CN103406365B - Cold rolling band steel plate shape intelligent optimization control method - Google Patents

Abstract

The invention provides a cold rolling band steel plate shape intelligent optimization control method. A plate shape deviation signal is divided into a left wave and right wave component, a middle wave and two-side wave component and a quarter wave and side middle wave component through model recognition. A fuzzy inference rule for cold rolling mill plate shape control is built. A generalized input variable and a generalized fuzzy subset of the variable are defined for the obtained fuzzy inference rule, and then a fuzzy membership function of a fuzzy set that the generalized input variable corresponds to is taken. Online adjusting quantity of a cold rolling mill roll inclining device, a working roll bending device and a middle roll bending device is calculated to obtain an online adjusting quantity calculation model of the cold rolling mill plate shape intelligent optimization control. Online adjustment is conducted according to the obtained online adjusting quantity calculation model. The problem of poor cold rolling band steel product plate shape control quality caused by the fact that transmission lag exists between a mill body and a plate shape instrument and affecting factors of the plate shape are not considered comprehensively is solved.

Description

A kind of cold-rolled strip steel shape intelligent optimized control method

Technical field

The invention belongs to cold-strip steel field, particularly relate to a kind of cold-rolled strip steel shape intelligent optimized control method.

Background technology

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

Strip Shape Control algorithm is the core content of cold-rolled strip steel shape control system, and its superiority-inferiority directly determines the quality of Strip Shape Control effect.The plate shape Multi-variables optimum design 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 milling train exit, then with the quadratic sum of the deviation between plate shape desired value and real-time measurement values for 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 pointed out that technique scheme key is the Strip Shape Control regulation and control efficiency coefficient that will obtain high-precision each plate shape regulation device.But, actual cold-rolling mill be a kind of nonlinearity, time become complicated physical system, still can not carry out Accurate Model to it at present, this also determines the Strip Shape Control regulation and control efficiency coefficient being difficult to obtain high-precision each plate shape regulation device, therefore can not obtain desirable Strip Shape Control effect.Therefore, even if modern cold rolling enterprise production line is configured with state-of-the-art flatness detection device, also still fail effectively to eliminate the flatness defect that cold-rolled steel strip products exists.

On the other hand, Strip Shape Control algorithm is the core content of cold-rolled strip steel shape control system, and its superiority-inferiority directly determines the quality of Strip Shape Control effect.The plate shape Multi-variables optimum design 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 milling train exit, then with the quadratic sum of the deviation between plate shape desired value and real-time measurement values for 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 is 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, tradition board-shape control method utilizes Shape signal in the past to control current plate shape, and this also determines and is difficult to obtain high-precision Strip Shape Control effect.Particularly cause tension force acute variation before and after roll-force fluctuation and milling train when being with steel acceleration and deceleration, these factors all can cause outlet belt plate shape defect.Therefore, even if modern cold rolling enterprise production line is configured with state-of-the-art flatness detection device, also still fail effectively to eliminate the flatness defect that cold-rolled steel strip products exists, the unrestrained defect of wave and limit particularly.

Summary of the invention

The technical problem to be solved in the present invention is: provide a kind of cold-rolled strip steel shape intelligent optimized control method, to solve due to the technical problem that there is transmission time lag between rolling mill body and plate profile instrument and consider these two kinds of reasons not comprehensive to plate shape influence factor and cause cold-rolled steel strip products Strip Shape Control of low quality.

The present invention for solving the problems of the technologies described above taked technical scheme is: a kind of cold-rolled strip steel shape intelligent optimized control method, is characterized in that: it comprises the following steps:

1) common flatness defect is classified, be divided into successively: wave in left side wave, the right unrestrained, middle wave, bilateral wave, four points of waves and limit; Wherein plate shape deviation signal is divided into left side wave and the right wave component Δ by pattern-recognition ₁, middle wave and bilateral unrestrained component Δ ₂, and four points of waves and limit in unrestrained component Δ ₃;

2) for the band steel of same specification, the fuzzy inference rule that cold-rolling mill shape controls is set up:

$\mathrm{IF}(Q-d_{11})\mathrm{is}{F}_{11}\mathrm{and}(Q-d_{12})\mathrm{is}{F}_{12},\mathrm{and}\·\·\·\mathrm{and}(Q-d_{{1n}_1})\mathrm{is}{F}_{{1n}_1},$

$(T-d_{21})\mathrm{is}{F}_{21}\mathrm{and}(T-d_{22})\mathrm{is}{F}_{22},\mathrm{and}...,\mathrm{and}(T-d_{{2n}_2})\mathrm{is}{F}_{{2n}_2},$

$({\mathrm{\Δ}}_1-d_{31})\mathrm{is}{F}_{31}\mathrm{and}({\mathrm{\Δ}}_1-d_{32})\mathrm{is}{F}_{32},\mathrm{and}...,\mathrm{and}({\mathrm{\Δ}}_1-d_{{3n}_3})\mathrm{is}{F}_{{3n}_3},$

$({\mathrm{\Δ}}_2-d_{41})\mathrm{is}{F}_{41}\mathrm{and}({\mathrm{\Δ}}_2-d_{42})\mathrm{is}{F}_{42},\mathrm{and}...,\mathrm{and}({\mathrm{\Δ}}_2-d_{{4n}_4})\mathrm{is}{F}_{{4n}_4},$

$({\mathrm{\Δ}}_3-d_{51})\mathrm{is}{F}_{51}\mathrm{and}({\mathrm{\Δ}}_3-d_{52})\mathrm{is}{F}_{52},\mathrm{and}...,\mathrm{and}({\mathrm{\Δ}}_3-d_{{5n}_5})\mathrm{is}{F}_{{5n}_5};$

$\mathrm{THEN}\begin{array}{c}\left[\begin{array}{c}{u}_1\\ u_2\\ u_3\end{array}\right]\end{array}={\left[\begin{array}{c}{a}_{F_{11}}\\ b_{F_{11}}\\ c_{F_{11}}\end{array}\right]}_{+...+}\left[\begin{array}{c}{a}_{F_{1n_1}}\\ b_{F_{{1n}_1}}\\ c_{F_{{1n}_1}}\end{array}\right]\text{+}{\left[\begin{array}{c}{a}_{F_{21}}\\ b_{F_{21}}\\ c_{F_{21}}\end{array}\right]}_{+...}+{\left[\begin{array}{c}{a}_{F_{{2n}_2}}\\ b_{F_{{2n}_2}}\\ c_{F_{{2n}_2}}\end{array}\right]}_{+...}+{\left[\begin{array}{c}{a}_{F_{51}}\\ b_{F_{51}}\\ c_{F_{51}}\end{array}\right]}_{+...}+\left[\begin{array}{c}{a}_{F_{{5n}_5}}\\ b_{F_{{5n}_5}}\\ c_{F_{{5n}_5}}\end{array}\right];$

Wherein, the Q real-time roll-force that is cold-rolling mill; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For Q being carried out in fuzzy inference rule the jth of linear transformation ₁Individual linear transformation point, and have j ₁=1,2 ..., n ₁, n ₁For Q being carried out in fuzzy inference rule the number of linear transformation; T is the real-time rolling backward pull of cold-rolling mill; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For T being carried out in fuzzy inference rule the jth of linear transformation ₂Individual linear transformation point, and have j ₂=1,2 ..., n ₂, n ₂For T being carried out in fuzzy inference rule the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₁Carry out the jth of linear transformation ₃Individual linear transformation point, and have j ₃=1,2 ..., n ₃, n ₃For in fuzzy inference rule to Δ ₁Carry out the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₂Carry out the jth of linear transformation ₄Individual linear transformation point, and have j ₄=1,2 ..., n ₄, n ₄For in fuzzy inference rule to Δ ₂Carry out the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₃Carry out the jth of linear transformation ₅Individual linear transformation point, and have j ₅=1,2 ..., n ₅, n ₅For in fuzzy inference rule to Δ ₁Carry out the number of linear transformation; u ₁For the cold-rolling mill roller arrangement on-line control amount of inclining, u ₂For cold rolling mill work roller roll-bending device on-line control amount, u ₃For cold-rolling mill intermediate calender rolls roll-bending device on-line control amount; With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant;

Due to with value, make above-mentioned fuzzy inference rule have bar;

3) to obtain bar fuzzy inference rule, definition broad sense input variable x _kwith its generalized fuzzy subset F _k:

$x_k=\left\{\begin{array}{c}Q-d_{1k},1\≤k\≤n_1,\\ {T-d}_{2(k-n_1)},n_1+1\≤k\≤n_1+n_2,\\ {\mathrm{\Δ}}_1-d_{3(k-n_1-n_2)},n_1+n_2+1\≤k\≤n_1+n_2+n_3,\\ {\mathrm{\Δ}}_2-d_{4(k-n_1-n_2-n_3)},n_1+n_2+n_3+1\≤k\≤n_1+n_2+n_3+n_4,\\ {{\mathrm{\Δ}}_3-d}_{5(k-n_1-n_2-n_3-n_4)},n_1+n_2+n_3+n_4+1\≤k\≤n_1+n_2+n_3+n_4+n_5,\end{array}\right.$

$F_k=\left\{\begin{array}{c}{F}_{1k},1\≤k\≤n_1,\\ F_{2(k-n_1)},n_1+1\≤k\≤n_1+n_2,\\ F_{3(k-n_1-n_2)},n_1+n_2+1\≤k\≤n_1+n_2+n_3,\\ F_{4(k-n_1-n_2-n_3)},n_1+n_2+n_3+1\≤k\≤n_1+n_2+n_3+n_4,\\ F_{5(k-n_1-n_2-n_3-n_4)},n_1+n_2+n_3+n_4+1\≤k\≤n_1+n_2+n_3+n_4+n_5,\end{array}\right.$

Then broad sense input variable x is got _kcorresponding fuzzy set with with with with with fuzzy membership functions be respectively with

$\left\{\begin{array}{c}{\mathrm{\μ}}_{P_k}\left(x_k\right)=e^{-\frac{1}{2}{(x_k-z_k)}^2}\\ {\mathrm{\μ}}_{N_k}\left(x_k\right)=e^{-\frac{1}{2}{(x_k+z_k)}^2}\end{array}\right.,$

In formula, z ^kfor being more than or equal to the constant of 0, relevant with the number of broad sense input variable; K is the sequence number of broad sense input variable, and k=1 ...,

4) utilization obtains the fuzzy set of bar fuzzy inference rule and broad sense input variable and correspondence with with with with with fuzzy membership functions be respectively with ask for cold-rolling mill to incline the on-line control amount of roller arrangement, working-roller bending device and intermediate calender rolls roll-bending device, obtain the on-line control amount computation model of following cold-rolling mill shape intelligent optimal control:

$\left[\begin{array}{c}{u}_1\\ u_2\\ u_3\end{array}\right]=\left[\begin{array}{c}\underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{a_{P_k}{e}^{z_k{x}_k}+a_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\\ \underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{b_{P_k}{e}^{z_k{x}_k}+b_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{{-z}_k{x}_k}}\\ \underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{c_{P_k}{e}^{z_k{x}_k}+c_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\end{array}\right];$

Wherein with for F _kfor P _ktime corresponding output constant, with for F _kfor N _ktime corresponding output constant, e is exponential function;

5) carry out inclining according to the on-line control amount computation model of the cold-rolling mill shape intelligent optimal control obtained the on-line control of roller arrangement, working-roller bending device and intermediate calender rolls roll-bending device.

Beneficial effect of the present invention is: use advanced fuzzy hyperbolic model modeling method to establish the real-time roll-force of cold-rolling mill, the real-time rolling backward pull of cold-rolling mill, the left side wave of plate shape deviation signal and the right wave component, the middle wave of plate shape deviation signal and bilateral unrestrained component, in four points of waves of plate shape deviation signal and limit, unrestrained component five kinds of system input quantities and cold-rolling mill incline roller arrangement on-line control amount, dynamic relationship between cold rolling mill work roller roll-bending device on-line control amount and cold-rolling mill intermediate calender rolls roll-bending device on-line control amount three kinds of system output quantities, set up the on-line control amount computation model of cold-rolling mill shape intelligent optimal control, overcome adverse effect cold-strip steel outlet Strip Shape Control produced owing to there is transmission time lag between rolling mill body and plate profile instrument, the real-time rolling backward pull of the real-time roll-force and cold-rolling mill that consider cold-rolling mill is to the negative effect of belt plate shape quality, effectively can eliminate cold-rolled steel strip products and there is common flatness defect, improve the quality of products.

Accompanying drawing explanation

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

Fig. 2 is milling train exit plate shape distribution map when not using control method of the present invention.

Fig. 3 is milling train exit plate shape distribution map after use control method of the present invention.

Detailed description of the invention

Below in conjunction with drawings and Examples, the present invention will be further described.

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

1) common flatness defect is classified, be divided into successively: wave in left side wave, the right unrestrained, middle wave, bilateral wave, four points of waves and limit; Wherein left side wave and the right is unrestrained, wave is flatness defect type contrary between two in middle wave and bilateral wave and four points of waves and limit, both at a time only there will be wherein a kind of flatness defect.Based on above-mentioned principle, plate shape deviation signal is divided into left side wave and the right wave component Δ by pattern-recognition ₁, middle wave and bilateral unrestrained component Δ ₂, and four points of waves and limit in unrestrained component Δ ₃(unit is I);

2) for the band steel of same specification, the fuzzy inference rule that cold-rolling mill shape controls is set up:

$\mathrm{IF}(Q-d_{11})\mathrm{is}{F}_{11}\mathrm{and}(Q-d_{12})\mathrm{is}{F}_{12},\mathrm{and}\·\·\·\mathrm{and}(Q-d_{{1n}_1})\mathrm{is}{F}_{{1n}_1},$

$(T-d_{21})\mathrm{is}{F}_{21}\mathrm{and}(T-d_{22})\mathrm{is}{F}_{22},\mathrm{and}...,\mathrm{and}(T-d_{{2n}_2})\mathrm{is}{F}_{{2n}_2},$

$({\mathrm{\Δ}}_1-d_{31})\mathrm{is}{F}_{31}\mathrm{and}({\mathrm{\Δ}}_1-d_{32})\mathrm{is}{F}_{32},\mathrm{and}...,\mathrm{and}({\mathrm{\Δ}}_1-d_{{3n}_3})\mathrm{is}{F}_{{3n}_3},$

$({\mathrm{\Δ}}_2-d_{41})\mathrm{is}{F}_{41}\mathrm{and}({\mathrm{\Δ}}_2-d_{42})\mathrm{is}{F}_{42},\mathrm{and}...,\mathrm{and}({\mathrm{\Δ}}_2-d_{{4n}_4})\mathrm{is}{F}_{{4n}_4},$

$({\mathrm{\Δ}}_3-d_{51})\mathrm{is}{F}_{51}\mathrm{and}({\mathrm{\Δ}}_3-d_{52})\mathrm{is}{F}_{52},\mathrm{and}...,\mathrm{and}({\mathrm{\Δ}}_3-d_{{5n}_5})\mathrm{is}{F}_{{5n}_5};$

$\mathrm{THEN}\begin{array}{c}\left[\begin{array}{c}{u}_1\\ u_2\\ u_3\end{array}\right]\end{array}={\left[\begin{array}{c}{a}_{F_{11}}\\ b_{F_{11}}\\ c_{F_{11}}\end{array}\right]}_{+...+}\left[\begin{array}{c}{a}_{F_{1n_1}}\\ b_{F_{{1n}_1}}\\ c_{F_{{1n}_1}}\end{array}\right]\text{+}{\left[\begin{array}{c}{a}_{F_{21}}\\ b_{F_{21}}\\ c_{F_{21}}\end{array}\right]}_{+...}+{\left[\begin{array}{c}{a}_{F_{{2n}_2}}\\ b_{F_{{2n}_2}}\\ c_{F_{{2n}_2}}\end{array}\right]}_{+...}+{\left[\begin{array}{c}{a}_{F_{51}}\\ b_{F_{51}}\\ c_{F_{51}}\end{array}\right]}_{+...}+\left[\begin{array}{c}{a}_{F_{{5n}_5}}\\ b_{F_{{5n}_5}}\\ c_{F_{{5n}_5}}\end{array}\right];$

Wherein, the real-time roll-force that Q is cold-rolling mill, unit is KN; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For Q being carried out in fuzzy inference rule the jth of linear transformation ₁Individual linear transformation point, and have j ₁=1,2 ..., n ₁, n ₁For Q being carried out in fuzzy inference rule the number of linear transformation; T is the real-time rolling backward pull of cold-rolling mill, and unit is KN; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For T being carried out in fuzzy inference rule the jth of linear transformation ₂Individual linear transformation point, and have j ₂=1,2 ..., n ₂,N ₂For T being carried out in fuzzy inference rule the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₁Carry out the jth of linear transformation ₃Individual linear transformation point, and have j ₃=1,2 ..., n ₃, n ₃For in fuzzy inference rule to Δ ₁Carry out the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₂Carry out the jth of linear transformation ₄Individual linear transformation point, and have j ₄=1,2 ..., n ₄, n ₄For in fuzzy inference rule to Δ ₂Carry out the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₃Carry out the jth of linear transformation ₅Individual linear transformation point, and have j ₅=1,2 ..., n ₅, n ₅For in fuzzy inference rule to Δ ₁Carry out the number of linear transformation; u ₁For the cold-rolling mill roller arrangement on-line control amount of inclining, unit is mm, u ₂For cold rolling mill work roller roll-bending device on-line control amount, unit is KN, u ₃For cold-rolling mill intermediate calender rolls roll-bending device on-line control amount, unit is KN; With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant;

Due to with value, make above-mentioned fuzzy inference rule have bar;

3) to obtain bar fuzzy inference rule, definition broad sense input variable x _kwith its generalized fuzzy subset F _k:

$x_k=\left\{\begin{array}{c}Q-d_{1k},1\≤k\≤n_1,\\ {T-d}_{2(k-n_1)},n_1+1\≤k\≤n_1+n_2,\\ {\mathrm{\Δ}}_1-d_{3(k-n_1-n_2)},n_1+n_2+1\≤k\≤n_1+n_2+n_3,\\ {\mathrm{\Δ}}_2-d_{4(k-n_1-n_2-n_3)},n_1+n_2+n_3+1\≤k\≤n_1+n_2+n_3+n_4,\\ {{\mathrm{\Δ}}_3-d}_{5(k-n_1-n_2-n_3-n_4)},n_1+n_2+n_3+n_4+1\≤k\≤n_1+n_2+n_3+n_4+n_5,\end{array}\right.$

$F_k=\left\{\begin{array}{c}{F}_{1k},1\≤k\≤n_1,\\ F_{2(k-n_1)},n_1+1\≤k\≤n_1+n_2,\\ F_{3(k-n_1-n_2)},n_1+n_2+1\≤k\≤n_1+n_2+n_3,\\ F_{4(k-n_1-n_2-n_3)},n_1+n_2+n_3+1\≤k\≤n_1+n_2+n_3+n_4,\\ F_{5(k-n_1-n_2-n_3-n_4)},n_1+n_2+n_3+n_4+1\≤k\≤n_1+n_2+n_3+n_4+n_5,\end{array}\right.$

Then broad sense input variable x is got _kcorresponding fuzzy set with with with with with fuzzy membership functions be respectively with

$\left\{\begin{array}{c}{\mathrm{\μ}}_{P_k}\left(x_k\right)=e^{-\frac{1}{2}{(x_k-z_k)}^2}\\ {\mathrm{\μ}}_{N_k}\left(x_k\right)=e^{-\frac{1}{2}{(x_k+z_k)}^2}\end{array}\right.,$

In formula, z ^kfor being more than or equal to the constant of 0, relevant with the number of broad sense input variable; K is the sequence number of broad sense input variable, and k=1 ...,

4) utilization obtains the fuzzy set of bar fuzzy inference rule and broad sense input variable and correspondence with with with with with fuzzy membership functions be respectively with ask for cold-rolling mill to incline the on-line control amount of roller arrangement, working-roller bending device and intermediate calender rolls roll-bending device, obtain the on-line control amount computation model of following cold-rolling mill shape intelligent optimal control:

$\left[\begin{array}{c}{u}_1\\ u_2\\ u_3\end{array}\right]=\left[\begin{array}{c}\underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{a_{P_k}{e}^{z_k{x}_k}+a_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\\ \underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{b_{P_k}{e}^{z_k{x}_k}+b_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{{-z}_k{x}_k}}\\ \underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{c_{P_k}{e}^{z_k{x}_k}+c_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\end{array}\right];$

Wherein with for F _kfor P _ktime corresponding output constant, with for F _kfor N _ktime corresponding output constant, e is exponential function;

5) carry out inclining according to the on-line control amount computation model of the cold-rolling mill shape intelligent optimal control obtained the on-line control of roller arrangement, working-roller bending device and intermediate calender rolls roll-bending device.Detailed description of the invention is: cold-rolling mill roller arrangement on-line control amount of inclining is u ₁, cold rolling mill work roller roll-bending device on-line control amount is u ₂, cold-rolling mill intermediate calender rolls roll-bending device on-line control amount is u ₃.

Four rollers, six roller single chassis or multi-frame tandem mills is can be used for based on cold-rolled strip steel shape intelligent optimized control method of the present invention.Below for a single chassis six-high cluster mill, six-high cluster mill the product of rolling can comprise common plate, high-strength steel, part stainless steel and silicon steel etc.The present embodiment rolling be middle high grade silicon steel, type is UCM milling train, and Strip Shape Control means comprise roller declination, the positive and negative roller of working roll, the positive roller of intermediate calender rolls, intermediate roll shifting and emulsion section cooling etc.Wherein intermediate roll shifting carries out presetting according to strip width, and Adjustment principle is alignd with steel edge portion at intermediate calender rolls body of roll edge, and also can consider interpolation correction by operation side, after being transferred to position, holding position is constant; Emulsion section cooling has larger characteristic time lag.Thus the Strip Shape Control means of on-line control mainly contain roller declination, the positive and negative roller of working roll, the positive roller of intermediate calender rolls three kinds.The basic mechanical design feature index of this unit and device parameter are:

Mill speed: Max 900m/min, draught pressure: Max 18000KN, maximum rolling force square: 140.3KN × m, coiling tension: Max 220KN, 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, intermediate calender rolls is traversing amount :-120 ~ 120mm axially, 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, be made up of single solid steel axle, a measured zone is divided in the width direction every 52mm or 26mm, surrounding vertically at measuring roller in each measured zone is uniform-distribution with four grooves to place magnetoelasticity force snesor, the outside of sensor wrap up by steel loop.Plate shape roller often rotates a circle, and can measure four times to Strip Shape.

Milling train exit plate shape distribution map before the control method of the present invention that gives Fig. 2 puts 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 exceeded 20I, has obviously flatness defect, have impact on product quality and economic benefit; This also illustrates the necessity of actual production to advanced Strip Shape Control technical research.Milling train exit plate shape distribution map after the control method of the present invention that gives Fig. 3 puts into operation.As seen from Figure 3, cold-rolling mill shape intelligent optimized control method of the present invention effectively eliminates plate shape deviation, and the plate shape deviation Maximum constraint of milling train exit plate shape, within 5I, significantly improves belt steel product exit plate shape, improves the strip shape quality of band.

Above embodiment, only for illustration of technological thought of the present invention and feature, its object is to enable those skilled in the art 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 all equivalent variations of doing according to disclosed principle, design philosophy or modification, all within the scope of the claims of the present invention.

Claims (1)

1. a cold-rolled strip steel shape intelligent optimized control method, is characterized in that: it comprises the following steps:

1) common flatness defect is classified, be divided into successively: wave in left side wave, the right unrestrained, middle wave, bilateral wave, four points of waves and limit; Wherein plate shape deviation signal is divided into left side wave and the right wave component Δ by pattern-recognition ₁, middle wave and bilateral unrestrained component Δ ₂, and four points of waves and limit in unrestrained component Δ ₃;

2) for the band steel of same specification, the fuzzy inference rule that cold-rolling mill shape controls is set up:

$\mathrm{IF}(Q-d_{11})\mathrm{is}{F}_{11}\mathrm{and}(Q-d_{12})\mathrm{is}{F}_{12},\mathrm{and}\·\·\·,\mathrm{and}(Q-d_{{1n}_1})\mathrm{is}{F}_{{1n}_1},$

$(T-d_{21})\mathrm{is}{F}_{21}\mathrm{and}(T-d_{22})\mathrm{is}{F}_{22},\mathrm{and}\·\·\·,\mathrm{and}(T-d_{{2n}_2})\mathrm{is}{F}_{{2n}_2},$

$({\mathrm{\Δ}}_1-d_{31})\mathrm{is}{F}_{31}\mathrm{and}({\mathrm{\Δ}}_1-d_{32})\mathrm{is}{F}_{32},\mathrm{and}\·\·\·,\mathrm{and}({\mathrm{\Δ}}_1-d_{{3n}_3})\mathrm{is}{F}_{3n_3},$

$({\mathrm{\Δ}}_2-d_{41})\mathrm{is}{F}_{41}\mathrm{and}({\mathrm{\Δ}}_2-d_{42})\mathrm{is}{F}_{42},\mathrm{and}\·\·\·,\mathrm{and}({\mathrm{\Δ}}_2-d_{{4n}_4})\mathrm{is}{F}_{4n_4},$

$({\mathrm{\Δ}}_3-d_{51})\mathrm{is}{F}_{51}\mathrm{and}({\mathrm{\Δ}}_3-d_{52})\mathrm{is}{F}_{52},\mathrm{and}\·\·\·,\mathrm{and}({\mathrm{\Δ}}_3-d_{{5n}_5})\mathrm{is}{F}_{5n_5};$

$\mathrm{THEN}\left[\begin{array}{c}{u}_1\\ u_2\\ u_3\end{array}\right]={\left[\begin{array}{c}{a}_{F_{11}}\\ b_{F_{11}}\\ c_{F_{11}}\end{array}\right]}_{+\·\·\·+}\left[\begin{array}{c}{a}_{F_{{1n}_1}}\\ b_{F_{1n_1}}\\ c_{F_{{1n}_1}}\end{array}\right]+{\left[\begin{array}{c}{a}_{F_{21}}\\ b_{F_{21}}\\ c_{F_{21}}\end{array}\right]}_{+\·\·\·}+{\left[\begin{array}{c}{a}_{F_{2n_2}}\\ b_{F_{2n_2}}\\ c_{F_{2n_2}}\end{array}\right]}_{+\·\·\·}+{\left[\begin{array}{c}{a}_{F_{51}}\\ b_{F_{51}}\\ c_{F_{51}}\end{array}\right]}_{+\·\·\·}+\left[\begin{array}{c}{a}_{F_{{5n}_5}}\\ b_{F_{5n_5}}\\ c_{F_{{5n}_5}}\end{array}\right];$

Wherein, the Q real-time roll-force that is cold-rolling mill; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For Q being carried out in fuzzy inference rule the jth of linear transformation ₁Individual linear transformation point, and have j ₁=1,2 ..., n ₁, n ₁For Q being carried out in fuzzy inference rule the number of linear transformation; T is the real-time rolling backward pull of cold-rolling mill; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values,When During for positive P, For When During for negative N, For For T being carried out in fuzzy inference rule the jth of linear transformation ₂Individual linear transformation point, and have j ₂=1,2 ..., n ₂, n ₂For T being carried out in fuzzy inference rule the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₁Carry out the jth of linear transformation ₃Individual linear transformation point, and have j ₃=1,2 ..., n ₃, n ₃For in fuzzy inference rule to Δ ₁Carry out the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₂Carry out the jth of linear transformation ₄Individual linear transformation point, and have j ₄=1,2 ..., n ₄, n ₄For in fuzzy inference rule to Δ ₂Carry out the number of linear transformation; For Corresponding fuzzy subset, comprises positive P and negative N two Linguistic Values, when During for positive P, For When During for negative N, For For in fuzzy inference rule to Δ ₃Carry out the jth of linear transformation ₅Individual linear transformation point, and have j ₅=1,2 ..., n ₅, n ₅For in fuzzy inference rule to Δ ₃Carry out the number of linear transformation; u ₁For the cold-rolling mill roller arrangement on-line control amount of inclining, u ₂For cold rolling mill work roller roll-bending device on-line control amount, u ₃For cold-rolling mill intermediate calender rolls roll-bending device on-line control amount; With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant, With For Corresponding output constant;

Due to with value, make above-mentioned fuzzy inference rule have bar;

3) to obtain bar fuzzy inference rule, definition broad sense input variable x _kwith its generalized fuzzy subset F _k:

$x_k=\left\{\begin{array}{c}Q-d_{1k},1\≤k\≤n_1,\\ T-d_{2(k-n_1)},n_1+1\≤k\≤n_1+n_2,\\ {\mathrm{\Δ}}_1-d_{3(k-n_1-n_2)},n_1+n_2+1\≤k\≤n_1+n_2+n_3,\\ {\mathrm{\Δ}}_2-d_{4(k-n_1-n_2-n_3)},n_1+n_2+n_3+1\≤k\≤n_1+n_2+n_3+n_4,\\ {\mathrm{\Δ}}_3-d_{5(k-n_1-n_2-n_3-n_4)},n_1+n_2+n_3+n_4+1\≤k\≤n_1+n_2+n_3+n_4+n_5,\end{array}\right.$

$F_k=\left\{\begin{array}{c}{F}_{1k},1\≤k\≤n_1,\\ F_{2(k-n_1)},n_1+1\≤k\≤n_1+n_2,\\ F_{3(k-n_1-n_2)},n_1+n_2+1\≤k\≤n_1+n_2+n_3,\\ F_{4(k-n_1-n_2-n_3)},n_1+n_2+n_3+1\≤k\≤n_1+n_2+n_3+n_4,\\ F_{5(k-n_1-n_2-n_3-n_4)},n_1+n_2+n_3+n_4+1\≤k\≤n_1+n_2+n_3+n_4+n_5,\end{array}\right.$

Then broad sense input variable x is got _kcorresponding fuzzy set with with with with with fuzzy membership functions be respectively with

$\left\{\begin{array}{c}{\mathrm{\μ}}_{P_k}\left(x_k\right)=e^{-\frac{1}{2}{(x_k-z_k)}^2}\\ {\mathrm{\μ}}_{N_k}\left(x_k\right)=e^{-\frac{1}{2}{(x_k+z_k)}^2}\end{array}\right.,$

In formula, z _kfor being more than or equal to the constant of 0, relevant with the number of broad sense input variable; K is the sequence number of broad sense input variable, and k=1 ...,

4) fuzzy membership functions obtained is utilized with ask for cold-rolling mill to incline the on-line control amount of roller arrangement, working-roller bending device and intermediate calender rolls roll-bending device, obtain the on-line control amount computation model of following cold-rolling mill shape intelligent optimal control:

$\left[\begin{array}{c}{u}_1\\ u_2\\ u_3\end{array}\right]=\left[\begin{array}{c}\underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{a_{P_k}{e}^{z_k{x}_k}+a_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\\ \underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{b_{P_k}{e}^{z_k{x}_k}+b_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\\ \underset{k=1}{\overset{\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{n}_i}{\mathrm{\Σ}}}\frac{c_{P_k}{e}^{z_k{x}_k}+c_{N_k}{e}^{-z_k{x}_k}}{e^{z_k{x}_k}+e^{-z_k{x}_k}}\end{array}\right];$

Wherein with for working as F _kfor P _ktime corresponding output constant, with for working as F _kfor N _ktime corresponding output constant, e is natural Exponents;

5) carry out inclining according to the on-line control amount computation model of the cold-rolling mill shape intelligent optimal control obtained the on-line control of roller arrangement, working-roller bending device and intermediate calender rolls roll-bending device.