CN115591947B - Distributed regulation and control method for strip quality in continuous rolling process - Google Patents

Distributed regulation and control method for strip quality in continuous rolling process Download PDF

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CN115591947B
CN115591947B CN202211609053.4A CN202211609053A CN115591947B CN 115591947 B CN115591947 B CN 115591947B CN 202211609053 A CN202211609053 A CN 202211609053A CN 115591947 B CN115591947 B CN 115591947B
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rack
data set
quality
process variables
variables
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CN115591947A (en
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姬亚锋
薛霖
马立峰
郭宇会
周娜
王娟
孙杰
彭文
孟媛
刘佩艳
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Taiyuan University of Science and Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B1/00—Metal-rolling methods or mills for making semi-finished products of solid or profiled cross-section; Sequence of operations in milling trains; Layout of rolling-mill plant, e.g. grouping of stands; Succession of passes or of sectional pass alternations
    • B21B1/22—Metal-rolling methods or mills for making semi-finished products of solid or profiled cross-section; Sequence of operations in milling trains; Layout of rolling-mill plant, e.g. grouping of stands; Succession of passes or of sectional pass alternations for rolling plates, strips, bands or sheets of indefinite length
    • B21B1/24—Metal-rolling methods or mills for making semi-finished products of solid or profiled cross-section; Sequence of operations in milling trains; Layout of rolling-mill plant, e.g. grouping of stands; Succession of passes or of sectional pass alternations for rolling plates, strips, bands or sheets of indefinite length in a continuous or semi-continuous process
    • B21B1/26—Metal-rolling methods or mills for making semi-finished products of solid or profiled cross-section; Sequence of operations in milling trains; Layout of rolling-mill plant, e.g. grouping of stands; Succession of passes or of sectional pass alternations for rolling plates, strips, bands or sheets of indefinite length in a continuous or semi-continuous process by hot-rolling, e.g. Steckel hot mill
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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Abstract

The invention discloses a distributed regulation and control method for the quality of a strip in a continuous rolling process, which comprises the following specific steps: acquiring historical data of a finish rolling process, including process variables and quality indexes; dividing the finish rolling flow into 7 subsystems, and obtaining an optimal data set of process variables and quality indexes of the subsystems according to the maximum correlation and minimum redundancy screening principle; mapping the optimal data set to a high-dimensional feature space by adopting a mixed kernel function method; and constructing a multi-system distributed coordination control model according to the optimal data set mapped into the high-dimensional feature space, introducing a group intelligent optimization algorithm, and realizing coordination control among the racks by adopting a cyclic correction strategy. The invention fully utilizes the prior rolling information, introduces a group intelligent optimization algorithm and a cyclic correction strategy, improves the coordination among the racks and meets the requirement of distributed regulation and control.

Description

Distributed regulation and control method for strip quality in continuous rolling process
Technical Field
The invention belongs to the field of rolling technology and control science and engineering, and particularly relates to a distributed regulation and control method for the quality of a strip in a continuous rolling process.
Background
The traditional control method for the hot continuous rolling process is distributed control based on a PID (proportion integration differentiation) controller, cannot systematically consider the coupling effect between production line parts, and is not beneficial to globally realizing the performance optimization of closed-loop control. The hot continuous rolling process is a typical multivariable, strong-coupling and nonlinear industrial production process, and the quality requirement of the market on hot rolled strips is higher and higher along with the high-end and intelligent transformation of the manufacturing industry in China. The plate shape and the plate thickness are important quality indexes which are particularly concerned by users, the plate shape and the plate thickness influence each other in actual production, the control effect of a hot continuous rolling production line is nearly limited due to the development of industrialization and informatization, the pursuit of quality is endless, and some key problems related to a rolling process are not completely solved. In terms of quality control, there have been many reports on the control of thickness and strip shape, and furthermore, the distributed control research for hot continuous rolling lines is also tedious. Therefore, timely introduction of advanced control strategies and presentation of novel distributed control systems have important guiding significance for improving the robustness of the whole production process and improving the quality of hot rolled strips.
Disclosure of Invention
In order to solve the problems, the invention provides a strip quality distributed control method in a continuous rolling process, which improves the performance of a hot continuous rolling strip quality control system and improves the quality of a hot continuous rolling strip steel product to a certain extent.
In order to achieve the aim, the invention provides a strip quality distributed regulation and control method in a continuous rolling process, which comprises the following steps:
acquiring historical data of a finish rolling process, wherein the historical data comprises: process variables and quality indicators;
dividing the finish rolling flow into 7 subsystems, dividing the process variables into different subsystems respectively, and obtaining an optimal data set of the process variables and quality indexes in each subsystem according to a maximum correlation and minimum redundancy screening principle;
mapping the preferred data set to a high-dimensional feature space by adopting a mixed kernel function method;
constructing a multi-system distributed coordination control model according to the preferred data set in the high-dimensional feature space;
and introducing a group intelligent optimization algorithm based on the multi-system distributed coordination control model, and realizing coordination control among the racks by adopting a cyclic correction strategy.
Preferably, the obtained historical data of the finish rolling process is as follows: rolling the plate strips of the same steel type, different steel grades, the same steel grade, different steel grades and different plate thicknesses to generate data; the process variables comprise rolling force, roll gap value, rolling speed, roll bending force and roll shifting amount; the quality indexes comprise thickness, width of the plate strip, convexity of the plate strip and flatness of the plate strip.
Preferably, the 7 subsystems dividing the finish rolling flow include: the device comprises a first rack, a second rack, a third rack, a fourth rack, a fifth rack, a sixth rack and a seventh rack.
Preferably, the method for dividing the process variables into different subsystems comprises the following steps:
Figure 228527DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 817772DEST_PATH_IMAGE002
a data set representing process variables of the process,
Figure 535192DEST_PATH_IMAGE003
a data set consisting of process variables for a first rack process,
Figure 563626DEST_PATH_IMAGE004
a data set consisting of process variables for the second rack,
Figure 253364DEST_PATH_IMAGE005
a data set consisting of process variables for the third rack,
Figure 810247DEST_PATH_IMAGE006
a data set consisting of process variables for the fourth rack,
Figure 14964DEST_PATH_IMAGE007
a data set consisting of process variables for the fifth rack,
Figure 885968DEST_PATH_IMAGE008
a data set consisting of process variables for the sixth rack,
Figure 226950DEST_PATH_IMAGE009
a data set consisting of process variables for the seventh rack;
Figure 892418DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 443485DEST_PATH_IMAGE011
a data set representing the quality indicator is presented,
Figure 852601DEST_PATH_IMAGE012
and the thickness and the convexity of the finished product corresponding to the quality index data set are represented.
Preferably, the method for obtaining the optimal data set of the process variables and the quality indexes in each subsystem according to the maximum correlation and minimum redundancy screening principle comprises the following steps:
obtaining the incidence relation between the process variable and the quality index in each subsystem according to the maximum correlation screening principle;
obtaining the redundancy relation between the process variables and the quality indexes in each subsystem according to the minimum redundancy screening principle;
respectively introducing a joint measurement coefficient to the process variable and the quality index in each subsystem based on the incidence relation and the redundancy relation to obtain an optimal data set of the process variable in each subsystem
Figure 48090DEST_PATH_IMAGE013
And a preferred data set of said quality indicators
Figure 415617DEST_PATH_IMAGE014
；
The formula corresponding to the maximum correlation principle is as follows:
Figure 126084DEST_PATH_IMAGE015
in the formula (I), wherein,
Figure 338891DEST_PATH_IMAGE016
represented as a set of input variables,
Figure 920045DEST_PATH_IMAGE017
a set of output variables is represented that are,
Figure 192895DEST_PATH_IMAGE018
and
Figure 266024DEST_PATH_IMAGE019
are respectively as
Figure 282521DEST_PATH_IMAGE016
And
Figure 983761DEST_PATH_IMAGE017
the elements (A) and (B) in (B),
Figure 427512DEST_PATH_IMAGE020
is defined as
Figure 581413DEST_PATH_IMAGE018
And
Figure 136022DEST_PATH_IMAGE019
maximum information coefficient therebetween;
the expression of the incidence relation between the process variables and the quality indexes is as follows:
Figure 160610DEST_PATH_IMAGE022
，
Figure 899896DEST_PATH_IMAGE023
in the formula (I), wherein,
Figure 275513DEST_PATH_IMAGE024
representing a set of process variables for each subsystem,
Figure 368234DEST_PATH_IMAGE025
a set of quality indicators is represented, and,
Figure 512908DEST_PATH_IMAGE026
and
Figure 564041DEST_PATH_IMAGE027
are respectively as
Figure 20430DEST_PATH_IMAGE028
And
Figure 916842DEST_PATH_IMAGE025
the elements (A) and (B) in (B),
Figure 650442DEST_PATH_IMAGE020
is defined as
Figure 606897DEST_PATH_IMAGE026
And
Figure 816161DEST_PATH_IMAGE027
maximum information coefficient therebetween;
the formula corresponding to the minimum redundancy screening principle is as follows:
Figure 516264DEST_PATH_IMAGE029
in the formula (I), wherein,
Figure 369951DEST_PATH_IMAGE016
in order to input the set of variables,
Figure 231727DEST_PATH_IMAGE018
and
Figure 662709DEST_PATH_IMAGE030
is composed of
Figure 900923DEST_PATH_IMAGE016
The elements (A) and (B) in (B),
Figure 874695DEST_PATH_IMAGE020
is composed of
Figure 32007DEST_PATH_IMAGE018
And
Figure 91230DEST_PATH_IMAGE030
maximum information coefficient therebetween;
the expression of the redundancy relationship between the process variables and the quality indexes is as follows:
Figure 133136DEST_PATH_IMAGE031
，
Figure 695835DEST_PATH_IMAGE032
in the formula (I), wherein,
Figure 289627DEST_PATH_IMAGE028
represents a set of process control variables for each subsystem,
Figure 570567DEST_PATH_IMAGE025
a set of quality indicators is represented, and,
Figure 416163DEST_PATH_IMAGE026
and
Figure 958003DEST_PATH_IMAGE033
is that
Figure 598063DEST_PATH_IMAGE024
The elements (A) and (B) in (B),
Figure 366299DEST_PATH_IMAGE027
and
Figure 15586DEST_PATH_IMAGE034
is that
Figure 552878DEST_PATH_IMAGE025
The elements (A) and (B) in (B),
Figure 488473DEST_PATH_IMAGE020
represents the maximum information coefficient between elements;
the joint measurement coefficient introduced by the process variable is as follows:
Figure 744005DEST_PATH_IMAGE035
；
the joint measurement coefficient introduced by the quality index is as follows:
Figure 665824DEST_PATH_IMAGE036
。
preferably, the expression of the mixing kernel function is:
Figure 323202DEST_PATH_IMAGE038
in the formula (I), the compound is shown in the specification,
Figure 429698DEST_PATH_IMAGE039
representing process variables
Figure 172526DEST_PATH_IMAGE040
And
Figure 898037DEST_PATH_IMAGE041
the mixing kernel function of (a) is,
Figure 409921DEST_PATH_IMAGE042
indicating quality index
Figure 687318DEST_PATH_IMAGE040
And
Figure 651863DEST_PATH_IMAGE041
the mixing kernel function of (a) is,
Figure 181065DEST_PATH_IMAGE043
and
Figure 547455DEST_PATH_IMAGE044
is at a subsystem
Figure 995754DEST_PATH_IMAGE045
The process variables of (1) are set,
Figure 713174DEST_PATH_IMAGE046
in order to be a quality index,
Figure 780487DEST_PATH_IMAGE047
、
Figure 1384DEST_PATH_IMAGE048
、
Figure 620584DEST_PATH_IMAGE049
are parameters of the corresponding kernel function, wherein,
Figure 559722DEST_PATH_IMAGE047
is a bandwidth parameter, the value of which is greater than 0, and
Figure 430726DEST_PATH_IMAGE050
。
preferably, the method for constructing the multi-system distributed coordination control model according to the preferred data set in the high-dimensional feature space comprises the following steps:
Figure 630763DEST_PATH_IMAGE051
in the formula (I), the compound is shown in the specification,
Figure 561810DEST_PATH_IMAGE052
is composed of
Figure 988243DEST_PATH_IMAGE053
And
Figure 397359DEST_PATH_IMAGE054
a combined input-output matrix, wherein,
Figure 717482DEST_PATH_IMAGE053
is a matrix composed of high-dimensional features whose process parameters are mapped to a high-dimensional space through a mixed kernel function,
Figure 819430DEST_PATH_IMAGE054
is a matrix composed of high-dimensional features which are mapped to a high-dimensional space through a mixed kernel function,
Figure 467580DEST_PATH_IMAGE055
for future output matrices, i.e. quality index matrices,
Figure 922528DEST_PATH_IMAGE056
for future input matrices, i.e. process variable matrices,
Figure 238103DEST_PATH_IMAGE057
and
Figure 370007DEST_PATH_IMAGE058
respectively, are the control matrices of the corresponding matrices,
Figure 771033DEST_PATH_IMAGE059
there is an unknown disturbance between the process variable and the quality index.
Preferably, a group intelligent optimization algorithm is introduced, a cyclic correction strategy is adopted, and the method for realizing coordination control among the racks comprises the following steps:
setting a comprehensive objective function related to the thickness and the plate shape and the dimension of an individual, wherein the dimension of the individual is set to be 14 dimensions, namely the outlet thickness and the convexity of 7 racks;
optimizing the outlet thickness and the convexity of each rack by taking the comprehensive objective function as a reference, and obtaining the optimal outlet thickness and the optimal convexity value of each rack after optimizing;
checking whether the outlet thickness and convexity of the end frame meet the comprehensive objective function, if so, terminating iteration, and otherwise, continuing the iteration until the error is within an allowable range;
after the iteration is ended, according to the optimal outlet thickness and the optimal convexity value of each rack, the rolling force, the roll bending force and the roll shifting amount applied by each rack are reversely deduced by combining the multi-system distributed coordination control model, and the specific strokes of the hydraulic cylinder and the roll bending cylinder are reversely deduced by combining the specific numerical values of the rolling force, the roll bending force and the roll shifting amount, so that the regulation and control of the process variables of the corresponding process are completed;
after the regulation and control of the process variables of the corresponding process are finished, the deviation vectors existing in the measured values and the target values of the thickness and the convexity of the outlet of the finished product are obtained through the measurement of a multifunctional instrument
Figure 521951DEST_PATH_IMAGE060
(ii) a According to a regulation matrix
Figure 692032DEST_PATH_IMAGE061
Calculating the adjustment amount required for eliminating the corresponding deviation vector
Figure 729258DEST_PATH_IMAGE062
(ii) a The production process is regulated and controlled in a circulating and reciprocating manner by taking time as a variable, so that the load distribution of each rack and the dynamic coordination among the racks are realized;
the expression of the comprehensive objective function is as follows:
Figure 883159DEST_PATH_IMAGE063
wherein, in the process,
Figure 437768DEST_PATH_IMAGE064
indicating the best output in the future, i.e. the best control effect set by the control system, of
Figure 462356DEST_PATH_IMAGE065
The corresponding optimal outlet thickness in the quality index is represented,
Figure 201642DEST_PATH_IMAGE066
the corresponding optimal plate shape in the quality index is shown.
Compared with the prior art, the invention has the following advantages and technical effects:
the invention provides a distributed regulation and control method for strip quality in a continuous rolling process, which screens data by using acquired field data and adopting the maximum correlation and minimum redundancy principle, and maps process variables and quality indexes in the data into a high-dimensional characteristic space by using a mixed kernel function method to lay a foundation for a multi-system distributed coordination control model. An objective function related to the thickness and the plate shape is established through a designed multi-system distributed coordination control model, and the thickness and the convexity of each rack are further optimized by using a group intelligent optimization algorithm with the objective function as a reference. And after the iteration of the algorithm is terminated, reversely deducing the rolling force, the roll bending force and the roll shifting amount of each rack through the thickness and the convexity of each rack, and then compensating and correcting the rolling force, the roll bending force and the roll shifting amount of each rack by adopting a cyclic correction strategy to realize the load distribution of each rack and the dynamic coordination among the racks.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a control schematic of an embodiment of the present invention;
FIG. 2 is a graphical representation of interaction information between a subsystem and a controller in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of an optimization algorithm for group intelligence optimization according to an embodiment of the present invention;
fig. 4 is a schematic configuration diagram of a distributed system process automation level according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
As shown in fig. 1-4, the invention provides a distributed regulation and control method for the quality of a strip in a continuous rolling process, which comprises the following steps:
s1: acquiring historical data of a finish rolling process, wherein the historical data comprises: process variables and quality indicators;
s2: dividing the finish rolling flow into 7 subsystems, dividing the process variables and the quality indexes into different subsystems respectively, and obtaining an optimal data set of the process variables and the quality indexes in each subsystem according to a maximum correlation and minimum redundancy screening principle;
s3: mapping the preferred data set to a high-dimensional feature space by adopting a hybrid kernel function method;
s4: constructing a multi-system distributed coordination control model according to the preferred data set in the high-dimensional feature space;
s5: and introducing a group intelligent optimization algorithm based on the multi-system distributed coordination control model, and realizing coordination control among the racks by adopting a cyclic correction strategy.
The acquiring of the historical data of the finish rolling process comprises the following steps:
s1.1, production data of the metallurgical industry, especially the strip steel production industry, has the characteristics of multiple isomerism, large volume, multiple variables and multiple granularities, data acquisition generally manages and configures the data by taking a real-time database as a basic data platform depending on an actual production field, and corresponding data are acquired by adopting a standard I/O driving interface provided by the real-time data platform based on data acquisition protocols such as OPC, ODBC and the like;
s1.2, rolling the data generated by the plate strips of the same steel type, different steel grades, the same steel grade, different steel grades and different plate thicknesses; the process variables comprise rolling force, roll gap value, rolling speed, roll bending force and roll shifting quantity, and the quality indexes comprise thickness, width of the plate strip, convexity of the plate strip and flatness of the plate strip.
The step of dividing the finish rolling flow into 7 subsystems comprises the following steps:
s2.1, a first rack, a second rack, a third rack, a fourth rack, a fifth rack, a sixth rack and a seventh rack;
s2.2, dividing the process variables into different subsystems to be represented as:
Figure 311681DEST_PATH_IMAGE001
，
Figure 404402DEST_PATH_IMAGE010
. Wherein the content of the first and second substances,
Figure 549075DEST_PATH_IMAGE002
a data set representing process variable data of a process,
Figure 334628DEST_PATH_IMAGE003
a data set consisting of process variables for a first rack process,
Figure 56597DEST_PATH_IMAGE004
a data set consisting of process variables for the second rack,
Figure 953009DEST_PATH_IMAGE005
a data set consisting of process variables for the third rack,
Figure 952189DEST_PATH_IMAGE006
a data set consisting of process variables for the fourth rack,
Figure 174223DEST_PATH_IMAGE007
a data set consisting of process variables for the fifth rack,
Figure 258853DEST_PATH_IMAGE008
a data set consisting of process variables for the sixth rack,
Figure 818011DEST_PATH_IMAGE009
a data set consisting of process variables for the seventh rack;
Figure 937276DEST_PATH_IMAGE011
representing a quality indicator data set.
Figure 64632DEST_PATH_IMAGE012
And the thickness and the convexity of the finished product corresponding to the quality index data set are represented.
S2.3, obtaining the optimal data set of the process variables and the quality indexes in the subsystem according to the maximum correlation and minimum redundancy screening principle, wherein the optimal data set comprises the following steps: the maximum correlation principle is used for establishing a strong correlation relationship between process variables and quality indexes, and the minimum redundancy is used for avoiding mutual influence between the process variables to the maximum extent;
wherein, the maximum correlation corresponding formula is:
Figure 636559DEST_PATH_IMAGE067
in the formula (I), wherein,
Figure 874774DEST_PATH_IMAGE016
represented as a set of input variables,
Figure 707600DEST_PATH_IMAGE017
a set of output variables is represented that are,
Figure 5858DEST_PATH_IMAGE018
and
Figure 330660DEST_PATH_IMAGE019
are respectively as
Figure 106986DEST_PATH_IMAGE016
And
Figure 794319DEST_PATH_IMAGE017
the elements (A) and (B) in (B),
Figure 263478DEST_PATH_IMAGE020
is defined as
Figure 544417DEST_PATH_IMAGE018
And
Figure 390014DEST_PATH_IMAGE019
the largest information coefficient in between. Migrating the above formula to the present invention is represented as:
Figure 197433DEST_PATH_IMAGE021
Figure 837493DEST_PATH_IMAGE023
in the formula (I), the compound is shown in the specification,
Figure 605728DEST_PATH_IMAGE024
representing a set of process variables for each subsystem,
Figure 723857DEST_PATH_IMAGE025
a set of quality indicators is represented, and,
Figure 651362DEST_PATH_IMAGE026
and
Figure 462323DEST_PATH_IMAGE027
are respectively as
Figure 717855DEST_PATH_IMAGE028
And
Figure 639675DEST_PATH_IMAGE025
the elements in (1), similarly,
Figure 562631DEST_PATH_IMAGE020
is defined as
Figure 669128DEST_PATH_IMAGE026
And
Figure 411956DEST_PATH_IMAGE027
the largest information coefficient in between. The invention establishes the correlation between the process variables and the quality indexes in a two-way manner, and is the basis for subsequently establishing a multi-system distributed coordination control model;
the formula corresponding to the minimum redundancy is:
Figure 137466DEST_PATH_IMAGE068
in the formula (I), wherein,
Figure 649350DEST_PATH_IMAGE016
in order to input the set of variables,
Figure 926748DEST_PATH_IMAGE018
and
Figure 891293DEST_PATH_IMAGE030
is composed of
Figure 420494DEST_PATH_IMAGE016
The elements (A) and (B) in (B),
Figure 786884DEST_PATH_IMAGE020
is composed of
Figure 235183DEST_PATH_IMAGE018
And
Figure 952604DEST_PATH_IMAGE030
the largest information coefficient in between. Migrating the above formula to the present invention is represented as:
Figure 19917DEST_PATH_IMAGE031
Figure 506393DEST_PATH_IMAGE069
in the formula (I), the compound is shown in the specification,
Figure 266539DEST_PATH_IMAGE024
representing sets of process control variables for each subsystem，
Figure 64730DEST_PATH_IMAGE025
A set of quality indicators is represented, and,
Figure 935734DEST_PATH_IMAGE026
and
Figure 11138DEST_PATH_IMAGE033
is that
Figure 66818DEST_PATH_IMAGE024
The elements (A) and (B) in (B),
Figure 227672DEST_PATH_IMAGE027
and
Figure 902367DEST_PATH_IMAGE034
is that
Figure 97856DEST_PATH_IMAGE025
The elements (A) and (B) in (B),
Figure 58859DEST_PATH_IMAGE020
represents the maximum information coefficient between elements;
introducing joint metric coefficients
Figure 238168DEST_PATH_IMAGE070
Let us order
Figure 450974DEST_PATH_IMAGE071
By definition, the stronger the correlation, the weaker the redundancy, and the better the matching degree of the process parameters and the quality indexes.
For process variables, there are
Figure 500970DEST_PATH_IMAGE072
For the quality index, there are
Figure 773820DEST_PATH_IMAGE073
In the same way as above, the first and second,
Figure 33900DEST_PATH_IMAGE074
obtaining a preferred data set by the above method
Figure 784818DEST_PATH_IMAGE013
And
Figure 954899DEST_PATH_IMAGE014
，
Figure 257705DEST_PATH_IMAGE074
，
Figure 146026DEST_PATH_IMAGE016
is composed of
Figure 700635DEST_PATH_IMAGE075
Abbreviations of (a).
The method adopting the mixed kernel function integrates the preferred data
Figure 725223DEST_PATH_IMAGE013
And
Figure 198930DEST_PATH_IMAGE014
respectively mapping to high-dimensional feature spaces, including:
s3.1, the mixed kernel function is an integrated form of a radial basis kernel function and a Sigmoid kernel function, and is expressed as follows:
Figure 308968DEST_PATH_IMAGE076
，
Figure 667269DEST_PATH_IMAGE077
in order to be a function of the mixing kernel,
Figure 546363DEST_PATH_IMAGE078
in order to be a function of the radial basis kernel,
Figure 456550DEST_PATH_IMAGE079
is Sigmoid coreThe function of the function is that of the function,
Figure 53885DEST_PATH_IMAGE080
the weight is represented, the mixed kernel function has stronger generalization capability compared with the kernel function, and the established multi-system distributed control model is more stable based on the data mapped to the high-dimensional space by the mixed kernel function. Migration to the present invention is represented as:
Figure 950296DEST_PATH_IMAGE082
in the formula (I), the compound is shown in the specification,
Figure 949476DEST_PATH_IMAGE039
representing process variables
Figure 764986DEST_PATH_IMAGE040
And
Figure 584037DEST_PATH_IMAGE041
the mixing kernel function of (a) is,
Figure 885657DEST_PATH_IMAGE083
indicating quality index
Figure 332818DEST_PATH_IMAGE040
And
Figure 991333DEST_PATH_IMAGE041
the mixing kernel function of (a) is,
Figure 32101DEST_PATH_IMAGE043
and
Figure 801474DEST_PATH_IMAGE044
to be in a subsystem
Figure 775246DEST_PATH_IMAGE045
The process variables of (1) are set,
Figure 932558DEST_PATH_IMAGE046
to be in a subsystem
Figure 991781DEST_PATH_IMAGE045
The quality index of (1).
Figure 502528DEST_PATH_IMAGE047
、
Figure 861965DEST_PATH_IMAGE048
、
Figure 924599DEST_PATH_IMAGE049
Are all necessary parameters of the corresponding kernel function. Wherein the content of the first and second substances,
Figure 736697DEST_PATH_IMAGE047
is a bandwidth parameter, the value of which is greater than 0, and
Figure 785556DEST_PATH_IMAGE050
。
s3.2 method of Mixed Kernel function optimization of data set
Figure 999499DEST_PATH_IMAGE013
And
Figure 498614DEST_PATH_IMAGE014
respectively mapping to high-dimensional feature space to respectively obtain
Figure 532429DEST_PATH_IMAGE053
And
Figure 384979DEST_PATH_IMAGE054
。
the method for constructing the multi-system distributed coordination control model according to the preferred data set mapped to the high-dimensional feature space comprises the following steps:
s4.1, constructing a multi-system distributed coordination control model
Figure 46904DEST_PATH_IMAGE084
In the formula (I), the compound is shown in the specification,
Figure 123444DEST_PATH_IMAGE052
is composed of
Figure 644556DEST_PATH_IMAGE053
And
Figure 566375DEST_PATH_IMAGE054
a combined input-output matrix, wherein,
Figure 817228DEST_PATH_IMAGE053
is a matrix composed of high-dimensional features which are mapped to a high-dimensional space by a mixed kernel function for process parameters,
Figure 64670DEST_PATH_IMAGE054
is a matrix composed of high-dimensional features which are mapped to a high-dimensional space through a mixed kernel function,
Figure 73077DEST_PATH_IMAGE055
for future output matrices, i.e. quality index matrices,
Figure 267429DEST_PATH_IMAGE056
for future input matrices, i.e. process variable matrices,
Figure 310471DEST_PATH_IMAGE057
and
Figure 56711DEST_PATH_IMAGE058
respectively, a control matrix corresponding to the matrix,
Figure 817993DEST_PATH_IMAGE059
unknown disturbances existing between process variables and quality indexes;
further, the air conditioner is provided with a fan,
Figure 550457DEST_PATH_IMAGE085
indicating the best output in the future, i.e. set by the control systemThe best control effect. In the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE086
represents the optimal set of outputs for each subsystem in the future,
Figure 979164DEST_PATH_IMAGE054
and
Figure 568409DEST_PATH_IMAGE086
the closer the control effect, the better. Therein
Figure 285829DEST_PATH_IMAGE065
The corresponding optimal outlet thickness in the quality index is represented,
Figure 25246DEST_PATH_IMAGE066
and (4) representing the corresponding optimal plate shape in the quality index. In the usual case of the use of a magnetic tape,
Figure 574039DEST_PATH_IMAGE066
the related measurement indexes comprise convexity and flatness, and the convexity is mainly considered in the invention.
S4.2, in conclusion, the comprehensive objective function concerning the thickness and the plate shape is set as follows:
Figure DEST_PATH_IMAGE087
the final actuator for adjusting the thickness and the plate shape is on the actions of the hydraulic cylinder and the roll bending cylinder. The process variables particularly relate to rolling force, roll bending force and roll shifting amount.
The method for realizing coordination control among the racks by adopting the loop correction strategy comprises the following steps:
s5.1, introducing a group intelligent optimization algorithm for optimization, wherein the optimization comprises the following steps: the group intelligent optimization algorithm abstracts the concrete activities of foraging, hunting and the like of organisms by means of the concept of bionics, the dimensionality of each individual in a population is the optimized variable number, the vector parameters in each dimensionality are variables to be optimized, in the embodiment, the dimensionality of each individual is set to be 14 dimensionalities, and the dimensionalities are respectively the outlet thickness and the convexity of 7 racks. And optimizing the outlet thickness and the convexity of each rack by taking the comprehensive objective function as a reference, obtaining the optimal outlet thickness and the optimal convexity value of each rack after optimization, checking whether the thickness and the convexity of the last rack meet the comprehensive objective function, meeting the condition and terminating iteration, and continuing iteration until the thickness and the convexity reach the error allowable range.
After the iteration is ended, according to the optimal outlet thickness and the optimal convexity value of each rack, a rolling force, a roll bending force and a roll shifting amount applied by each rack are reversely deduced by combining a multi-system distributed coordination control model, and the specific strokes of a hydraulic cylinder and a roll bending cylinder are reversely deduced by combining the specific numerical values of the rolling force, the roll bending force and the roll shifting amount, so that the regulation and control of corresponding process variables are completed;
s5.2, the circulation optimization strategy is to obtain deviation vectors of measured values and target values of the outlet thickness and the convexity of the finished product through measurement of a multifunctional instrument after the regulation and control of the corresponding variables are completed
Figure 740709DEST_PATH_IMAGE060
According to a regulatory matrix
Figure 945426DEST_PATH_IMAGE061
The adjustment quantity required for eliminating the deviation can be obtained by fast corresponding calculation
Figure 82009DEST_PATH_IMAGE062
. The cycle optimization strategy takes time as a variable to carry out the regulation and control on the production process in a cycle-to-cycle manner, and realizes the load distribution of each rack and the dynamic coordination among the racks.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (3)

1. A distributed regulation and control method for the quality of a strip in a continuous rolling process is characterized by comprising the following steps:
obtaining historical data of a finish rolling process, wherein the historical data comprises: process variables and quality indicators;
dividing the finish rolling flow into 7 subsystems, dividing the process variables into different subsystems, and obtaining an optimal data set of the process variables and quality indexes in each subsystem according to a maximum correlation and minimum redundancy screening principle;
mapping the preferred data set to a high-dimensional feature space by adopting a mixed kernel function method;
constructing a multi-system distributed coordination control model according to the preferred data set in the high-dimensional feature space;
introducing a group intelligent optimization algorithm based on the multi-system distributed coordination control model, and realizing coordination control among the racks by adopting a cyclic correction strategy;
a group intelligent optimization algorithm is introduced, a cyclic correction strategy is adopted, and the method for realizing coordination control among the racks comprises the following steps:
setting a comprehensive objective function related to the thickness and the plate shape and the dimension of an individual, wherein the dimension of the individual is set to be 14 dimensions, namely the outlet thickness and the convexity of 7 racks;
optimizing the outlet thickness and the convexity of each rack by taking the comprehensive objective function as a reference, and obtaining the optimal outlet thickness and the optimal convexity value of each rack after optimization;
checking whether the outlet thickness and convexity of the end frame meet the comprehensive objective function, if so, terminating iteration, and otherwise, continuing the iteration until the error is within an allowable range;
after iteration is ended, according to the optimal outlet thickness and the optimal convexity value of each rack, the rolling force, the roll bending force and the roll shifting quantity applied by each rack are reversely deduced by combining the multi-system distributed coordination control model, and the specific strokes of the hydraulic cylinder and the roll bending cylinder are reversely deduced by combining the specific values of the rolling force, the roll bending force and the roll shifting quantity, so that the regulation and control of the process variables of the corresponding process are completed;
after the regulation and control of the process variable of the corresponding process are finished, the process is startedThe measured value of the outlet thickness and convexity of the finished product and the deviation vector existing in the target value are obtained by measurement of an excessive function instrument
Figure QLYQS_1
(ii) a According to a regulation matrix
Figure QLYQS_2
Calculating the adjustment amount required for eliminating the corresponding deviation vector
Figure QLYQS_3
(ii) a The production process is regulated and controlled in a circulating and reciprocating manner by taking time as a variable, so that the load distribution of each rack and the dynamic coordination among the racks are realized;
the expression of the comprehensive objective function is as follows:
Figure QLYQS_4
wherein, in the step (A),
Figure QLYQS_5
indicating the best output in the future, i.e. the best control effect set by the control system, of
Figure QLYQS_6
The corresponding optimum outlet thickness in the quality indicator is indicated,
Figure QLYQS_7
representing the corresponding optimal plate shape in the quality index;
the 7 subsystems that divide the finish rolling flow include: the device comprises a first rack, a second rack, a third rack, a fourth rack, a fifth rack, a sixth rack and a seventh rack;
the method for dividing the process variables into different subsystems comprises the following steps:
Figure QLYQS_10
wherein the content of the first and second substances,
Figure QLYQS_13
a data set representing process variable data of a process,
Figure QLYQS_14
a data set consisting of process variables for a first rack process,
Figure QLYQS_9
a data set composed of process variables for the second rack,
Figure QLYQS_11
a data set consisting of process variables for the third rack,
Figure QLYQS_15
a data set consisting of process variables for the fourth rack,
Figure QLYQS_16
a data set of process variables for the fifth rack,
Figure QLYQS_8
a data set consisting of process variables for the sixth rack,
Figure QLYQS_12
a data set consisting of process variables for the seventh rack;
Figure QLYQS_17
wherein the content of the first and second substances,
Figure QLYQS_18
a data set representing the quality indicator is presented,
Figure QLYQS_19
the thickness and the convexity of the finished product corresponding to the quality index data set are represented;
the method for obtaining the optimal data set of the process variables and the quality indexes in each subsystem according to the maximum correlation and minimum redundancy screening principle comprises the following steps:
obtaining the incidence relation between the process variable and the quality index in each subsystem according to the maximum correlation screening principle;
obtaining the redundancy relation between the process variables and the quality indexes in each subsystem according to the minimum redundancy screening principle;
respectively introducing a joint measurement coefficient to the process variable and the quality index in each subsystem based on the incidence relation and the redundancy relation to obtain an optimal data set of the process variable in each subsystem
Figure QLYQS_20
And a preferred data set of said quality indicators
Figure QLYQS_21
；
The formula corresponding to the maximum correlation screening principle is as follows:
Figure QLYQS_22
in the formula (I), wherein,
Figure QLYQS_25
represented as a set of input variables,
Figure QLYQS_27
a set of output variables is represented that are,
Figure QLYQS_24
and
Figure QLYQS_26
are respectively as
Figure QLYQS_28
And
Figure QLYQS_29
the elements in (A) and (B) are selected,
Figure QLYQS_23
is defined as
Figure QLYQS_30
And
Figure QLYQS_31
maximum information coefficient in between;
the expression of the incidence relation between the process variables and the quality indexes is as follows:
Figure QLYQS_32
，
Figure QLYQS_33
in the formula (I), wherein,
Figure QLYQS_38
representing a set of process variables for each subsystem,
Figure QLYQS_40
a set of quality indicators is represented, and,
Figure QLYQS_34
and
Figure QLYQS_36
are respectively as
Figure QLYQS_39
And
Figure QLYQS_41
the elements in (A) and (B) are selected,
Figure QLYQS_35
is defined as
Figure QLYQS_37
And
Figure QLYQS_42
maximum information coefficient of (2);
the formula corresponding to the minimum redundancy screening principle is as follows:
Figure QLYQS_43
in the formula (I), wherein,
Figure QLYQS_48
in order to input the set of variables,
Figure QLYQS_49
and
Figure QLYQS_44
is composed of
Figure QLYQS_46
The elements (A) and (B) in (B),
Figure QLYQS_47
is composed of
Figure QLYQS_50
And
Figure QLYQS_45
maximum information coefficient in between;
the expression of the redundancy relationship between the process variables and the quality indexes is as follows:
Figure QLYQS_52
，
Figure QLYQS_54
in the formula (I), wherein,
Figure QLYQS_55
represents a set of process control variables for each subsystem,
Figure QLYQS_51
a set of quality indicators is represented, and,
Figure QLYQS_57
and
Figure QLYQS_58
is that
Figure QLYQS_59
The elements (A) and (B) in (B),
Figure QLYQS_53
and
Figure QLYQS_56
is that
Figure QLYQS_60
The elements (A) and (B) in (B),
Figure QLYQS_61
represents the maximum information coefficient between elements;
the joint measurement coefficient introduced by the process variable is as follows:
Figure QLYQS_62
；
the joint measurement coefficient introduced by the quality index is as follows:
Figure QLYQS_63
；
the expression of the mixing kernel function is as follows:
Figure QLYQS_65
Figure QLYQS_68
in the formula (I), the compound is shown in the specification,
Figure QLYQS_73
representing process variables
Figure QLYQS_66
And
Figure QLYQS_70
the mixing kernel function of (a) is,
Figure QLYQS_74
indicating quality index
Figure QLYQS_75
And
Figure QLYQS_64
the mixing kernel function of (a) is,
Figure QLYQS_69
and
Figure QLYQS_77
is at a subsystem
Figure QLYQS_78
The process variables of (1) are set to,
Figure QLYQS_67
in order to be a quality index,
Figure QLYQS_71
are parameters of the corresponding kernel function, wherein,
Figure QLYQS_72
is a bandwidth parameter, the value of which is greater than 0, and
Figure QLYQS_76
。
2. the distributed regulation and control method for the quality of the strip in the continuous rolling process according to claim 1, wherein the obtained historical data of the finish rolling process is as follows: rolling the plate strips of the same steel type, different steel grades, the same steel grade, different steel grades and different plate thicknesses to generate data; the process variables comprise rolling force, roll gap value, rolling speed, roll bending force and roll shifting amount; the quality indexes comprise thickness, width of the plate strip, convexity of the plate strip and flatness of the plate strip.
3. The method for the distributed regulation and control of the plate strip quality in the continuous rolling process according to claim 1, wherein the method for constructing the multi-system distributed coordination control model according to the preferred data set in the high-dimensional characteristic space comprises the following steps:
Figure QLYQS_81
in the formula (I), the compound is shown in the specification,
Figure QLYQS_83
is composed of
Figure QLYQS_86
And
Figure QLYQS_80
a combined input-output matrix, wherein,
Figure QLYQS_84
is a matrix composed of high-dimensional features whose process parameters are mapped to a high-dimensional space through a mixed kernel function,
Figure QLYQS_87
is a matrix composed of high-dimensional features which are mapped to a high-dimensional space through a mixed kernel function,
Figure QLYQS_89
for future output matrices, i.e. quality index matrices,
Figure QLYQS_79
for future input matrices, i.e. process variable matrices,
Figure QLYQS_82
and
Figure QLYQS_85
respectively, are the control matrices of the corresponding matrices,
Figure QLYQS_88
there is an unknown disturbance between the process variable and the quality index.
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