CN103592852A - PID (Proportion Integration Differentiation) controller optimizing design method based on particle swarm membrane algorithm - Google Patents

Abstract

The invention relates to a PID controller parameter optimizing method, and especially discloses a PID controller optimizing design method based on a particle swarm membrane algorithm. The method comprises that an optimal particle is obtained from a swarm, different dimension values of the particle are successively assigned to to-be-optimized parameters Kp, Ki and Kd, a control system model is operated, performance indexes output by the system are obtained, and it is determined whether the evolution rule is used again to search for the optimal particle so that the optimal control effect can be obtained. The PID controller optimizing design method combines membrane computing with traditional PSO (Particle Swarm Optimization), fully utilizes the partition function and transport communication rules, can obtain a set of more proper PID control parameters, and finally enables a controlled system to achieve the optimal control effect. According the PID controller optimizing design method, the controlled system can obtain the set of more proper PID control parameters and achieves the optimal control effect under the condition that the controller structure is not changed.

Description

PID controller optimization method for designing based on population film algorithm

Technical field

The present invention relates to industrial process control technology field, particularly a kind of PID(proportional-integral-differential based on population film algorithm) controller parameter optimization method.

Background technology

Being created in when greatly enhancing productivity of industrial automation brought huge economic benefit, as the control system of its key link, no matter be that theory or technology application aspect have all obtained developing rapidly.In industrial process, when control system puts into operation, must be optimized controller, make control system obtain as much as possible optimum control performance to obtain larger economic benefit.The optimal design of controller generally comprises two aspects, i.e. the optimization of controller architecture and the optimization of controller parameter.The optimization, the particularly Proportional coefficient K of PID controller of the controller parameter that controller optimization design of the present invention just refers to _p, storage gain K _iwith differential gain K _doptimization.

In general, after the control strategy of control system is determined, the structure of controller just can be decided.And the parameter of controller is very large to the performance impact of control system, if choose improperly, can make the hydraulic performance decline of whole system, when serious, even also can cause that system produces unsteady phenomena.PID controller because it is simple in structure, good stability, easily realize and robustness good, in industrial automation, application is always extensive.So, after the structure of control strategy and control system is determined, how by PID controller being optimized to design to obtain better PID controller parameter, thereby make the control effect of controlled system reach the more excellent even optimum strong practical significance that has.

At present, conventional PID controller optimization method comprises classic method and intelligent method.Conventional PID controllers Optimization Design Method mainly comprises Ziegler-Nichols method (Z-N method), simplicial method, relay setting method etc., the common advantage of these methods is simple, but they have identical problem in PID controller optimization design process, the optimization that is all difficult to realize parameter is adjusted, easily produce vibration and large overshoot, and the adjusting time is often longer.Intelligent PID controller Optimization Design Method is easily absorbed in precocious phenomenon at high order system or in the process of optimization of pure lag system system and cannot obtains global optimum as genetic algorithm (GA), particle cluster algorithm (PSO) etc., thereby makes controlled system cannot obtain satisfied control effect.

Summary of the invention

For above-mentioned technical matters, target of the present invention is that a kind of PID controller optimization method for designing based on population film algorithm is provided.

The present invention solves the problems of the technologies described above, and the technical scheme of employing is that the PID controller optimization method for designing based on population film algorithm, comprises the following steps:

A, initialization algorithm parameter, comprise underlying membrane quantity m, inertia weight w, acceleration factor c ₁, c ₂, dimension D, algorithm termination condition and membrane structure, m is natural number, m>=1;

B, initialization population, produce one by n the molecular particle population flying according to certain speed of grain in D dimension space, and between position x, the flying speed v of each particle of random initializtion and the movement area of particle, then each particle in this population is assigned randomly in m layer underlying membrane, and guarantees to have at least in a particle individuality and top layer film and do not comprise any particle in every layer of underlying membrane; Initialization is as follows:

w ₀＝λ，

${\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">w</figure-callout>}}_1=q_1{q}_2{q}_3<figure-callout\; id="1"\; label="...\; q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">\; <figure-callout\; id="1"\; label="...\; q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">.</figure-callout>\; </figure-callout>..q_{n_{}}{{}_{}}_{}{{}_1}_{}$ ，1≤n ₁＜n-m+1，

${\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">w</figure-callout>}}_2={\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}{\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}<figure-callout\; id="2"\; label="...\; q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">.</figure-callout>..q_{n_{}}{{}_2}_{}$ ，n ₁+1≤n ₂＜n-m+2，

……

$w_m=q_{n_{(m-1)}+1}{q}_{n_{(m-1)}+2}...q_n,$

W wherein ₀represent the initial object in the film of top layer, λ represents null character string, w _i(1≤i≤m) represents the initial object in i layer underlying membrane, and n is natural number, n>=1, and n>=m, and n represents Population Size, q _i(1≤i≤n) represents a particle individuality;

In C, every layer of underlying membrane, the independent evolutionary rule of PSO that uses carries out optimizing respectively;

Concrete steps are:

(1) particle every layer of underlying membrane being obtained during Random assignment in step B is as the population at individual of this layer of underlying membrane, and population is as the population scale of this layer of underlying membrane;

(2) independently calculate the fitness function of each particle in every layer of underlying membrane and store fitness value;

(3) best values fitness value of each particle and this particle being lived through (is recorded as individual optimal value and with symbol p _irepresent) relatively, if better, so using this fitness value as the current individual optimal value of this particle storage;

(4) by the individual optimal value p of each particle _iwith current underlying membrane in-group optimal value p _grelatively, if better, colony's optimal value the storage in current underlying membrane using the individual optimal value of this particle so;

(5) in every layer of underlying membrane, by formula (1) and (2), each particle is carried out to speed and position renewal;

$v_i^{(t+1)}={\mathrm{wv}}_i^{\left(t\right)}+c_1{r}_1(p_i^{\left(t\right)}-x_i^{\left(t\right)})+c_2{r}_2(p_g^{\left(t\right)}-x_i^{\left(t\right)})$

$x_i^{(t+1)}=x_i^{\left(t\right)}+v_i^{(t+1)}$

Wherein, v _i=(v _i1, v _i2..., v _iD) ^tthe speed that represents i particle in population, x _i=(x _i1, x _i2..., x _iD) ^tthe position that represents i particle, p _i=(p _i1, p _i2..., p _iD) ^tthe individual optimal value that represents each particle, p _g=(p _g1, p _g2..., p _gD) ^tthe colony's optimal value that represents population, g represents the call number of the desired positions of all particles experience in population, t and t+1 represent respectively t and t+1 speed and position renewal, the transposition of T representing matrix, r ₁, r ₂be illustrated in the random number that interval [0,1] changes;

(6), if meet termination condition, in underlying membrane, optimizing finishes and proceeds to next step, i.e. step D; Otherwise return, carry out (2) step;

D, judge in each underlying membrane that whether all objects evolve completely, completely proceed to next step if evolve, otherwise return to step C;

E, m layer underlying membrane and top layer be intermembranous carries out information interchange, every layer of underlying membrane and the intermembranous execution transhipment in top layer with exchange regularly, the optimum individual in every layer of underlying membrane is all delivered in the film of top layer, now in the film of top layer, comprise m particle;

F, m particle carries out fitness value evaluation in his-and-hers watches tunic successively;

G, in the film of top layer, m particle, select colony's optimum individual, and upgrade the current optimal value p of colony with this individuality _g;

H, by each dimension value of top layer Mo Zhong colony optimal particle successively assignment, give parameter K to be optimized _p, K _iand K _d; K wherein _pfor PID controller scale-up factor, K _ifor PID controller storage gain, K _dfor the PID controller differential gain;

I, operational system model;

J, output controlled system performance index;

K, determine whether and meet end condition, if meet, proceed to next step, otherwise jump to step C;

The PID controller parameter that L, the output of top layer film are optimized.

Recommend, in steps A, velocity factor c ₁, c ₂in interval [0,2] value.

Preferably, in steps A, described membrane structure is single-layer membrane structure, wherein comprises m layer underlying membrane and top layer film 0; Its expression formula is: [ ₀[ ₁] ₁, [ ₂] ₂, [ ₃] ₃..., [ _m] _m] ₀.

The invention has the beneficial effects as follows, in the situation that not redesigning controller architecture, can make controlled system obtain one group of more appropriate pid control parameter (K _p, K _iand K _d), thereby make controlled system reach better control effect.The thought that the present invention calculates film combines with traditional PS O, the sectoring function that makes full use of film calculating exchanges rule with transhipment, make the sub-population in every layer of underlying membrane in iteration, all there is strong uncertainty each time, thereby the diversity that keeps population, make the inventive method can effectively avoid precocious phenomenon and effectively improve its global optimizing ability, therefore can obtain one group of more appropriate pid control parameter (K _p, K _i, K _d), finally make controlled system reach better control effect.

Accompanying drawing explanation

Fig. 1 is process flow diagram of the present invention;

Fig. 2 is single-layer membrane structure;

Fig. 3 is the closed loop unit-step nsponse curve of object I;

Fig. 4 is the closed loop unit-step nsponse curve of object II;

Fig. 5 is the closed loop unit-step nsponse curve of object III;

Fig. 6 is the closed loop unit-step nsponse curve of object IV.

Embodiment

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

The present invention is based on the PID controller optimization method for designing of population film algorithm, after obtaining the optimal particle of population, by each dimension value of this particle successively assignment, give parameter K to be optimized _p, K _iand K _d, then operation control system model obtains the output performance index of this system, and determines whether and again adopt evolutionary rule to carry out optimizing to impel system to obtain more excellent control effect.Process flow diagram of the present invention as shown in Figure 1, is optimized design according to following steps:

1. initialization algorithm parameter.Comprise underlying membrane quantity m, inertia weight w, acceleration factor c ₁, c ₂(conventionally in interval [0,2] value), dimension D, algorithm termination condition and membrane structure.The present invention adopts single-layer membrane structure in recommending: [ ₀[ ₁] ₁, [ ₂] ₂, [ ₃] ₃..., [ _m] _m] ₀, as shown in Figure 2, wherein comprise m layer underlying membrane and top layer film 0;

2. initialization population.Produce one by n the molecular particle population flying according to certain speed of grain in D dimension space, and [v between position x, the flying speed v of each particle of random initializtion and the movement area of particle _min, v _max] (v _min, v _maxbe respectively minimum value and the maximal value of speed v), then each particle in this population is assigned randomly in m layer underlying membrane, and guarantees in every layer of underlying membrane, to have a particle individuality at least, and in the film of top layer, do not comprise any particle.Initialization is as follows:

w ₀＝λ，

${\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">w</figure-callout>}}_1=q_1{q}_2{q}_3<figure-callout\; id="1"\; label="...\; q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">\; <figure-callout\; id="1"\; label="...\; q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">.</figure-callout>\; </figure-callout>..q_{n_{}}{{}_{}}_{}{{}_1}_{}$ ，1≤n ₁＜n-m+1，

${\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">w</figure-callout>}}_2={\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}{\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}<figure-callout\; id="2"\; label="...\; q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">.</figure-callout>..q_{n_{}}{{}_2}_{}$ ，n ₁+1≤n ₂＜n-m+2，

……

$w_m=q_{n_{(m-1)}+1}{q}_{n_{(m-1)}+2}...q_n,$

W wherein ₀represent the initial object in the film of top layer, λ represents null character string, w _i(1≤i≤m) represents the initial object in i layer underlying membrane, and n represents Population Size, q _i(1≤i≤n) represents a particle individuality;

3. in every layer of underlying membrane, the independent evolutionary rule of PSO that uses carries out optimizing respectively.Concrete steps are:

(1) particle every layer of underlying membrane being obtained during Random assignment in the 2nd step is as the population at individual of this layer of underlying membrane, and population is as the population scale of this layer of underlying membrane;

(2) independently calculate the fitness function of each particle in every layer of underlying membrane and store fitness value;

(3) best values fitness value of each particle and this particle being lived through (is recorded as individual optimal value and with symbol p _irepresent) relatively, if better, so using this fitness value as the current individual optimal value of this particle storage;

(4) by the individual optimal value p of each particle _iwith current underlying membrane in-group optimal value p _grelatively, if better, colony's optimal value the storage in current underlying membrane using the individual optimal value of this particle so;

(5) in every layer of underlying membrane, by formula (1) and (2), each particle is carried out to speed and position renewal;

$v_i^{(t+1)}={\mathrm{wv}}_i^{\left(t\right)}+c_1{r}_1(p_i^{\left(t\right)}-x_i^{\left(t\right)})+c_2{r}_2(p_g^{\left(t\right)}-x_i^{\left(t\right)})---\left(1\right)$

$x_i^{(t+1)}=x_i^{\left(t\right)}+v_i^{(t+1)}---\left(2\right)$

Wherein, v _i=(v _i1, v _i2..., v _iD) ^tthe speed that represents i particle in population, x _i=(x _i1, x _i2..., x _iD) ^tthe position that represents i particle, p _i=(p _i1, p _i2..., p _iD) ^tthe individual optimal value that represents each particle, p _g=(p _g1, p _g2..., p _gD) ^tthe colony's optimal value that represents population, g represents the call number of the desired positions of all particles experience in population, t and t+1 represent respectively t and t+1 speed and position renewal, the transposition of T representing matrix, r ₁, r ₂be illustrated in the random number that interval [0,1] changes.

(6), if meet termination condition, in underlying membrane, optimizing finishes and proceeds to next step, i.e. the 4th step; Otherwise return, carry out (2) step;

4. judge in each underlying membrane whether all objects evolve completely, if evolve, completely proceed to next step, and the 5th step, carries out the 3rd step otherwise return;

5.m layer underlying membrane and top layer be intermembranous carries out information interchange.Every layer of underlying membrane transported with the intermembranous execution in top layer and exchanges rule, and the optimum individual in every layer of underlying membrane is all delivered in the film of top layer, now in the film of top layer, comprises m particle;

6. m particle carries out fitness value evaluation in his-and-hers watches tunic successively;

7. in the film of top layer, m particle, select colony's optimum individual, and upgrade the current optimal value p of colony with this individuality _g;

8. by each dimension value of top layer Mo Zhong colony optimal particle successively assignment, give parameter K to be optimized _p, K _iand K _d;

9. operational system model;

10. export controlled system performance index;

11. determine whether and meet end condition, if meet, algorithm proceeds to next step, i.e. the 12nd step, otherwise jump to the 3rd step;

The PID controller parameter that 12. top layer film outputs are optimized.

Embodiment

Adopt the present invention to carry out the design of PID controller optimization to four classical single-input single-outputs (SISO) controlled device, the mathematical model of four objects is as shown in the formula shown in (3)～(6), be respectively one order inertia and add three rank linear system and fourth-order linear system, the wherein G that pure lag system (models of most chemical processes), second-order inertia add pure lag system (lag time constant is larger), non-minimum phase ₁(s), G ₂(s), G ₃and G (s) ₄(s) difference indicated object I, II, III and IV are at the function in complex frequency domain (s territory).The performance index that adopt in embodiment comprise individual event performance index overshoot (T _s), adjusting time (σ) and absolute value error integral performance index (IAE), adopt Matlab7.5 as implementation tool, operating system be Windows XP, CPU be AMD Sempron 1.6GHz and in save as on the computing machine of 1GB and solve.

Object I:

$G_1\left(s\right)=\frac{1}{s+1}{e}^{-0.2s}---\left(3\right)$

Object II:

$G_2\left(s\right)=\frac{0.33}{134{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">s</figure-callout>}}^2+18.5s+1}{e}^{-18.25s}---\left(4\right)$

Object III:

$G_3\left(s\right)=\frac{1-1.4s}{{(1+s)}^3}---\left(5\right)$

Object IV:

$G_4\left(s\right)=\frac{1}{(1+s){(s/6+1)}^3}---\left(6\right)$

The specific implementation step of the PID controller optimization method for designing of this example based on population film algorithm is as follows:

Step1: initialization algorithm parameter.Algorithm parameter arranges as follows: underlying membrane quantity m=16, inertia weight ω=0.6, acceleration factor c ₁=c ₂=2, the dimension D=3 of each particle, top layer film iterations MaxIter=10, underlying membrane iterations MaxIter _i=10 (1≤i≤16) and membrane structure [ ₀[ ₁] ₁, [ ₂] ₂, [ ₃] ₃..., [ ₁₆] ₁₆] ₀, wherein 0 is top layer film.

Step2: initialization population.Produce one by 30 molecular particle populations that fly according to certain speed of grain in 3 dimension spaces, and between position x, the flying speed v of each particle of random initializtion and the movement area of particle [1,1], then each particle in this population is assigned randomly in 16 layers of underlying membrane, and guarantees to have at least in every layer of underlying membrane one by one in body and top layer film and do not comprise any particle.Multiset initialization is as follows:

w ₀＝λ，

${\mathrm{<figure-callout\; id="1"\; label="w"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">w</figure-callout>}}_1=q_1{q}_2{q}_3...{\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">\; <figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>\; </figure-callout>}}_{n_{}{}_{}}$ ，1≤n ₁＜15，

${\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">w</figure-callout>}}_2={\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}{\mathrm{<figure-callout\; id="1"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}...{\mathrm{<figure-callout\; id="2"\; label="q\; n"\; filenames="HDA0000425776270000021.png"\; state="\{\{state\}\}">q</figure-callout>}}_{n_{}}$ ，n ₁+1≤n ₂＜16，

……

${\mathrm{<figure-callout\; id="16"\; label="w"\; filenames="HDA0000425776270000031.png"\; state="\{\{state\}\}">w</figure-callout>}}_{16}=q_{n_{15}+1}{q}_{n_{15}+2}...q_{30},$

Wherein Population Size is 30, q _i(1≤i≤30) represent a particle individuality;

Step3: in every layer of underlying membrane, the independent evolutionary rule of PSO that uses carries out optimizing respectively.Concrete steps are:

(1) particle every layer of underlying membrane being obtained during Random assignment in the 2nd step is as the population at individual of this layer of underlying membrane, and population is as the population scale of this layer of underlying membrane;

(2) independently calculate the fitness function IAE of each particle in every layer of underlying membrane and store fitness value, wherein the mathematic(al) representation of IAE as shown in (7), the deviation of e (t) representative system wherein;

$\mathrm{IAE}=\underset{0}{\overset{\&infin;}{\&Integral;}}\left|e\left(t\right)\right|\mathrm{dt}---\left(7\right)$

(3) best values fitness value of each particle and this particle being lived through (is recorded as individual optimal value and with symbol p _irepresent) relatively, if better, so using this fitness value as the current individual optimal value of this particle storage;

(4) by the individual optimal value p of each particle _iwith current underlying membrane in-group optimal value p _grelatively, if better, colony's optimal value the storage in current underlying membrane using the individual optimal value of this particle so;

(5) in every layer of underlying membrane, by formula (1) and (2), each particle is carried out to speed and position renewal,

$v_i^{(t+1)}={\mathrm{wv}}_i^{\left(t\right)}+c_1{r}_1(p_i^{\left(t\right)}-x_i^{\left(t\right)})+c_2{r}_2(p_g^{\left(t\right)}-x_i^{\left(t\right)})$ With

$x_i^{(t+1)}=x_i^{\left(t\right)}+v_i^{(t+1)}$

(6) if meet termination condition, i.e. Iter _i=MaxIter _i=10 (1≤i≤16), in underlying membrane, optimizing finishes and proceeds to next step, i.e. Step4; Otherwise return, carry out (2) step;

Step4: judge in each underlying membrane whether all objects evolve completely, completely proceed to next step if evolve, and Step5, carries out Step3 otherwise return;

Step5:16 layer underlying membrane and top layer be intermembranous carries out information interchange.Every layer of underlying membrane transported with the intermembranous execution in top layer and exchanges rule, and the optimum individual in every layer of underlying membrane is all delivered in the film of top layer, now in the film of top layer, comprises 16 particles;

Step6: in his-and-hers watches tunic, 16 particles carry out fitness value evaluation successively, fitness function adopts IAE, and its mathematic(al) representation is as shown in (7);

Step7: select colony's optimum individual 16 particles in the film of top layer, and upgrade the current optimal value p of colony with this individuality _g;

Step8: give parameter K to be optimized by each dimension value of top layer Mo Zhong colony optimal particle successively assignment _p, K _iand K _d;

Step9: operational system model;

Step10: output controlled system performance index;

Step11: determine whether and meet end condition, i.e. Ite=MaxIter=10, if meet, algorithm proceeds to next step, i.e. Step12, otherwise jump to Step3;

Step12: the PID controller parameter that film output in top layer is optimized.

Table 1 is the present invention and traditional Z-N method (Z-N), standard genetic algorithm (SGA), have PID controller parameter (Kp, Ki and Kd) that the film optimized algorithm (dsDNA-MC) of DNA double chain structure obtains when same object being carried out to controller optimization design and performance index overshoot (Ts), the contrast of adjusting time (σ), absolute value error integral performance index (IAE).Fig. 3～Fig. 6 is the closed loop step response curve comparison diagram that the present invention and above three kinds of methods obtain when object I to IV being carried out to controller optimization design.From table 1 and Fig. 3-6, can find out, when the inventive method adopts population film algorithm (PSOPS) to carry out controller optimization design, the performance of system be improved significantly.For object I, object II and object IV, adopt PSOPS algorithm to be optimized the PID control system after design to controller, there is less adjusting time, overshoot and performance index value, and effect is better than Z-N method, SGA method and dsDNA-MC method far away; For object III, as can be seen from Figure 5, the unit-step nsponse curve that Z-N method, SGA method and dsDNA-MC method obtain is slightly better than the response curve under PSOPS algorithm, but the system performance index value that PSOPS algorithm obtains is better than this three far away, and has response speed faster.

PID controller parameter and performance index that four kinds of methods of table 1 obtain

To sum up, the three rank linear systems and the fourth-order linear system that while adopting the present invention to carry out the design of PID controller optimization, can make one, second-order inertia add pure lag system system, non-minimum phase all obtain good performance index, the controller that the inventive method obtains has good quality, thereby makes system obtain satisfied control effect.

Claims (3)

1. the PID controller optimization method for designing based on population film algorithm, comprises the following steps:

A, initialization algorithm parameter, comprise underlying membrane quantity m, inertia weight w, acceleration factor c ₁, c ₂, dimension D, algorithm termination condition and membrane structure, m is natural number, m>=1;

B, initialization population, produce one by n the molecular particle population flying according to certain speed of grain in D dimension space, and between position x, the flying speed v of each particle of random initializtion and the movement area of particle, then each particle in this population is assigned randomly in m layer underlying membrane, and guarantees to have at least in a particle individuality and top layer film and do not comprise any particle in every layer of underlying membrane; Initialization is as follows:

w ₀＝λ，

$w_1=q_1{q}_2{q}_3...q_{n_1}$ ，1≤n ₁＜n-m+1，

$w_2=q_{n_1+1}{q}_{n_1+2}...q_{n_2}$ ，n ₁+1≤n ₂＜n-m+2，

……

$w_m=q_{n_{(m-1)}+1}{q}_{n_{(m-1)}+2}...q_n,$

W wherein ₀represent the initial object in the film of top layer, λ represents null character string, w _i(1≤i≤m) represents the initial object in i layer underlying membrane, and n is natural number, n>=1, and n>=m, and n represents Population Size, q _i(1≤i≤n) represents a particle individuality;

In C, every layer of underlying membrane, the independent evolutionary rule of PSO that uses carries out optimizing respectively;

Concrete steps are:

(1) particle every layer of underlying membrane being obtained during Random assignment in step B is as the population at individual of this layer of underlying membrane, and population is as the population scale of this layer of underlying membrane;

(2) independently calculate the fitness function of each particle in every layer of underlying membrane and store fitness value;

(3) best values fitness value of each particle and this particle being lived through (is recorded as individual optimal value and with symbol p _irepresent) relatively, if better, so using this fitness value as the current individual optimal value of this particle storage;

(4) by the individual optimal value p of each particle _iwith current underlying membrane in-group optimal value p _grelatively, if better, colony's optimal value the storage in current underlying membrane using the individual optimal value of this particle so;

(5) in every layer of underlying membrane, by formula (1) and (2), each particle is carried out to speed and position renewal;

$v_i^{(t+1)}={\mathrm{wv}}_i^{\left(t\right)}+c_1{r}_1(p_i^{\left(t\right)}-x_i^{\left(t\right)})+c_2{r}_2(p_g^{\left(t\right)}-x_i^{\left(t\right)})$

$x_i^{(t+1)}=x_i^{\left(t\right)}+v_i^{(t+1)}$

Wherein, v _i=(v _i1, v _i2..., v _iD) ^tthe speed that represents i particle in population, x _i=(x _i1, x _i2..., x _iD) ^tthe position that represents i particle, p _i=(p _i1, p _i2..., p _iD) ^tthe individual optimal value that represents each particle, p _g=(p _g1, p _g2..., p _gD) ^tthe colony's optimal value that represents population, g represents the call number of the desired positions of all particles experience in population, t and t+1 represent respectively t and t+1 speed and position renewal, the transposition of T representing matrix, r ₁, r ₂be illustrated in the random number that interval [0,1] changes;

(6), if meet termination condition, in underlying membrane, optimizing finishes and proceeds to next step, i.e. step D; Otherwise return, carry out (2) step;

D, judge in each underlying membrane that whether all objects evolve completely, completely proceed to next step if evolve, otherwise return to step C;

E, m layer underlying membrane and top layer be intermembranous carries out information interchange, every layer of underlying membrane and the intermembranous execution transhipment in top layer with exchange regularly, the optimum individual in every layer of underlying membrane is all delivered in the film of top layer, now in the film of top layer, comprise m particle;

F, m particle carries out fitness value evaluation in his-and-hers watches tunic successively;

G, in the film of top layer, m particle, select colony's optimum individual, and upgrade the current optimal value p of colony with this individuality _g;

H, by each dimension value of top layer Mo Zhong colony optimal particle successively assignment, give parameter K to be optimized _p, K _iand K _d; K wherein _pfor PID controller scale-up factor, K _ifor PID controller storage gain, K _dfor the PID controller differential gain;

I, operational system model;

J, output controlled system performance index;

K, determine whether and meet end condition, if meet, proceed to next step, otherwise jump to step C;

The PID controller parameter that L, the output of top layer film are optimized.

2. the PID controller optimization method for designing based on population film algorithm according to claim 1, is characterized in that, in steps A, and velocity factor c ₁, c ₂in interval [0,2] value.

3. the PID controller optimization method for designing based on population film algorithm according to claim 1, is characterized in that, in steps A, described membrane structure is single-layer membrane structure, wherein comprises m layer underlying membrane and top layer film 0; Its expression formula is: [ ₀[ ₁] ₁, [ ₂] ₂, [ ₃] ₃..., [ _m] _m] ₀.

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