CN104834329A - Method adopting fuzzy control to adjust genetic algorithm so as to optimize parameters and application of method - Google Patents
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Abstract
The invention provides a method adopting fuzzy control to adjust a genetic algorithm so as to optimize parameters and application of the method. According to the method of the invention, a genetic algorithm (GA) for global optimization is selected to perform automatic optimization solving on adjustment of a subordinating degree function, parameter correction of a fuzzy control rule analytic expression and position change adjustment of an output clavate subordinating degree function. The method includes the steps that the genetic algorithm is utilized to perform self-correction and self-adjustment on a fuzzy subordinating degree function curve, parameters p and q in the fuzzy control analytic expression U=f (E,C,p,q) and the position of the output clavate subordinating degree function, and therefore, fuzzy control satisfying requirements of experiences can be realized, and control accuracy and stability can be greatly improved, and designed control quality can be realized, and better control quality of a furnace temperature control system can be realized, and the system can be constantly in an optimized state in an operating process assuredly.
Description
Technical Field
The invention belongs to the technical field of kiln temperature control, and particularly relates to a method for adjusting genetic algorithm optimization parameters through fuzzy control and application thereof.
Background
The furnace temperature control system is a complex system, has the characteristics of nonlinearity, time-varying parameters, a large number of control variables, controlled quantity delay and the like, and has interference of a plurality of uncertain factors. The traditional fuzzy controller is difficult to ensure that the control effect of the system is always in a relatively ideal state in various actually complex kiln thermal parameter control systems.
Disclosure of Invention
The invention aims to provide a method for adjusting genetic algorithm optimization parameters through fuzzy control and application thereof, and aims to solve the problem that the actual control effect of a traditional fuzzy controller on various kiln thermal parameters is not ideal.
The invention is realized in such a way that a method for adjusting the optimization parameters of the genetic algorithm by fuzzy control comprises the adjustment of a fuzzy membership function curve, and the adjustment of the fuzzy membership function curve comprises the following steps:
moving the distance d to the triangle vertex of the triangle membership function curve of the input fuzzy variable, and selecting a three-bit binary code to encode the distance d;
simultaneously coding the input deviation E and the deviation change C in the triangular membership function curve;
and according to the coding rule, randomly extracting a plurality of schemes as chromosomes to carry out optimization operation of the genetic input fitness function.
Preferably, the input deviation E and the deviation variation C are coded in the form of:
ne1ne2…ne18nc1nc2…nc18；
where n is the fractional step, E is the subscript deviation E, and C is the subscript deviation change C.
Preferably, the fitness function is:
wherein, for fitness, the change eiIs the deviation value after the ith sampling.
Preferably, the genetic optimization operations comprise in particular replication operations, crossover operations and mutation operations.
Preferably, the method for adjusting the optimization parameters of the genetic algorithm by fuzzy control further comprises a modification of a fuzzy control analytic expression, and the modification of the fuzzy control analytic expression comprises the following steps:
dividing an output response curve of a given system into four regions longitudinally according to E, C signs, and segmenting transversely according to the absolute value of the deviation E to obtain different segments;
defining the control rules of different sections as follows by using a fuzzy control analytic expression:
(i＝1，2；j＝1，2；x＝1,2,3,4)；
in the formula,<>for rounding up the rounding symbols, x is four different segments, px、qxFor making an intelligenceCan self-adjust the factor, and px+qx1, E is the variance, C is the variance;
adopting genetic algorithm to intelligently regulate the factor p in the fuzzy control analytic expressionx、qxAnd carrying out optimization correction on the value.
Preferably, the intelligent regulating factor p in the fuzzy control analytic expression is adjusted by a genetic algorithmx、qxThe optimization and correction of the value comprises the following steps:
consider px+qx1, p in four regions1,p2,p3,p4Respectively representing by four-digit binary BCD codes, randomly selecting, connecting the codes into a binary character string, and randomly extracting four schemes as chromosomes to carry out genetic operation;
the genetic optimization procedure according to claim 3.
Preferably, the method for adjusting genetic algorithm optimization parameters by fuzzy control further comprises outputting a bar-shaped spacing adjustment, wherein the outputting the bar-shaped spacing adjustment comprises the following steps:
encoding the output bar-shaped spacing, wherein the encoding of the output bar-shaped spacing is similar to the encoding of the distance d in the adjustment of the input membership function, and the difference is that the leftmost displacement and the rightmost displacement of the input membership function are removed;
the genetic optimization procedure according to claim 3.
The invention further provides application of the method for adjusting the genetic algorithm optimization parameters through fuzzy control in a kiln temperature control system.
The invention provides a method for fuzzy control and adjustment of genetic algorithm optimization parameters, namely, adjustment of membership function, fuzzy control rule analytic formula parameter correction and position change adjustment of output rod-shaped membership function, and automatic optimization solution of the membership function by using global optimized Genetic Algorithm (GA), wherein the method comprises the steps of self-correcting and self-adjusting parameters p and q in fuzzy membership function curve, fuzzy control analytic formula U (E, C, p and q) and output membership rod-shaped membership function position by using the genetic algorithm, thereby realizing empirical fuzzy control, greatly improving control precision and stability, and achieving designed control quality, the system achieves better control quality and can be ensured to be always in an optimized state in the running process.
Drawings
FIG. 1 is a schematic diagram of the left-right movement range of the input variable E, C according to the embodiment of the present invention;
FIG. 2 is a schematic diagram showing the left and right movement of the bar position of the bar membership function of the output variable in the embodiment of the present invention;
FIG. 3 is a diagram of assistance in solving for an output expression in an embodiment of the present invention;
FIG. 4 is a graph comparing an adjustment curve and a no adjustment curve of an input membership function according to an embodiment of the present invention;
FIG. 5 shows fuzzy control rule p according to an embodiment of the present inventionx、qxAnd comparing the adjusting response curve with the position adjusting response curve of the output membership function.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1 input membership function adjustment in fuzzy control
The membership function curve of the fuzzy variable can take various shapes, such as bell shape, trapezoid shape, triangle shape and bar shape. Since the normal distribution curve is closer to human thinking, the conventional fuzzy controller generally adopts an isosceles triangle membership curve close to the normal distribution curve as a membership function of an input variable, and performs equidistant quantization as shown in fig. 1. In fact, the system is complex and varies, if the regular isosceles triangles which are symmetrical and uniformly distributed are selected, the requirements of dynamic response and steady-state precision are difficult to meet at the same time, therefore, the invention adopts a genetic algorithm to automatically adjust the positions of the vertexes of the triangles (the positions of the bottom edges are kept unchanged), thereby improving the control precision and the like. The plus-minus 6-level discretization is still adopted, plus-minus 3-level (large, medium and small) fuzzification is still adopted, in order to avoid vertex overlapping, the distance of left and right movement is limited within one level, and a distance d of right movement is definediAnd a left shift distance-diMaximum not exceeding 60% of one level, Z at 0OThe vertex of the curve is unchanged, as shown in detail below:
(1) encoding
And selecting a three-bit binary code for the moving distance d of the vertex of the input triangular membership function, and coding according to the coding rule shown in the table 1. The input contains two variables, namely a deviation E and a deviation change C, and E, C is encoded simultaneously in the form of:
ne1ne2…ne18nc1nc2…nc18 (1)
in the formula (1), n is a step, E is a subscript deviation E, and C is a subscript deviation change C.
And according to the coding rule, randomly extracting four schemes as chromosomes to carry out genetic optimization operation.
TABLE 1 coding table
(1) Fitness function
The chromosome with high fitness is selected, the genetic gene of the chromosome is inherited in the next generation, and the chromosome with low fitness is eliminated, wherein the fitness function selected by the invention is as follows:
in the formula (2), the fitness is changed by eiIs the deviation value after the ith sampling.
(2) Genetic manipulation
a. Copy operation
Randomly generating N groups of serial code character strings, i.e. N is the number of chromosomes and is expressed as N' & fi/∑fiDetermining the number of i-th individuals that should replicate themselves in the next generation, N' being the number of populations involved in replication, fiIs the fitness value of the ith individual.
b. Crossover operation
The crossover operation allows the creation of new chromosomes, allowing new points in the search space to be tested, assuming two schemes i, ii are chosen as chromosomes, such as:
chromosome A010111101001100011000001111101010001
Chromosome B110101100001101100110101001011010000
Randomly generating an exchange position point, such as the right part 0011 … and 1100 … of the 14 th position in the exchange chromosomes A, B, then the two next generation chromosome strings are:
A' 010 111 101 001 101 100 110 101 001 011 010 000
B' 110 101 100 001 100 011 000 001 111 101 010 001
taking the cross probability fcAt 0.6, only 60% of the population is participating in the crossover operation.
c. Mutation operation
Mutation is the random change of the value of a bit in a string from 1 to 0, or from 0 to 1, with a small probability (otherwise the transition is too large and tends to oscillate). By mutation operation, the diversity of genetic gene types in the population can be ensured, local solution can be avoided, and high-quality optimized solution can be obtained, such as chromosome A2The 6 th site of the chromosome is mutated, and the mutated chromosome is:
A” 010 110 101 001 101 100 110 101 001 011 010 000
taking the variation probability fmSince the above chromosome string is 36 bits, the mutation operation can be performed once after 3 generations by using the accumulation algorithm.
Example 2 correction of parameters in fuzzy control analytic equation
For a given system, the output response curve is longitudinally divided into four zones according to E, C signs, the output response curve is transversely segmented according to the magnitude of the absolute value of the deviation E, when the 'zones' are different, the control rule is also different, alpha in the control analytic formula U- < alpha E + (1-alpha) C > is changed accordingly, therefore, intelligent adjusting factors p and q (namely, the upper and lower limit values of the automatic change of alpha) of the upper and lower limit values automatically changing along with the magnitude of the | E | gear value are introduced for adjustment, and then the fuzzy control analytic formula is changed into:
(i＝1，2；j＝1，2；x＝1,2,3,4) (3)
wherein,<>is taken for roundingWhole symbol, x four different segments, px、qxIs an intelligent self-adjusting factor, and px+qxWhen the value is 1, E is a variation, and C is a variation.
Obviously, only by choosing appropriate p in different "sectionsx、qxThe value is such that the design requirement is met, and the invention also applies genetic algorithms to px、qxAnd carrying out optimization correction on the value.
(1) Encoding
When only p is consideredx+qxWhen 1 is the case, qx＝1-pxSo that different p are selectedxThe value of q is obtainedxThe value is obtained. P in four regions1,p2,p3,p4Respectively expressed by four-digit binary BCD codes, randomly selected, connected into a binary character string, and randomly extracted four schemes as chromosomes to carry out genetic operation, as shown in Table 2:
TABLE 2 encoding of parameter p in improved fuzzy control analytic expressions
| p value | p1 | p2 | p3 | p4 |
| Scheme I | x11x12x13x14 | x21x22x23x24 | x31x32x33x34 | x41x42x43x44 |
| Scheme II | y11y12y13y14 | y21y22y23y24 | y31y32y33y34 | y41y42y43y44 |
| Scheme III | z11z12z13z14 | z21z22z23z24 | z31z32z33z34 | z41z42z43z44 |
| Scheme IV | v11v12v13v14 | v21v22v23v24 | v31v32v33v34 | v41v42v43v44 |
(2) Genetic manipulation
The operations of replication, crossover and mutation are similar to those described above, and no more description is given, and an ideal scheme can be selected through genetic algorithm calculation simulation, such as:
chromosome x 0110011101010111
Conversion to decimal digit string: chromosome x' 6757
From px+qxWhen 1, p may be taken1＝0.6，p2＝0.7，p3＝0.5，p40.7, with Uijk＝f(Eix,Cjx,px,qx) The kiln is controlled to obtain good control effect.
Example 3 output rod spacing adjustment
After the adjustment of membership function and control rule, the regulation of output U bar position is also crucial, because it directly regulates the controlled object, if it is not accurate, it affects the control effect, and it can use genetic algorithm to ensure the original 0 bar position is not moved, and output bar spacing sigmaiAnd-sigmaiThe output membership function curve of the adjustment and optimization is shown in FIG. 2.
(1) Encoding
The code of the output bar-shaped distance is the same as the code of the input membership function, only 3-bit binary codes are adopted, but because the valve opening degree is limited, the leftmost displacement and the rightmost displacement must be removed to avoid overflow, four schemes can be randomly selected as chromosomes for genetic optimization, and the codes are shown in table 3:
TABLE 3 four-chromosome coding of output rod-shaped position shift distance
| Value of sigma | σ′3 | σ′2 | σ′1 | σ1 | σ2 | σ3 |
| Scheme I | x′31x′32x′33 | x′21x′22x′23 | x′11x′12x′13 | x11x12x13 | x21x22x23 | x31x32x33 |
| Scheme II | y′31y′32y′33 | y′21y′22y′23 | y′11y′12y′13 | y11y12y13 | y21y22y23 | y31y32y33 |
| Scheme III | z′31z′32z′33 | z′21z′22z′23 | z′11z′12z′13 | z11z12z13 | z21z22z23 | z31z32z33 |
| Scheme IV | v′31v′32v′33 | v′21v′22v′23 | v′11v′12v′13 | v11v12v13 | v21v22v23 | v31v32v33 |
(2) Genetic manipulation
The genetic manipulation is the same as before. For a specific kiln temperature control object, a group of optimized series corresponding to each output gear is obtained through genetic algorithm GA calculation operation and simulation analysis, and the series is shown in the following table 4:
TABLE 4 number of stages corresponding to each optimized gear
| Gear | NB | NM | NS | OK | PS | PM | PB |
| Corresponding number of stages | －6.2 | －3.6 | －1.8 | 0 | 2.2 | 4.2 | 6 |
Example 4 maximum correction adjustment calculation
The output U fuzzifies the variables using ± 6 levels of discretization and ± 3 levels, i.e. three levels of large, medium and small, which is only analyzed by taking the case of E, C in 0 to 2 levels as an example, see fig. 3.
If the measured deviation isValue ex0.6 grade, deviation variation value cxGrade 2.3, from the above figure it is clear that E1＝int(ex0/2) gear E2＝E1+1 ═ 1 gear; c1＝int(cx2) 1 st gear, C2＝C1+ 1-2 gear (d)x、σxAll are left shift amounts, take-0.6 level, p takes 0.8), and are obtained by the similar triangle proportional relation:
A、0≤ex＜2-d ∴ <math>
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is obtained by the formula (3): u shape11＝－1、U12＝－2、U21＝－1、U22＝－2,
The method has the following obvious advantages: n is11＝-2、n12＝-4、n21＝-2、n22＝-4，σij＝σx＝-0.6，
The following steps are provided:and
outputs can be obtained with conservative aggressive decisions:
before non-adjustment of nuIs level-2.4, so the maximum adjustable correction is:
Δn＝nu-n'u2.4- (-3.27) ═ 0.87 (grade)
This amount is sufficient to satisfy the maximum adjustment amount, where no correction of the p-coefficient is included.
Example 5 analysis of simulation examples
A three-order control system mathematical model with a pure hysteresis link is given:
the simulation results are shown in fig. 4 and 5, where "1" represents the simulation curve without any adjustment (U ═ 0.5E +0.5C > analytic control type), "2" represents the simulation curve for adjusting the pitch of the peaks of the membership function, "3" represents the response curve for adjusting the fuzzy control law parameters, and "4" represents the output rod-shaped position adjustment response curve. As can be seen from the simulation graph, the overshoot after the genetic algorithm optimization is reduced, the response time is shortened, and the method is obviously superior to unadjusted fuzzy control.
Compared with the defects and shortcomings of the prior art, the invention has the following beneficial effects: the invention greatly improves the control precision and stability of the furnace temperature control system, achieves the designed control quality, enables the system to achieve better control quality, and can ensure that the system is always in an optimized state in the operation process.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A method for adjusting genetic algorithm optimization parameters by fuzzy control is characterized in that the method for adjusting the genetic algorithm optimization parameters by the fuzzy control comprises the adjustment of a fuzzy membership function curve, and the adjustment of the fuzzy membership function curve comprises the following steps:
moving the distance d to the triangle vertex of the triangle membership function curve of the input fuzzy variable, and selecting a three-bit binary code to encode the distance d;
simultaneously coding the input deviation E and the deviation change C in the triangular membership function curve;
and according to the coding rule, randomly extracting a plurality of schemes as chromosomes to carry out optimization operation of the genetic input fitness function.
2. The method for adjusting the optimized parameters of genetic algorithms by fuzzy control as claimed in claim 1, wherein said input deviations E and deviation variances C are coded in the form of:
ne1ne2…ne18nc1nc2…nc18；
where n is the fractional step, E is the subscript deviation E, and C is the subscript deviation change C.
3. The method for fuzzy control tuning genetic algorithm optimization parameters of claim 2, wherein said fitness function is:
wherein, for fitness, the change eiIs the deviation value after the ith sampling.
4. The method for fuzzy control tuning genetic algorithm optimization parameters of claim 3, wherein said genetic optimization operations specifically include replication operations, crossover operations, and mutation operations.
5. The method for adjusting the optimized parameters of the genetic algorithm by fuzzy control of claim 1, further comprising the step of modifying the fuzzy control analytic expression, wherein the step of modifying the fuzzy control analytic expression comprises the steps of:
dividing an output response curve of a given system into four regions longitudinally according to E, C signs, and segmenting transversely according to the absolute value of the deviation E to obtain different segments;
defining the control rules of different sections as follows by using a fuzzy control analytic expression:
(i＝1，2；j＝1，2；x＝1,2,3,4)；
in the formula,<>for rounding up the rounding symbols, x is four different segments, px、qxIs an intelligent self-adjusting factor, and px+qx1, E is the variance, C is the variance;
adopting genetic algorithm to intelligently regulate the factor p in the fuzzy control analytic expressionx、qxAnd carrying out optimization correction on the value.
6. The method for adjusting the optimized parameters of genetic algorithm according to the fuzzy control of claim 5, wherein the genetic algorithm is used to adjust the intelligent adjustment factor p in the fuzzy control analytic expressionx、qxThe optimization and correction of the value comprises the following steps:
consider px+qx1, p in four regions1,p2,p3,p4Respectively representing by four-digit binary BCD codes, randomly selecting, connecting the codes into a binary character string, and randomly extracting four schemes as chromosomes to carry out genetic operation;
the genetic optimization procedure according to claim 3.
7. The method for fuzzy control tuning genetic algorithm optimization parameters of claim 1, wherein said method for fuzzy control tuning genetic algorithm optimization parameters further comprises outputting a rod-shaped spacing tuning, said outputting a rod-shaped spacing tuning comprising the steps of:
encoding the output bar-shaped spacing, wherein the encoding of the output bar-shaped spacing is similar to the encoding of the distance d in the adjustment of the input membership function, and the difference is that the leftmost displacement and the rightmost displacement of the input membership function are removed;
the genetic optimization procedure according to claim 3.
8. Use of the method for fuzzy control of adjustment of genetic algorithm optimization parameters according to claims 1 to 7 in a kiln temperature control system.
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| CN108820036A (en) * | 2018-05-30 | 2018-11-16 | 厦门大学 | A kind of In-wheel motor driving automobile electric power-assisted steering controlling method |
| CN109683476A (en) * | 2018-12-10 | 2019-04-26 | 武汉船用电力推进装置研究所（中国船舶重工集团公司第七一二研究所） | A kind of parsing Oriented Fuzzy Control System |
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| CN105425588B (en) * | 2015-12-09 | 2018-08-17 | 天津大学 | A kind of fuzzy control method improving indoor environment reverse design efficiency |
| CN105589332A (en) * | 2016-01-20 | 2016-05-18 | 中国矿业大学 | Method for balancing ball-beam system based on SFLA fuzzy controller |
| CN105589332B (en) * | 2016-01-20 | 2019-03-29 | 中国矿业大学 | A kind of balance ball-beam system method based on SFLA fuzzy controller |
| CN106650934A (en) * | 2016-10-17 | 2017-05-10 | 东南大学 | Method for accurately identifying parameters of thermal process state-space model by adopting improved genetic optimization algorithm |
| CN106650934B (en) * | 2016-10-17 | 2018-11-09 | 东南大学 | The method that thermal process state-space model parameter is accurately recognized using improved genetic Optimization Algorithm |
| CN108820036A (en) * | 2018-05-30 | 2018-11-16 | 厦门大学 | A kind of In-wheel motor driving automobile electric power-assisted steering controlling method |
| CN109683476A (en) * | 2018-12-10 | 2019-04-26 | 武汉船用电力推进装置研究所（中国船舶重工集团公司第七一二研究所） | A kind of parsing Oriented Fuzzy Control System |
| CN112486224A (en) * | 2020-12-10 | 2021-03-12 | 安徽环瑞电热器材有限公司 | Bathroom equipment temperature control system and temperature control method thereof |
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