CN103418619A - Cold-rolled strip steel plate shape prediction control method - Google Patents

Abstract

The invention provides a cold-rolled strip steel plate shape prediction control method, comprising the following steps: for cold-rolled strip steel of the same specification, establishing a corresponding plate shape prediction control fuzzy reasoning model; setting fuzzy membership functions of various parameters in the plate shape prediction control fuzzy reasoning model by being combined with the characteristics of influence of rolling force change on the strip steel plate shape; establishing plate shape fuzzy prediction control models by utilizing a Takagi-Sugeno fuzzy model modeling rule; selecting a corresponding plate shape fuzzy prediction control model to carry out online adjustment on a working roller bending device. A dynamic relationship is established among rolling force variation, forward pull variation of a roll mill, backward pull variation of the roll mill, and online adjustment variable of the working roller bending device by using a fuzzy modeling method, the adverse effect of transmission time lag existing between the roll mill body and a plate shape instrument on the plate shape control at the outlet of the cold-rolled strip steel, and two familiar plate shape defects of intermediate waves and edge waves existing in the cold-rolled strip steel products are effectively overcome.

Description

A kind of cold-rolled strip steel shape forecast Control Algorithm

Technical field

The invention belongs to the cold-strip steel field, relate in particular to a kind of cold-rolled strip steel shape forecast Control Algorithm.

Background technology

In recent years, continuous expansion along with the cold-rolled steel strip products scope of application, the user is also more and more higher to the requirement of belt plate shape quality, thus the cold-rolled strip steel shape control technology become one by modern steel enterprise and large-scale research institution the emphasis research topic especially paid attention to.The principle that plate shape is controlled is to calculate by layout board shape control algolithm the regulated quantity that each plate shape is controlled actuator, each plate shape control measures is cooperatively interacted, farthest to eliminate the deviation between plate shape desired value and actual measured value.

Plate shape control algolithm is the core content of cold-rolled strip steel shape control system, and its superiority-inferiority has directly determined that plate shape is controlled the quality of effect.The plate shape multivariable optimal control method based on feedback control idea extensively adopted at present is by configuring the real-time Shape signal of contact plate profile instrument on-line measurement in the milling train exit, then the quadratic sum of the deviation between plate shape desired value and real-time measurement values of take is the evaluation index function, each plate shape regulation device on-line control amount when cycle calculations makes this evaluation index function obtain minimum of a value.It is to be noted, owing to there being the transmission time lag between rolling mill body and plate profile instrument, the Shape signal of a certain moment plate profile instrument actual measurement is not the instant Shape signal of milling train outlet, that is to say, the tradition board-shape control method is to utilize Shape signal in the past to control current plate shape, and this has also determined that the high-precision plate shape of very difficult acquisition is controlled effect.Particularly when the acceleration and deceleration of band steel, cause roll-force fluctuation and milling train front and back tension force acute variation, these factors all can cause outlet belt plate shape defect.Therefore, even modern cold rolling enterprise production line disposes state-of-the-art plate shape checkout gear, also still fail effectively to eliminate the flatness defect that cold-rolled steel strip products exists, particularly middle wave and limit wave defect.

Summary of the invention

The technical problem to be solved in the present invention is: provide a kind of cold-rolled strip steel shape forecast Control Algorithm, to solve owing to existing the transmission time lag to cause cold-rolled steel strip products plate shape to control technical problem of low quality between rolling mill body and plate profile instrument.

The present invention solves the problems of the technologies described above taked technical scheme to be:

A kind of cold-rolled strip steel shape forecast Control Algorithm, it is characterized in that: it comprises the following steps:

1), for the cold-strip steel of same specification, set up corresponding plate shape PREDICTIVE CONTROL Fuzzy Inference Model:

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁；

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₂；

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₃；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₄；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₅；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₆；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₇；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₈；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₉；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁₀；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₁₁；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₁₂；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₁₂；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₁₄；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₁₅；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₁₆；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₁₇；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₁₈；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁₉；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₂₀；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₂₁；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₂₂；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₂₃；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₂₄；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₂₅；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₂₆；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₂₇；

Wherein, the roll-force measured value that Δ F is current control cycle and the roll-force measured value of a upper control cycle poor, FP, FZ, FN be respectively describe single order plate shape deviation for just, zero, negative fuzzy number; The milling train forward pull measured value of the milling train forward pull measured value that Δ T1 is current control cycle and a upper control cycle poor, T1P, T1Z, T1N be respectively describe second order plate shape deviation for just, zero, negative fuzzy number; The milling train backward pull measured value of the milling train backward pull measured value that Δ T2 is current control cycle and a upper control cycle poor, T2P, T2Z, T2N be respectively describe quadravalence plate shape deviation for just, zero, negative fuzzy number; U is the on-line control amount at working-roller bending device; U _jFor in j bar fuzzy rule bottom working roll roll-bending device on-line control amount reference value, in the manually-operated Heuristics, obtain, j=1,2 ..., 27;

2) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ F in conjunction with roll-force:

Δ F is about the fuzzy membership functions of FP:

$f_{\mathrm{FP}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;F}>\mathrm{\&alpha;}\\ \frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},0\&le;\mathrm{\&Delta;F}\&le;\mathrm{\&alpha;}\\ 0,\mathrm{\&Delta;F}<0\end{array}\right.,$

Here, α is that Δ F is positive threshold value, for just, for negative, lower same during be less than-α of Δ F while in the described plate shape of step 1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ F is greater than α;

Δ F is about the fuzzy membership functions of FZ:

$f_{\mathrm{FZ}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;F}>\mathrm{\&alpha;}\\ 1-\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},0\&le;\mathrm{\&Delta;F}\&le;\mathrm{\&alpha;}\\ 1+\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},-\mathrm{\&alpha;}\&le;\mathrm{\&Delta;F}<0\\ 0,\mathrm{\&Delta;F}<-\mathrm{\&alpha;}\end{array}\right.,$

Δ F is about the fuzzy membership functions of FN:

$F_{\mathrm{FN}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;F}>0\\ -\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},-\mathrm{\&alpha;}\&le;\mathrm{\&Delta;F}\&le;0\\ 1,\mathrm{\&Delta;F}<-\mathrm{\&alpha;}\end{array}\right.;$

3) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ T1 in conjunction with the milling train forward pull:

Δ T1 is about the fuzzy membership functions of T1P:

$f_{T1P}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1>\mathrm{\&beta;}\\ \frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">\; <figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>\; </figure-callout>}1,0\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1\&le;\mathrm{\&beta;}\\ 0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<0\end{array}\right.,$

Here, β is that Δ T1 is positive threshold value, for just, for negative, lower same during be less than-β of Δ T1 while in the described plate shape of step 1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ T1 is greater than β;

Δ T1 is about the fuzzy membership functions of T1Z:

$f_{T1Z}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1>\mathrm{\&beta;}\\ 1-\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">\; <figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>\; </figure-callout>}\mathrm{1,0}\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1\&le;\mathrm{\&beta;}\\ 1+\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1,-\mathrm{\&beta;}\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<0\\ 0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<-\mathrm{\&beta;}\end{array}\right.,$

Δ T1 is about the fuzzy membership functions of T1N:

$f_{T1N}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1>0\\ -\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1,-\mathrm{\&beta;}\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1\&le;0\\ 1,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<-\mathrm{\&beta;}\end{array}\right.;$

4) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ T2 in conjunction with the milling train backward pull:

Δ T2 is about the fuzzy membership functions of T2P

$f_{T2P}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;T}2>\mathrm{\&gamma;}\\ \frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;<figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}\mathrm{2,0}\&le;\mathrm{\&Delta;T}2\&le;\mathrm{\&gamma;}\\ 0,\mathrm{\&Delta;T}2<0\end{array}\right.,$

Here, γ is that Δ T2 is positive threshold value, for just, for negative, lower same during be less than-γ of Δ T2 while in the described plate shape of step (1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ T2 is greater than γ;

Δ T2 is about the fuzzy membership functions of T2Z:

$f_{T2Z}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}2>\mathrm{\&gamma;}\\ 1-\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;<figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}\mathrm{2,0}\&le;\mathrm{\&Delta;T}2\&le;\mathrm{\&gamma;}\\ 1+\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}2,-\mathrm{\&gamma;}\&le;\mathrm{\&Delta;T}2<0\\ 0,\mathrm{\&Delta;T}2<-\mathrm{\&gamma;}\end{array}\right.,$

Δ T2 is about the fuzzy membership functions of T2N:

$f_{T2N}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}2>0\\ -\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}2,-\mathrm{\&gamma;}\&le;\mathrm{\&Delta;T}2\&le;0\\ 1,\mathrm{\&Delta;T}2<-\mathrm{\&gamma;}\end{array}\right.;$

5) utilize Takagi-Sugeno fuzzy model modeling rule to set up as lower plate shape Fuzzy Predictive Control model:

$U=\underset{j=1}{\overset{27}{\mathrm{\&Sigma;}}}{h}_j\&times;U_j,$

H wherein _iFor i fuzzy membership of plate shape Fuzzy Predictive Control model, and:

h ₁=f _FP(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，h ₂=f _FP(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，

h ₃=f _FP(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，h ₄=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，

h ₅=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，h ₆=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，

h ₇=f _FP(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，h ₈=f _FP(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，

h ₉=f _FP(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)，h ₁₀=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，

h ₁₁=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，h ₁₂=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，

h ₁₃=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，h ₁₄=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，

h ₁₅=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，h ₁₆=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，

h ₁₇=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，h ₁₈=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)，

h ₁₉=f _FN(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，h ₂₀=f _FN(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，

h ₂₁=f _FN(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，h ₂₂=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，

h ₂₃=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，h ₂₄=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，

h ₂₅=f _FN(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，h ₂₆=f _FN(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，

h ₂₇=f _FN(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)；

6) for the cold-strip steel of determining specification, select corresponding plate shape Fuzzy Predictive Control model to carry out the on-line control of working-roller bending device.

Press such scheme, described step 6) comprises following steps:

6.1) judge whether the current control cycle k of plate shape PREDICTIVE CONTROL arrives, if arrive, do not continue to wait for;

6.2) current control cycle k is while arriving, and reads the relevant actual measurement parameter in current control cycle: the milling train backward pull measured value T2 (k-1) of the milling train forward pull measured value T1 (k-1) of the roll-force measured value F (k-1) of the milling train backward pull measured value T2 (k) of the milling train forward pull measured value T1 (k) of the roll-force measured value F (k) of current control cycle, current control cycle, current control cycle, a upper front control cycle, a upper control cycle, a upper control cycle;

6.3) calculate respectively roll-force measured value poor of the roll-force measured value of current control cycle and a upper control cycle: Δ F=F (k)-F (k-1), the milling train forward pull measured value of the milling train forward pull measured value of current control cycle and a upper control cycle poor: Δ T1=T1 (k)-T1 (k-1), the milling train backward pull measured value of the milling train backward pull measured value of current control cycle and a upper control cycle poor: Δ T2=T2 (k)-T2 (k-1);

6.4) the Δ F, the Δ T1 that obtain and Δ T2 substitution plate shape Fuzzy Predictive Control model are obtained to the on-line control amount U of working-roller bending device;

6.5) the on-line control amount U of working-roller bending device is sent to the PLC controlled for working-roller bending device, realize the On-line Control of working-roller bending device;

6.6) the current control cycle k of plate shape PREDICTIVE CONTROL is set as to k+1, repeating step 6.1) to 6.5), realize the online loop control of working-roller bending device.

Beneficial effect of the present invention is: the present invention uses fuzzy Modeling Method to set up the roll-force variable quantity, milling train forward pull variable quantity, dynamic relationship between milling train backward pull variable quantity and working-roller bending device on-line control amount, set up plate shape Fuzzy Predictive Control model, overcome owing to existing the transmission time lag cold-strip steel exit plate shape to be controlled to the adverse effect produced between rolling mill body and plate profile instrument, reduced the negative effect to the belt plate shape quality of roll-force variation and tension variation, can effectively eliminate middle wave and two kinds of common flatness defects of limit wave that cold-rolled steel strip products exists, improve the quality of products.

The accompanying drawing explanation

The method flow diagram that Fig. 1 is one embodiment of the invention.

Fig. 2 is the milling train exit plate shape distribution map while not using the inventive method.

Fig. 3 is the milling train exit plate shape distribution map after use the inventive method.

The specific embodiment

Below in conjunction with the drawings and specific embodiments, the invention will be further described so that those skilled in the art the present invention may be better understood and can be implemented, but illustrated embodiment is not as a limitation of the invention.

The method flow diagram that Fig. 1 is one embodiment of the invention, it comprises the following steps:

1), for the cold-strip steel of same specification, set up corresponding plate shape PREDICTIVE CONTROL Fuzzy Inference Model:

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁；

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₂；

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₃；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₄；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₅；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₆；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₇；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₈；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₉；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁₀；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₁₁；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₁₂；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₁₂；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₁₄；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₁₅；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₁₆；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₁₇；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₁₈；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁₉；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₂₀；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₂₁；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₂₂；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₂₃；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₂₄；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₂₅；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₂₆；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₂₇；

Wherein, the roll-force measured value that Δ F is current control cycle and the roll-force measured value of a upper control cycle poor, unit is KN, FP, FZ, FN be respectively describe single order plate shape deviation for just, zero, negative fuzzy number; The milling train forward pull measured value of the milling train forward pull measured value that Δ T1 is current control cycle and a upper control cycle poor, unit is KN, T1P, T1Z, T1N be respectively describe second order plate shape deviation for just, zero, negative fuzzy number; The milling train backward pull measured value of the milling train backward pull measured value that Δ T2 is current control cycle and a upper control cycle poor, unit is KN, T2P, T2Z, T2N be respectively describe quadravalence plate shape deviation for just, zero, negative fuzzy number; U is the on-line control amount at working-roller bending device, and unit is KN; U _jFor in j bar fuzzy rule bottom working roll roll-bending device on-line control amount reference value, in the manually-operated Heuristics, obtain, unit is KN, j=1,2 ..., 27;

2) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ F in conjunction with roll-force:

Δ F is about the fuzzy membership functions of FP:

$f_{\mathrm{FP}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;F}>\mathrm{\&alpha;}\\ \frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},0\&le;\mathrm{\&Delta;F}\&le;\mathrm{\&alpha;}\\ 0,\mathrm{\&Delta;F}<0\end{array}\right.,$

Here, α is that Δ F is positive threshold value, and unit is KN, for just, for negative, lower same during be less than-α of Δ F while in the described plate shape of step 1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ F is greater than α;

Δ F is about the fuzzy membership functions of FZ:

$f_{\mathrm{FZ}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;F}>\mathrm{\&alpha;}\\ 1-\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},0\&le;\mathrm{\&Delta;F}\&le;\mathrm{\&alpha;}\\ 1+\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},-\mathrm{\&alpha;}\&le;\mathrm{\&Delta;F}<0\\ 0,\mathrm{\&Delta;F}<-\mathrm{\&alpha;}\end{array}\right.,$

Δ F is about the fuzzy membership functions of FN:

$F_{\mathrm{FN}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;F}>0\\ -\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},-\mathrm{\&alpha;}\&le;\mathrm{\&Delta;F}\&le;0\\ 1,\mathrm{\&Delta;F}<-\mathrm{\&alpha;}\end{array}\right.;$

3) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ T1 in conjunction with the milling train forward pull:

Δ T1 is about the fuzzy membership functions of T1P:

$f_{T1P}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1>\mathrm{\&beta;}\\ \frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">\; <figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>\; </figure-callout>}1,0\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1\&le;\mathrm{\&beta;}\\ 0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<0\end{array}\right.,$

Here, β is that Δ T1 is positive threshold value, and unit is KN, for just, for negative, lower same during be less than-β of Δ T1 while in the described plate shape of step 1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ T1 is greater than β;

Δ T1 is about the fuzzy membership functions of T1Z:

$f_{T1Z}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1>\mathrm{\&beta;}\\ 1-\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">\; <figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>\; </figure-callout>}\mathrm{1,0}\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1\&le;\mathrm{\&beta;}\\ 1+\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1,-\mathrm{\&beta;}\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<0\\ 0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<-\mathrm{\&beta;}\end{array}\right.,$

Δ T1 is about the fuzzy membership functions of T1N:

$f_{T1N}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1>0\\ -\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1,-\mathrm{\&beta;}\&le;\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1\&le;0\\ 1,\mathrm{\&Delta;<figure-callout\; id="1"\; label="T"\; filenames="HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}1<-\mathrm{\&beta;}\end{array}\right.;$

4) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ T2 in conjunction with the milling train backward pull:

Δ T2 is about the fuzzy membership functions of T2P

$f_{T2P}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;T}2>\mathrm{\&gamma;}\\ \frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;<figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}\mathrm{2,0}\&le;\mathrm{\&Delta;T}2\&le;\mathrm{\&gamma;}\\ 0,\mathrm{\&Delta;T}2<0\end{array}\right.,$

Here, γ is that Δ T2 is positive threshold value, and unit is KN, for just, for negative, lower same during be less than-γ of Δ T2 while in the described plate shape of step (1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ T2 is greater than γ;

Δ T2 is about the fuzzy membership functions of T2Z:

$f_{T2Z}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}2>\mathrm{\&gamma;}\\ 1-\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;<figure-callout\; id="0"\; label="T"\; filenames="HDA0000372651660000021.png,HDA0000372651660000022.png"\; state="\{\{state\}\}">T</figure-callout>}\mathrm{2,0}\&le;\mathrm{\&Delta;T}2\&le;\mathrm{\&gamma;}\\ 1+\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}2,-\mathrm{\&gamma;}\&le;\mathrm{\&Delta;T}2<0\\ 0,\mathrm{\&Delta;T}2<-\mathrm{\&gamma;}\end{array}\right.,$

Δ T2 is about the fuzzy membership functions of T2N:

$f_{T2P}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}2>0\\ -\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}2,-\mathrm{\&gamma;}\&le;\mathrm{\&Delta;T}2\&le;0\\ 1,\mathrm{\&Delta;T}2<-\mathrm{\&gamma;}\end{array}\right.;$

5) utilize Takagi-Sugeno fuzzy model modeling rule to set up as lower plate shape Fuzzy Predictive Control model:

$U=\underset{j=1}{\overset{27}{\mathrm{\&Sigma;}}}{h}_j\&times;U_j,$

H wherein _iFor i fuzzy membership of plate shape Fuzzy Predictive Control model, and:

h ₁=f _FP(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，h ₂=f _FP(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，

h ₃=f _FP(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，h ₄=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，

h ₅=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，h ₆=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，

h ₇=f _FP(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，h ₈=f _FP(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，

h ₉=f _FP(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)，h ₁₀=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，

h ₁₁=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，h ₁₂=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，

h ₁₃=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，h ₁₄=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，

h ₁₅=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，h ₁₆=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，

h ₁₇=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，h ₁₈=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)，

h ₁₉=f _FN(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，h ₂₀=f _FN(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，

h ₂₁=f _FN(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，h ₂₂=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，

h ₂₃=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，h ₂₄=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，

h ₂₅=f _FN(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，h ₂₆=f _FN(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，

h ₂₇=f _FN(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)；

6) for the cold-strip steel of determining specification, select corresponding plate shape Fuzzy Predictive Control model to carry out the on-line control of working-roller bending device, in the present embodiment, it specifically comprises following steps:

6.1) judge whether the current control cycle k of plate shape PREDICTIVE CONTROL arrives, if arrive, do not continue to wait for;

6.2) current control cycle k is while arriving, and reads the relevant actual measurement parameter in current control cycle: the milling train backward pull measured value T2 (k-1) of the milling train forward pull measured value T1 (k-1) of the roll-force measured value F (k-1) of the milling train backward pull measured value T2 (k) of the milling train forward pull measured value T1 (k) of the roll-force measured value F (k) of current control cycle, current control cycle, current control cycle, a upper front control cycle, a upper control cycle, a upper control cycle;

6.3) calculate respectively roll-force measured value poor of the roll-force measured value of current control cycle and a upper control cycle: Δ F=F (k)-F (k-1), the milling train forward pull measured value of the milling train forward pull measured value of current control cycle and a upper control cycle poor: Δ T1=T1 (k)-T1 (k-1), the milling train backward pull measured value of the milling train backward pull measured value of current control cycle and a upper control cycle poor: Δ T2=T2 (k)-T2 (k-1);

6.4) the Δ F, the Δ T1 that obtain and Δ T2 substitution plate shape Fuzzy Predictive Control model are obtained to the on-line control amount U of working-roller bending device;

6.5) the on-line control amount U of working-roller bending device is sent to the PLC controlled for working-roller bending device, realize the On-line Control of working-roller bending device;

6.6) the current control cycle k of plate shape PREDICTIVE CONTROL is set as to k+1, repeating step 6.1) to 6.5), realize the online loop control of working-roller bending device.

Can be used for four rollers, six roller single chassis or multi-frame tandem mills based on cold-rolled strip steel shape thickness of slab integrated control method of the present invention.Below to take a single chassis six-high cluster mill be example, but the product of six-high cluster mill rolling comprises common plate, high-strength steel, part stainless steel and silicon steel etc.The present embodiment rolling be middle high grade silicon steel, type is the UCM milling train, plate shape control device comprises that roller declination, the positive and negative roller of working roll, the positive roller of intermediate calender rolls, intermediate roll shifting and emulsion section are cooling etc.Wherein intermediate roll shifting is presetted according to strip width, and adjusting principle is that intermediate calender rolls body of roll edge is alignd with steel edge portion, also can be considered to add a correction by operation side, is transferred to a rear holding position constant; Emulsion section is cooling has larger characteristic time lag.Thereby the plate shape control device of on-line control mainly contains three kinds of roller declinations, the positive and negative roller of working roll, the positive roller of intermediate calender rolls.Basic mechanical design feature index and the device parameter of this unit are:

Mill speed: Max900m/min, draught pressure: Max18000KN, maximum rolling force square: 140.3KN * m, coiling tension: Max220KN, main motor current: 5500KW;

Supplied materials thickness range: 1.8～2.5mm, supplied materials width range: 850～1280mm, outgoing gauge scope: 0.3mm～1.0mm;

Work roll diameter: 290～340mm, working roll height: 1400mm, intermediate calender rolls diameter: 440～500mm, intermediate calender rolls height: 1640mm, backing roll diameter: 1150～1250mm, backing roll height: 1400mm;

Every side work roll bending power :-280～350KN, every side intermediate calender rolls bending roller force: 0～500KN, the axial traversing amount of intermediate calender rolls :-120～120mm, auxiliary hydraulic system pressure: 14MPa, balance bending system pressure: 28MPa, press down system pressure: 28MPa.

Plate profile instrument adopts ABB AB's plate shape roller of Sweden, the roller footpath 313mm of this plate shape roller, by the solid steel axle, formed, broad ways is divided into a measured zone every 52mm or 26mm, be uniform-distribution with four grooves to place magnetoelasticity power sensor in the surrounding of measuring roller vertically in each measured zone, the outside of sensor is wrapped up by steel loop.Plate shape roller often rotates a circle, and can measure four times Strip Shape.

Measuring cell is installed and is used for measuring in real time mill rolling force, at the inlet of rolling mill place, tensiometer is installed and is used for measuring the milling train forward pull, in the milling train exit, tensiometer is installed and is used for measuring the milling train backward pull.

For the present embodiment, Fig. 2 has provided the milling train exit plate shape distribution map of sheet shape prediction and control method of the present invention before putting into operation, now adopts regulative mode manually to carry out cold-rolled strip steel shape control.As seen from Figure 2, the plate shape deviation maximum of milling train exit plate shape has even surpassed 20I, has very significantly limit wave flatness defect, has affected product quality and economic benefit; This has also illustrated the necessity of actual production to advanced plate shape control technology research and development.Milling train exit plate shape distribution map after Fig. 3 has provided control method of the present invention and puts into operation.As seen from Figure 3, the method of the invention has effectively been eliminated plate shape deviation, and the plate shape deviation Maximum constraint of milling train exit plate shape, in 5I, does not have obvious limit wave or middle unrestrained flatness defect, significantly improve belt steel product exit plate shape, improved the strip shape quality of band.

Above embodiment is only for technological thought of the present invention and characteristics are described, its purpose is to make those skilled in the art can understand content of the present invention and implement according to this.The scope of the claims of the present invention is not limited to above-described embodiment, and the disclosed principle of all foundations, equivalent variations or the modification that design philosophy is done, all within the scope of the claims of the present invention.

Claims (2)

1. a cold-rolled strip steel shape forecast Control Algorithm, it is characterized in that: it comprises the following steps:

1), for the cold-strip steel of same specification, set up corresponding plate shape PREDICTIVE CONTROL Fuzzy Inference Model:

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁；

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₂；

IFΔF?is?FP?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₃；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₄；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₅；

IFΔF?is?FP?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₆；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₇；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₈；

IFΔF?is?FP?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₉；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁₀；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₁₁；

IFΔF?is?FZ?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₁₂；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₁₂；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₁₄；

IFΔF?is?FZ?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₁₅；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₁₆；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₁₇；

IFΔF?is?FZ?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₁₈；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2P?and，THEN?U=U ₁₉；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2Z?and，THEN?U=U ₂₀；

IFΔF?is?FN?and?IFΔT1is?T1P?and?IFΔT2is?T2N?and，THEN?U=U ₂₁；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2P?and，THEN?U=U ₂₂；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2Z?and，THEN?U=U ₂₃；

IFΔF?is?FN?and?IFΔT1is?T1Z?and?IFΔT2is?T2N?and，THEN?U=U ₂₄；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2P?and，THEN?U=U ₂₅；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2Z?and，THEN?U=U ₂₆；

IFΔF?is?FN?and?IFΔT1is?T1N?and?IFΔT2is?T2N?and，THEN?U=U ₂₇；

Wherein, the roll-force measured value that Δ F is current control cycle and the roll-force measured value of a upper control cycle poor, FP, FZ, FN be respectively describe single order plate shape deviation for just, zero, negative fuzzy number; The milling train forward pull measured value of the milling train forward pull measured value that Δ T1 is current control cycle and a upper control cycle poor, T1P, T1Z, T1N be respectively describe second order plate shape deviation for just, zero, negative fuzzy number; The milling train backward pull measured value of the milling train backward pull measured value that Δ T2 is current control cycle and a upper control cycle poor, T2P, T2Z, T2N be respectively describe quadravalence plate shape deviation for just, zero, negative fuzzy number; U is the on-line control amount at working-roller bending device; U _jFor in j bar fuzzy rule bottom working roll roll-bending device on-line control amount reference value, in the manually-operated Heuristics, obtain, j=1,2 ..., 27;

2) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ F in conjunction with roll-force:

Δ F is about the fuzzy membership functions of FP:

$f_{\mathrm{FP}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;F}>\mathrm{\&alpha;}\\ \frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},0\&le;\mathrm{\&Delta;F}\&le;\mathrm{\&alpha;}\\ 0,\mathrm{\&Delta;F}<0\end{array}\right.,$

Here, α is that Δ F is positive threshold value, for just, for negative, lower same during be less than-α of Δ F while in the described plate shape of step 1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ F is greater than α;

Δ F is about the fuzzy membership functions of FZ:

$f_{\mathrm{FZ}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;F}>\mathrm{\&alpha;}\\ 1-\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},0\&le;\mathrm{\&Delta;F}\&le;\mathrm{\&alpha;}\\ 1+\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},-\mathrm{\&alpha;}\&le;\mathrm{\&Delta;F}<0\\ 0,\mathrm{\&Delta;F}<-\mathrm{\&alpha;}\end{array}\right.,$

Δ F is about the fuzzy membership functions of FN:

$F_{\mathrm{FN}}\left(\mathrm{\&Delta;F}\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;F}>0\\ -\frac{1}{\mathrm{\&alpha;}}\mathrm{\&Delta;F},-\mathrm{\&alpha;}\&le;\mathrm{\&Delta;F}\&le;0\\ 1,\mathrm{\&Delta;F}<-\mathrm{\&alpha;}\end{array}\right.;$

3) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ T1 in conjunction with the milling train forward pull:

Δ T1 is about the fuzzy membership functions of T1P:

$f_{T1P}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;T}1>\mathrm{\&beta;}\\ \frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;T}1,0\&le;\mathrm{\&Delta;T}1\&le;\mathrm{\&beta;}\\ 0,\mathrm{\&Delta;T}1<0\end{array}\right.,$

Here, β is that Δ T1 is positive threshold value, for just, for negative, lower same during be less than-β of Δ T1 while in the described plate shape of step 1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ T1 is greater than β;

Δ T1 is about the fuzzy membership functions of T1Z:

$f_{T1Z}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}1>\mathrm{\&beta;}\\ 1-\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;T}\mathrm{1,0}\&le;\mathrm{\&Delta;T}1\&le;\mathrm{\&beta;}\\ 1+\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;T}1,-\mathrm{\&beta;}\&le;\mathrm{\&Delta;T}1<0\\ 0,\mathrm{\&Delta;T}1<-\mathrm{\&beta;}\end{array}\right.,$

Δ T1 is about the fuzzy membership functions of T1N:

$f_{T1N}\left(\mathrm{\&Delta;T}1\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}1>0\\ -\frac{1}{\mathrm{\&beta;}}\mathrm{\&Delta;T}1,-\mathrm{\&beta;}\&le;\mathrm{\&Delta;T}1\&le;0\\ 1,\mathrm{\&Delta;T}1<-\mathrm{\&beta;}\end{array}\right.;$

4) change the influencing characterisitic of belt plate shape set to the following fuzzy membership functions about Δ T2 in conjunction with the milling train backward pull:

Δ T2 is about the fuzzy membership functions of T2P

$f_{T2P}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}1,\mathrm{\&Delta;T}2>\mathrm{\&gamma;}\\ \frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}\mathrm{2,0}\&le;\mathrm{\&Delta;T}2\&le;\mathrm{\&gamma;}\\ 0,\mathrm{\&Delta;T}2<0\end{array}\right.,$

Here, γ is that Δ T2 is positive threshold value, for just, for negative, lower same during be less than-γ of Δ T2 while in the described plate shape of step (1) PREDICTIVE CONTROL fuzzy reasoning control model, thinking that Δ T2 is greater than γ;

Δ T2 is about the fuzzy membership functions of T2Z:

$f_{T2Z}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}2>\mathrm{\&gamma;}\\ 1-\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}\mathrm{2,0}\&le;\mathrm{\&Delta;T}2\&le;\mathrm{\&gamma;}\\ 1+\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}2,-\mathrm{\&gamma;}\&le;\mathrm{\&Delta;T}2<0\\ 0,\mathrm{\&Delta;T}2<-\mathrm{\&gamma;}\end{array}\right.,$

Δ T2 is about the fuzzy membership functions of T2N:

$f_{T2P}\left(\mathrm{\&Delta;T}2\right)=\left\{\begin{array}{c}0,\mathrm{\&Delta;T}2>0\\ -\frac{1}{\mathrm{\&gamma;}}\mathrm{\&Delta;T}2,-\mathrm{\&gamma;}\&le;\mathrm{\&Delta;T}2\&le;0\\ 1,\mathrm{\&Delta;T}2<-\mathrm{\&gamma;}\end{array}\right.;$

5) utilize Takagi-Sugeno fuzzy model modeling rule to set up as lower plate shape Fuzzy Predictive Control model:

$U=\underset{j=1}{\overset{27}{\mathrm{\&Sigma;}}}{h}_j\&times;U_j,$

H wherein _iFor i fuzzy membership of plate shape Fuzzy Predictive Control model, and:

h ₁=f _FP(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，h ₂=f _FP(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，

h ₃=f _FP(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，h ₄=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，

h ₅=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，h ₆=f _FP(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，

h ₇=f _FP(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，h ₈=f _FP(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，

h ₉=f _FP(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)，h ₁₀=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，

h ₁₁=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，h ₁₂=f _FZ(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，

h ₁₃=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，h ₁₄=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，

h ₁₅=f _FZ(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，h ₁₆=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，

h ₁₇=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，h ₁₈=f _FZ(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)，

h ₁₉=f _FN(ΔF)×f _T1P(ΔT1)×f _T2P(ΔT2)，h ₂₀=f _FN(ΔF)×f _T1P(ΔT1)×f _T2Z(ΔT2)，

h ₂₁=f _FN(ΔF)×f _T1P(ΔT1)×f _T2N(ΔT2)，h ₂₂=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2P(ΔT2)，

h ₂₃=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2Z(ΔT2)，h ₂₄=f _FN(ΔF)×f _T1Z(ΔT1)×f _T2N(ΔT2)，

h ₂₅=f _FN(ΔF)×f _T1N(ΔT1)×f _T2P(ΔT2)，h ₂₆=f _FN(ΔF)×f _T1N(ΔT1)×f _T2Z(ΔT2)，

h ₂₇=f _FN(ΔF)×f _T1N(ΔT1)×f _T2N(ΔT2)；

6) for the cold-strip steel of determining specification, select corresponding plate shape Fuzzy Predictive Control model to carry out the on-line control of working-roller bending device.

2. cold-rolled strip steel shape forecast Control Algorithm according to claim 1, it is characterized in that: described step 6) comprises following steps:

6.1) judge whether the current control cycle k of plate shape PREDICTIVE CONTROL arrives, if arrive, do not continue to wait for;

6.2) current control cycle k is while arriving, and reads the relevant actual measurement parameter in current control cycle: the milling train backward pull measured value T2 (k-1) of the milling train forward pull measured value T1 (k-1) of the roll-force measured value F (k-1) of the milling train backward pull measured value T2 (k) of the milling train forward pull measured value T1 (k) of the roll-force measured value F (k) of current control cycle, current control cycle, current control cycle, a upper front control cycle, a upper control cycle, a upper control cycle;

6.3) calculate respectively roll-force measured value poor of the roll-force measured value of current control cycle and a upper control cycle: Δ F=F (k)-F (k-1), the milling train forward pull measured value of the milling train forward pull measured value of current control cycle and a upper control cycle poor: Δ T1=T1 (k)-T1 (k-1), the milling train backward pull measured value of the milling train backward pull measured value of current control cycle and a upper control cycle poor: Δ T2=T2 (k)-T2 (k-1);

6.4) the Δ F, the Δ T1 that obtain and Δ T2 substitution plate shape Fuzzy Predictive Control model are obtained to the on-line control amount U of working-roller bending device;

6.5) the on-line control amount U of working-roller bending device is sent to the PLC controlled for working-roller bending device, realize the On-line Control of working-roller bending device;

6.6) the current control cycle k of plate shape PREDICTIVE CONTROL is set as to k+1, repeating step 6.1) to 6.5), realize the online loop control of working-roller bending device.

Images