CN103357669B - Plate model prediction control method - Google Patents

Abstract

The invention relates to a plate model prediction control method and belongs to the technical field of steel rolling quality control. The plate model prediction control method includes: when a rolling control computer detects that a band steel speed is lower than a preset value, taking a secondary plate shape component and a first plate shape component in an actual plate shape curve as target values of model parameter optimization; obtaining a static secondary plate shape component, a static first plate shape component, a dynamic secondary plate shape component and a dynamic first plate shape component according to a static prediction model and a dynamic prediction model; after the static secondary plate shape component and the static first plate shape component are taken as control initial values, respectively comparing the dynamic secondary plate shape component and the dynamic first plate shape component with the corresponding target values, and outputting corresponding control parameters according to comparing results. Reasonable prediction parameters are adopted to substitute for actual measuring parameters for comparison and control, so that control hysteresis when the band steel rotating speed is low can be avoided, existing plate shape feedback control can be supplemented, and cold rolling quality is guaranteed.

Description

A kind of Plate model prediction control method

Technical field

The present invention relates to a kind of method of steel rolling quality control, especially a kind of Plate model prediction control method, belong to steel rolling Quality Control Technology field.

Background technology

The plate shape of cold-strip steel directly has influence on the productivity ratio of downstream industry, lumber recovery and the height of cost and the outward appearance of product, and therefore Strip Shape Control has great importance for the quality improving cold-strip steel.

Plate shape with steel comprises the size index of two aspects in length and breadth, and with regard to band steel is longitudinal, usual plate shape refers to glacing flatness, is commonly called as shape wave, namely along the planarization on strip length direction; With regard to band steel laterally with regard to, plate shape institute refers to the section configuration being with steel, and the thickness distribution namely on plate width direction, convexity is that the transverse plate shape commonly used the most represents index.

According to the applicant understood, what adopt in current automatic strip shape control system is plate shape FEEDBACK CONTROL, namely be worth to being with the actual plate shape of steel by plate shape measurement roller and plate shape measurement systems axiol-ogy, the deviation that automatic strip shape control in controller is worth according to target flatness and actual plate shape, calculates the setting value of executing agency.As the process that application number plat control system technical scheme disclosed in the Chinese patent application of CN200510028316.2 is exactly according to actual measurement board form data, obtain after treatment surveying Shape signal; Calculated by plate shape deviation, deduct target flatness with actual measurement plate shape, obtain deviation Shape signal, thus carry out Strip Shape Control.But it is less than the plate shape closed-loop control cycle based on the sense cycle of plate shape measurement system, namely its controlled condition is that strip speed is greater than certain value and just can comes into operation.Because plate profile instrument distance rolling mill 5 frame (last frame) outlet has certain distance, when strip speed is lower than certain value, flatness detection signal lag time τ will be greater than plate shape closed-loop control cycle T, and control system will be difficult to stability contorting.In order to ensure the stability of closed-loop control link, control system gain value is had to less, and result causes the deleterious of plate shape closed-loop control.Further retrieval finds, application number is the Chinese patent application of 200810207919.2---in the sheet shape prediction and control method of model adaptation, by real data, Strip Shape Control model is revised, but because the change of previous moment board form data do not considered by this shape prediction model, when real data changes more violent in Dynamic Rolling Process process time, Strip Shape Control model easily causes the instability of system.

Application number is the method for designing that the patent of invention of CN200810011561.6 discloses a kind of cold-rolled strip steel shape control objectives model, determines according to the mathematical constraint that structure & working mechanism and the target flatness of cold-rolled strip steel shape feature, milling train Strip Shape Control actuator should meet the Mathematical Modeling that description strip profile and flatness controls; According to the different process quality requirement of the kind of rolled band steel and specification, roll rear different disposal operation and change to determine the different controling parameters in plate shape object module to roll wear in the requirement of belt plate shape and the operation of rolling and roll thermal crown, form different target flatness curves, for process control of cold rolling calculated with mathematical model and the real-time Strip Shape Control of basic automatization.Its object module mainly solves the desired value of plate shape flatness control, off-line simulation static settings, instead of Dynamic Regulating Process.

In addition, application number is that the patent of invention of CN200910011950.3 discloses a kind of optimizing regulating and controlling efficiency coefficient of board shape controlling actuator of cold rolling mill method, establish the plate shape regulation and control efficiency coefficient priori value table under different rolling operating point, in table, one group of strip width value and the corresponding rolling operating point of rolling force value, its operating point, border is determined according to the position of actual rolling operating point in table, weight factor is set by the similarity degree of operating point, border parameter and actual rolling operating point parameter, the plate shape regulation and control efficiency coefficient under actual rolling operating point is obtained by the priori efficiency coefficient weighted superposition of boundary point, the plate shape using online self learning model and actual measurement board form data to update in table regulates and controls efficiency coefficient precision, the regulation and control efficiency coefficient of accurate profile regulation mechanism can be obtained, and be applied in closed loop plat control system, there is higher Strip Shape Control precision.Its Problems existing does not have to solve the problem owing to surveying delayed the carried out open loop Strip Shape Control of plate shape or closed-loop control instability under low-speed conditions.

Summary of the invention

The object of the invention is to: the deficiency existed for above-mentioned prior art, propose a kind of can effectively solve strip speed lower time control the plate shape Plate model prediction control method of lag issues, thus supplementing as existing plate shape FEEDBACK CONTROL, for ensureing that the quality of cold-rolled strip steel shape creates more favorably condition.

From background technology, in current cold-strip steel automatic control system, determining target flatness curve y (x)=A ₀+ A ₁x+A ₂x ², i.e. A ₀, A ₁, A ₂after determining, when surveying the once item of plate shape curve, quadratic term coefficient and aim curve and not being inconsistent, computer can by running existing program, according to the comparative result of surveying plate shape component and target flatness component (once item, quadratic term coefficient), the controlled quentity controlled variables such as the roller declination of electronically operated mill and bending roller force, try hard to make actual measurement coefficient convergence target factor, just due to this feedback closed loop, control when strip speed is lower cannot simultaneously match, is difficult to play required feedback modifiers effect.

Therefore, in order to realize the above object, Plate model prediction control method of the present invention primarily of control five stand mill roll control computer and be arranged in final milling train output plate profile instrument form control system, the signal output part of described plate profile instrument connects the corresponding ports of roll control computer, and the control output end of described roll control computer is connected to the controlled end of each roll respectively; When roll control COMPUTER DETECTION to strip speed lower than predetermined value time, carry out basic Recurrence Process according to the following steps and control:

The first step, data processing is carried out to one group of standard actual measurement board form data of plate profile instrument collection, obtain the secondary plate shape component in actual plate shape curve and a plate shape component, as the desired value of Model Parameter Optimization;

Second step, according to static prediction model, try to achieve the static secondary plate shape component A in desirable rolling situation ₂(t) and a static plate shape component A ₁(t);

Secondary plate shape static prediction model is:

$A_2\left(t\right)=F_I\left(t\right)\×K_{F_{I}2}+F_W\left(t\right)\×K_{F_{W}2}+I_{\mathrm{MR}}\left(t\right)\×K_{I_{\mathrm{MR}}2}+P\left(t\right)\×K_{P2}+T_5\left(t\right)\×K_{T_{5}2}+T_{45}\left(t\right)\×K_{T_{45}2}$ In formula:

A ₂(t)---be the static secondary plate shape component of t;

F _i(t)---be t intermediate calender rolls bending roller force mean value, unit: ton;

F _w(t)---be t work roll bending power mean value, unit: ton;

I _mR(t)---be t upper and lower intermediate calender rolls string roller amount mean value, unit: millimeter;

P (t)---be t roll-force, unit: ton;

T ₅(t)---be t 5 rack outlet tension force, unit: ton;

T ₄₅(t)---be t 4 ~ 5 interstand tension, unit: ton;

k _p2, --be respectively intermediate calender rolls bending roller force, work roll bending power, intermediate roll shifting amount, roll-force, 5 rack outlet tension force, 4 ~ 5 interstand tensions to the influence coefficient of static secondary plate shape component;

One time plate shape static prediction model is:

$A_1\left(t\right)=F_{\mathrm{ID}}\left(t\right)\×K_{F_{\mathrm{ID}}1}+F_{\mathrm{WD}}\left(t\right)\×K_{F_{\mathrm{WD}}1}+I_{\mathrm{MRD}}\left(t\right)\×K_{I_{\mathrm{MRD}}1}+I_P\left(t\right)\×K_{I_{P}1}$

In formula:

A ₁(t)---be static state plate shape component of t;

F _iD(t)---for t intermediate calender rolls bending roller force is poor, unit: ton

F _wD(t)---for t work roll bending power is poor, unit: ton;

I _mRD(t)---for t upper and lower intermediate calender rolls shifting amount is poor, unit: millimeter;

I _p(t)---be t roller declination amount, unit: micron;

---be respectively that intermediate calender rolls bending roller force is poor, work roll bending power is poor, intermediate calender rolls shifting amount is poor, roller declination amount is to the influence coefficient of a static plate shape component;

3rd step, according to dynamical prediction model, try to achieve in actual rolling situation dynamic prediction model prediction secondary plate shape component A ₂(t) and a plate shape component A ₁(t);

Secondary plate shape dynamical prediction model is:

$A_2\left(t\right)=A_2(t-1)\×K_2+{\mathrm{\ΔF}}_I\left(t\right)\×K_{F_{I}2}+{\mathrm{\ΔF}}_W\left(t\right)\×K_{F_{W}2}+{\mathrm{\ΔI}}_{\mathrm{MR}}\left(t\right)\×K_{I_{\mathrm{MR}}2}+\mathrm{\ΔP}\left(t\right)\×K_{P2}+$

${\mathrm{\ΔT}}_5\left(t\right)\×K_{T_{5}2}+{\mathrm{\ΔT}}_{45}\left(t\right)\×K_{T_{45}2}$

In formula,

A ₂(t)---be the dynamic secondary plate shape component when previous moment;

A ₂(t-1)---be the dynamic secondary plate shape component of previous moment (i.e. t-1 moment);

Δ F _i(t)---be t intermediate calender rolls bending roller force increment, unit: ton;

Δ F _w(t)---be t work roll bending power increment, unit: ton;

Δ P (t)---be t roll-force increment, unit: ton;

Δ T ₅(t)---be t 5 rack outlet tension increment, unit: ton;

Δ T ₄₅(t)---be the tension increment between t 4 ~ 5 frame, unit: ton;

One time plate shape dynamical prediction model is:

$A_1\left(t\right)=A_1(t-1)\×K_1+{\mathrm{\ΔF}}_{\mathrm{ID}}\left(t\right)\×K_{F_{\mathrm{ID}}1}+{\mathrm{\ΔF}}_{\mathrm{WD}}\left(t\right)\×K_{F_{\mathrm{WD}}1}+\mathrm{\Δ}{I}_P\left(t\right)\×K_{I_{P}1}$

In formula,

A ₁(t-1)---be the dynamic plate shape component of previous moment (i.e. t-1 moment);

Δ F _iD(t)---be t intermediate calender rolls bending roller force difference increment, Δ F _iD(t)=F _iD(t)-F _iD(t-1);

Δ F _wD(t)---be t work roll bending power difference increment, Δ F _wD(t)=F _wD(t)-F _wD(t-1);

Δ I _p(t)---be t roller declination amount increment, Δ I _p(t)=I _p(t)-I _p(t-1);

4th step, after exporting initial control signal with the comparative result of corresponding described desired value to each roll respectively with static secondary plate shape component and a static plate shape component, respectively with dynamic secondary plate shape component with a dynamic plate shape component compared with corresponding described desired value, and export dynamic control signal according to comparative result to each roll.

After adopting the present invention, control is compared owing to replacing actual measurement parameter with rational Prediction Parameters, control hysteresis when strip speed therefore can be avoided lower, thus supplementing as existing plate shape FEEDBACK CONTROL, for ensureing that the quality of cold-rolled strip steel shape creates more favorably condition.

Accompanying drawing explanation

Fig. 1 is the plat control system schematic diagram of one embodiment of the invention.

Fig. 2 is the flow chart of one embodiment of the invention control procedure.

Fig. 3 is dynamic sheet shape asymmetry part deviation measured waveform figure.In figure, abscissa is sampling instant, and ordinate is the deviation of asymmetry part A1.

Fig. 4 is dynamic sheet shape symmetrical components deviation measured waveform figure.In figure, abscissa is sampling instant, and ordinate is the deviation of symmetrical components A2.

Fig. 5 is the forecast bending roller force increment changing trend diagram that symmetrical plate shape component deviation causes.

Detailed description of the invention

The plat control system of the present embodiment as shown in Figure 1, mainly comprises control output end and is connected to respectively by interface unit the plate profile instrument 2 that the roll control computer 1 of each roll and signal output part connect roll control computer corresponding ports.Testing agency's plate shape measurement roll spacing in plate profile instrument 2 is from 5# frame (being also called for short 5 frames) outlet distance 1=3.2m, as plate shape closed-loop control cycle T=0.65s, then as the speed of service V < 282m/min of 5# rack outlet band steel, occur that flatness detection signal lag τ=1/V=3.2 ÷ 282 × 60=0.68s is greater than the situation of Strip Shape Control cycle T=0.65s.Now, computer for controlling automatically switch to Plate model prediction control method of the present invention when roll control COMPUTER DETECTION to strip speed lower than predetermined value time, carry out according to the following steps controlling (see Fig. 2):

The first step, adopt least square method to return with conic section to one group of standard actual measurement board form data of plate profile instrument collection, obtain secondary plate shape component (the i.e. quadratic term coefficient A in actual plate shape curve ₂---reflect symmetrical plate shape) and a plate shape component (i.e. Monomial coefficient A ₁---reflection asymmetric plate shape), as the desired value of Model Parameter Optimization;

This enforcement plate profile instrument adopts 40 passages, 12 passages wherein, and each passage is separated by 52mm; Everybody 14 passages of both sides, each passage is separated by 26mm.On each passage, radial load is converted to the signal of telecommunication by pressure measurement sensor, and plate shape measurement system is processed the signal of telecommunication sent and calculates, and obtains actual Shape signal.After removing noise processed, least square method is adopted to return with conic section to plate profile instrument detection signal.Actual measurement plate shape can be expressed from the next:

y(x)＝A ₀+A ₁x+A ₂x ²

In formula, y (x) is actual measurement Flatness Pattern identification fitting function, A ₀~ A ₂be constant, wherein A ₂, A ₁represent secondary plate shape component (i.e. symmetrical plate shape) and a plate shape component (i.e. asymmetric plate shape) of rolled band steel respectively, it is respective panels shape executing agency characteristic respectively, the bending roller force of the corresponding working roll of symmetrical plate shape component and intermediate calender rolls, the corresponding roller declination of asymmetric plate shape component.A ₁x represents the linear segment in actual measurement plate shape; A ₂x ²represent parabola branch in actual measurement plate shape.

Native system adopts plate profile instrument to be along the horizontal tension detect plate profile instrument being divided into 40 sections of band steel, first the normalization on plate width direction is carried out in regression process, a reference axis is regarded as by plate profile instrument, with the center of plate profile instrument for initial point, then the position of each measuring section on number axis is known, as shown in table 1.

The position of measuring section in reference axis when table 1 covers completely with steel

i	1	2	3	......	20	......	38	39	40
										x i	-1.000	-0.950	-0.900	......	-0.050	......	0.900	0.950	1.000
y i	y 1	y 2	y 3	......	y 20	......	y 38	y 39	y 40

In table 1, i is measuring section numbering, x _ibe i-th section of coordinate position on number axis, namely leave the distance at initial point (Dai Gang center), y _ifor the plate shape measured value on i-th section.If the plate of actual measurement band steel is wide can not cover all passages of plate profile instrument, then need the smallest passage min and the largest passages max that calculate effective overlay area.For the wide 847mm of plate, drafting board shape as shown in Figure 2 in fact, its smallest passage min=7, largest passages max=31.Can obtain thus:

$y_{\min }=A_0+A_1{x}_{\min }+A_2{x}_{\min }^2+e_{\min }$

. .

. .

. .

$y_{\max }=A_0+A_1{x}_{\max }+A_2{x}_{\max }^2+e_{\max }$

And remember:

$Y=\left[\begin{array}{c}{y}_{\min }\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ y_{\max }\end{array}\right],$ $X=\left[\begin{array}{ccc}1& x_{\min }& x_{\min }^2\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ 1& x_{\max }& x_{\max }^2\end{array}\right],$ $\mathrm{\&theta;}=\left[\begin{array}{c}{A}_0\\ A_1\\ A_2\end{array}\right]$

Plate shape characteristic coefficient A ₀~ A ₂error of fitting should be made to meet:

value minimum, namely to this function Q (θ)=(Y-X θ) ^t(Y-X θ) minimizing can try to achieve A ₀~ A ₂.

Second step, according to static prediction model, try to achieve the static secondary plate shape component A in desirable rolling situation ₂(t) and a static plate shape component A ₁t (), compares with corresponding target flatness component, respectively as initially controlling foundation;

Secondary plate shape static prediction model is:

$A_2\left(t\right)=F_I\left(t\right)\&times;K_{F_{I}2}+F_W\left(t\right)\&times;K_{F_{W}2}+I_{\mathrm{MR}}\left(t\right)\&times;K_{I_{\mathrm{MR}}2}+P\left(t\right)\&times;K_{P2}+T_5\left(t\right)\&times;K_{T_{5}2}+T_{45}\left(t\right)\&times;K_{T_{45}2}$ In formula:

A ₂(t)---be the static secondary plate shape component of t;

F _i(t)---be t intermediate calender rolls bending roller force mean value, namely WS side (active side) and DS side (transmission side) intermediate calender rolls bending roller force is equaled the half of sum, unit: ton (10KN);

F _w(t)---be t work roll bending power mean value, namely WS side and DS side work roll bending power is equaled the half of sum, unit: ton (10KN);

I _mR(t)---be t upper and lower intermediate calender rolls string roller amount mean value, i.e. upper and lower intermediate calender rolls string roller amount the half of sum, unit: millimeter (mm);

P (t)---be t roll-force, unit: ton (10KN);

T ₅(t)---be t 5 rack outlet tension force, unit: ton (10KN);

T ₄₅(t)---be t 4 ~ 5 interstand tension, unit: ton (10KN);

k _p2, --be respectively intermediate calender rolls bending roller force, work roll bending power, intermediate roll shifting amount, roll-force, 5 rack outlet tension force, 4 ~ 5 interstand tensions to the influence coefficient of above-mentioned secondary plate shape.(some groups of real data x values of rolling same size band steel gained and A under identical rolling condition before obtaining ₂most young waiter in a wineshop or an inn's method is adopted to take advantage of matching to obtain);

Consider that roller declination amount, work roll bending power are poor, intermediate calender rolls bending roller force difference and intermediate calender rolls string roller amount difference be to plate shape (asymmetric plate shape) component A ₁(t), setting up a plate shape static prediction model is:

$A_1\left(t\right)=F_{\mathrm{ID}}\left(t\right)\&times;K_{F_{\mathrm{ID}}1}+F_{\mathrm{WD}}\left(t\right)\&times;K_{F_{\mathrm{WD}}1}+I_{\mathrm{MRD}}\left(t\right)\&times;K_{I_{\mathrm{MRD}}1}+I_P\left(t\right)\&times;K_{I_{P}1}$

In formula:

A ₁(t)---be static state plate shape component of t;

F _iD(t)---for t intermediate calender rolls bending roller force is poor, unit: ton (10KN);

F _wD(t)---for t work roll bending power is poor, unit: ton (10KN);

I _mRD(t)---for t upper and lower intermediate calender rolls shifting amount is poor, unit: millimeter (mm);

I _p(t)---be t roller declination amount, unit: micron (um);

---be respectively that intermediate calender rolls bending roller force is poor, work roll bending power is poor, intermediate calender rolls shifting amount is poor, roller declination amount is to the influence coefficient of an above-mentioned plate shape (some groups of real data x of rolling same size band steel gained and A under identical rolling condition before obtaining ₁most young waiter in a wineshop or an inn's method is adopted to take advantage of matching to obtain);

3rd step, the subsequent time prediction of plate shape value precision obtained due to plate shape static prediction model can not meet actual requirement, are incorporated herein previous moment plate shape component, build plate shape dynamical prediction model.After introducing the plate shape component of previous moment, with the plate shape components subtract of current time, quite and the increment of lead-in plate shape component.According to dynamical prediction model, try to achieve the secondary plate shape component A of the dynamic prediction model prediction in actual rolling situation ₂(t) and a plate shape component A ₁(t), and compare, as the foundation of dynamic prediction model parameters revision with corresponding actual measurement plate shape component respectively;

Secondary plate shape dynamical prediction model is:

$A_2\left(t\right)=A_2(t-1)\&times;K_2+{\mathrm{\&Delta;F}}_I\left(t\right)\&times;K_{F_{I}2}+{\mathrm{\&Delta;F}}_W\left(t\right)\&times;K_{F_{W}2}+{\mathrm{\&Delta;I}}_{\mathrm{MR}}\left(t\right)\&times;K_{I_{\mathrm{MR}}2}+\mathrm{\&Delta;P}\left(t\right)\&times;K_{P2}+$

${\mathrm{\&Delta;T}}_5\left(t\right)\&times;K_{T_{5}2}+{\mathrm{\&Delta;T}}_{45}\left(t\right)\&times;K_{T_{45}2}$

In formula,

A ₂(t)---be the dynamic secondary plate shape component when previous moment;

A ₂(t-1)---be the dynamic secondary plate shape component of previous moment (i.e. t-1 moment);

Δ F _i(t)---be t intermediate calender rolls bending roller force increment;

Δ F _w(t)---be t work roll bending power increment;

Δ P (t)---be t roll-force increment;

Δ T ₅(t)---be t 5 rack outlet tension increment;

Δ T ₄₅(t)---be the tension increment between t 4 ~ 5 frame;

One time plate shape dynamical prediction model is:

$A_1\left(t\right)=A_1(t-1)\&times;K_1+{\mathrm{\&Delta;F}}_{\mathrm{ID}}\left(t\right)\&times;K_{F_{\mathrm{ID}}1}+{\mathrm{\&Delta;F}}_{\mathrm{WD}}\left(t\right)\&times;K_{F_{\mathrm{WD}}1}+\mathrm{\&Delta;}{I}_P\left(t\right)\&times;K_{I_{P}1}$

In formula,

A ₁(t-1)---be the dynamic plate shape component of previous moment (i.e. t-1 moment);

Δ F _iD(t)---be t intermediate calender rolls bending roller force difference increment, Δ F _iD(t)=F _iD(t)-F _iD(t-1);

Δ F _wD(t)---be t work roll bending power difference increment, Δ F _wD(t)=F _wD(t)-F _wD(t-1);

Δ I _p(t)---be t roller declination amount increment, Δ I _p(t)=I _p(t)-I _p(t-1);

4th step, using static secondary plate shape component and a static plate shape component after control initial value, respectively with dynamic secondary plate shape component with a dynamic plate shape component compared with corresponding desired value, and export corresponding controling parameters according to comparative result.

Because the increment of work roll bending power is all consistent with the action effect of the increment of intermediate calender rolls bending roller force and variation tendency, these two amounts can be merged into a controlled quentity controlled variable increment: Δ F (t)=α Δ F _i(t)+(1-α) Δ F _wt (), wherein α=0 ~ 1, get 0.5 in current procedure, this can adjust according to on-the-spot plate shape situation.Meanwhile, due in the actual operation of rolling, intermediate roll shifting amount is generally substantially constant, and therefore secondary plate shape dynamical prediction model can be reduced to:

$A_2\left(t\right)=A_2(t-1)\&times;K_2+\mathrm{\&Delta;F}\left(t\right)\&times;K_{F2}+\mathrm{\&Delta;P}\left(t\right)\&times;K_{P2}+\mathrm{\&Delta;}{T}_5\left(t\right)\&times;K_{T_{5}2}+{\mathrm{\&Delta;T}}_{45}\left(t\right)\&times;K_{T_{45}2}.$

In like manner, its plate shape dynamical prediction model is:

$A_1\left(t\right)=A_1(t-1)\&times;K_1+{\mathrm{\&Delta;F}}_{\mathrm{ID}}\left(t\right)\&times;K_{F_{\mathrm{ID}}1}+{\mathrm{\&Delta;F}}_{\mathrm{WD}}\left(t\right)\&times;K_{F_{\mathrm{WD}}1}+{\mathrm{\&Delta;I}}_P\left(t\right)\&times;K_{I_{P}1}$

4th step, using static secondary plate shape component and a static plate shape component after control initial value, respectively with dynamic secondary plate shape component with a dynamic plate shape component compared with corresponding desired value, and export corresponding controling parameters according to comparative result.

After this, circulate above control procedure, thus Strip Shape Control is constantly optimized.

Parameter on the right of above-mentioned each formula equal sign of the present embodiment can obtain respectively by prior art means such as actual measurement, data processings, after therefore bringing formula into, can obtain required calculated value.

Utilize the actual production data of a certain coiled strip steel, adopt the method for the present embodiment to carry out simulation calculation.Under condition in the speed difference 10m/min in former and later two moment, Fig. 3 and Fig. 4 sets forth asymmetric plate shape component deviation and the symmetrical model prediction value of plate shape symmetrical components deviation and the relative error of actual value, and Fig. 5 gives the change trend curve of the work roll bending power increment that result rolling optimization obtains.As can be seen from Fig. 3 and Fig. 4, the relative error of asymmetric plate shape component and symmetrical plate shape component is substantially all less than 10-1 (namely 10%) and illustrates that plate shape dynamical prediction model can improve prediction of plate shape precision effectively, meets the required precision of plat control system.

In a word, the present embodiment carries out least square method by sheet shape measurer actual measurement and carries out recurrence decomposition with conic section after the plate shape of band steel transmission delay, then together with the mode input amount in corresponding moment, forecast model coefficient is revised, through revised model coefficient, be used for by Strip Shape Control amount on the one hand, roll-force and tension force etc. is pre-drafting board shape more accurately, be used for dynamic optimal ground on the other hand and determine Strip Shape Control amount, realize the Accurate Prediction of plate shape and the dynamic optimum control of plate shape, improve strip shape quality and the lumber recovery of band steel, improve stability and the reliability of milling train operation simultaneously.

Claims (3)

1. a Plate model prediction control method, primarily of control five stand mill roll control computer and be arranged in final milling train output plate profile instrument form control system, the signal output part of described plate profile instrument connects the corresponding ports of roll control computer, and the control output end of described roll control computer is connected to the controlled end of each roll respectively; When roll control COMPUTER DETECTION to strip speed lower than predetermined value time, carry out basic Recurrence Process according to the following steps and control:

The first step, data processing is carried out to one group of standard actual measurement board form data of plate profile instrument collection, obtain the secondary plate shape component in actual plate shape curve and a plate shape component, as the desired value of Model Parameter Optimization;

Second step, according to static prediction model, try to achieve the static secondary plate shape component A in desirable rolling situation _2s(t) and a static plate shape component A _1s(t);

Secondary plate shape static prediction model is:

$A_{2s}\left(t\right)=F_I\left(t\right)\&times;K_{F_{I}2}+F_W\left(t\right)\&times;K_{F_{W}2}+I_{\mathrm{MR}}\left(t\right)\&times;K_{I_{\mathrm{MR}}2}+P\left(t\right)\&times;K_{P2}+T_5\left(t\right)\&times;K_{T_{5}2}+T_{45}\left(t\right)\&times;K_{T_{45}2}$

In formula:

A _2s(t)---be the static secondary plate shape component of t;

F _i(t)---be t intermediate calender rolls bending roller force mean value, unit: ton;

F _w(t)---be t work roll bending power mean value, unit: ton;

I _mR(t)---be t upper and lower intermediate roll shifting amount mean value, unit: millimeter;

P (t)---be t roll-force, unit: ton;

T ₅(t)---be t 5 rack outlet tension force, unit: ton;

T ₄₅(t)---be t 4 ~ 5 interstand tension, unit: ton;

--be respectively intermediate calender rolls bending roller force, work roll bending power, intermediate roll shifting amount, roll-force, 5 rack outlet tension force, 4 ~ 5 interstand tensions to the influence coefficient of static secondary plate shape component;

One time plate shape static prediction model is:

$A_{1s}\left(t\right)=F_{\mathrm{ID}}\left(t\right)\&times;K_{F_{\mathrm{ID}}1}+F_{\mathrm{WD}}\left(t\right)\&times;K_{F_{\mathrm{WD}}1}+I_{\mathrm{MRD}}\left(t\right)\&times;K_{I_{\mathrm{MRD}}1}+I_P\left(t\right)\&times;K_{I_{P}1}$

In formula:

A _1s(t)---be static state plate shape component of t;

F _iD(t)---for t intermediate calender rolls bending roller force is poor, unit: ton

F _wD(t)---for t work roll bending power is poor, unit: ton;

I _mRD(t)---for t upper and lower intermediate roll shifting amount is poor, unit: millimeter;

I _p(t)---be t roller declination amount, unit: micron;

---be respectively that intermediate calender rolls bending roller force is poor, work roll bending power is poor, intermediate roll shifting amount is poor, roller declination amount is to the influence coefficient of a static plate shape component;

3rd step, according to dynamical prediction model, try to achieve in actual rolling situation dynamic prediction model prediction secondary plate shape component A ₂(t) and a plate shape component A ₁(t);

Secondary plate shape dynamical prediction model is:

$\begin{array}{c}{A}_2\left(t\right)=A_2(t-1)\&times;K_2+\mathrm{\&Delta;}{F}_I\left(t\right)\&times;K_{F_{I}2}+\mathrm{\&Delta;}{F}_W\left(t\right)\&times;K_{F_{W}2}+\mathrm{\&Delta;}{I}_{\mathrm{MR}}\left(t\right)\&times;K_{I_{\mathrm{MR}}2}+\mathrm{\&Delta;P}\left(t\right)\&times;K_{P2}+\\ \mathrm{\&Delta;}{T}_5\left(t\right)\&times;K_{T_{5}2}+\mathrm{\&Delta;}{T}_{45}\left(t\right)\&times;K_{T_{45}2}\end{array}$

In formula,

A ₂(t)---be the dynamic secondary plate shape component of t;

A ₂(t-1)---be the dynamic secondary plate shape component in t-1 moment;

Δ F _i(t)---be t intermediate calender rolls bending roller force increment, unit: ton;

Δ F _w(t)---be t work roll bending power increment, unit: ton;

Δ I _mR(t)---be t upper and lower intermediate roll shifting amount mean value increment, unit: millimeter;

Δ P (t)---be t roll-force increment, unit: ton;

Δ T ₅(t)---be t 5 rack outlet tension increment, unit: ton;

Δ T ₄₅(t)---be the tension increment between t 4 ~ 5 frame, unit: ton;

K ₂---be t-1 moment secondary plate shape influence coefficient;

One time plate shape dynamical prediction model is:

$A_1\left(t\right)=A_1(t-1)\&times;K_1+\mathrm{\&Delta;}{F}_{\mathrm{ID}}\left(t\right)\&times;K_{F_{\mathrm{ID}}1}+\mathrm{\&Delta;}{F}_{\mathrm{WD}}\left(t\right)\&times;K_{F_{\mathrm{WD}}1}+\mathrm{\&Delta;}{I}_{\mathrm{MRD}}\left(t\right)\&times;K_{I_{\mathrm{MRD}}1}+\mathrm{\&Delta;}{I}_P\left(t\right)\&times;K_{I_{P}1}$

In formula,

A ₁(t)---be the dynamic plate shape component of t;

A ₁(t-1)---a t-1 moment dynamic plate shape component;

Δ F _iD(t)---be t intermediate calender rolls bending roller force difference increment, Δ F _iD(t)=F _iD(t)-F _iD(t-1);

Δ F _wD(t)---be t work roll bending power difference increment, Δ F _wD(t)=F _wD(t)-F _wD(t-1);

Δ I _mRD(t)---be t upper and lower intermediate roll shifting amount difference increment, unit: millimeter;

Δ I _p(t)---be t roller declination amount increment, Δ I _p(t)=I _p(t)-I _p(t-1);

K ₁---be t-1 moment plate shape influence coefficient;

4th step, after exporting initial control signal with the comparative result of corresponding described desired value to each roll respectively with static secondary plate shape component and a static plate shape component, respectively with dynamic secondary plate shape component with a dynamic plate shape component compared with corresponding described desired value, and export dynamic control signal according to comparative result to each roll.

2. Plate model prediction control method according to claim 1, is characterized in that: described predetermined value is the plate shape closed-loop control cycle.

3. Plate model prediction control method according to claim 2, is characterized in that: the data processing in the described first step returns with conic section for adopting least square method.