CN106440844B - A kind of grate-cooler scraper velocity control method - Google Patents
A kind of grate-cooler scraper velocity control method Download PDFInfo
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- CN106440844B CN106440844B CN201610573475.9A CN201610573475A CN106440844B CN 106440844 B CN106440844 B CN 106440844B CN 201610573475 A CN201610573475 A CN 201610573475A CN 106440844 B CN106440844 B CN 106440844B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D15/00—Handling or treating discharged material; Supports or receiving chambers therefor
- F27D15/02—Cooling
- F27D15/0206—Cooling with means to convey the charge
- F27D15/0213—Cooling with means to convey the charge comprising a cooling grate
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
- F27D2019/0028—Regulation
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Feedback Control In General (AREA)
Abstract
The invention belongs to clinker industrial production automatic control process field, specifically a kind of grate-cooler scraper velocity control method.This method comprises: one, establish fitness function；Two, initiation parameter；Three, initialization population；Four, each particle adaptive value of the first generation is calculated by fitness function, selects minimum adaptive value as global optimal adaptation value, then using each particle of the first generation as individual optimum position, adaptive value is as individual optimal adaptation value；Five, kick probability factor is calculated；Six, global optimal adaptation value and global optimum position enter iterative cycles；Seven, population optimal adaptation value and global optimum position are obtained.The present invention is a kind of to combine simulated annealing technology with particle swarm algorithm, optimization PID controller parameter, it realizes that speed regulation process is more stable to the control of the higher precision of grate-cooler scraper velocity, reduces the grate-cooler scraper velocity control method based on simulated annealing particle swarm algorithm of vibration and impact.
Description
Technical field
The invention belongs to clinker industrial production automatic control process field, specifically a kind of cooling machine scraper speed
Spend control method.
Background technique
Grate-cooler is the important corollary equipment of cement industry clinker burning production system, is widely used in cement industry clinker
On production line.Nowadays, grate-cooler has evolved to forth generation, and with the promotion of production capacity, driving method is from Mechanical Driven
Change hydraulic-driven into.Thus, it adjusts hydraulic control grate-cooler scraper velocity and adjustment process stationarity, accuracy, reduce fortune
Row impact and vibration etc. become the main contents of cement clinker grate cooler control process.
To realize that grate-cooler scraper velocity is adjusted and control, the present invention use PID control.Determining grate-cooler scraper velocity
Under the precondition of control system model and control strategy, how Kp (proportionality coefficient), the Ts of design optimization PID controller (are accumulated
Point time constant), the parameters such as TD (derivative time constant) become the key for influencing this system control effect.
Currently, there are commonly particle swarm algorithm, genetic algorithm and people in the intelligent method of PID controller parameter optimization
Group hunting algorithm etc., but local minimum solution is often easily fallen into actual operation, the dispersibility for occurring searching for is deteriorated, and the overall situation is searched
Reduced capability can not search the deficiency more preferably solved, to influence the control effect of controlled system.
Summary of the invention
Simulated annealing technology is combined with particle swarm algorithm the present invention provides a kind of, using PID control, optimization
PID controller parameter realizes the control to the higher precision of grate-cooler scraper velocity, and speed regulation process is more stable, reduces vibration
Dynamic and impact the grate-cooler scraper velocity control method based on simulated annealing particle swarm algorithm, overcomes existing cooling machine scraper
The problem of speed cannot be controlled effectively.
Technical solution of the present invention is described with reference to the drawings as follows:
A kind of grate-cooler scraper velocity control method, the control method the following steps are included:
Step 1: establishing the fitness function of grate-cooler scraper velocity；
Step 2: the parameter of the fitness function of initialization grate-cooler scraper velocity；
Step 3: N number of solution X of initialization fitness function, wherein the vector of each solution is by three parameter groups of PID controller
At so the vector dimension D=3 of solution, the dimension of the speed V of each solution are identical as each solution dimension of X；
Step 4: calculating the adaptive value of each solution of the first generation by the fitness function in step 1, select the smallest
For adaptive value as global optimal adaptation value zbest, corresponding solution is global optimum Gbest, then using each solution of the first generation as a
Body optimum solution Pi, adaptive value is as individual solution optimal adaptation value gbest；
Step 5: calculating kick probability factor T；
Step 6: overall situation optimal adaptation value zbest and overall situation optimum position GbestInto iterative cycles Maxlter；
Step 7: obtaining zbest and Gbest, GbestIt is exactly the optimized parameter in the fitness function of grate-cooler scraper velocity.
Fitness function in the step 1 is specific as follows:
Wherein e (t) is the setting value of PID controller and the systematic error of value of feedback, and u (t) is PID controller output, ω 2
It is weight with ω 1, in order to avoid system exports overshoot, is controlled using punishment, if when e (t) < 0,
Wherein ω 3 is weight, under normal conditions ω 1=0.999, ω 2=0.001, ω 3=100.
The parameter initialized in the step 2 includes number of iterations Maxlter, current iteration number t, experience control parameter c1
And c2, wherein c1Adjust the solution adjustment step-length mobile to individual optimum solution direction, c2Adjust movement of the solution to global optimum direction
Step-length, convergence coefficient lamda, convergence coefficient be reduce kick probability factor linearly, maximum weight ωmax, minimum weight
ωmin, compressibility factorWherein compressibility factor is according to formulaIt acquires, C=c1+c2, and C > 4, make
Whole size is demodulated with control and constraint.
The matrix initially dissolved in the step 3:Kp、Ki、Kd
The range [0100] of parameter；
Initialize rate matrices:The range of Vp, Vi, Vd parameter
[01], maximum speed V is initializedmaxWith minimum speed Vmin。
Kick probability factor T is calculated by following formula in the step 5:
T=-Fitness (Gbest)/log(0.2)。 (3)
Specific step is as follows for the step 6:
(1) optimal adaptation value Fitness (G is calculatedbest), the kick probability for calculating each solution passes through following formula:
Wherein Δ f=Fitness (Xi)-Fitness (Gbest), Fitness (Xi) is the adaptive value currently solved, i=1~
N；
(2) the kick probability Tu of each solution and random number random is compared in the section 0-1, if the kick of solution is general
Rate is greater than random number random, and solution Xi just replaces Gbest, the problem of easily sinking into local minimum solution in iteration searching process is avoided,
Otherwise do not replace, introducing kick probability is that iteration optimizing is avoided precocious phenomenon occur, prevents optimizing from finding better solution；
(3) weight ω is calculated, following formula is passed through:
Weight ω, which is acted on, improves ability of searching optimum and optimizing later period local search ability optimizing early period；
(4) renewal speed V (N, D) passes through following formula:
Vi (t+1)=ω × Vi (t)+c1×r1×(Pi-Xi(t))+c2×r2×(Gbest-Xi(t)) (6)
Wherein t is number of iterations；Velocity magnitude when Vi (t) is i-th of the t times iteration, r1,r2It is to be taken on (0,1) section
From equally distributed random number；
(5) more new explanation X (N, D), passes through following formula:
WhereinC=c1+c2, and C > 4, the introducing of compressibility factor preferably command deployment speed prevent
Excessive velocities miss optimum value, solution when X i (t) is i-th of the t times iteration；
(6) logic judgment:
It calculates each and solves updated adaptive value Fitness (Xi)；Individual optimal adaptation value and individual optimum position
Update, if updated adaptive value Fitness (Xi) is less than individual optimal adaptation value gbest, then individual optimal adaptation value
Gbest=Fitness (Xi), individual optimum position PiIt is equal to Xi；The update of global optimal adaptation value and individual fitness, when
The adaptive value Fitness (Xi) of preceding solution is less than global adaptive value zbest, then zbest=Fitness (Xi), global optimum position
Gbest=x (i)；
(7) update of T and t: T=lamda*T, t=t+1, wherein reduction of the T with the number of iterations, kick probability value
It will level off to zero；
(8) judge whether t is equal to Maxlter, if it is equal, step 6 is jumped out, enters step seven.
It is led the beneficial effects of the present invention are: phenomena such as population (PSO) algorithm is simply easy to accomplish, however easily appearance is precocious
Cause is unable to global optimizing.Since the present invention is controlled by PID controller, grate-cooler scraper velocity control of the present invention can be converted
The problem of at PID controller parameter optimal solution is found.In view of the above-mentioned problems, introducing kick probability, it is joined the present invention by temperature
Several control, i.e. probability size reduce with the decline of temperature, and temperature parameter reduces with the increase of the number of iterations, because
This is avoided that phenomena such as precocity, accelerates solving speed and guarantees the dispersibility of solution.Determine grate-cooler scraper velocity control system
After transmission function and setting value are unit step, simulation results show, the present invention uses the simulated annealing grain of pid parameter adjusting
The parameter for the PID that swarm optimization adjusts out system, the ginseng with particle swarm algorithm, genetic algorithm and crowd's searching algorithm optimization
Number is compared, when number of iterations is equal, ability of searching optimum enhancing, and, guarantee the dispersibility understood, avoid conventional particle group's algorithm
The convergence rate of the problems such as being easily trapped into precocity, adaptive value is also accelerated, by the algorithm optimization go out pid control parameter (Kp,
Ki, Kd), the control precision of grate-cooler scraper velocity control system is improved, shock and vibration are also the smallest in control process,
The requirement for being conducive to the high cooling efficiency for meeting grate-cooler and high heat recovery efficiency is conducive to the development of cement industry.
Detailed description of the invention
Fig. 1 is that simulated annealing population adjusts schematic diagram to PID controller parameter；
Fig. 2 is simulated annealing particle group optimizing curve graph；
Fig. 3 is simulated annealing population adaptive value curve graph；
Fig. 4 is particle group optimizing curve graph；
Fig. 5 is population adaptive value curve graph；
Fig. 6 is people's group hunting Optimal Curve figure；
Fig. 7 is people's group hunting adaptive value curve graph；
Fig. 8 is genetic optimization curve graph；
Fig. 9 is genetic adaptation value curve graph；
Figure 10 is grate-cooler scraper velocity control system jump response composition error curve graph；
Figure 11 is that grate-cooler scraper velocity control system jump responds comprehensive output curve diagram；
Figure 12 is flow chart of the present invention.
Specific embodiment
With reference to the accompanying drawings and embodiments, the invention will be described in further detail.
The present invention is the grate-cooler scraper velocity control method based on simulated annealing particle swarm algorithm.By electrichydraulic control theory
It can be concluded that the electro-hydraulic position servo system of a valve control, chooses suitable parameter and carries out simulation study, can obtain grate-cooler speed
The closed loop transfer function, of control systemAnd by simulated annealing particle swarm algorithm
Pid parameter optimization Simulation is carried out to system function.
Simulated annealing population to PID controller (proportional-integral derivative controller) parameter tuning schematic diagram refering to fig. 1
It is shown.
Refering to fig. 12, grate-cooler scraper velocity control method the following steps are included:
Step 1), the fitness function for establishing grate-cooler scraper velocity of simulated annealing particle swarm algorithm: fitness function
It is the performance for evaluating optimizing solution, the fitness function that wherein the remaining victory prestige of fitness function reference uses, specific function is such as
Under:
Wherein e (t) is systematic error, and u (t) is controller output, and ω 2 and ω 1 are weight.In order to avoid system output is super
It adjusts, is controlled using punishment, if when e (t) < 0
ω 1=0.999 under normal conditions, ω 2=0.001, ω 3=100, fitness function reflect tri- parameters of PID
Control effect, the smaller reflection control effect of adaptive value are more preferable.
Step 2), the parameter of the fitness function of grate-cooler scraper velocity is initialized, following parameter is all the artificial debugging phase
Between empirical value and optimum efficiency value, number of iterations Maxlter=100, current iteration number t, Studying factors c1=2.1 and c2=
2.1, temperature cooling ratio lamda=0.6, compressibility factorWherein compressibility factor is the formula quoting doctor Clerc and proposingIt acquires, C=c1+c2, and C > 4, ωmax=0.9, ωmin=0.45.
Step 3), initialization population, the number N=30 in population X, wherein the position vector of each particle is by PID (ratio
Example-Integrated Derivative) three parameter compositions (the dimension D=3 of position vector), the velocity vector dimension and each grain of each particle
The dimension of son is identical；
The range [0 100] of Kp, Ki, Kd parameter；
The range [0 1] of Vp, Vi, Vd parameter, Vmax
=1；Vmin=-1；
Step 4) adaptive value that each particle of the first generation is calculated by formula (3) and formula (2), select the smallest fit
It should be worth as global optimal adaptation value zbest, corresponding particle is global optimum position Gbest, then each particle of the first generation
As individual optimum position Pi, adaptive value is as individual optimal adaptation value gbest, wherein global optimum position is exactly PID control
The optimization control parameter of system.
Step 5), kick probability factor T is calculated, following formula is passed through:
T=-Fitness (Gbest)/log(0.2) (4)
The formula is the initialization annealing temperature formula in simulated annealing.
Step 6), into iterative cycles Maxlter；
(1) optimal adaptation value Fitness (G is calculatedbest), the kick probability for calculating each particle passes through following formula:
The formula comes from and is introduced into kick new probability formula in simulated annealing；
Wherein Δ f=Fitness (x (i))-Fitness (Gbest), Fitness (x (i)) are the adaptation of current particle
Value, i=1~30.
(2) the kick probability of each particle and the random number random in the section 0-1 are compared, if the kick of particle is general
Rate is greater than random number random, and particle x (i) just replaces Gbest, otherwise do not replace.
(3) weighted value ω is calculated, following formula is passed through:
The formula is to propose weight computing formula by doctor SHI；
(4) (30,3) renewal speed V pass through following formula:
Vi (t+1)=ω × vi (t)+c1×r1×(Pi-xi(t))+c2×r2×(Gbest-xi(t)) (7)
The formula is the speed more new formula that doctor SHI proposes, wherein t is number of iterations；Vi (t) is the t times iteration of particle i
When speed, r1,r2It is to obey equally distributed random number on (0,1) section；Vi (t+1) is judged, ifvi (t+1) >
Vmax, vi (t+1)=Vmax；If vi (t+1) < Vmin, vi (t+1)=Vmin.
(5) position X (30,3) are updated, pass through following formula:
The formula is the location update formula proposed by doctor Clerc,
WhereinC=c1+c2, and C > 4, xi (t) be the t times iteration of particle i when position.
(6) logic judgment:
The updated adaptive value Fitness (x (i)) of each particle is calculated, formula 3 and formula 2 are passed through；It is individual best
The update of adaptive value and individual optimum position, if updated adaptive value Fitness (x (i)) is less than individual optimal adaptation value
Gbest, then individual optimal adaptation value gbest=Fitness (x (i)), individual optimum position PiIt is equal to x (i)；It is global best
The update of adaptive value and individual fitness, the adaptive value Fitness (x (i)) of current particle are less than global adaptive value zbest, then
Zbest=Fitness (x (i)), global optimum position Gbest=x (i).
(7) T and t with new: T=lamda*T (9), t=t+1 (10).
(8) judge whether t is equal to Maxlter, if it is equal, step 6 is jumped out, enters step seven.
It is simulated annealing particle group optimizing curve graph refering to Fig. 2, is simulated annealing population adaptive value curve refering to Fig. 3
Figure, be particle group optimizing curve graph refering to Fig. 4, be population adaptive value curve graph refering to Fig. 5, be that crowd's search is excellent refering to Fig. 6
Change curve graph, refering to Fig. 7 be crowd search for adaptive value curve, be genetic optimization curve refering to Fig. 8, refering to Fig. 9 be genetic adaptation
It is worth curve.The comparison for optimizing curve first obtains simulated annealing population solution by the comparison of four Optimal Curve figures
It is dispersed big, ability of searching optimum be it is strongest in these four algorithms, avoid particle swarm algorithm in this way and be easily trapped into part
The problem of easily going to the lavatory.And the convergence curve for adapting to value function is not slowed down by the influence of kick probability, or even also speeds up
Convergence rate.
Table 1 be the present invention with particle swarm algorithm, genetic algorithm and crowd's searching algorithm in identical controlled device, (grate-cooler is fast
Spend the transmission function G1 (s) of control system) in the case where obtain PID controller parameter (Kp, Ki, Kd) and setting value and response
Between error (when number of iterations is equal to 100) contrast table.
| Optimization algorithm | Kp | Ki | Kd | Error between setting value and response |
| PSO | 0.1094 | 67.6368 | 0 | -3.784268729588e-04 |
| GA | 0.2695 | 67.2737 | 0 | 4.910661970256580e-04 |
| SOA | 0.0409 | 62.6602 | 0 | 1.287183486353705e-05 |
| Simulated annealing particle swarm algorithm | 0.0013 | 61.4727 | 0 | 3.648913215914540e-06 |
1 parameter of table and error contrast table
The parameter that intelligent algorithm optimization is come is applied to controlled device (the transmission function G1 of grate-cooler speed control system
(s)), by Figure 10 and Figure 11, sufficient it can be proved that the parameter control effect that simulated annealing particle group optimizing comes out plus table 1
It is best in these algorithms.This meets the high request to the control of grate-cooler scraper velocity, i.e. scraper velocity control is accurate, adjusts
Fast process is steady and vibration is minimum with impact, ensure that grate-cooler high cooling efficiency and high recovery efficiency.
Claims (1)
1. a kind of grate-cooler scraper velocity control method, which is characterized in that the control method the following steps are included:
Step 1: establishing the fitness function of grate-cooler scraper velocity；
Step 2: the parameter of the fitness function of initialization grate-cooler scraper velocity；
Step 3: N number of solution X of initialization fitness function, wherein the vector of each solution is made of three parameters of PID controller,
So the vector dimension D=3 of solution, the dimension of the speed V of each solution are identical as each solution dimension of X；
Step 4: calculating the adaptive value of each solution of the first generation by the fitness function in step 1, the smallest adaptation is selected
For value as global optimal adaptation value zbest, corresponding solution is global optimum Gbest, then using each solution of the first generation as it is individual most
Good solution Pi, adaptive value is as individual solution optimal adaptation value gbest；
Step 5: calculating kick probability factor T；
Step 6: overall situation optimal adaptation value zbest and overall situation optimum position GbestInto iterative cycles Maxlter；
Step 7: obtaining zbest and Gbest, GbestIt is exactly the optimized parameter in the fitness function of grate-cooler scraper velocity；
Fitness function in the step 1 is specific as follows:
Wherein e (t) is the setting value of PID controller and the systematic error of value of feedback, and u (t) is PID controller output, ω 2 and ω
1 is weight, in order to avoid system exports overshoot, is controlled using punishment, if when e (t) < 0,
Wherein ω 3 is weight, under normal conditions ω 1=0.999, ω 2=0.001, ω 3=100；
The parameter initialized in the step 2 includes number of iterations Maxlter, current iteration number t, experience control parameter c1And c2,
Wherein c1Adjust the solution adjustment step-length mobile to individual optimum solution direction, c2Adjust the step of the movement of solution to global optimum direction
Length, convergence coefficient lamda, convergence coefficient be reduce kick probability factor linearly, maximum weight ωmax, minimum weight
ωmin, compressibility factorWherein compressibility factor is according to formulaIt acquires, C=c1+c2, and C > 4, make
Whole size is demodulated with control and constraint；
The matrix initially dissolved in the step 3:Kp, Ki, Kd parameter
Range [0 100]；
Initialize rate matrices:The range [0 1] of Vp, Vi, Vd parameter,
Initialize maximum speed VmaxWith minimum speed Vmin；Kick probability factor T is calculated by following formula in the step 5:
T=-Fitness (Gbest)/log(0.2)； (3)
Specific step is as follows for the step 6:
(1) optimal adaptation value Fitness (G is calculatedbest), the kick probability for calculating each solution passes through following formula:
Wherein Δ f=Fitness (Xi)-Fitness (Gbest), Fitness (Xi) is the adaptive value currently solved, i=1~N；
(2) the kick probability Tu of each solution and random number random is compared in the section 0-1, if the kick probability of solution is big
In random number random, solves Xi and just replace Gbest, the problem of easily sinking into local minimum solution in iteration searching process is avoided, otherwise
Do not replace, introducing kick probability is that iteration optimizing is avoided precocious phenomenon occur, prevents optimizing from finding better solution；
(3) weight ω is calculated, following formula is passed through:
Weight ω, which is acted on, improves ability of searching optimum and optimizing later period local search ability optimizing early period；
(4) renewal speed V (N, D) passes through following formula:
Vi (t+1)=ω × Vi (t)+c1×r1×(Pi-Xi(t))+c2×r2×(Gbest-Xi(t)) (6)
Wherein t is number of iterations；Velocity magnitude when Vi (t) is i-th of the t times iteration, r1,r2It is to be obeyed on (0,1) section
The random number of even distribution；
(5) more new explanation X (N, D), passes through following formula:
WhereinC=c1+c2, and C > 4, the introducing of compressibility factor preferably command deployment speed prevent speed
It is too fast to miss optimum value, solution when Xi (t) is i-th of the t times iteration；
(6) logic judgment:
It calculates each and solves updated adaptive value Fitness (Xi)；Individual optimal adaptation value and individual optimum position are more
Newly, if updated adaptive value Fitness (Xi) is less than individual optimal adaptation value gbest, then individual optimal adaptation value gbest
=Fitness (Xi), individual optimum position PiIt is equal to Xi；The update of global optimal adaptation value and individual fitness, it is current to solve
Adaptive value Fitness (Xi) be less than global adaptive value zbest, then zbest=Fitness (Xi), global optimum position Gbest
=x (i)；
(7) update of T and t: T=lamda*T, t=t+1, wherein with the reduction of the number of iterations, kick probability value will also become T
It is bordering on zero；
(8) judge whether t is equal to Maxlter, if it is equal, step 6 is jumped out, enters step seven.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3208741A (en) * | 1960-09-24 | 1965-09-28 | Rheinische Kalksteinwerk G M B | Method and system for the automatic controlling of grid coolers or traveling grids |
| CN103116280A (en) * | 2013-01-16 | 2013-05-22 | 北京航空航天大学 | Microminiature unmanned aerial vehicle longitudinal control method with random delay of distributed network |
| CN103629941A (en) * | 2013-09-13 | 2014-03-12 | 葛洲坝宜城水泥有限公司 | Grate cooler used on cement production line and provided with automatic grate conveying speed regulating device |
| CN104503242A (en) * | 2014-12-24 | 2015-04-08 | 浙江邦业科技有限公司 | Cement grate cooler self-adaptive model prediction controller |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3208741A (en) * | 1960-09-24 | 1965-09-28 | Rheinische Kalksteinwerk G M B | Method and system for the automatic controlling of grid coolers or traveling grids |
| CN103116280A (en) * | 2013-01-16 | 2013-05-22 | 北京航空航天大学 | Microminiature unmanned aerial vehicle longitudinal control method with random delay of distributed network |
| CN103629941A (en) * | 2013-09-13 | 2014-03-12 | 葛洲坝宜城水泥有限公司 | Grate cooler used on cement production line and provided with automatic grate conveying speed regulating device |
| CN104503242A (en) * | 2014-12-24 | 2015-04-08 | 浙江邦业科技有限公司 | Cement grate cooler self-adaptive model prediction controller |
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