CN108763779A - A kind of method that the particle cluster algorithm of application enhancements controls quadrotor drone - Google Patents
A kind of method that the particle cluster algorithm of application enhancements controls quadrotor drone Download PDFInfo
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Abstract
The present invention relates to unmanned plane automation fields.A kind of method that the particle cluster algorithm of application enhancements controls quadrotor drone, it is random to establish initialization population, if there are the fitness values of particle to be less than population optimal particle in population when current iterationP g , population optimal particleP g It is substituted for the particle；If there are single particle fitness values to be less than individual history optimal particle when current iterationP j , individual history optimal particleP j It is substituted for the particle, according to formulaCalculate the adjustment probability of populationP, the present invention is intersected by introducing, the mechanism of mutation algorithm is improved heredity-particle cluster algorithm, is improved timeliness, is realized and be precisely controlled to modern unmanned plane.
Description
Technical field
The present invention relates to unmanned plane automation fields.
Background technology
The stability contorting of unmanned plane is one of core and emphasis of UAV system research, its research not only has great
Realistic meaning, and have far-reaching theory significance.Fly control algorithm design influenced the stability of unmanned plane during flying with
Flexibility.However quadrotor drone is in control aspect that there are many problems, if unmanned plane response speed is slow, oscillation is apparent etc..
In the past few decades, various control algolithms are studied, are used in the stability contorting of unmanned plane.In order to improve control
The mode of the precision generally use optimizing algorithm combination controller of system controls unmanned plane.Controller is commonly used to be had
PID controller, Backstepping controller, sliding mode controller etc..Wherein PID controller because its letter answer, efficient advantage it is extensive
Be applied in modern unmanned aerial vehicle control system.But the control effect of PID controller it is excessive dependent on ratio, integral, micro-
Divide the setting of these three parameters, so three parameters of Tuning PID Controller become the emphasis for improving control accuracy.Typically
These three parameters are improved using optimizing algorithm, common optimizing algorithm has particle cluster algorithm, fuzzy algorithmic approach, heredity to calculate
Method, neural network algorithm etc..
Particle cluster algorithm has the advantages of simple structure and easy realization.It is often applied in three parameters of Tuning PID Controller.But
It is that, with the operation of algorithm, particle is susceptible to the situation for making algorithm terminate too early because of Premature Convergence, i.e., that usually says is " early
It is ripe ".The thought intersect in order to avoid this situation is usually introduced into particle cluster algorithm in genetic algorithm, to make a variation improves particle
Particle information in group.The situation of local optimum that can largely improve is done so, but also inevitable simultaneously
The time for increasing algorithm, control efficiency is affected, control is this to be for the exigent controller of timeliness for flying for this
It is unacceptable.
Invention content
The technical problem to be solved by the present invention is to：It is how technically existing insufficient for heredity-particle cluster algorithm, add
To improve, the stability contorting to unmanned plane is realized.
The technical solution adopted in the present invention is：What a kind of particle cluster algorithm of application enhancements controlled quadrotor drone
Method is carried out according to following step
Step 1: random establish initialization population, population number q because be with population to PID controller three
A parameter is adjusted, then the dimension of algorithm is d=3, and the position Xi=(xi1, xi2, xi3) of i-th of particle represents i-th
The location components of [1,3] dimension of son, the speed for corresponding to i-th of particle in the population in PID controller respectively are Vi=
(vi1, vi2, vi3) represent i-th of particle [1,3] dimension velocity component, q generally takes the natural number more than 100, and i is small
In the natural number equal to q；
Step 2: the fitness value of any one particle is expressed asT is time, e (t) tables
Show the deviation between the theoretical output signals r (t) of system and real output signal y (t), i.e. e (t)=r (t)-y (t), this changes
For when population in fitness value minimum particle as population optimal particle Pg, in population each particle in current iteration and
The particle of fitness value minimum is as individual history optimal particle P when current iteration pervious iterationj；
Step 3: the position and speed information of population more new particle according to the following formula, if population when current iteration
It is middle that there are the fitness values of particle to be less than population optimal particle Pg, population optimal particle PgIt is substituted for the particle；If when current iteration
There are single particle fitness values to be less than individual history optimal particle Pj, individual history optimal particle PjIt is substituted for the particle
Wherein,Indicate the particle i velocity components that d is tieed up at the kth iteration in population,It indicates in population
The particle i velocity components that d is tieed up in+1 iteration of kth, PgdIndicate optimal particle P when kth time iterationgD dimension component,
PjdIndicate history optimal particle P when kth time iterationjIn the component of d dimensions, d is the dimension of the population less than or equal to D, and k is repeatedly
Generation number, r1And r2A random number between [0,1], c1And c2For Studying factors (usually taking 2),It indicates in population
The particle i location components that d is tieed up at the kth iteration,Indicate what the d in+1 iteration of kth of the particle i in population were tieed up
Location components, w are inertia weight (usually taking the number between (0,1))；
Step 4: the adjustment probability P of population is calculated according to formula P=α+Ge β, if meeting P > Pm, then follow the steps
Five, otherwise execute step 8, PmArbitrary value for setting in (0,1) section, α and β are the regulation coefficient of given probability, Ge
For group optimal particle PgWith individual history optimal particle PjNo change or the iterations aggregate-value for changing very little, kind when iteration
Group's optimal particle PgWith individual history optimal particle PjIt all updates, shows that the of overall importance of population need not well carry out population
It adjusts, step 8 is jumped directly to, if population optimal particle PgWith individual history optimal particle PjContinuously several times iteration all without
Change or vary less, Ge will become larger, and corresponding this, which adjusts probability value, to become larger, and pressure adjustment is carried out as Ge=1/ β
Execute step 5；
Step 5: utilizing formulaCalculate current iteration when population in Arbitrary Particles u1 with
Population optimal particle PgThe distance between,Indicate optimal particle PgThe location components of d dimensions, xu1dIndicate particle u1 the
The location components of d dimensions show that population aggregation needs to carry out crossover operation to particle, execute step if l is less than threshold value △ φ
Rapid six, otherwise return to step three, wherein △ φ=| iter/itermax|n× (ub-lb), iter are indicated until current iteration
Iterations, itermaxIndicate that maximum iterations, ub indicate the upper limit X of particle position in populationmax, lb expression grains
The lower limit X of particle position in subgroupmin；
Step 6: according to formulaCrossover operation is carried out to particle, is used if adaptive value becomes smaller
Particle after intersection replaces the particle, executes step 5 to next particle later, until all particles all intersect in population
After execute step 8, otherwise execute step 7 to particle carry out mutation operation, xu1For the position of the particle of current operation,
xu2To remove x in populationu1The position of any one outer particle, x 'u1It is xu1Particle position after intersection, x 'u2It is xu2Intersect
Particle position afterwards, z are D dimension random number series its numerical value between (0,1)；
Step 7: to the particle after crossover operation according to formula x "u1=x 'u1+(1-iter/itermax)γ(ub-x’u1) or
x”u1=x 'u1-(1-iter/itermax)γ(x’u1- lb) to carrying out mutation operation, x "u1For the particle after variation；If after variation
Particle fitness value is less than individual history optimal particle PjFitness value, then the history optimal particle P of the particlejReplace with change
The particle reduced after different, if the particle fitness value after variation is less than population optimal particle PgFitness value.Then population is optimal
Particle PgThe particle reduced after variation is replaced with, step 5 is executed to next particle later, until all particles in population
Step 8 is executed after all making a variation；
Step 8: loop iteration next time is carried out, and return to step two is until suitable population optimal particle PgWith individual history
Optimal particle PjFitness value both less than or until reaching iterations maximum value equal to preset value or iterations.
Step 9: the component of 3 dimensions of the value of the fitness minimum finally obtained through algorithm as PID controller three
A parameter carries out flight control to unmanned plane.
As a kind of preferred embodiment：In step 3, w value ranges are between (0,1).
As a kind of preferred embodiment：In step 8, iterations maximum value is more than or equal to 100.
The beneficial effects of the invention are as follows：The present invention is by introducing the mechanism of intersection, mutation algorithm to heredity-particle cluster algorithm
It is improved, improves timeliness, realize and modern unmanned plane is precisely controlled.Present invention eliminates in traditional improved method
The intersection of redundancy, mutation operation are capable of the raising efficiency of algorithm of high degree.It is particularly suitable for effective exigent winged
Control system.
Description of the drawings
Fig. 1 is flow diagram of the present invention；
The relationship of heredity-population adaptive value and iterations of Fig. 2 routines；
The relationship of Fig. 3 population adaptive value and iterations of the present invention；
Fig. 4 conventional genetics-population PID control pitch angle curve of output；
Fig. 5 population PID control pitch angle curves of output of the present invention；
Specific implementation mode
Due to the poor in timeliness of traditional heredity-particle cluster algorithm, it is extremely difficult to the requirement that modern unmanned plane is precisely controlled.
So this algorithm is in order to solve these problems, first, by calculate distance and setting in population between adjacent particles threshold value it
Between be compared to each other to judge
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with 1 to 5 pairs of this hairs of attached drawing
Bright further description.
Establish quadrotor drone mathematical model
Wherein Fi(i=1,2,3,4) is respectively four rotor lifts of aircraft, U in formula1For aircraft vertical direction
Total life, U2For roll input control quantity, U3For pitching input control quantity, U4To yaw input control quantity, Ω is rotor rotating speed, b
It is lift coefficient, kk is resistance coefficient.
Quadrotor mathematical model after simplification is：
Wherein C is cos, S sin.Ix, Iy, Iz are respectively body coordinate system x, y, the moment of inertia in z-axis；L ' is rotation
Fore-and-aft distance of the wing center to body barycenter；M is the quality of quadrotor body；φ, θ, ψ are respectively the roll angle of quadrotor, are bowed
The elevation angle and yaw angle.
For simplify control scheme, the form that quadrotor model conversion is transmission function.Binding experiment room build four
Rotor craft housing construction data, mathematical model, the transmission function for providing pitch angle are：
The transmission function of roll angle is：
The transmission function of yaw angle is：
u1、u2、u3Respectively the control input quantity of pitch angle, roll angle, yaw angle, s are Laplace operator.It calculates in detail
Method explanation
Step 1: random establish initialization population, population number q because be with population to PID controller three
A parameter is adjusted, then the dimension of algorithm is 3.The position X of i-th of particlei=(xi1, xi2, xi3) represent i-th particle
The location components of [1,3] dimension, the speed for corresponding to i-th of particle in the population in PID controller respectively are Vi=(vi1,
vi2, vi3) represent i-th of particle [1,3] dimension velocity component, q generally takes the natural number more than 100, i be less than etc.
In the natural number of q.
Step 2: in order to examine selected particle the good and bad ITEA that chooses as error performance index, any one particle
Fitness value is expressed asT is the time, and e (t) indicates the theoretical output signals r (t) and reality of system
Deviation between output signal y (t), i.e. e (t)=r (t)-y (t), when current iteration in population fitness value minimum particle
As population optimal particle Pg, fitness value is most in current iteration and current iteration pervious iteration for each particle in population
Small particle is as individual history optimal particle Pj；
Step 3: the position and speed information of population more new particle according to the following formula, if population when current iteration
It is middle that there are the fitness values of particle to be less than population optimal particle Pg, population optimal particle PgIt is substituted for the particle；If when current iteration
There are single particle fitness values to be less than individual history optimal particle Pj, individual history optimal particle PjIt is substituted for the particle
Wherein,Indicate the particle i velocity components that d is tieed up at the kth iteration in population,It indicates in population
The particle i velocity components that d is tieed up in+1 iteration of kth, PgdIndicate optimal particle P when kth time iterationgD dimension component,
PjdIndicate history optimal particle P when kth time iterationjIn the component of d dimensions, d is the dimension of the population less than or equal to D, and k is repeatedly
Generation number, r1And r2A random number between [0,1], c1And c2For Studying factors (usually taking 2),It indicates in population
The particle i location components that d is tieed up at the kth iteration,Indicate what the d in+1 iteration of kth of the particle i in population were tieed up
Location components, w are inertia weight (usually taking the number between (0,1))；
Step 4: the adjustment probability P of population is calculated according to formula P=α+Ge β, if meeting P > Pm, then follow the steps
Five, otherwise execute step 8, PmArbitrary value for setting in (0,1) section, α and β are the regulation coefficient of given probability, Ge
For group optimal particle PgWith individual history optimal particle PjNo change or the iterations aggregate-value for changing very little, kind when iteration
Group's optimal particle PgWith individual history optimal particle PjIt all updates, shows that the of overall importance of population need not well carry out population
It adjusts, step 8 is jumped directly to, if population optimal particle PgWith individual history optimal particle PjContinuously several times iteration all without
Change or vary less, Ge will become larger, and corresponding this, which adjusts probability value, to become larger, and pressure adjustment is carried out as Ge=1/ β
Execute step 5；
Step 5: utilizing formulaCalculate current iteration when population in Arbitrary Particles u1 with
Population optimal particle PgThe distance between,Indicate optimal particle PgThe location components of d dimensions, xu1dIndicate particle u1 d
The location components of dimension show that population aggregation needs to carry out crossover operation to particle, execute step if l is less than threshold value △ φ
Rapid six, otherwise return to step three, wherein △ φ=| iter/itermax|n× (ub-lb) iter are indicated until current iteration
Iterations, itermax
Indicate that maximum iterations, ub indicate the upper limit X of particle position in populationmax, lb indicate population in particle
The lower limit X of positionmin；
Step 6: according to formulaCrossover operation is carried out to particle, is used if adaptive value becomes smaller
Particle after intersection replaces the particle, executes step 5 to next particle later, until all particles all intersect in population
After execute step 8, otherwise execute step 7 to particle carry out mutation operation, xu1For the position of the particle of current operation,
xu2To remove x in populationu1The position of any one outer particle, x 'u1It is xu1Particle position after intersection, x 'u2It is xu2Intersect
Particle position afterwards, z are D dimension random number series its numerical value between (0,1)；
Step 7: to the particle after crossover operation according to formula x "u1=x 'u1+(1-iter/itermax)γ(ub-x’u1) or
x”u1=x 'u1-(1-iter/itermax)γ(x’u1- lb) to carrying out mutation operation, x "u1For the particle after variation；If after variation
Particle fitness value is less than individual history optimal particle PjFitness value, then the history optimal particle P of the particlejReplace with change
The particle reduced after different, if the particle fitness value after variation is less than population optimal particle PgFitness value.Then population is optimal
Particle PgThe particle reduced after variation is replaced with, step 5 is executed to next particle later, until all particles in population
Step 8 is executed after all making a variation；
Step 8: loop iteration next time is carried out, and return to step two is until suitable population optimal particle PgWith individual history
Optimal particle PjFitness value both less than or until reaching iterations maximum value equal to preset value or iterations.
Step 9: the component of 3 dimensions of the value of the fitness minimum finally obtained through algorithm as PID controller three
A parameter carries out flight control to unmanned plane.
Step 10: the controlled quentity controlled variable u (t) that PID controller is calculated by PID control rule, PID control rule is as follows：
U (t) is delivered in unmanned plane model, unmanned plane mathematical model input control quantity u (t), and exports obtained reality
Actual value y (t).
Based on algorithm initialization parameter in the above theoretical procedure and table 1, simulation comparison figure is obtained.
1 algorithm initialization parameter of table
| Parameter | Numerical value |
| Dimension | 3 |
| Inertia weight | W=0.6 |
| Accelerated factor | c1=c2=2 |
| The weights of variation | γ=2 |
| Minimum adaptive value | 0.1 |
| Population scale | 100 |
| Particle rapidity range | [- 1,1] |
| Maximum iteration | 100 |
| Probability coefficent of the present invention | α=0.001, β=0.005 |
| Tri- parameter areas of PID | [0,300] |
Simulation result
2. pitch angle pid parameter of table and each performance indicator
The effect of conventional particle group algorithm and particle cluster algorithm Tuning PID parameters of the present invention in contrast table 2, can make as
Lower analysis：
(1) particle cluster algorithm of the present invention adjusts the overshoot smaller of PID, and fitness value is lower, then flight system is more
Stablize.
(2) particle cluster algorithm of the present invention adjust PID regulating time and the rise time it is shorter, then the response of flight system is more
Soon.
Comparison diagram 2 and Fig. 3, which can be analyzed, obtains particle cluster algorithm of the present invention compared with heredity-particle cluster algorithm iterations more
Few, convergence rate is faster and the optimal value fitness that finds is lower.
Comparison diagram 4 and Fig. 5, which can be analyzed, show that particle cluster algorithm of the present invention is being adjusted and controlled compared with heredity-particle cluster algorithm
Faster, control efficiency higher, and overshoot smaller, control effect is more for particle cluster algorithm response speed of the present invention when pitch angle processed
It is ideal.
Claims (3)
1. a kind of method that the particle cluster algorithm of application enhancements controls quadrotor drone, which is characterized in that according to following
Step carries out：
Step 1: random establish initialization population, population number q, because being three ginsengs with population to PID controller
Number is adjusted, then the dimension of algorithm is d=3, the position X of i-th of particlei=(xi1, xi2, xi3) represent the of i-th of particle
The location components of [1,3] dimension, the speed for corresponding to i-th of particle in the population in PID controller are Vi=(vi1, vi2, vi3)
Represent the velocity component of [1,3] dimension of i-th of particle, q generally takes the natural number more than 100, i be less than or equal to q from
So number；
Step 2: the fitness value of any one particle is expressed asT is the time, and e (t) indicates system
Deviation between the theoretical output signals r (t) and real output signal y (t) of system, i.e. e (t)=r (t)-y (t), when current iteration
The particle of fitness value minimum is as population optimal particle P in populationg, each particle is in current iteration and this in population
The particle of fitness value minimum is as individual history optimal particle P when iteration pervious iterationj；
Step 3: the position and speed information of population more new particle according to the following formula, if being deposited in population when current iteration
It is less than population optimal particle P in the fitness value of particleg, population optimal particle PgIt is substituted for the particle；If existing when current iteration
Single particle fitness value is less than individual history optimal particle Pj, individual history optimal particle PjIt is substituted for the particle；
Wherein,Indicate the particle i velocity components that d is tieed up at the kth iteration in population,Indicate the particle i in population
The velocity component that d is tieed up in+1 iteration of kth, PgdIndicate optimal particle P when kth time iterationgIn the component of d dimensions, PjdTable
History optimal particle P when showing kth time iterationjIn the component of d dimensions, d is the dimension of the population less than or equal to D, and k is iteration time
Number, r1And r2A random number between [0,1], c1 and c2 are Studying factors,Indicate the particle i in population in kth time
The location components that d is tieed up when iteration,Indicate that the location components that d is tieed up in+1 iteration of kth of the particle i in population, w are
Inertia weight；
Step 4: the adjustment probability P of population is calculated according to formula P=α+Ge β, if meeting P > Pm, then follow the steps five, it is no
Then follow the steps eight, PmArbitrary value for setting in (0,1) section, α and β are the regulation coefficients of given probability, and Ge is group
Body optimal particle PgWith individual history optimal particle PjNo change or the iterations aggregate-value for changing very little, population is most when iteration
Excellent particle PgWith individual history optimal particle PjIt all updates, shows that the of overall importance of population need not well be adjusted population,
Step 8 is jumped directly to, if population optimal particle PgWith individual history optimal particle PjContinuously iteration is all unchanged several times
Or vary less, Ge will become larger, and corresponding this, which adjusts probability value, to become larger, and carry out that adjustment is forced to be held as Ge=1/ β
Row step 5；
Step 5: utilizing formulaArbitrary Particles u1 and population be most in population when calculating current iteration
Excellent particle PgThe distance between,Indicate optimal particle PgThe location components of d dimensions, xu1dIndicate particle u1 d dimensions
Location components show that population aggregation needs to carry out crossover operation to particle, execute step 6 if l is less than threshold value △ φ, no
Then return to step three, wherein △ φ=| iter/itermax|n× (ub-lb), iter indicate the iteration until current iteration
Number, itermaxIndicate that maximum iterations, ub indicate the upper limit X of particle position in populationmax, lb indicate population in
The lower limit X of particle positionmin；
Step 6: according to formulaCrossover operation is carried out to particle, with intersection if adaptive value becomes smaller
Particle afterwards replaces the particle, executes step 5 to next particle later, until all particles all intersect and finish in population
After execute step 8, otherwise execute step 7 to particle carry out mutation operation, xu1For the position of the particle of current operation, xu2For
X is removed in populationu1The position of any one outer particle, x 'u1It is xu1Particle position after intersection, x 'u2It is xu2After intersection
Particle position, z are D dimension random number series its numerical value between (0,1)；
Step 7: to the particle after crossover operation according to formula x "u1=x 'u1+(1-iter/itermax)γ(ub-x’u1) or x "u1
=x 'u1-(1-iter/itermax)γ(x’u1- lb) to carrying out mutation operation, x "u1For the particle after variation；If the grain after variation
Sub- fitness value is less than individual history optimal particle PjFitness value, then the history optimal particle P of the particlejReplace with variation
The particle reduced afterwards, if the particle fitness value after variation is less than population optimal particle PgFitness value.The then optimal grain of population
Sub- PgThe particle that reduces after variation is replaced with, step 5 is executed to next particle later, until all particles are all in population
Step 8 is executed after variation；
Step 8: loop iteration next time is carried out, and return to step two is until suitable population optimal particle PgWith the optimal grain of individual history
Sub- PjFitness value both less than or until reaching iterations maximum value equal to preset value or iterations.
Step 9: the global optimum finally obtained the i.e. component of 3 dimensions of the particle of fitness minimum is controlled respectively as PID
Three parameters of device processed are transformed into the domains s, and flight control is carried out to unmanned plane.
2. the method that the particle cluster algorithm of application enhancements according to claim 1 controls quadrotor drone, feature
It is：In step 3, w value ranges are between (0,1), c1=c2=2.
3. the method that the particle cluster algorithm of application enhancements according to claim 1 controls quadrotor drone, feature
It is：In step 8, iterations maximum value is more than or equal to 100.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109947124A (en) * | 2019-04-25 | 2019-06-28 | 南京航空航天大学 | Improve particle swarm algorithm Optimization of Fuzzy PID unmanned helicopter attitude control method |
| CN110399697A (en) * | 2019-08-02 | 2019-11-01 | 南京航空航天大学 | Control distribution method based on the aircraft for improving genetic learning particle swarm algorithm |
| CN110580077A (en) * | 2019-08-20 | 2019-12-17 | 广东工业大学 | maximum power extraction method of photovoltaic power generation system and related device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109947124A (en) * | 2019-04-25 | 2019-06-28 | 南京航空航天大学 | Improve particle swarm algorithm Optimization of Fuzzy PID unmanned helicopter attitude control method |
| CN110399697A (en) * | 2019-08-02 | 2019-11-01 | 南京航空航天大学 | Control distribution method based on the aircraft for improving genetic learning particle swarm algorithm |
| CN110399697B (en) * | 2019-08-02 | 2023-07-25 | 南京航空航天大学 | Aircraft control distribution method based on improved genetic learning particle swarm algorithm |
| CN110580077A (en) * | 2019-08-20 | 2019-12-17 | 广东工业大学 | maximum power extraction method of photovoltaic power generation system and related device |
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Application publication date: 20181106 |