CN110134095A - The method and terminal device of Power Plant Thermal analog control system optimization - Google Patents
The method and terminal device of Power Plant Thermal analog control system optimization Download PDFInfo
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Abstract
The present invention is suitable for Power Plant Thermal analog quantity automatic control technology field, provide the method and terminal device of a kind of Power Plant Thermal analog control system optimization, this method comprises: obtaining the mathematical model of analog quantity object by recognizing to the analog quantity process object model of acquisition；According to the mathematical model of analog quantity object, the parameter of the analog control system controller model of acquisition is optimized, obtains target controller model；When the performance of the target controller model does not meet preset need, it reacquires analog quantity process object model and executes subsequent operation, terminate process when the performance of the target controller model meets preset need, so as to optimize to Power Plant Thermal analog control system, control system is made to have the characteristics that control precision is high, overshoot is small, control speed is fast, strong antijamming capability.
Description
Technical field
The invention belongs to Power Plant Thermal analog quantity automatic control technology fields more particularly to a kind of Power Plant Thermal to simulate
The method and terminal device of amount control system optimization.
Background technique
Analog control system refer to analog quantity parameter related in industrial control field carry out continuous closed-loop control, make by
Control analog quantity parameter value maintains setting range or the as expected general name of the automatic control system of object variations.
It in Power Plant Thermal control field, often needs to control the analog quantity in production process, such as thermal power plant's control
Load control system, the control of Stream temperature degree or out of stock control system in system processed etc..The control effect of analog control system is good
The bad production cost and Business Economic Benefit that will have a direct impact on enterprise.Therefore, the adjusting of analog control system is raw in enterprise
It produces most important in operational process.
However the controlled device of analog control system has large time delay characteristic, multiple analog quantitys mostly in process of production
Between interact, be mutually coupled, control process is influenced by many disturbing factors, therefore traditional is led based on proportional, integral-
Often there is control precision in the automatic control system of number (Proportion Integration Differentiation, PID)
It is low, control speed is slow, phenomena such as system overshoot is big, in addition, traditional analog amount control system parameter regulation is completely dependent on debugging
The field experience of personnel, technical staff's professional standards are irregular, and adjustment method subjectivity is strong so that analog quantity automatically control at
For a difficult point of industrial control field.
Summary of the invention
In view of this, the embodiment of the invention provides a kind of method of Power Plant Thermal analog control system optimization and ends
End equipment, to solve, the control precision that analog quantity automatically controls in the prior art is low, control speed is slow and system overshoot is big
The problem of.
The first aspect of the embodiment of the present invention provides a kind of method of Power Plant Thermal analog control system optimization, packet
It includes:
A obtains Power Plant Thermal analog quantity process object model；
B recognizes the analog quantity process object model, obtains the mathematical model of analog quantity object；
C obtains Power Plant Thermal analog control system controller model；
D, according to the mathematical model of the analog quantity object, to the parameter of the analog control system controller model into
Row optimization, obtains target controller model；
E repeats step A to step D, directly when the performance of the target controller model does not meet preset need
Terminate process when meeting preset need to the performance of the target controller model.
In one embodiment, the analog quantity process object model is
Wherein, the W (S) indicates the analog quantity process object model, and the Y (s) indicates process variable output function
Laplace transform, it is describedIndicate the Laplace transform of input function, a and the b respectively indicate each level
Number, the m indicate the maximum order of molecule, and the n indicates the maximum order of denominator, and the s indicates complex variable.
In one embodiment, artificial bee colony algorithm is mixed to the analog quantity process object by changing the ACABC of strategy
Model is recognized, and is optimized to the parameter of the analog control system controller model, wherein the change plan
ACABC slightly mixes artificial bee colony algorithm
A, initialization algorithm parameter, the parameter include population scale NP and maximum cycle；
B, NP initial solution of random initializtion, and the fitness of all initial solutions is calculated, obtain initial globally optimal solution；
C, gathering honey bee carry out new nectar source according to all initial solutions and search for, and obtain the first new explanation, and it is new to calculate described first
The fitness of solution；
D updates the first new explanation neighborhood search number according to first new explanation；
E, according to the fitness of the fitness of the initial solution and first new explanation, calculate each nectar source be observed bee with
With follow probability；
F, according to the initial solution and first new explanation, to the initial solution and the corresponding nectar source position of first new explanation
It sets and is analyzed, distribution estimation is carried out to target nectar source, obtains the dominant group of gathering honey bee；
G carries out distribution estimation according to dominant group of the Gauss marginal probability distribution estimation method to the gathering honey bee, obtains
Distribution probability；
H, observation bee follow probability to carry out new nectar source search according to, or according to copula Estimation of Distribution Algorithm with
Marginal probability distribution calculates, and obtains the second new explanation, and calculate the fitness of second new explanation；
I, investigation bee carry out new nectar source search at random, obtain third new explanation, and calculate the fitness of the third new explanation；
J, according to the fitness of the initial solution, the fitness of first new explanation, second new explanation fitness and
The fitness of the third new explanation regard the maximum solution of fitness as globally optimal solution；
Whether k, detection global search reach the maximum cycle, follow when the global search is not up to the maximum
When ring number, step c to step k is continued to execute, terminates process when global search reaches the maximum cycle.
It is in one embodiment, described that first new explanation neighborhood search number is updated according to first new explanation, comprising:
When the fitness of first new explanation is more than or equal to the fitness currently solved, then the first new explanation neighborhood search
Number is 0, and first new explanation is the new explanation for obtain when the search of nectar source according to the current solution；
When the fitness of first new explanation is less than the fitness currently solved, then the first new explanation neighborhood search number is in original
Add 1 on the basis of neighborhood search number.
In one embodiment, it calculates the observed bee in each nectar source according to the initial solution and the new explanation described and follows
Probability after, further includes:
According to the fitness of the fitness of the initial solution and the new explanation, according to fitness to the initial solution and described
New explanation is ranked up.
In one embodiment, the gathering honey bee carries out new nectar source search according to all initial solutions, obtains the first new explanation, packet
It includes:
According toObtain the first new explanation；
Wherein, the Vi jIndicate first new explanation, it is describedThe first new explanation is corresponding described in expression initial solution works as
Preceding solution, it is describedIndicate the solution randomly selected in current nectar source, it is describedIndicate the random number in (- 1,1) range, it is described
YjIndicate that current globally optimal solution, the st indicate that current iteration number, the M indicate maximum cycle；
It is described according to the fitness of the initial solution and the fitness of first new explanation, calculate each nectar source and be observed bee
What is followed follows probability, comprising:
According toCalculate each nectar source be observed bee follow follow probability；
Wherein, the piIndicate i-th of nectar source be observed bee follow follow probability, the fitiIndicate the suitable of i-th of solution
Response, the N indicate the total number of the fitness of the initial solution and the fitness of first new explanation；
The dominant group for obtaining gathering honey bee, comprising:
According toObtain the dominant group of gathering honey bee；
Wherein, the S indicates the dominant group quantity of gathering honey bee, and the s' indicates optimum selecting probability；
It is described that distribution estimation is carried out according to dominant group of the Gauss marginal probability distribution estimation method to the gathering honey bee, it obtains
Obtain distribution probability, comprising:
According toObtain distribution probability；
Wherein, the N (μj,σj 2) indicate distribution probability；
It is described to be calculated according to copula Estimation of Distribution Algorithm and marginal probability distribution, obtain the second new explanation, comprising:
According toObtain the second new explanation；
Wherein, describedIndicate second new explanation, the ujIt indicates in [0,1] section by being uniformly distributed the only of generation
Vertical variate-value, the θ indicate copula Estimation of Distribution Algorithm parameter, and the L indicates to obey of the second new explanation of Joint Distribution
Number, the v indicate stochastic variable, and the e indicates natural constant, and the F (v) indicates empirical distribution function, the j representation dimension
Variable, the σjIndicate variance of random variable, the μjIndicate stochastic variable mean value；
The investigation bee carries out new nectar source search at random, obtains third new explanation, comprising:
According to Xi(n)=Xmin+rand(0,1)(Xmax-Xmin) third new explanation is obtained,
Wherein, the Xi(n) third new explanation, the X are indicatedmax, the XminRespectively indicate maximal solution in all solutions, most
It goes to the lavatory.
In one embodiment, described that the analog quantity process object model is recognized, obtain the number of analog quantity object
Learn model, comprising:
Obtain the mathematical model of proportional-integral-differential PID controller；
Parameter in the mathematical model of the fixed PID controller, using the corresponding process values of analog quantity, predetermined target value
And control instruction applies step disturbance to analog control system, mixes artificial bee colony algorithm using the ACABC for changing strategy
Parameter in analog quantity process object model described in search iteration, when the ACABC for changing strategy mixes artificial bee colony algorithm
The corresponding fitness function value of the evaluation index of use is optimal, and obtains the mathematical model of analog quantity object.
In one embodiment, the mathematical model of the PID controller is
Wherein, the KDIndicate differential coefficient, the KPIndicate proportionality coefficient, the K1Indicate integral coefficient, the s table
It gives instructions in reply variable.
In one embodiment, the analog control system controller model is
Wherein, the Cm(s) analog control system controller model, the G are indicatedm(s)-Indicate the mould of controlled device
Part in type, the f (s) indicate that low-pass filter, the λ indicate filter temporal parameter, and 0≤λ < 1, and the r is indicated
Guarantee Cm(s) reasonable order.
In one embodiment, the mathematical model according to the analog quantity object, using the ACABC for changing strategy
Mixing artificial bee colony algorithm optimizes the parameter of the analog control system controller model, obtains target controller mould
Type, comprising:
The mathematical model of the analog quantity object is updated in the analog control system controller model, in acquisition
The closed loop mathematical model of mould controller；
According to the closed loop mathematical model of the internal mode controller, the parameter of analog control system controller model is determined；
Step is applied to analog control system using the corresponding process values of analog quantity, predetermined target value and control instruction
Disturbance, using analog control system controller described in the ACABC mixing artificial bee colony algorithm search iteration for changing strategy
The parameter of model, when the corresponding fitness letter of evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses
Numerical value is optimal, and obtains the parameter of target controller model；
According to the parameter of the target controller model, target controller model is obtained.
In one embodiment, the closed loop mathematical model of the internal mode controller is
Wherein, the Cm(s) ' indicate the closed loop mathematical model of the internal mode controller, the W (s)-Indicate that there is minimum
The transmission function of phase characteristic, the W (s)+Indicate all-pass filter transmission function.
In one embodiment, the evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function is
Wherein, the evaluation index that the fit indicates that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function, the ISE indicates that integrated square error, the OS indicate that maximum overshoot, the T indicate to stablize the time,
The α1, the α2With the α3Respectively indicate weight of each evaluation index in fitness function, the ymaxIndicate process variable
Export maximum value, the ysIndicate the setting value of process variable, the e (t) indicates deviation, and the r (t) indicates the setting of process variable
Value, the c (t) indicate the measured value of process variable.
The second aspect of the embodiment of the present invention provides a kind of device of Power Plant Thermal analog control system optimization, packet
It includes:
Module is obtained, for obtaining Power Plant Thermal analog quantity process object model；
Module is recognized, for recognizing to the analog quantity process object model, obtains the mathematical modulo of analog quantity object
Type；
The acquisition module is also used to obtain Power Plant Thermal analog control system controller model；
Optimization module, for the mathematical model according to the analog quantity object, to the analog control system controller
The parameter of model optimizes, and obtains target controller model；
Control module, for controlling the acquisition when the performance of the target controller model does not meet preset need
Module continues to operate, and terminates process when the performance of the target controller model meets preset need.
The third aspect of the embodiment of the present invention provides a kind of terminal device, comprising: memory, processor and is stored in
In the memory and the computer program that can run on the processor, when the processor executes the computer program
Realize the step as described in the method that Power Plant Thermal analog control system optimizes.
The fourth aspect of the embodiment of the present invention provides a kind of computer readable storage medium, comprising: the computer can
It reads storage medium and is stored with computer program, such as Power Plant Thermal analog quantity is realized when the computer program is executed by processor
Step described in the method for control system optimization.
Existing beneficial effect is the embodiment of the present invention compared with prior art: by the analog quantity process object to acquisition
Model is recognized, and the mathematical model of analog quantity object is obtained；According to the mathematical model of analog quantity object, to the analog quantity of acquisition
The parameter of control system controller model optimizes, and obtains target controller model；When the property of the target controller model
When can not meet preset need, reacquires analog quantity process object model and execute subsequent operation, until the target control
The performance of device model processed terminates process when meeting preset need, excellent so as to carry out to Power Plant Thermal analog control system
Change, control system is made to have the characteristics that control precision is high, overshoot is small, control speed is fast, strong antijamming capability.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is that the implementation process of the method for Power Plant Thermal analog control system optimization provided in an embodiment of the present invention is shown
It is intended to；
Fig. 2 is showing for the realization process of the ACABC mixing artificial bee colony algorithm provided in an embodiment of the present invention for changing strategy
It is intended to；
Fig. 3 is the schematic diagram provided in an embodiment of the present invention for obtaining target controller model；
Fig. 4 is the exemplary diagram of the device of Power Plant Thermal analog control system optimization provided in an embodiment of the present invention；
Fig. 5 is the schematic diagram of terminal device provided in an embodiment of the present invention.
Specific embodiment
In being described below, for illustration and not for limitation, the tool of such as particular system structure, technology etc is proposed
Body details, to understand thoroughly the embodiment of the present invention.However, it will be clear to one skilled in the art that there is no these specific
The present invention also may be implemented in the other embodiments of details.In other situations, it omits to well-known system, device, electricity
The detailed description of road and method, in case unnecessary details interferes description of the invention.
In order to illustrate technical solutions according to the invention, the following is a description of specific embodiments.
Fig. 1 is that the implementation process of the method for Power Plant Thermal analog control system provided in an embodiment of the present invention optimization is shown
It is intended to, details are as follows.
Step 101, Power Plant Thermal analog quantity process object model is obtained.
Optionally, common single-input single-output Power Plant Thermal analog quantity process object model can transmit letter with frequency domain
Number is expressed as follows:
Wherein, the W (S) indicates the analog quantity process object model, and the Y (s) indicates process variable output function
Laplace transform, it is describedIndicate the Laplace transform of input function, a and the b respectively indicate each level
Number, the m indicate the maximum order of molecule, and the n indicates the maximum order of denominator, and the s indicates complex variable.
Molecule and each level number a of denominator in the analog quantity process object model are determined by identificationn、bm, can be obtained mould
The mathematical model of analog quantity process object, to prepare to advanced optimize.
Step 102, the analog quantity process object model is recognized, obtains the mathematical model of analog quantity object.
Optionally, artificial bee colony algorithm is mixed to the analog quantity process pair by changing the ACABC of strategy in step 102
As model is recognized, the ACABC mixing artificial bee colony algorithm for changing strategy, which changes in basic artificial bee colony algorithm, observes bee
The local search mechanism for following mechanism and algorithm, by copula Estimation of Distribution Algorithm (Estimation of
Distribution Algorithm, EDA) it is combined with artificial bee colony algorithm, and increased in bee colony neighborhood search strategy
Globally optimal solution component, to improve the local search ability of algorithm.In basic artificial bee colony algorithm, observation bee is obtained in bee colony
The nectar source information of all gathering honey bees, and optimum selecting gathering honey bee is followed, and is searched further near its nectar source.It improves
The ACABC algorithm of search strategy selects nectar source using adaptive preferentially ratio, carries out to these high-quality nectar sources
Copula distribution estimation, and nectar source inferior, instead the new honey that distribution estimates are removed according to survival of the fittest principle
Source.So ensure that algorithm search is carried out towards optimal direction always.
Optionally, as shown in Fig. 2, the ACABC mixing artificial bee colony algorithm for changing strategy realizes that process includes the following steps.
Step 201, initialization algorithm parameter, the parameter include population scale NP and maximum cycle.
Optionally, maximum cycle can be indicated using M.The parameter can also include local neighborhood search limitation time
Number LIMIT and copula EDA parameter θ.
Step 202, NP initial solution of random initializtion, and the fitness of all initial solutions is calculated, obtain the initial overall situation most
Excellent solution.
Optionally, initial solution is nectar source position, and the fitness of initial solution can be the nectar amount in nectar source, the nectar in nectar source
Amount is more, and the fitness of solution is higher, more advantageous to bee colony.Initial globally optimal solution is the most nectar source position of nectar amount.
Optionally, initial solution can be X={ Xi| i=1,2,3..., NP }.
Step 203, gathering honey bee carries out new nectar source search according to all initial solutions, obtains the first new explanation, and described in calculating
The fitness of first new explanation.
It may include three kinds of bees in the ACABC mixing artificial bee colony of improved search strategy: gathering honey bee, observation bee and investigation
Bee.Gathering honey bee generally finds new nectar source, the observation bee honey new according to the information searching that gathering honey bee is shared using previous nectar source
Source, investigation bee find new nectar source at random.
Every gathering honey bee, can be according in the progress nectar source search nearby of current solution
Wherein, the Vi jIndicate first new explanation, it is describedThe first new explanation is corresponding described in expression initial solution works as
Preceding solution, it is describedIndicate the solution randomly selected in current nectar source, it is describedIndicate the random number in (- 1,1) range, it is described
YjIndicate that current globally optimal solution, the st indicate that current iteration number, the M indicate maximum cycle.
Step 204, the first new explanation neighborhood search number is updated according to first new explanation.
Optionally, this step includes:
When the fitness of first new explanation is more than or equal to the fitness currently solved, i.e. the first new explanation is better than current
Solution, then the first new explanation neighborhood search number is 0, and first new explanation obtains when carrying out nectar source search according to the current solution
New explanation；
When the fitness of first new explanation is less than the fitness currently solved, i.e. the first new explanation is also poorer than current solution,
Then the first new explanation neighborhood search number adds 1 on the basis of former neighborhood search number.
Optionally, it if the neighborhood search number currently solved is equal to local neighborhood search limited number of times LIMIT, abandons pair
Current solution nearby carries out finding new nectar source.
Step 205, it according to the fitness of the fitness of the initial solution and first new explanation, calculates each nectar source and is seen
That examines that bee follows follows probability.
Optionally, this step can basisFollowing of calculating that each nectar source is observed that bee follows is general
Rate；Wherein, the piIndicate i-th of nectar source be observed bee follow follow probability, the fitiIndicate the fitness of i-th of solution,
The N indicates the total number of the fitness of the initial solution and the fitness of first new explanation.
It optionally, can also include: the adaptation of the fitness according to the initial solution and the new explanation after this step
Degree, is ranked up the initial solution and the new explanation according to fitness.Being ranked up to all solutions can be convenient in step 206
All nectar source positions are analyzed, distribution estimation is carried out to high-quality nectar source.
Step 206, corresponding to the initial solution and first new explanation according to the initial solution and first new explanation
Nectar source position is analyzed, and is carried out distribution estimation to target nectar source, is obtained the dominant group of gathering honey bee.
Optionally, target nectar source is the high-quality nectar source in nectar source.
Optionally, the dominant group that this step obtains gathering honey bee can basisObtain gathering honey bee
Dominant group；Wherein, the S indicates the dominant group quantity of gathering honey bee, and the s' indicates optimum selecting probability.Optionally,
The dominant group of gathering honey bee is the group that the high gathering honey bee of fitness of the nectar source position searched is constituted.
Step 207, distribution is carried out according to dominant group of the Gauss marginal probability distribution estimation method to the gathering honey bee to estimate
Meter obtains distribution probability.
Optionally, this step basisObtain distribution probability；Wherein, the N
(μj,σj 2) indicate distribution probability.
Step 208, observation bee follows probability to carry out new nectar source search according to, or is distributed and is estimated according to copula
Algorithm and marginal probability distribution calculate, and obtain the second new explanation, and calculate the fitness of second new explanation.
Observation bee followed in the stage, and every observation bee is according to the gathering honey bee for following probability selection to be followed, if the gathering honey bee
Belong to dominant group, then observes bee and directly scanned in its neighborhood, calculate new solution；Otherwise, it is distributed and is estimated according to copula
Method and marginal probability distribution calculate, and generate new nectar source solution, and calculate the fitness function value of new individual.
Optionally, it is calculated according to copula Estimation of Distribution Algorithm and marginal probability distribution, obtains the second new explanation, may include
According toObtain the second new explanation；
Wherein, describedIndicate second new explanation, the ujIt indicates in [0,1] section by being uniformly distributed the only of generation
Vertical variate-value, the θ indicate copula Estimation of Distribution Algorithm parameter, and the L indicates to obey of the second new explanation of Joint Distribution
Number, the v indicate stochastic variable, and the e indicates natural constant, and the F (v) indicates empirical distribution function, the j representation dimension
Variable, the σjIndicate variance of random variable, the μjIndicate stochastic variable mean value.
Step 209, investigation bee carries out new nectar source search at random, obtains third new explanation, and calculate the third new explanation
Fitness.
Optionally, third new explanation is obtained in step 209 may include: according to Xi(n)=Xmin+rand(0,1)(Xmax-
Xmin) third new explanation is obtained,
Wherein, the Xi(n) third new explanation, the X are indicatedmax, the XminRespectively indicate maximal solution in all solutions, most
It goes to the lavatory.
Step 210, according to the fitness of the initial solution, the fitness of first new explanation, second new explanation it is suitable
The fitness of response and the third new explanation regard the maximum solution of fitness as globally optimal solution.
Step 211, whether detection global search reaches the maximum cycle, when the global search is not up to described
When maximum cycle, step 203 is continued to execute to step 211, knot when global search reaches the maximum cycle
Line journey.
Optionally, after obtaining the ACABC for changing strategy mixing artificial bee colony algorithm, it can use and change strategy
ACABC mixing artificial bee colony algorithm recognizes the analog quantity process object model, obtains the mathematical modulo of analog quantity object
Type.Optionally, the mathematical model of PID controller is obtained first；Then the ginseng in the mathematical model of the PID controller is fixed
Number applies step disturbance to analog control system using the corresponding process values of analog quantity, predetermined target value and control instruction,
Using the ginseng in analog quantity process object model described in the ACABC mixing artificial bee colony algorithm search iteration for changing strategy
Number, when the corresponding fitness function value of evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses reaches
It is optimal, obtain the mathematical model of analog quantity object.
Optionally, Power Plant Thermal analog control system is based on PID controller control mode, the number of PID controller
Learning model can be described as follows:
Wherein, the KDIndicate differential coefficient, the KPIndicate proportionality coefficient, the K1Indicate integral coefficient, the s table
It gives instructions in reply variable.
Optionally, in identification optimization process, change the evaluation index that the ACABC mixing artificial bee colony algorithm of strategy uses
There are three: integrated square error, maximum overshoot and stable time, corresponding fitness function can be described as follows:
Wherein, the evaluation index that the fit indicates that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function, the ISE indicates that integrated square error, the OS indicate that maximum overshoot, the T indicate to stablize the time,
Stablizing the time indicates since disturbance to process variable output stable time within the allowable range, the α1, the α2And institute
State α3Respectively indicate weight of each evaluation index in fitness function, the ymaxIndicate that process variable exports maximum value, the ys
Indicate the setting value of process variable, the e (t) indicates deviation, and the r (t) indicates the setting value of process variable, and the c (t) indicated
The measured value of journey amount.Optionally, after the mathematical model and fitness function that PID controller has been determined, system can be applied
Add step disturbance, using analog quantity process object described in the ACABC mixing artificial bee colony algorithm search iteration for changing strategy
Parameter in model, is optimal fitness function value, to obtain the mathematical model of analog quantity object.
Optionally, conventional PID controllers are substituted using internal mode controller, and artificial using the ACABC mixing for changing strategy
Ant colony algorithm optimizes internal model controller parameter, has to big inertia, large time delay, the object that non-linear and interference is more good
Control effect.
Step 103, Power Plant Thermal analog control system controller model is obtained.
Optionally, conventional PID controllers are replaced to control Power Plant Thermal analog quantity using internal mode controller, it is described
Analog control system controller model is that the mathematical model of internal mode controller is
Wherein, the Y (s) indicates the output of the mathematical model of internal mode controller, and the d (s) indicates disturbance, the Cm
(s) feedforward internal mode controller model, the G are indicatedp(s) controlled device, the G are indicatedm(s) model of controlled device, institute are indicated
Stating R (s) indicates the reference input of system.
In order to design stability can Project Realization internal mode controller, by Gm(s) model decomposition is Gm +、Gm -Two parts, i.e. Gm
(s)=Gm(s)+·Gm(s)-。
The form for then designing internal mode controller is
Wherein, the Cm(s) analog control system controller model, the G are indicatedm(s)-Indicate the mould of controlled device
Part in type, the f (s) indicate that low-pass filter, the λ indicate filter temporal parameter, and the size of 0≤λ < 1, λ will certainly
Determine the response speed of closed loop output, the r indicates to guarantee Cm(s) reasonable order.
Step 104, according to the mathematical model of the analog quantity object, to the analog control system controller model
Parameter optimizes, and obtains target controller model.
Optionally, as shown in figure 3, step 104 may comprise steps of.
Step 301, the mathematical model of the analog quantity object is updated to the analog control system controller model
In, obtain the closed loop mathematical model of internal mode controller.
Optionally, the closed loop mathematical model of the internal mode controller of this step acquisition is
Wherein, the Cm(s) ' indicate the closed loop mathematical model of the internal mode controller, the W (s)-Indicate that there is minimum
The transmission function of phase characteristic, the W (s)+Indicate all-pass filter transmission function.
Step 302, according to the closed loop mathematical model of the internal mode controller, analog control system controller model is determined
Parameter.
Optionally, the parameter of analog control system controller model is filter temporal parameter lambda and order r.
Step 303, using the corresponding process values of analog quantity, predetermined target value and control instruction to analog control system
Apply step disturbance, using Analog control system described in the ACABC mixing artificial bee colony algorithm search iteration for changing strategy
The parameter of system controller model, when the evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function value is optimal, and obtains the parameter of target controller model.
Optionally, the corresponding fitness of evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses
Function is
Wherein, the evaluation index that the fit indicates that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function, the ISE indicates that integrated square error, the OS indicate that maximum overshoot, the T indicate to stablize the time,
The α1, the α2With the α3Weight of each evaluation index in fitness function is respectively indicated, optionally, in this step, α1
=1, α2=6, α3=0.6, the ymaxIndicate that process variable exports maximum value, the ysIndicate the setting value of process variable, the e
(t) deviation is indicated, the r (t) indicates the setting value of process variable, and the c (t) indicates the measured value of process variable.
Optionally, defining nectar source formal similarity is X={ X1,X2, X1Indicate filter temporal parameter lambda, X2Indicate order r.
Step 304, according to the parameter of the target controller model, target controller model is obtained.
After obtaining target controller model, the performance of target controller is detected, i.e. execution step 105.
Step 105, whether the performance for detecting the target controller model meets preset need.
Optionally, when the performance of the target controller model meets preset need, step 106 is executed, when described
When the performance of target controller model does not meet preset need, step 101 is repeated to step 104, until the target control
The performance of device model processed terminates process when meeting preset need.
Step 106, the target controller model is exported to Analog control object.
It optionally, then can be by the target controller of acquisition when the performance of target controller model meets preset need
The corresponding target controller of model comes into operation.
The method of above-mentioned Power Plant Thermal analog control system optimization, by being distinguished to analog quantity process object model
Know, obtains the mathematical model of analog quantity object；Then Power Plant Thermal analog control system controller model is obtained；According to institute
The mathematical model for stating analog quantity object optimizes the parameter of the analog control system controller model, obtains target
Controller model makes control system have control precision so as to optimize to Power Plant Thermal analog control system
The characteristics of height, overshoot are small, control speed is fast, strong antijamming capability, made it possible in unmanned the case where intervening or intervening on a small quantity
Under, automatic identification system model simultaneously carries out optimal controller parameter, has the advantages that debug convenient, strong operability.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
Corresponding to the method for the optimization of Power Plant Thermal analog control system described in foregoing embodiments, Fig. 4 shows this
The exemplary diagram of the device for the Power Plant Thermal analog control system optimization that inventive embodiments provide.As shown in figure 4, the device can
To include: to obtain module 401, identification module 402, optimization module 403 and control module 404.
Module 401 is obtained, for obtaining Power Plant Thermal analog quantity process object model；
Module 402 is recognized, for recognizing to the analog quantity process object model, obtains the mathematics of analog quantity object
Model；
The acquisition module 401, is also used to obtain Power Plant Thermal analog control system controller model；
Optimization module 403 controls the analog control system for the mathematical model according to the analog quantity object
The parameter of device model optimizes, and obtains target controller model；
Control module 404, for being obtained described in control when the performance of the target controller model does not meet preset need
Modulus block continues to operate, and terminates process when the performance of the target controller model meets preset need.
Optionally, the analog quantity process object model is
Wherein, the W (S) indicates the analog quantity process object model, and the Y (s) indicates process variable output function
Laplace transform, it is describedIndicate the Laplace transform of input function, a and the b respectively indicate each level
Number, the m indicate the maximum order of molecule, and the n indicates the maximum order of denominator, and the s indicates complex variable.
Optionally, the identification module 402, is also used to: a, initialization algorithm parameter, the parameter include population scale NP
And maximum cycle；B, NP initial solution of random initializtion, and the fitness of all initial solutions is calculated, obtain the initial overall situation most
Excellent solution；C, gathering honey bee carry out new nectar source according to all initial solutions and search for, and obtain the first new explanation, and calculate first new explanation
Fitness；D updates the first new explanation neighborhood search number according to first new explanation；E, according to the fitness of the initial solution and
The fitness of first new explanation, calculate each nectar source be observed bee follow follow probability；F, according to the initial solution and institute
The first new explanation is stated, the initial solution and the corresponding nectar source position of first new explanation are analyzed, target nectar source is divided
Cloth estimation, obtains the dominant group of gathering honey bee；G, according to Gauss marginal probability distribution estimation method to the advantage of the gathering honey bee
Group carries out distribution estimation, obtains distribution probability；H, observation bee follow probability to carry out new nectar source search, Huo Zhegen according to
It is calculated according to copula Estimation of Distribution Algorithm and marginal probability distribution, obtains the second new explanation, and calculate the adaptation of second new explanation
Degree；I, investigation bee carry out new nectar source search at random, obtain third new explanation, and calculate the fitness of the third new explanation；J, root
According to the fitness of the initial solution, the fitness of first new explanation, the fitness of second new explanation and the third new explanation
Fitness, by fitness it is maximum solution be used as globally optimal solution；Whether k, detection global search reach the largest loop time
Number, when the global search is not up to the maximum cycle, continues to execute step c to step k, until global search reaches
Terminate process when to the maximum cycle.
Optionally, the identification module 402 updates the first new explanation neighborhood search number, Ke Yiyong according to first new explanation
In when the fitness of first new explanation is more than or equal to the fitness currently solved, then the first new explanation neighborhood search number is
0, first new explanation is the new explanation for obtain when the search of nectar source according to the current solution；Or fitting when first new explanation
When response is less than the fitness currently solved, then the first new explanation neighborhood search number adds 1 on the basis of former neighborhood search number.
Optionally, the identification module 402 calculates each nectar source and is seen described according to the initial solution and the new explanation
It after examining the probability that bee follows, can be also used for: according to the fitness of the fitness of the initial solution and the new explanation, according to suitable
Response is ranked up the initial solution and the new explanation.
Optionally, the identification module 402 can be used for obtaining the mathematical model of PID controller；The fixed PID control
Parameter in the mathematical model of device gives analog quantity control using the corresponding process values of analog quantity, predetermined target value and control instruction
System processed applies step disturbance, using analog quantity described in the ACABC mixing artificial bee colony algorithm search iteration for changing strategy
Parameter in process object model, when the evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function value be optimal, obtain analog quantity object mathematical model.
Optionally, the mathematical model of the PID controller is
Wherein, the KDIndicate differential coefficient, the KPIndicate proportionality coefficient, the K1Indicate integral coefficient, the s table
It gives instructions in reply variable.
Optionally, the optimization module 403, for the mathematical model of the analog quantity object to be updated to the analog quantity
In control system controller model, the closed loop mathematical model of internal mode controller is obtained；According to the closed loop number of the internal mode controller
Model is learned, determines the parameter of analog control system controller model；Use the corresponding process values of analog quantity, predetermined target value with
And control instruction applies step disturbance to analog control system, is calculated using the ACABC mixing artificial bee colony for changing strategy
The parameter of analog control system controller model described in method search iteration, when the ACABC for changing strategy mixes artificial bee
The corresponding fitness function value of evaluation index that group's algorithm uses is optimal, and obtains the parameter of target controller model；According to
The parameter of the target controller model obtains target controller model.
Optionally, the closed loop mathematical model of the internal mode controller is
Wherein, the Cm(s) ' indicate the closed loop mathematical model of the internal mode controller, the W (s)-Indicate that there is minimum
The transmission function of phase characteristic, the W (s)+Indicate all-pass filter transmission function.
Optionally, the corresponding fitness of evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses
Function is
Wherein, the evaluation index that the fit indicates that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding
Fitness function, the ISE indicates that integrated square error, the OS indicate that maximum overshoot, the T indicate to stablize the time,
The α1, the α2With the α3Respectively indicate weight of each evaluation index in fitness function, the ymaxIndicate process variable
Export maximum value, the ysIndicate the setting value of process variable, the e (t) indicates deviation, and the r (t) indicates the setting of process variable
Value, the c (t) indicate the measured value of process variable.
The device of above-mentioned Power Plant Thermal analog control system optimization, by identification module to analog quantity process object mould
Type is recognized, and the mathematical model of analog quantity object is obtained；Then it obtains module and obtains Power Plant Thermal analog control system
Controller model；According to the mathematical model of the analog quantity object, optimization module is to the analog control system controller mould
The parameter of type optimizes, and obtains target controller model, excellent so as to carry out to Power Plant Thermal analog control system
Change, so that control system is had the characteristics that control precision is high, overshoot is small, control speed is fast, strong antijamming capability, make it possible to
It is unmanned intervene or it is a small amount of intervene in the case where, automatic identification system model simultaneously carries out optimal controller parameter, have debugging it is convenient,
The advantages of strong operability.
Fig. 5 is the schematic diagram for the terminal device that one embodiment of the invention provides.As shown in figure 5, the terminal of the embodiment is set
Standby 500 include: processor 501, memory 502 and are stored in the memory 502 and can transport on the processor 501
Capable computer program 503, such as the program of Power Plant Thermal analog control system optimization.The processor 501 executes institute
The step in the embodiment of the method for above-mentioned Power Plant Thermal analog control system optimization, example are realized when stating computer program 503
Step 101 as shown in Figure 1 to 106 perhaps step 201 shown in Fig. 2 to step 211 or step 301 shown in Fig. 3 to
Processor 501 described in step 304 realizes the function of each module in above-mentioned each Installation practice when executing the computer program 503
Can, such as the function of module 401 to 404 shown in Fig. 4.
Illustratively, the computer program 503 can be divided into one or more program modules, it is one or
Multiple program modules are stored in the memory 502, and are executed by the processor 501, to complete the present invention.Described one
A or multiple program modules can be the series of computation machine program instruction section that can complete specific function, and the instruction segment is for retouching
The computer program 503 is stated in the device or terminal device 500 that the Power Plant Thermal analog control system optimizes
Implementation procedure.For example, the computer program 503, which can be divided into, obtains module 401, identification module 402, optimization module
403 and control module 404, each module concrete function is as shown in figure 4, this is no longer going to repeat them.
The terminal device 500 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.The terminal device may include, but be not limited only to, processor 501, memory 502.It will be understood by those skilled in the art that
Fig. 5 is only the example of terminal device 500, does not constitute the restriction to terminal device 500, may include more or more than illustrating
Few component perhaps combines certain components or different components, such as the terminal device can also be set including input and output
Standby, network access equipment, bus etc..
Alleged processor 501 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 502 can be the internal storage unit of the terminal device 500, such as terminal device 500 is hard
Disk or memory.The memory 502 is also possible to the External memory equipment of the terminal device 500, such as the terminal device
The plug-in type hard disk being equipped on 500, intelligent memory card (Smart Media Card, SMC), secure digital (Secure
Digital, SD) card, flash card (Flash Card) etc..Further, the memory 502 can also both include the terminal
The internal storage unit of equipment 500 also includes External memory equipment.The memory 502 for store the computer program with
And other programs and data needed for the terminal device 500.The memory 502 can be also used for temporarily storing defeated
Out or the data that will export.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.Each functional unit in embodiment, module can integrate in one processing unit, can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units, it is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.In addition, each function list
Member, the specific name of module are also only for convenience of distinguishing each other, the protection scope being not intended to limit this application.Above system
The specific work process of middle unit, module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
The scope of the present invention.
In embodiment provided by the present invention, it should be understood that disclosed device/terminal device and method, it can be with
It realizes by another way.For example, device described above/terminal device embodiment is only schematical, for example, institute
The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, such as
Multiple units or components can be combined or can be integrated into another system, or some features can be ignored or not executed.Separately
A bit, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device
Or the INDIRECT COUPLING or communication connection of unit, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated module/unit be realized in the form of SFU software functional unit and as independent product sale or
In use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-mentioned implementation
All or part of the process in example method, can also instruct relevant hardware to complete, the meter by computer program
Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on
The step of stating each embodiment of the method.Wherein, the computer program includes computer program code, the computer program
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..Computer-readable Jie
Matter may include: can carry the computer program code any entity or device, recording medium, USB flash disk, mobile hard disk,
Magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as does not include electric carrier signal and electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions
Believe signal.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (15)
1. a kind of method of Power Plant Thermal analog control system optimization characterized by comprising
A obtains Power Plant Thermal analog quantity process object model；
B recognizes the analog quantity process object model, obtains the mathematical model of analog quantity object；
C obtains Power Plant Thermal analog control system controller model；
D carries out the parameter of the analog control system controller model excellent according to the mathematical model of the analog quantity object
Change, obtains target controller model；
E repeats step A to step D, Zhi Daosuo when the performance of the target controller model does not meet preset need
Stating when the performance of target controller model meets preset need terminates process.
2. the method for Power Plant Thermal analog control system optimization as described in claim 1, which is characterized in that the simulation
Measuring process object model is
Wherein, the W (S) indicates the analog quantity process object model, and the Y (s) indicates the La Pu of process variable output function
Lars transformation, it is describedIndicate the Laplace transform of input function, a and the b respectively indicate each level number, institute
Stating m indicates the maximum order of molecule, and the n indicates the maximum order of denominator, and the s indicates complex variable.
3. the method for Power Plant Thermal analog control system optimization as claimed in claim 2, which is characterized in that pass through change
The ACABC mixing artificial bee colony algorithm of strategy recognizes the analog quantity process object model, and to the analog quantity
The parameter of control system controller model optimizes, wherein the ACABC mixing artificial bee colony algorithm for changing strategy includes:
A, initialization algorithm parameter, the parameter include population scale NP and maximum cycle；
B, NP initial solution of random initializtion, and the fitness of all initial solutions is calculated, obtain initial globally optimal solution；
C, gathering honey bee carry out new nectar source according to all initial solutions and search for, and obtain the first new explanation, and calculate first new explanation
Fitness；
D updates the first new explanation neighborhood search number according to first new explanation；
E calculates each nectar source and is observed what bee followed according to the fitness of the fitness of the initial solution and first new explanation
Follow probability；
F, according to the initial solution and first new explanation, to the initial solution and the corresponding nectar source position of first new explanation into
Row analysis carries out distribution estimation to target nectar source, obtains the dominant group of gathering honey bee；
G carries out distribution estimation according to dominant group of the Gauss marginal probability distribution estimation method to the gathering honey bee, is distributed
Probability；
H, observation bee follow probability to carry out new nectar source search according to, or according to copula Estimation of Distribution Algorithm and edge
Probability distribution calculates, and obtains the second new explanation, and calculate the fitness of second new explanation；
I, investigation bee carry out new nectar source search at random, obtain third new explanation, and calculate the fitness of the third new explanation；
J, according to the fitness of the initial solution, the fitness of first new explanation, the fitness of second new explanation and described
The fitness of third new explanation regard the maximum solution of fitness as globally optimal solution；
Whether k, detection global search reach the maximum cycle, when the global search is not up to the largest loop time
When number, step c to step k is continued to execute, terminates process when global search reaches the maximum cycle.
4. the method for Power Plant Thermal analog control system optimization as claimed in claim 3, which is characterized in that the basis
First new explanation updates the first new explanation neighborhood search number, comprising:
When the fitness of first new explanation is more than or equal to the fitness currently solved, then the first new explanation neighborhood search number
It is 0, first new explanation is the new explanation for obtain when the search of nectar source according to the current solution；
When the fitness of first new explanation is less than the fitness currently solved, then the first new explanation neighborhood search number is in former neighborhood
Add 1 on the basis of searching times.
5. the method for Power Plant Thermal analog control system optimization as claimed in claim 3, which is characterized in that at described
According to the initial solution and the new explanation, calculates each nectar source and is observed after the probability that bee follows, further includes:
According to the fitness of the fitness of the initial solution and the new explanation, according to fitness to the initial solution and the new explanation
It is ranked up.
6. the method for Power Plant Thermal analog control system optimization as claimed in claim 3, which is characterized in that the gathering honey
Bee carries out new nectar source according to all initial solutions and searches for, and obtains the first new explanation, comprising:
According toObtain the first new explanation；
Wherein, the Vi jIndicate first new explanation, it is describedIndicate the corresponding current solution of the first new explanation described in initial solution,
It is describedIndicate the solution randomly selected in current nectar source, it is describedIndicate the random number in (- 1,1) range, the YjIt indicates
Current globally optimal solution, the st indicate that current iteration number, the M indicate maximum cycle；
It is described according to the fitness of the initial solution and the fitness of first new explanation, calculate each nectar source and be observed bee and follow
Follow probability, comprising:
According toCalculate each nectar source be observed bee follow follow probability；
Wherein, the piIndicate i-th of nectar source be observed bee follow follow probability, the fitiIndicate the adaptation of i-th of solution
Degree, the N indicate the total number of the fitness of the initial solution and the fitness of first new explanation；
The dominant group for obtaining gathering honey bee, comprising:
According toObtain the dominant group of gathering honey bee；
Wherein, the S indicates the dominant group quantity of gathering honey bee, and the s' indicates optimum selecting probability；
It is described that distribution estimation is carried out according to dominant group of the Gauss marginal probability distribution estimation method to the gathering honey bee, divided
Cloth probability, comprising:
According toObtain distribution probability；
Wherein, the N (μj,σj 2) indicate distribution probability；
It is described to be calculated according to copula Estimation of Distribution Algorithm and marginal probability distribution, obtain the second new explanation, comprising:
According toObtain the second new explanation；
Wherein, describedIndicate second new explanation, the ujIt indicates in [0,1] section by the independent change for being uniformly distributed generation
Magnitude, the θ indicate copula Estimation of Distribution Algorithm parameter, and the L indicates to obey the number of the second new explanation of Joint Distribution, institute
Stating v indicates stochastic variable, and the e indicates natural constant, and the F (v) indicates empirical distribution function, the j representation dimension variable,
The σjIndicate variance of random variable, the μjIndicate stochastic variable mean value；
The investigation bee carries out new nectar source search at random, obtains third new explanation, comprising:
According to Xi(n)=Xmin+rand(0,1)(Xmax-Xmin) third new explanation is obtained,
Wherein, the Xi(n) third new explanation, the X are indicatedmax, the XminRespectively indicate maximal solution in all solutions, minimal solution.
7. the method for Power Plant Thermal analog control system optimization as claimed in claim 3, which is characterized in that described to institute
It states analog quantity process object model to be recognized, obtains the mathematical model of analog quantity object, comprising:
Obtain the mathematical model of proportional-integral-differential PID controller；
Parameter in the mathematical model of the fixed PID controller, using the corresponding process values of analog quantity, predetermined target value and
Control instruction applies step disturbance to analog control system, using the ACABC mixing artificial bee colony algorithm search for changing strategy
Parameter in analog quantity process object model described in iteration, when the ACABC mixing artificial bee colony algorithm for changing strategy uses
The corresponding fitness function value of evaluation index be optimal, obtain analog quantity object mathematical model.
8. the method for Power Plant Thermal analog control system optimization as claimed in claim 7, which is characterized in that the PID
The mathematical model of controller is
Wherein, the KDIndicate differential coefficient, the KPIndicate proportionality coefficient, the K1Indicate that integral coefficient, the s indicate multiple
Variable.
9. the method for Power Plant Thermal analog control system optimization as described in claim 1, which is characterized in that the simulation
Amount control system controller model is
Wherein, the Cm(s) analog control system controller model, the G are indicatedm(s)-In the model for indicating controlled device
Part, the f (s) indicates that low-pass filter, the λ indicate filter temporal parameter, and 0≤λ < 1, and the r indicates to guarantee
Cm(s) reasonable order.
10. the method for Power Plant Thermal analog control system optimization as claimed in claim 3, which is characterized in that described
According to the mathematical model of the analog quantity object, the parameter of the analog control system controller model is optimized, is obtained
Target controller model, comprising:
The mathematical model of the analog quantity object is updated in the analog control system controller model, internal model control is obtained
The closed loop mathematical model of device processed；
According to the closed loop mathematical model of the internal mode controller, the parameter of analog control system controller model is determined；
Apply step to analog control system using the corresponding process values of analog quantity, predetermined target value and control instruction to disturb
It is dynamic, using analog control system controller mould described in the ACABC mixing artificial bee colony algorithm search iteration for changing strategy
The parameter of type, when the corresponding fitness function of evaluation index that the ACABC mixing artificial bee colony algorithm for changing strategy uses
Value is optimal, and obtains the parameter of target controller model；
According to the parameter of the target controller model, target controller model is obtained.
11. the method for Power Plant Thermal analog control system optimization as claimed in claim 10, which is characterized in that in described
The closed loop mathematical model of mould controller is
Wherein, the Cm(s) ' indicate the closed loop mathematical model of the internal mode controller, the W (s)-Indicate that there is minimum phase
The transmission function of characteristic, the W (s)+Indicate all-pass filter transmission function.
12. the method that the Power Plant Thermal analog control system as described in claim 7 or 10 optimizes, which is characterized in that institute
State change the ACABC corresponding fitness function of evaluation index that uses of mixing artificial bee colony algorithm of strategy for
Wherein, the evaluation index that the fit indicates that the ACABC mixing artificial bee colony algorithm for changing strategy uses is corresponding suitable
Response function, the ISE indicate that integrated square error, the OS indicate maximum overshoot, and the T indicates to stablize the time, described
α1, the α2With the α3Respectively indicate weight of each evaluation index in fitness function, the ymaxIndicate process variable output
Maximum value, the ysIndicating the setting value of process variable, the e (t) indicates deviation, and the r (t) indicates the setting value of process variable,
The c (t) indicates the measured value of process variable.
13. a kind of device of Power Plant Thermal analog control system optimization characterized by comprising
Module is obtained, for obtaining Power Plant Thermal analog quantity process object model；
Module is recognized, for recognizing to the analog quantity process object model, obtains the mathematical model of analog quantity object；
The acquisition module is also used to obtain Power Plant Thermal analog control system controller model；
Optimization module, for the mathematical model according to the analog quantity object, to the analog control system controller model
Parameter optimize, obtain target controller model；
Control module, for controlling the acquisition module when the performance of the target controller model does not meet preset need
Continue to operate, terminates process when the performance of the target controller model meets preset need.
14. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 12 when executing the computer program
The step of any one the method.
15. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In when the computer program is executed by processor the step of any one of such as claim 1 to 12 of realization the method.
Priority Applications (1)
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