mi> <mi>t</mi> <mo>=</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <munderover> <mo>&amp;Integral;</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <msub> <mi>t</mi> <mn>2</mn> </msub> </munderover> <mo>|</mo> <mrow> <msub> <mi>h</mi> <mi>c</mi> </msub> <mo>-</mo> <mi>h</mi> </mrow> <mo>|</mo> <mi>d</mi> <mi>t</mi> <mo>+</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <munderover> <mo>&amp;Integral;</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <msub> <mi>t</mi> <mn>2</mn> </msub> </munderover> <mo>|</mo> <mrow> <msub> <mi>&amp;theta;</mi> <mi>c</mi> </msub> <mo>-</mo> <msub> <mi>&amp;theta;</mi> <mrow> <mi>c</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> </mrow> <mo>|</mo> <mi>d</mi> <mi>t</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, hcFor specify glide path highly instruct, h for carrier-borne aircraft highly, θcTo refer to the angle of pitch of inner ring automatic pilot Order, θcssFor stable state when the angle of pitch instruction, w1And w2For weight, t1And t2The period being concerned about for designer；
    Step 4：Guidance law parameter to be optimized is set in Simulink
    Parameter in optimized algorithm is passed into Simulink models, in the initialization function of Simulink modules be loaded into The .mat files of guidance law parameter value, the guidance law parameter defined is write in guidance law parametric gain module in Simulink Variable name；
    Step 5：Optimize map compass operator using dove group and carry out optimizing
    Using the group position and speed of initialization, global optimum position X is chosen according to initial individual cost function valueg；Root According to formula Vi(t)=Vi(t-1)·e-Rt+rand·(Xg-Xi(t-1)) (1) and formula Xi(t)=Xi(t-1)+Vi(t) in (2) The each individual of formula renewal speed ViWith position xi, the cost function value of newly-generated particle is calculated, if the cost of new particle Functional value is lower than the cost function value of global optimum position, then newly-generated particle position is defined as new global optimum position Put Xg；Optimizing is carried out using map compass operator repeatedly, until operation algebraically is more than map compass operator maximum algebraically NC1When stop Only；
    Step 6：Optimize terrestrial reference operator using dove group and carry out optimizing
    By the use of map compass operator optimizing result as terrestrial reference operator initial population, according to formula Xc(t)=∑ Xi(t)· fitness(Xi(t))/∑fitness(Xi(t)) (4) and formula Xi(t)=Xi(t-1)+rand·(Xc(t)-Xi(t-1))(5) In each individual of formula renewal speed ViWith position xi, the cost function value of newly-generated particle is calculated, if the generation of new particle Valency functional value is lower than the cost function value of global optimum position, then newly-generated particle position is defined as new global optimum Position Xg；According to formula Xp(t)=Np(t-1)/2 (3) calculate the Population of new population, and the result calculated according to formula (3) is given up Cost function larger portion individual in colony is abandoned, preferably colony is next as colony's progress is retained in selection current group Optimizing is taken turns, carries out optimizing using terrestrial reference operator repeatedly, until operation algebraically is more than terrestrial reference operator maximum algebraically NC2When stop；
    Step 7：Store results are simultaneously verified
    The result of terrestrial reference arithmetic operators optimization is considered as final guidance law optimum results, and this result is stored in .mat files, The .mat files are called in Simulink modules, are emulated using the guidance law parameter of optimization, observation carrier-borne aircraft is done in wake flow The lower tracking accuracy to specifying glide path is disturbed, assesses the result of guidance law optimization；If being unsatisfied with to optimum results, during adjusting and optimizing The cost function used, restarting algorithm optimizes, until obtaining satisfied optimum results.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103971180A (en) * 2014-05-09 2014-08-06 北京航空航天大学 Continuous optimization problem solving method based on pigeon-inspired optimization

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Pigeon-Inspired optimization approach to multiple UAVs formation reconfiguration controller design;Xiaomin Zhang等;《Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference》;20140810;第2707-2712页 *
小型舰载无人机侧向自主着舰引导技术;郑峰婴等;《南京航空航天大学学报》;20130228;第45卷(第1期);第82-87页 *

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