CN106440844A - Grate cooler scraper blade speed controlling method - Google Patents

Abstract

The invention belongs to the field of cement clinker industrial production automatic control processes, and particularly relates to a grate cooler scraper blade speed controlling method. The method comprises the steps that 1, a fitness function is built; 2, parameters are initialized; 3, a popularization is initialized; 4, the adaptive value of each particle of the first generation is calculated through the fitness function, the smallest adaptive value is selected out as the global optimum adaptive value, then, each particle of the first generation is used as the individual optimum position, and the adaptive value of each particle is used as the individual optimum adaptive value; 5, a sudden jump probability factor is calculated; 6, the global optimum adaptive value and the global optimum position enter an iterative loop; and 7, the particle swarm optimum adaptive value and the global optimum position are obtained. According to the grate cooler scraper blade speed controlling method based on simulated annealing particle swarm optimization, the simulated annealing technology and particle swarm optimization are combined, PID controller parameters are optimized, higher-precision control over the grate cooler scraper blade speed is achieved, the speed adjusting process is more stable, and vibration and impact are reduced.

Description

A kind of grate-cooler scraper velocity control method

Technical field

The invention belongs to clinker commercial production automatic control process field, specifically a kind of cooling machine scraper speed Degree control method.

Background technology

Grate-cooler is the important corollary equipment of cement industry clinker burning production system, is widely used in cement industry grog On production line.Nowadays, grate-cooler has evolved to forth generation, and with the lifting of production capacity, its type of drive is from Mechanical Driven Change hydraulic-driven into.Thus, adjust hydraulic control grate-cooler scraper velocity and adjust process stationarity, accuracy, reduce fortune Row impact and vibration etc. become the main contents of cement clinker grate cooler control process.

Adjust and control for realizing grate-cooler scraper velocity, the present invention adopts PID control.Determining grate-cooler scraper velocity Under the precondition of control system model and control strategy, how the Kp (proportionality coefficient) of design optimization PID controller, Ts are (long-pending Point time constant), the parameter such as TD (derivative time constant) become the key of impact the system control effect.

At present, in the intelligent method that PID controller parameter optimizes, conventional has particle cluster algorithm, genetic algorithm and people Group hunting algorithm etc., but often easily it is absorbed in local minimum solution in practical operation, dispersibility search is deteriorated, and the overall situation is searched Reduced capability is it is impossible to search the deficiency of more preferable solution, thus affecting the control effect of controlled system.

Content of the invention

The invention provides simulated annealing technology is combined by one kind with particle cluster algorithm, using PID control, optimization PID controller parameter, realizes the control of the higher precision to grate-cooler scraper velocity, and speed regulation process is more steady, reduction is shaken The grate-cooler scraper velocity control method based on simulated annealing particle cluster algorithm that is dynamic and impacting, overcomes existing cooling machine scraper Speed cannot get the problem of effective control.

Technical solution of the present invention is described with reference to the drawings as follows：

A kind of grate-cooler scraper velocity control method, this control method comprises the following steps：

Step one, set up 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 group of PID controller Become, thus solution vector dimension D=3, each solution the dimension of speed V with each solve X dimension identical；

Step 4, calculated by the fitness function in step one first generation each solution adaptive value, select minimum Adaptive value is global optimum G as overall optimal adaptation value zbest, corresponding solution_best, then using each solution of the first generation as individual Body optimum solution P_i, its adaptive value is as individual solution optimal adaptation value gbest；

Step 5, calculating kick probability factor T；

Step 6, overall optimal adaptation value zbest and overall optimum position G_bestEnter iterative cycles Maxlter；

Step 7, draw zbest and G_best, G_bestBe exactly grate-cooler scraper velocity fitness function in optimized parameter.

Fitness function in described step one is specific as follows：

Wherein e (t) is the systematic error with value of feedback for the setting value of PID controller, and u (t) exports for PID controller, ω 2 It is weights with ω 1, in order to avoid system exports overshoot, using punishment control, if e (t)<When 0,

Wherein ω 3 is weights, ω 1=0.999 under normal circumstances, ω 2=0.001, ω 3=100.

In described step 2, initialized parameter includes number of iterations Maxlter, current iteration number t, experience control parameter c₁ And c₂, wherein c₁Adjust the step-length to individual optimum solution direction movement for the solution adjustment, c₂Adjust the movement to global optimum direction for the solution Step-length, convergence coefficient lamda, convergence coefficient be so that kick probability factor is linearly reduced, maximum weights ω_max, minimum weights ω_min, compressibility factorWherein compressibility factor is according to formulaTry to achieve, C=c₁+c₂, and C>4, its work With controlling and constraining the whole size of demodulation.

The matrix initially dissolved in described step 3：Kp、Ki、Kd The scope [0100] of parameter；

Initialization rate matrices：The scope of Vp, Vi, Vd parameter [01], initialize maximal rate V_maxWith minimum speed V_min.

In described step 5, kick probability factor T is calculated by equation below：

T=-Fitness (G_best)/log(0.2). (3)

The comprising the following steps that of described step 6：

(1) calculate optimal adaptation value Fitness (G_best), the kick probability calculating each solution passes through equation below：

Wherein Δ f=Fitness (Xi)-Fitness (G_best), Fitness (Xi) is the adaptive value of current solution, i=1～ N；

(2) each solution kick probability Tu and 0-1 interval in random number random be compared, if solution kick general Rate is more than random number random, and solution Xi just replaces G_best, it is to avoid easily sink into the problem of local minimum solution in iteration searching process, Otherwise do not replace, introducing kick probability is to avoid iteration optimizing precocious phenomenon, makes optimizing can not find more preferable solution；

(3) calculate weights ω, by equation below：

Weights ω effect optimizing early stage improves ability of searching optimum and optimizing later stage local search ability；

(4) renewal speed V (N, D), by equation below：

Vi (t+1)=ω × Vi (t)+c₁×r₁×(P_i-Xi(t))+c₂×r₂×(G_best-Xi(t)) (6)

Wherein t is number of iterations；Vi (t) is velocity magnitude during i-th the t time iteration, r₁,r₂It is clothes on (0,1) interval From equally distributed random number；

(5) more new explanation X (N, D), by equation below：

WhereinC=c₁+c₂, and C>4, the introducing of compressibility factor better controls over search speed to be prevented Excessive velocities miss optimum, and X i (t) is solution during i-th the t time iteration；

(6) logical judgment：

Calculate the adaptive value Fitness (Xi) after each solution updates；Individual optimal adaptation value and individual optimum position Renewal, if the adaptive value Fitness (Xi) after updating is less than individual optimal adaptation value gbest, then individual optimal adaptation value Gbest=Fitness (Xi), individual optimum position P_iIt is equal to Xi；Overall optimal adaptation value and the renewal of individual fitness, when The adaptive value Fitness (Xi) of front solution is less than overall adaptive value zbest, then zbest=Fitness (Xi), overall optimum position Gbest=x (i)；

(7) renewal of T and t：T=lamda*T, t=t+1, wherein T are with the reduction of iterationses, kick probit To level off to zero；

(8) judge whether t is equal to Maxlter, if equal, jump out step 6, enter step 7.

The invention has the beneficial effects as follows：Population (PSO) algorithm is simply easily realized, but the phenomenons such as precocity easily and lead Cause is unable to global optimizing.Because the present invention is by PID controller control, grate-cooler scraper velocity therefore of the present invention controls and can convert Become to find the problem of PID controller parameter optimal solution.The present invention is directed to the problems referred to above, introduces kick probability, it is joined by temperature The control of number, that is, probability size reduce with the decline of temperature, and temperature parameter reduces with the increase of iterationses, because This is avoided that the phenomenons such as precocity, the dispersibility accelerated solving speed and ensure solution.Determine grate-cooler scraper velocity control system After transmission function and setting value are unit step, simulation results show, the simulated annealing grain that the present invention is adjusted using pid parameter The parameter of the PID that swarm optimization is adjusted out to system, the ginseng with particle cluster algorithm, genetic algorithm and crowd's searching algorithm optimization Number is compared, and when number of iterations is equal, ability of searching optimum strengthens, it is ensured that the dispersibility understanding, it is to avoid conventional particle group's algorithm The problems such as be easily trapped into precocity, the convergence rate of adaptive value is also accelerated, the pid control parameter that gone out by this algorithm optimization (Kp, Ki, Kd), the control accuracy of grate-cooler scraper velocity control system is improved, shock and vibration in control process are also minimum, Be conducive to meeting the requirement of the high cooling efficiency of grate-cooler and high heat recovery efficiency, be conducive to the development of cement industry.

Brief description

Fig. 1 adjusts to PID controller parameter schematic diagram for simulated annealing population；

Fig. 2 is simulated annealing particle group optimizing curve chart；

Fig. 3 is simulated annealing population adaptive value curve chart；

Fig. 4 is particle group optimizing curve chart；

Fig. 5 is population adaptive value curve chart；

Fig. 6 behaviour group hunting Optimal Curve figure；

Fig. 7 behaviour group hunting adaptive value curve chart；

Fig. 8 is genetic optimization curve chart；

Fig. 9 is genetic adaptation value curve chart；

Figure 10 is grate-cooler scraper velocity control system jump response synthetic error curve chart；

Figure 11 is the comprehensive output curve diagram of grate-cooler scraper velocity control system jump response；

Figure 12 is flow chart of the present invention.

Specific embodiment

Below in conjunction with the accompanying drawings and embodiment, the present invention is described in further detail.

The present invention is the grate-cooler scraper velocity control method based on simulated annealing particle cluster algorithm.Theoretical by electrichydraulic control The electro-hydraulic position servo system of a valve control can be drawn, choose suitable parameter and carry out simulation study, cooling motor speed can be drawn The closed loop transfer function of control systemAnd by simulated annealing particle cluster algorithm Pid parameter optimization Simulation is carried out to system function.

Simulated annealing population refers to Fig. 1 to PID controller (proportional-integral derivative controller) parameter tuning schematic diagram Shown.

Refering to Figure 12, grate-cooler scraper velocity control method comprises the following steps：

Step one), the fitness function setting up grate-cooler scraper velocity of simulated annealing particle cluster algorithm：Fitness function It is used to evaluate the performance of optimizing solution, wherein fitness function quotes the fitness function of Yu Shengwei use, and concrete function is such as Under：

Wherein e (t) is systematic error, and u (t) exports for controller, and ω 2 and ω 1 is weights.In order to avoid system output is super Adjust, using punishment control, if e (t)<When 0

ω 1=0.999 under normal circumstances, ω 2=0.001, ω 3=100, fitness function reflects tri- parameters of PID Control effect, adaptive value less reflection control effect is more preferable.

Step 2), the parameter of the fitness function of initialization grate-cooler scraper velocity, 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= 2.1st, temperature cooling ratio lamda=0.6, compressibility factorWherein compressibility factor is to quote the formula of doctor's Clerc propositionTry to achieve, C=c₁+c₂, and C>4,ω_max=0.9, ω_min=0.45.

Step 3), initialize population, the position vector of number N=30 in population X, wherein each particle is by PID (ratio Example-Integrated Derivative) three parameters composition (the dimension D=3 of position vector), the velocity dimension of each particle and each grain The dimension of son is identical；

The scope [0 100] of Kp, Ki, Kd parameter；

The scope [0 1] of Vp, Vi, Vd parameter, Vmax= 1；Vmin=-1；

Step 4) adaptive value of each particle of the first generation is calculated by formula (3) and formula (2), select fitting of minimum Should be worth as overall optimal adaptation value zbest, corresponding particle is overall optimum position Gbest, then each particle of the first generation As individual optimum position Pi, its adaptive value is exactly PID control as individual optimal adaptation value gbest, wherein overall optimum position The optimization control parameter of system.

Step 5), calculate kick probability factor T, by equation below：

T=-Fitness (G_best)/log(0.2) (4)

This formula is the initialization annealing temperature formula in simulated annealing.

Step 6), enter iterative cycles Maxlter；

(1) calculate optimal adaptation value Fitness (G_best), the kick probability calculating each particle passes through equation below：

This formula is derived 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 random number random in the kick probability of each particle and 0-1 interval is compared, if the kick of particle is general Rate is more than random number random, and particle x (i) just replaces G_best, otherwise do not replace.

(3) calculate weighted value ω, by equation below：

This formula is to propose weight computing formula by doctor SHI；

(4) renewal speed V (30,3), by equation below：

Vi (t+1)=ω × vi (t)+c₁×r₁×(P_i-xi(t))+c₂×r₂×(G_best-xi(t)) (7)

This formula is the speed more new formula that doctor SHI proposes, and wherein t is number of iterations；Vi (t) is the t time iteration of particle i When speed, r₁,r₂It is to obey equally distributed random number on (0,1) interval；Vi (t+1) is judged, ifvi (t+1)> Vmax, vi (t+1)=Vmax；if vi(t+1)<Vmin, vi (t+1)=Vmin.

(5) more new position X (30,3), by equation below：

This formula is the location updating formula being proposed by doctor Clerc,

WhereinC=c₁+c₂, and C>4, xi (t) is position during particle the t time iteration of i.

(6) logical judgment：

Calculate the adaptive value Fitness (x (i)) after each particle updates, by formula 3 and formula 2；Individual optimal Adaptive value and the renewal of individual optimum position, if the adaptive value Fitness (x (i)) after updating is less than individual optimal adaptation value Gbest, then individual optimal adaptation value gbest=Fitness (x (i)), individual optimum position P_iIt is equal to x (i)；The overall situation is optimal Adaptive value and the renewal of individual fitness, the adaptive value Fitness (x (i)) of current particle is less than overall adaptive value zbest, then Zbest=Fitness (x (i)), overall 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 equal, jump out step 6, enter step 7.

It is simulated annealing particle group optimizing curve chart, refering to Fig. 3 is simulated annealing population adaptive value curve refering to Fig. 2 Scheme, be particle group optimizing curve chart refering to Fig. 4, be population adaptive value curve chart refering to Fig. 5, be that people's group hunting is excellent refering to Fig. 6 Change curve chart, be people's group hunting adaptive value curve refering to Fig. 7, be genetic optimization curve refering to Fig. 8, be genetic adaptation refering to Fig. 9 Value curve.It is optimized the contrast of curve first, simulated annealing population solution is drawn by the contrast of four Optimal Curve figures Dispersibility is big, and ability of searching optimum is the strongest in these four algorithms, so avoids particle cluster algorithm and is easily trapped into local The problem easily gone to the lavatory.And adapting to the convergence curve of value function is not subject to the impact of kick probability to slow down, or even also speeds up Convergence rate.

Table 1 be the present invention with particle cluster algorithm, genetic algorithm and crowd's searching algorithm in identical controlled device (cooling machine speed Transmission function G1 (s) of degree control system) in the case of obtain PID controller parameter (Kp, Ki, Kd) and setting value and response value Between error (when number of iterations is equal to 100) contrast table.

Optimized algorithm	Kp	Ki	Kd	Error between setting value and response value
					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 cluster algorithm	0.0013	61.4727	0	3.648913215914540e-06

Table 1 parameter and error contrast table

The parameter that intelligent algorithm optimizes out is applied to controlled device (transmission function G1 of grate-cooler speed control system (s)), by Figure 10 and Figure 11, may certify that plus table 1 foot, simulated annealing particle group optimizing parameter control effect out It is best in these algorithms.This meets the high request that grate-cooler scraper velocity is controlled, and that is, scraper velocity controls accurately, adjusts Fast process steadily and vibration minimum with impact it is ensured that grate-cooler high cooling efficiency and high organic efficiency.

Claims (6)

1. a kind of grate-cooler scraper velocity control method is it is characterised in that this control method comprises the following steps：

Step one, set up 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, the vector of wherein each solution is made up of three parameters of PID controller, So solution vector dimension D=3, each solution the dimension of speed V with each solve X dimension identical；

Step 4, calculated by the fitness function in step one first generation each solution adaptive value, select minimum adaptation As overall optimal adaptation value zbest, corresponding solution is global optimum G to value_best, then using each solution of the first generation as individuality Good solution P_i, its adaptive value is as individual solution optimal adaptation value gbest；

Step 5, calculating kick probability factor T；

Step 6, overall optimal adaptation value zbest and overall optimum position G_bestEnter iterative cycles Maxlter；

Step 7, draw zbest and G_best, G_bestBe exactly grate-cooler scraper velocity fitness function in optimized parameter.

2. a kind of grate-cooler scraper velocity control method according to claim 1 is it is characterised in that in described step one Fitness function is specific as follows：

$Fitness={\&Integral;}_0^{\mathrm{\&infin;}}\&lsqb;\mathrm{\&omega;}1\left|e\left(t\right)\right|+\mathrm{\&omega;}2\&times;u\left(t\right)\&times;u\left(t\right)\&rsqb;dt---\left(1\right)$

Wherein e (t) is the systematic error with value of feedback for the setting value of PID controller, and u (t) exports for PID controller, ω 2 and ω 1 is weights, in order to avoid system exports overshoot, using punishment control, if e (t)<When 0,

$Fitness={\&Integral;}_0^{\mathrm{\&infin;}}\&lsqb;\mathrm{\&omega;}1\left|e\left(t\right)\right|+\mathrm{\&omega;}2\&times;u\left(t\right)\&times;u\left(t\right)+\mathrm{\&omega;}3\left|e\right(t\left)\right|\&rsqb;dt---\left(2\right)$

Wherein ω 3 is weights, ω 1=0.999 under normal circumstances, ω 2=0.001, ω 3=100.

3. a kind of grate-cooler scraper velocity control method according to claim 1 is it is characterised in that in described step 2 just The parameter of beginningization includes number of iterations Maxlter, current iteration number t, experience control parameter c₁And c₂, wherein c₁Adjust solution adjustment to The step-length of individual optimum solution direction movement, c₂Adjust the step-length of the movement to global optimum direction for the solution, convergence coefficient lamda, Convergence coefficient be so that kick probability factor is linearly reduced, maximum weights ω_max, minimum weights ω_min, compressibility factorWherein press The contracting factor is according to formulaTry to achieve, C=c₁+c₂, and C>4, its effect controls and constraint demodulation is whole big Little.

4. a kind of grate-cooler scraper velocity control method according to claim 1 is it is characterised in that in described step 3 just Begin the matrix dissolved：The scope [0100] of Kp, Ki, Kd parameter；

Initialization rate matrices：The scope [01] of Vp, Vi, Vd parameter, just Beginningization maximal rate V_maxWith minimum speed V_min.

5. a kind of grate-cooler pid control parameter setting method according to claim 1 is it is characterised in that described step 5 Middle kick probability factor T is calculated by equation below：

T=-Fitness (G_best)/log(0.2). (3)

6. a kind of grate-cooler scraper velocity control method according to claim 1 is it is characterised in that the tool of described step 6 Body step is as follows：

(1) calculate optimal adaptation value Fitness (G_best), the kick probability calculating each solution passes through equation below：

$Tu\left(i\right)=\frac{\exp \left(-\frac{\mathrm{\&Delta;}f}{\mathrm{\&tau;}}\right)}{{\mathrm{\&Sigma;}}_{i=1}^N\exp \left(-\frac{\mathrm{\&Delta;}f}{\mathrm{\&tau;}}\right)};---\left(4\right)$

Wherein Δ f=Fitness (Xi)-Fitness (G_best), Fitness (Xi) is the adaptive value of current solution, i=1～N；

(2) each solution kick probability Tu and 0-1 interval in random number random be compared, if solution kick probability big In random number random, solve Xi and just replace G_best, it is to avoid easily sink into the problem of local minimum solution in iteration searching process, otherwise Do not replace, introducing kick probability is to avoid iteration optimizing precocious phenomenon, makes optimizing can not find more preferable solution；

(3) calculate weights ω, by equation below：

$\mathrm{\&omega;}={\mathrm{\&omega;}}_{max}-\frac{t*({\mathrm{\&omega;}}_{max}-{\mathrm{\&omega;}}_{\min })}{tmax}---\left(5\right)$

Weights ω effect optimizing early stage improves ability of searching optimum and optimizing later stage local search ability；

(4) renewal speed V (N, D), by equation below：

Vi (t+1)=ω × Vi (t)+c₁×r₁×(P_i-Xi(t))+c₂×r₂×(G_best-Xi(t)) (6)

Wherein t is number of iterations；Vi (t) is velocity magnitude during i-th the t time iteration, r₁,r₂It is to obey all on (0,1) interval The random number of even distribution；

(5) more new explanation X (N, D), by equation below：

WhereinC=c₁+c₂, and C>4, the introducing of compressibility factor better controls over search speed prevents speed Too fast miss optimum, Xi (t) is solution during i-th the t time iteration；

(6) logical judgment：

Calculate the adaptive value Fitness (Xi) after each solution updates；Individual optimal adaptation value and individual optimum position are more Newly, if the adaptive value Fitness (Xi) after updating is less than individual optimal adaptation value gbest, then individual optimal adaptation value gbest =Fitness (Xi), individual optimum position P_iIt is equal to Xi；Overall optimal adaptation value and the renewal of individual fitness, current solution Adaptive value Fitness (Xi) be less than overall adaptive value zbest, then zbest=Fitness (Xi), overall optimum position Gbest =x (i)；

(7) renewal of T and t：T=lamda*T, t=t+1, wherein T also will become with the reduction of iterationses, kick probit It is bordering on zero；

(8) judge whether t is equal to Maxlter, if equal, jump out step 6, enter step 7.