CN110362889A - A kind of MPBLDCLM Multipurpose Optimal Method based on genetic algorithm - Google Patents
A kind of MPBLDCLM Multipurpose Optimal Method based on genetic algorithm Download PDFInfo
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Abstract
The MPBLDCLM Multipurpose Optimal Method based on genetic algorithm that the invention discloses a kind of, establishes MPBLDCLM mathematical model of optimizing design, the determination including optimization design variable, objective function and constraint condition；The feasible solution of optimization problem is encoded；Assessment and selection fitness function, a certain number of populations primary are randomly generated in area of feasible solution, calculate the individual adaptation degree in population；Selection, intersection, variation and increasing are executed repeatedly to tie up, and when all the carrying simplexs of individual are all evolved to mark simplex entirely in population, are stopped operation, are obtained Approximate Global Optimal Solution.Optimal speed of the present invention is fast, at low cost, high-efficient, improve global precision and convergence rate, this method is highly suitable in the multiple-objection optimization to MPBLDCLM, make motor under the premise of meeting its performance requirement and constant outer dimension, average thrust, force oscillation, efficiency can effectively be optimized.
Description
Technical field
The present invention relates to techniques of linear motor field, especially a kind of moving-magnetic type DC permanent-magnetic brushless based on genetic algorithm
The Multipurpose Optimal Method of linear motor (MPBLDCLM).
Background technique
Moving-magnetic type permanent magnet brushless direct current linear motor is a kind of high power driving motor.There is crowd in linear motor field
More advantages, as control performance is good, speed-regulating range width, starting thrust are big, runs smoothly.Due to moving-magnetic type DC permanent-magnetic brushless straight line
Motor has the above plurality of advantages, receives extensive attention in recent years, especially directly drives in lathe, train, electromagnetic launch servo system
It is more and more applied in the straight drive field such as system.But there is also some for moving-magnetic type permanent magnet brushless direct current linear motor itself simultaneously
Problem, such as operational efficiency is not high, thrust output fluctuation is big, optimization design difficulty is larger, these problems limit moving-magnetic type
The application of permanent magnet brushless direct current linear motor industrially.
For moving-magnetic type permanent magnet brushless direct current linear motor Optimal Structure Designing, the method being suggested has: using reason
Moving-magnetic type permanent magnet brushless direct current linear motor structure is optimized by design method of the analysis in conjunction with finite element simulation,
But due to parameter optimization need it is a large amount of call computer models to obtain its output, so the low optimal speed of computational efficiency it is slow, at
The deficiencies of this height, low efficiency, extends the period of design of electrical motor.
Summary of the invention
It is an object of the invention to by a kind of MPBLDCLM Multipurpose Optimal Method based on genetic algorithm, for tradition
Motor optimization has that blindness, low efficiency provide one kind and can rapidly and efficiently realize setting for multiple performance object synchronization optimization
Meter method.
The technical solution for realizing the aim of the invention is as follows: a kind of multiple-objection optimization side MPBLDCLM based on genetic algorithm
Method, the specific steps are as follows:
Step 1, the optimized variable parameter for determining moving-magnetic type permanent magnet brushless direct current linear motor；
Step 2 determines objective function and constraint condition that moving-magnetic type permanent magnet brushless direct current linear motor needs to optimize；
Step 3 carries out real coding to problem parameter collection, while the vertex for carrying simplex and Based on Integer Labelling information being drawn
Enter coding；
Step 4, the fitness function for choosing genetic algorithm, and according to the variable of moving-magnetic type permanent magnet brushless direct current linear motor
Parameter generates the population P primary of genetic algorithm0(t), population P primary is calculated0(t) the carrying simplex of each individual in calculates
P in population primary0(t) individual adaptation degree；
Step 5 executes selection, intersection, variation repeatedly and increases dimension, improves group's fitness, and individual moves closer to optimal solution,
Until meeting defined convergence foundation, globally optimal solution is finally obtained.
Compared with prior art, the present invention its remarkable advantage is:
1) the MPBLDCLM Multipurpose Optimal Method based on genetic algorithm in the present invention can realize the multiple performance of motor
Optimize while target.
2) genetic algorithm in the present invention is further improved variation and probability of crossover, and increases dimension operator,
So that algorithm can reach good effect of optimization with fast convergence.
3) genetic algorithm in the present invention overcomes the blindness occurred in traditional optimization process, improves motor optimization speed
Degree and efficiency, and there is universality, potential global convergence and validity.
Detailed description of the invention
Fig. 1 is the flow chart of the MPBLDCLM Multipurpose Optimal Method the present invention is based on genetic algorithm.
Specific embodiment
With reference to the accompanying drawing and specific embodiment makes further description to the present invention.
A kind of MPBLDCLM Multipurpose Optimal Method based on genetic algorithm of the present invention, initially sets up moving-magnetic type brushless, permanently
DC linear electric motor mathematical model of optimizing design, including the determination of optimization design variable, objective function and constraint condition；It is right
The feasible solution of optimization problem is encoded；Assessment and selection fitness function, are randomly generated certain amount in area of feasible solution
Population primary, calculate the individual adaptation degree in population primary；Selection, intersection, variation are executed repeatedly and increases dimension, until primary kind
When all the carrying simplexs of individual all evolve to mark simplex entirely in group, stops operation, obtain Approximate Global Optimal Solution.
In conjunction with Fig. 1, the MPBLDCLM Multipurpose Optimal Method based on genetic algorithm in the present embodiment, method and step is as follows:
Step 1, the Optimal Parameters variable for determining moving-magnetic type permanent magnet brushless direct current linear motor.Its Optimal Parameters variable includes
The long w of motor axial length L, permanent magnetpm, permanent magnet thickness hpm, stator yoke thickness hys, stator groove width ws, stator groove depth hs, gas length
g.These parametric variables directly determine the performance of motor, by determining that these Optimal Parameters variables may finally be quickly obtained
The smallest optimal solution of force oscillation.
Step 2 determines target and constraint condition that moving-magnetic type permanent magnet brushless direct current linear motor needs to optimize.Moving-magnetic type is forever
The objective function of magnetic brushless direct-current linear motor optimization is to maximize motor in the case that motor outer dimension is certain and averagely export
Thrust and efficiency minimize force oscillation；Constraint condition refers to the limit to practical problem that must satisfy in optimization problem
Condition processed, the optimization constraint condition of moving-magnetic type permanent magnet brushless direct current linear motor are respectively motor stator winding current density g1
(X), stator surface is averaged flux density g2(X), output power g3(X), the long g of permanent magnet4(X) and gas length g5(X)。
Moving-magnetic type permanent magnet brushless direct current linear motor is averaged thrust output FaveExpression formula:
In formula, FrTo ignore average electromagnetic push when inductance, KEFor the motor coefficient of potential, U is motor terminal voltage, and E is electricity
Machine back-emf, t1For open pipe shutdown after time of afterflow, T be a state angle commutating period time, κ be the commutating period time with
Electromagnetic time constant ratio, I0For initial current value, R is winding resistance.
Moving-magnetic type permanent magnet brushless direct current linear motor thrust output fluctuates FrippleExpression formula:
In formula, FmaxIt is maximum thrust, FminFor minimum thrust.
Moving-magnetic type permanent magnet brushless direct current linear motor efficiency eta expression formula:
In formula, v is Rated motor speed, and I is phase winding current effective value.
According to design requirement goal-selling function F (x) expression formula:
F (x)=- w1Fave+w2Fripple+w3η (4)
In formula, w1、w2、w3Respectively the weight coefficient of average thrust, force oscillation and efficiency, expression formula are as follows:
The constraint function expression formula of moving-magnetic type permanent magnet brushless direct current linear motor:
In formula, g1It (X) is motor stator winding current density, g2(X) it is averaged flux density for stator surface, g3It (X) is output work
Rate, g4(X) long for permanent magnet, g5It (X) is gas length.
Step 3 is encoded according to the variable parameter collection of moving-magnetic type permanent magnet brushless direct current linear motor, and is generated based on something lost
The population P primary of propagation algorithm0(t), and the fitness function based on genetic algorithm is chosen.And it simultaneously will carrying using real coding
The vertex of simplex and Based on Integer Labelling information introduce coding, and coding form is as follows:
{x,f(x),yi,f(yi),l(yi) | i=0,1,2 } (7)
In formula, x is individual variable in population, and f (x) is the target function value of individual variable x, yiFor the list of individual variable x
Pure shape vertex, f (yi) it is yiFunctional value, l (yi) it is obtained simplex vertex yiBased on Integer Labelling.
Step 4, according to the variable parameter of moving-magnetic type permanent magnet brushless direct current linear motor, the variable parameter includes motor shaft
To length L, the long w of permanent magnetpm, permanent magnet thickness hpm, stator yoke thickness hys, stator groove width ws, stator groove depth hs, gas length g, generate
Population P primary based on genetic algorithm0(t)。
The population P primary0(t) initial individuals composition as shown by:
X=[L, wpm,hpm,hys,ws,hs,g]T (8)
It is as follows to set objective function expression formula:
When it is required be objective function minimum value when fitness function F ' (x) expression formula:
When it is required be objective function maximum value when fitness function F " (x) expression formula:
In formula, F (x) is objective function, MmaxFor population P primary0(t) maximum value of objective function F (x), M inminIt is first
For population P0(t) minimum value of objective function F (x), M inmaxAnd MminAll in accordance with to population P primary0(t) objective function F (x) in
Valuation depending on.
Step 5, to population P primary0(t) carrying simplex vertex carries out Based on Integer Labelling in, calculates individual adaptation degree, repeatedly
It executes selection, intersection, variation and increases dimension genetic manipulation, improve group's fitness, individual moves closer to optimal solution, advises until meeting
Fixed convergence foundation, finally obtains globally optimal solution.
Step 5-1, apply selection operator selects individual inheritance into progeny population from parent group, is carried according to individual
Individual is divided into first, second, the third three classes by the label information on simplex vertex, and wherein the individual carrying simplex of Class A is almost complete marks
Simplex；It is entirely not identical that Class B obtains individual carrying simplex the Vertex Labeling；Remaining individual is included into Class C.Individual is by first, second, third
Sequence arranges, and different Class B individual preferentially enters parent population to carrying simplex the Vertex Labeling entirely, while algorithm is using " mixed
Close outstanding " strategy, retain the optimization individual in parent and filial generation；
Step 5-2, crossover operator is applied to parent population, operating process is divided into two steps: simple first, in accordance with individual carrying
Shape almost full mark simplex label information classify, then will belong between inhomogeneity and the individual of different bearer simplex with
Probability 1 carries out whole discrete crossover operation；
Step 5-3, compare the target function value on the identical simplex vertex of label first to find out target function value maximum
Vertex, maximum value vertex is denoted as y0, remaining vertex is denoted as y1, y2；Reconnect vertex y1, y2, obtained rib is denoted as λ (0), into
Row simplex search obtains being exactly simplex that new search arrives with the simplex that λ (0) is common edge and adjoining.In new simplex
In appoint take a little as new individual, calculate the Vertex Labeling of the simplex, execute the operation repeatedly until obtaining the Vertex Labeling
Different simplexs, when individual carrying simplex whole in population is all evolved to mark simplex entirely, termination algorithm.Full mark is single
The approximate optimal solution of the corresponding individual as majorized function of pure shape.
Claims (8)
1. a kind of MPBLDCLM Multipurpose Optimal Method based on genetic algorithm, which is characterized in that steps are as follows:
Step 1, the optimized variable parameter for determining moving-magnetic type permanent magnet brushless direct current linear motor；
Step 2 determines objective function and constraint condition that moving-magnetic type permanent magnet brushless direct current linear motor needs to optimize；
Step 3 carries out real coding to problem parameter collection, while the vertex for carrying simplex and Based on Integer Labelling information being introduced and compiled
Code；
Step 4, the fitness function for choosing genetic algorithm, and joined according to the variable of moving-magnetic type permanent magnet brushless direct current linear motor
Number, generates the population P primary of genetic algorithm0(t), population P primary is calculated0(t) the carrying simplex of each individual in calculates P0
(t) individual adaptation degree in；
Step 5 executes selection, intersection, variation repeatedly and increases dimension, improves P0(t) fitness, so that individual therein moves closer to
Optimal solution finally obtains globally optimal solution until meeting defined convergence foundation.
2. the MPBLDCLM Multipurpose Optimal Method according to claim 1 based on genetic algorithm, it is characterised in that: step
In rapid 1, the optimized variable parameter includes: motor axial length L, the long w of permanent magnetpm, permanent magnet thickness hpm, stator yoke thickness hys, it is fixed
The wide w of pilot trenchs, stator groove depth hs, gas length g.
3. the MPBLDCLM Multipurpose Optimal Method according to claim 1 based on genetic algorithm, it is characterised in that: step
In rapid 2, the objective function of moving-magnetic type permanent magnet brushless direct current linear motor optimization is in the case where motor outer dimension is certain, most
Bigization motor is averaged thrust output and efficiency, minimizes force oscillation；Constraint condition refers to must satisfy in optimization problem
The restrictive condition to practical problem, the optimization constraint condition of moving-magnetic type permanent magnet brushless direct current linear motor is respectively motor stator
Winding current density g1(X), stator surface is averaged flux density g2(X), output power g3(X), the long g of permanent magnet4(X) and gas length g5
(X)。
4. the MPBLDCLM Multipurpose Optimal Method according to claim 1 or described in 3 based on genetic algorithm, feature exist:
Goal-selling function F (x) is shown below:
In formula, N is objective function number, fiIt (x) is single target function expression；
In formula, wiFor weight coefficient, expression formula is as follows:
Default constraint function are as follows:
In formula, PNFor rated output power, X indicates solution space vector.
5. the MPBLDCLM Multipurpose Optimal Method according to claim 1 based on genetic algorithm, it is characterised in that: step
In rapid 3, real coding is used to problem parameter collection, the coding form is shown below:
{x,f(x),yi,f(yi),l(yi) | i=0,1,2 }
In formula, x is the individual variable in population primary, and f (x) is the target function value of individual variable x, yiFor the list of individual variable x
Pure shape vertex, f (yi) it is simplex vertex yiFunctional value, l (yi) it is obtained simplex vertex yiBased on Integer Labelling, i is a
The corresponding serial number of body variable x.
6. the MPBLDCLM Multipurpose Optimal Method according to claim 1 based on genetic algorithm, it is characterised in that: step
In rapid 4, the fitness function is shown below:
When it is required be objective function minimum value when fitness function F ' (x) expression formula:
When it is required be objective function maximum value when fitness function F " (x) expression formula:
In formula, F (x) is objective function, MmaxFor population P primary0(t) maximum value of objective function F (x), M inminFor population primary
P0(t) minimum value of objective function F (x), M inmaxAnd MminAll in accordance with to population P primary0(t) valuation of objective function F (x) in
Depending on.
7. the MPBLDCLM Multipurpose Optimal Method according to claim 1 or described in 6 based on genetic algorithm, feature exist
In: in step 4, the population P primary0(t) initial individuals composition as shown by:
X=[x1,x2,x3,…,xn-1,xn]T
X indicates that solution space vector, n indicate the design variable number of moving-magnetic type permanent magnet brushless direct current linear motor, and the member in X in formula
Element is by variable parameter x1,x2,x3,…,xn-1,xnComposition.
8. the MPBLDCLM Multipurpose Optimal Method according to claim 1 based on genetic algorithm, which is characterized in that step
In rapid 5, including the following steps:
Step 5-1, apply selection operator selects individual inheritance into progeny population from the parent group of population primary, parent kind
Full mark simplex in group is directly entered progeny population, applies selection operator to progeny population according to the outstanding strategy of father and son's mixing；
Step 5-2, progeny population is classified according to the simplex label information of almost marking entirely of individual carrying simplex, then will
Belong between inhomogeneity and the individual of different bearer simplex and whole discrete crossover operation is carried out with probability 1, it is simple to non-full mark
The preferential variation of individual in shape；
Step 5-3, applying increasing dimension operator keeps the individual Xiang Quanbiao simplex with identical label close, when whole in progeny population
When the carrying simplex of individual all evolves to mark simplex entirely, algorithm is terminated, complete to mark the corresponding individual as optimization letter of simplex
Several approximate optimal solutions.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114745310A (en) * | 2022-03-31 | 2022-07-12 | 工银科技有限公司 | Method and device for determining flow threshold based on genetic algorithm |
| CN115332780A (en) * | 2022-06-27 | 2022-11-11 | 中国舰船研究设计中心 | Ultra-wideband energy selection surface design and optimization method |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114745310A (en) * | 2022-03-31 | 2022-07-12 | 工银科技有限公司 | Method and device for determining flow threshold based on genetic algorithm |
| CN114745310B (en) * | 2022-03-31 | 2024-01-12 | 工银科技有限公司 | Flow threshold determining method and device based on genetic algorithm |
| CN115332780A (en) * | 2022-06-27 | 2022-11-11 | 中国舰船研究设计中心 | Ultra-wideband energy selection surface design and optimization method |
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