Genetic algorithm and fuzzy PID compound control fixed star spectrum simulation method

Abstract

The invention discloses a fixed star spectrum simulation method based on genetic algorithm and fuzzy PID compound control. Firstly, a fuzzy PID controller structure with double inputs: spectral intensity deviation # imgabs0 # and deviation rate # imgabs1 # and three outputs: proportionality coefficient increment # imgabs2 #, integral coefficient increment # imgabs3 # and differential coefficient increment # imgabs4 # is established. Then, basic work such as membership function selection, fuzzy rule making and defuzzification is completed; and finally, by means of a genetic algorithm, through coding mode formulation, initial population generation, fitness function selection and genetic operator determination, a fuzzy PID algorithm process is optimized. Practice verifies that in 3000K-9000K typical color temperature spectrum simulation, compared with a traditional fuzzy PID control algorithm, the error is remarkably reduced, and the simulation precision is greatly improved. And the performance is excellent when the full spectrum band simulation of the AM1.5 solar spectrum is carried out. The algorithm can accurately simulate a spectrum curve containing complex details, greatly enhances the accuracy and reliability of spectrum simulation, and provides key technical support for the fields of solar research, optical detection and the like.

Description

Technical Field

[0001] The present invention relates to the field of control, and in particular to a stellar spectrum simulation method based on a genetic algorithm and fuzzy PID composite control. Background Art

[0002] The spectral shapes between the minimum units of spectral fitting on the DMD have different functional distribution characteristics, and the radiance between different micromirrors of the DMD is nonlinear. At the same time, studies have shown that there are multiple correlations in the distribution between the minimum units of multi-correlation spectral fitting. This has caused the traditional PID control algorithm to be unable to solve the problems of increased spectral simulation errors due to the multiple correlations of independent variables, and sometimes target limitations and modulation underfitting will occur during the modulation process.

[0003] Therefore, a stellar spectrum simulation method based on genetic algorithm and fuzzy PID composite control is needed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a stellar spectrum simulation method based on a genetic algorithm and fuzzy PID composite control.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A stellar spectrum simulation method based on a genetic algorithm and fuzzy PID composite control comprises the following steps:

[0007] S1, spectral wavelength Corresponding spectral intensity deviation and deviation rate As input variable, increment by proportional coefficient , integral coefficient increment and differential coefficient increment For the output variables, a dual-input and three-output fuzzy PID controller structure is constructed;

[0008] S2. After building the dual-input and three-output fuzzy PID controller structure, membership function selection, fuzzy rule formulation and defuzzification are carried out;

[0009] S3. Use genetic algorithm to optimize the fuzzy PID algorithm process. The basic elements of genetic algorithm to optimize the fuzzy PID algorithm process are mainly coding method formulation, initial population generation, fitness function selection and genetic operation operator determination.

[0010] Specifically, in step S2, the membership function is selected to adopt a triangular function shape.

[0011] Specifically, the fuzzy rules in step S2 are based on , and The impact on output characteristics and actual engineering data in existing technologies are constructed through different deviation amounts and deviation rates.

[0012] Specifically, the defuzzification in step S2 is performed using a centroid method.

[0013] Specifically, the encoding method in step S3 is a hybrid encoding method, that is, the fuzzy rules are represented by decimal numbers, and the membership parameters are represented by floating-point numbers.

[0014] Specifically, the initial population in step S3 is generated by a program random method, and the generation rule is:

[0015] S1, generated random initial population matrix;

[0016] S2. All individuals in the population are different from each other, that is, for any ,have ;

[0017] The values of the same gene position of these two individuals are different, that is, for any have ;

[0018] S3. Set the evolutionary algebra timer at the same time , set the maximum evolutionary generations , then the termination condition is ;

[0019] Specifically, according to the fitness function selection rule and the spectral distribution function fitting evaluation method, the fitness function is selected as

[0020] ;

[0021] Where, is the fuzzy PID output node value; is the expected output value.

[0022] Specifically, the genetic operation operators in step S3 mainly include a selection operator, a crossover operator, and a mutation operator.

[0023] The beneficial effects of the present invention are:

[0024] A stellar spectrum simulation method combining genetic algorithms and fuzzy-PID control demonstrates significant advantages. When simulating spectra at typical color temperatures of 3000K, 6000K, and 9000K, the error was reduced by 1.26 times, 1.25 times, and 1.43 times, respectively, compared to the fuzzy-PID control algorithm. Within the 3000K-9000K color temperature range, the error remained stable at -2.91% to 2.94%, improving simulation accuracy by 1.4 times. The algorithm also performed exceptionally well when simulating the full AM1.5 solar spectrum, achieving an error better than ±3% in the smooth region and close to ±6% in the non-smooth region. The maximum error at 767nm was -5.93%, a 3.82-fold reduction compared to the fuzzy-PID control algorithm. Simulation accuracy in the smooth region of the spectrum was similar to that in the 3000K-9000K color temperature range, with little difference in the non-smooth region. This shows that the algorithm can excellently simulate spectral curves containing peak and trough details, significantly improve the accuracy and reliability of spectral simulation, and provide strong support for solar energy research, optical detection and other fields.

[0025] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a curve diagram of the unoptimized membership function shown in the present invention;

[0027] Figure 2 This is a flow chart of optimizing the fuzzy PID algorithm using a genetic algorithm as shown in the present invention;

[0028] Figure 3 The present invention shows Membership function curves before and after optimization;

[0029] Figure 4 The present invention shows Membership function curves before and after optimization;

[0030] Figure 5 The present invention shows Membership function curves before and after optimization;

[0031] Figure 6 The present invention shows Membership function curves before and after optimization;

[0032] Figure 7 The present invention shows Membership function curves before and after optimization;

[0033] Figure 8The 3000K color temperature spectrum simulation curve and spectrum simulation error diagram shown in the present invention;

[0034] Figure 9 The 6000K color temperature spectrum simulation curve and spectrum simulation error diagram shown in the present invention;

[0035] Figure 10 The 9000K color temperature spectrum simulation curve and spectrum simulation error diagram shown in the present invention;

[0036] Figure 11 This is a graph showing the spectrum simulation error test results of the fuzzy PID control algorithm in the 3000K-9000K color temperature range shown in the present invention;

[0037] Figure 12 This is a graph showing the test results of spectrum simulation error of the genetic algorithm optimized fuzzy PID algorithm in the color temperature range of 3000K-9000K shown in the present invention;

[0038] Figure 13 This is a simulated curve diagram of the AM1.5 solar spectrum shown in the present invention;

[0039] Figure 14 This is a curve diagram of the AM1.5 solar spectrum simulation error shown in the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the embodiments described are some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without inventive effort shall fall within the scope of protection of the present invention. In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation, and therefore should not be construed as limiting the present invention. In addition, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can mean fixed connection, detachable connection, or integral connection; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; or internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances. In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] The present application provides a stellar spectrum simulation method using a genetic algorithm and fuzzy PID composite control, comprising the following steps:

[0042] S1, build fuzzy PID controller structure; Corresponding spectral intensity deviation and deviation rate As input variable, increment by proportional coefficient , integral coefficient increment and differential coefficient increment As the output variable, a dual-input and three-output fuzzy PID controller structure is constructed. and The expression is

[0043] ; (1)

[0044] Where, and The wavelength is The simulated spectral intensity and target spectral intensity; is the spectral intensity deviation of the current state; is the spectral intensity deviation from the previous state.

[0045] S2. After building the fuzzy PID controller structure, the membership function selection, fuzzy rule formulation and defuzzification are carried out; the fuzzy set is divided into 5 fuzzy subsets , respectively negative large ( )、Negative small( ),zero( ), Zheng Xiao ( ) and Zhengda ( ); The domain area covered by the fuzzy subset membership function directly affects the performance of the fuzzy PID controller, while the shapes of the fuzzy membership function such as triangle, Gaussian, and bell have little effect on it; therefore, in order to facilitate the optimization of the membership function, the membership function adopts the shape of a triangular function, and the unoptimized The membership function curve is as follows Figure 1 As shown. , and The impact on output characteristics and actual engineering data in existing technologies are used to establish fuzzy rules through different deviation amounts and deviation rates:

[0046] (1) When the deviation is large, Take the larger value to improve the system response speed. Take a small value to avoid differential overflow caused by the instantaneous increase of the deviation at the beginning. Take a small value or zero to avoid excessive system overshoot;

[0047] (2) When the deviation amount and deviation rate are medium, It is appropriate to make it smaller to ensure the system response speed while reducing the system overshoot. and The value is moderate;

[0048] (3) When the deviation is small, and Take the largest value to ensure the steady-state performance of the system. At the same time, considering the anti-interference ability of the system, avoid output response oscillation. When the deviation rate is large, When the deviation rate is small, Take the larger one;

[0049] The initial , and The fuzzy rules are shown in Table 1.

[0050]

[0051] Defuzzification; The result of fuzzy reasoning is fuzzy quantity, but in the actual control process, accurate quantity is required. Therefore, the centroid method is used for defuzzification operation. The final output value expression of fuzzy reasoning is

[0052] ; (2)

[0053] Where, , , The exact value of the fuzzy controller output variation defuzzification, , , is the value in the domain of fuzzy control quantity, , , for , , The degree of membership.

[0054] S3. Genetic algorithm optimization fuzzy PID algorithm process design

[0055] The basic elements of the genetic algorithm optimization fuzzy PID algorithm process are mainly the formulation of encoding method, generation of initial population, selection of fitness function and determination of genetic operation operator.

[0056] Specifically, this application adopts a hybrid coding method, that is, the fuzzy rules are represented in decimal and the membership parameters are represented in floating point numbers, in order to reduce the optimization time and reduce the complexity of the coding. The code is {1, 2, 3, 4, 5}, and 0 indicates that there is no fuzzy rule. Each group of membership uses each membership function vertex as the optimization parameter, that is, each group has 13 membership function parameters to be optimized. At this time, the membership function code can be recorded as , then the fuzzy rules and membership functions are jointly encoded into a mixed code with a length of 140 bits.

[0057] Specifically, the initial population is generated randomly using a program, and the generation rules are as follows:

[0058] (1) Generate a random initial population matrix;

[0059] (2) All individuals in the population are different from each other, that is, for any ,have ;

[0060] The values of the same gene position of these two individuals are different, that is, for any have ;

[0061] (3) Set the evolutionary algebra timer at the same time , set the maximum evolutionary generations , then the termination condition is .

[0062] Specifically, according to the fitness function selection rule and the spectral distribution function fitting evaluation method, the fitness function is selected as

[0063] ; (3)

[0064] Where, is the fuzzy PID output node value; is the expected output value.

[0065] Specifically, genetic operation operators mainly include selection, crossover and mutation operators;

[0066] (1) Selecting an operator

[0067] The roulette method is used, that is, the distribution is based on the dominant proportion of fitness, and the probability of inheriting offspring is

[0068] (4)

[0069] Where, is the population size; For individuals Adaptability; Different from The count number of .

[0070] (2) Crossover operator

[0071] The crossover operator uses the random number comparison method to determine whether a crossover operation is required. The crossover operation criterion is:

[0072] (5)

[0073] Where, The random number generated for this round; is the crossover probability.

[0074] To ensure randomness, the crossover operation uses the real number crossover method, the formula is

[0075] (6)

[0076] Where, and Indicates the and The individual's chromosomes; A random number ranging from 0 to 1.

[0077] (3) Mutation operator

[0078] The mutation operator uses the random number comparison method to determine whether a mutation operation is required. The mutation operation criterion is:

[0079] (7)

[0080] Where, The random number generated for this round; is the mutation probability.

[0081] The mutation operation formula is

[0082] (8)

[0083] Where, It is the maximum value that a single chromosome in a single sample can take; Is the minimum value that a single sample and a single chromosome can take. The genetic algorithm optimization fuzzy PID algorithm process is as follows Figure 2 shown.

[0084] In order to further verify the generalization and spectral detail simulation capabilities of the genetic algorithm optimized fuzzy PID algorithm, the following examples are listed for further explanation.

[0085] Example 1: Comparative experiment of fuzzy rules and membership functions before and after optimization

[0086] To obtain more effective fuzzy rules and membership functions, the color temperature range was expanded from 3000K-9000K to 2500K-10000K. Therefore, six color temperatures (2500K, 3000K, 5000K, 6000K, 9000K, and 10000K) were selected as simulated target spectrum training samples. Fuzzy rules and membership functions were obtained after iterative optimization. The initialization parameters for the genetic algorithm-optimized fuzzy PID algorithm are shown in Table 2.

[0087]

[0088] After iterative optimization 、 、 The fuzzy PID control rules are shown in Table 3.

[0089]

[0090] According to Table 3, after optimization by genetic algorithm, In the early stage of adjustment, a larger value is used to increase the response speed. In the middle stage of adjustment, a smaller value is used to make the system have a smaller overshoot and ensure a certain response speed. In the later stage of adjustment, the value is increased to reduce the static error and improve the control accuracy. In the early stage of adjustment, to prevent integral saturation, the value is small or even zero. In the middle stage of adjustment, to avoid affecting stability, the value is moderate. In the late stage of adjustment, the value is increased to enhance the integral effect and reduce the static error of adjustment. Taking a larger value at the beginning of adjustment can obtain smaller or even avoid overshoot; taking a smaller value in the middle of adjustment and decreasing the value in the later stage can reduce the braking effect of the controlled process and compensate for the extended adjustment time caused by the larger value at the beginning of adjustment.

[0091] Before and after genetic algorithm optimization For comparison of membership function curves, see Figures 3 to 7 ,In the figure, the solid line part is the membership function before ,optimization, and the dotted line part is the membership function curve after ,optimization.

[0092] Example 2: Comparative experiment on spectrum simulation accuracy of fuzzy PID control algorithm and genetic algorithm optimized fuzzy PID algorithm

[0093] In the 450nm-1000nm spectral range, in the 3000K-9000K color temperature range, three typical characteristic target spectrum simulation curve shapes of 3000K, 6000K and 9000K were selected as the simulated target spectrum. The stellar optical radiation characteristics simulation algorithm experimental device was used to conduct the genetic algorithm optimization fuzzy PID algorithm simulation accuracy experiment of three typical color temperature spectra. The experimental test results are shown in Figures 8 to 10 .

[0094] according to Figures 8 to 10 It can be seen that the genetic algorithm optimized fuzzy PID algorithm has good simulation results for spectral curves with different distribution trends, and has little correlation with the trend of the spectral curve. The spectral simulation errors of three typical color temperatures of 3000K, 6000K and 9000K are shown in Table 4.

[0095]

[0096] According to Table 4, at three typical color temperatures of 3000K, 6000K, and 9000K, the spectrum simulation errors of the genetic algorithm optimized fuzzy PID algorithm are -2.79%, 2.92%, and 2.75%, respectively.

[0097] The spectral simulation error of the PID control algorithm is related to the shape of the target spectral curve. It is larger in areas where the spectral simulation curve is weak relative to the spectral radiation intensity. For example, the spectral simulation error near 450nm for a 3000K color temperature is larger than that near 1000nm, and the spectral simulation error near 450nm for a 9000K color temperature is smaller than that near 1000nm. However, due to the more uniform distribution of the spectral curve at a 6000K color temperature, this phenomenon is less pronounced. However, the spectral simulation error of the fuzzy PID control algorithm is more uniformly distributed within the 450nm-1000nm range for the three typical color temperatures of 3000K, 6000K, and 9000K. Therefore, there is no significant correlation between the spectral simulation error of the fuzzy PID control algorithm and the shape of the spectral curve. Table 5 shows the spectral simulation errors of the PID and fuzzy PID control algorithms for the three typical color temperatures of 3000K, 6000K, and 9000K.

[0098]

[0099] In order to facilitate the comparison of the spectrum simulation errors of the fuzzy PID control algorithm and the genetic algorithm optimized fuzzy PID algorithm at three typical color temperatures of 3000K, 6000K and 9000K, the relevant data in Table 4 and Table 5 are re-tabulated as shown in Table 6.

[0100]

[0101] According to Table 6, at three typical color temperatures of 3000K, 6000K, and 9000K, the spectrum simulation error of the genetic algorithm optimized fuzzy PID algorithm is reduced by 1.26 times, 1.25 times, and 1.43 times, respectively, compared with the fuzzy PID control algorithm.

[0102] Example 3: Verification of spectral simulation accuracy of fuzzy PID algorithm optimized by genetic algorithm

[0103] In order to verify the simulation accuracy of the fuzzy PID control algorithm for various color temperature spectra, the fuzzy PID control algorithm was used to simulate the spectrum curve of the color temperature range of 3000K-9000K (at intervals of 500K) within the 450nm-1000nm spectrum range. The spectrum simulation error test results can be found in Figure 11 .

[0104] In order to verify the simulation accuracy of the genetic algorithm optimized fuzzy PID algorithm for various color temperature spectra, the genetic algorithm optimized fuzzy PID algorithm was used to simulate the spectrum curve of the 3000K-9000K color temperature range (interval of 500K) within the 450nm-1000nm spectrum range. The spectrum simulation error test results can be found in Figure 12 .

[0105] according to Figure 12It can be concluded that the spectral simulation error of the genetic algorithm optimized fuzzy PID algorithm is between -2.91% and 2.94% in the color temperature range of 3000K-9000K, indicating that the genetic algorithm optimized fuzzy PID algorithm has a good simulation effect in the color temperature range of 3000K-9000K.

[0106] according to Figure 11 It can be seen that the maximum spectrum simulation error of the fuzzy PID control algorithm is 4.12% in the color temperature range of 3000K-9000K; according to Figure 12 It can be seen that the maximum spectrum simulation error of the genetic algorithm optimized fuzzy PID algorithm in the 3000K-9000K color temperature range is 2.94%. Compared with the fuzzy PID control algorithm, the spectrum simulation accuracy of the genetic algorithm optimized fuzzy PID algorithm is 1.4 times higher, indicating that the spectrum simulation accuracy of the genetic algorithm optimized fuzzy PID algorithm is higher.

[0107] Example 4 AM1.5 solar spectrum simulation experiment using genetic algorithm to optimize fuzzy PID algorithm

[0108] Based on the optimized fuzzy rules and membership functions, the AM1.5 solar spectrum was used as the simulation target to verify the ability of the genetic algorithm to optimize the fuzzy PID algorithm to simulate the spectral details. The simulation test results of the genetic algorithm optimized fuzzy PID algorithm on the AM1.5 solar spectrum can be found in the following table. Figure 13-14 .

[0109] according to Figure 13-14 It can be seen that the spectral simulation error of the genetic algorithm optimized fuzzy PID algorithm is within ±3% in the relatively smooth areas of the AM1.5 solar spectrum. However, when there is a large slope mutation in the shape of the spectral curve, the spectral simulation error can only converge to within ±6%, among which the spectral simulation error at 767nm is as large as -5.93%. This shows that although the spectral simulation error of the AM1.5 solar spectrum in the non-smooth area is larger than the spectral curve in the 3000K-9000K color temperature range, the spectral simulation error in the smooth area is very close to that in the 3000K-9000K color temperature range. This shows that when the simulation of very obvious spectral characteristic peaks is not required, the genetic algorithm optimized fuzzy PID algorithm still has good performance for spectral curves with detailed features such as peaks and troughs.

[0110] In summary, compared with the fuzzy PID control algorithm, the spectrum simulation errors of the three typical color temperatures of 3000K, 6000K and 9000K are reduced by 1.26 times, 1.25 times and 1.43 times respectively. The spectrum simulation errors of the fuzzy PID algorithm optimized by the genetic algorithm are distributed between -2.91% and 2.94% in the color temperature range of 3000K-9000K. The spectrum simulation accuracy of the fuzzy PID algorithm optimized by the genetic algorithm is 1.4 times higher than that of the fuzzy PID control algorithm. The fuzzy PID algorithm optimized by the genetic algorithm has a better effect on AM1. .5 All spectral segments of the solar spectrum have good simulation effects. The spectral simulation error in the smoother area is better than ±3%, and the spectral simulation error in the non-smooth area is close to ±6%. The maximum spectral simulation error at 767nm is -5.93%, which is 3.82 times smaller than that of the fuzzy PID control algorithm. The simulation accuracy of the spectral smooth area is very close to that in the color temperature range of 3000K-9000K, and the difference in the non-smooth area is not much. This shows that the genetic algorithm optimized fuzzy PID algorithm has a good simulation ability for spectral curves with detailed features such as peaks and troughs.

[0111] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A stellar spectrum simulation method based on genetic algorithm and fuzzy PID composite control, characterized in that: The following steps are involved: S1, spectral wavelength Corresponding spectral intensity deviation and deviation rate As input variable, increment by proportional coefficient , integral coefficient increment and differential coefficient increment For the output variables, a dual-input and three-output fuzzy PID controller structure is constructed; S2. After building the dual-input and three-output fuzzy PID controller structure, membership function selection, fuzzy rule formulation and defuzzification are carried out; S3. Use genetic algorithm to optimize the fuzzy PID algorithm process. The basic elements of genetic algorithm to optimize the fuzzy PID algorithm process are mainly coding method formulation, initial population generation, fitness function selection and genetic operation operator determination.

2. The stellar spectrum simulation method of genetic algorithm and fuzzy PID composite control according to claim 1, characterized in that: In step S2, the membership function is selected to adopt a triangular function shape.

3. The stellar spectrum simulation method of genetic algorithm and fuzzy PID composite control according to claim 1, characterized in that: The fuzzy rules in step S2 are based on , and The impact on output characteristics and actual engineering data in existing technologies are constructed through different deviation amounts and deviation rates.

4. The stellar spectrum simulation method of genetic algorithm and fuzzy PID composite control according to claim 1, characterized in that: The defuzzification in step S2 is performed using the centroid method.

5. The stellar spectrum simulation method of genetic algorithm and fuzzy PID composite control according to claim 1, characterized in that: The encoding method in step S3 uses a hybrid encoding method, that is, the fuzzy rules are represented by decimal numbers, and the membership parameters are represented by floating point numbers.

6. The stellar spectrum simulation method of genetic algorithm and fuzzy PID composite control according to claim 1, characterized in that: The initial population in step S3 is generated by random program, and the generation rule is: S1, generated random initial population matrix; S2. All individuals in the population are different from each other, that is, for any ,have ; The values of the same gene position of these two individuals are different, that is, for any have ; S3. Set the evolutionary algebra timer at the same time , set the maximum evolutionary generations , then the termination condition is ; Specifically, according to the fitness function selection rule and the spectral distribution function fitting evaluation method, the fitness function is selected as ； Where, is the fuzzy PID output node value; is the expected output value.

7. The stellar spectrum simulation method of genetic algorithm and fuzzy PID composite control according to claim 1, characterized in that: The genetic operation operators in step S3 mainly include a selection operator, a crossover operator and a mutation operator.

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