CN103064293A - Chemical process decoupling non-minimal realization state space linear quadric form control method - Google Patents

Chemical process decoupling non-minimal realization state space linear quadric form control method Download PDF

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CN103064293A
CN103064293A CN 201310018108 CN201310018108A CN103064293A CN 103064293 A CN103064293 A CN 103064293A CN 201310018108 CN201310018108 CN 201310018108 CN 201310018108 A CN201310018108 A CN 201310018108A CN 103064293 A CN103064293 A CN 103064293A
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decoupling zero
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张日东
吴锋
张乐
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Hangzhou Dianzi University
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Hangzhou Dianzi University
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Abstract

The invention relates to a chemical process decoupling non-minimal realization state space linear quadric form control method. According to an existing traditional and simple control method, control parameters are fully depended on experience of technical workers, and therefore control effect is unsatisfying. According to method, a decoupling state space model is established based on a chemical process model so as to obtain basic process characteristics; a linear quadric form control loop is established based on the decoupling state space model; and parameters of a linear quadric form controller are computed to carry out linear quadric form control on the whole of process objects. By means of data acquisition, process processing, mechanism forecasting, data drive, optimization and the like, the chemical process decoupling non-minimal realization state linear quadric form control method is achieved, and the chemical process decoupling non-minimal realization state linear quadric form control method can effectively improve control accuracy and stability, effectively bring convenience to design of controllers, guarantee improvement of performance, and meanwhile meet given production performance indexes.

Description

The non-Minimal Realization state space of chemical process decoupling zero Linear-Quadratic Problem control method
Technical field
The invention belongs to technical field of automation, relate to the non-Minimal Realization state space of a kind of chemical process decoupling zero Linear-Quadratic Problem control method.
Background technology
Chemical process is the important component part of China's process flow industry process, and its requirement is to supply with qualified industrial products, to satisfy the needs of China's industrial development.As an industrial significant subject, the raising of Producing Process of Processing Industry level plays vital effect to the raising of whole economic performance of industrial enterprises.For this reason, each main technologic parameters of production run must strict control.Along with industrial expansion and more and more higher to the requirement of quality, energy resource consumption and the environmental protection of product; control accuracy to industrial process requires also more and more stricter; though traditional control method has satisfied certain requirement; but be difficult to further promote the control level, add the technological process more complex.Simple single loop process control can't have been satisfied the requirement of control accuracy and stationarity, and product percent of pass is low, and unit efficiency is low.And traditional simple control device is adopted in control basically in the present actual industrial, and the control parameter relies on technician's experience fully, and production cost is increased, and the control effect is very undesirable.China's Chemical Engineering Process Control and optimisation technique are relatively backward, and energy consumption is high, and control performance is poor, and automaticity is low, are difficult to adapt to energy-saving and emission-reduction and the indirect demand of environmental protection, this wherein directly one of influence factor be the control program problem of system.
Summary of the invention
Target of the present invention is the weak point for existing Chemical Processing Systems control technology, and the non-Minimal Realization state space of a kind of chemical process decoupling zero Linear-Quadratic Problem control method is provided.The method has remedied the deficiency of traditional control method, and when guaranteeing that control has higher precision and stability, the form that also guarantees is simple and satisfy the needs of actual industrial process.
The inventive method is at first set up the decoupling zero state-space model based on the chemical process model, excavates basic process characteristic; Then set up the Linear-Quadratic Problem control loop based on this decoupling zero state-space model; By calculating the parameter of linear quadratic type controller, process object whole implementation Linear-Quadratic Problem is controlled at last.
Technical scheme of the present invention is to process, predict the means such as mechanism, data-driven, optimization by data acquisition, process, established the non-Minimal Realization state space of a kind of chemical process decoupling zero Linear-Quadratic Problem control method, but utilize the precision of the method Effective Raise control, improve the control smoothness.
The step of the inventive method comprises:
(1) utilize the chemical process model to set up the decoupling zero state-space model, concrete grammar is:
At first gather the inputoutput data of chemical process, it is as follows to utilize these data to set up input/output model:
Figure 2013100181089100002DEST_PATH_IMAGE002
Wherein
Figure 2013100181089100002DEST_PATH_IMAGE004
,
Figure 2013100181089100002DEST_PATH_IMAGE006
,
Figure 2013100181089100002DEST_PATH_IMAGE008
Be respectively output vector
Figure 2013100181089100002DEST_PATH_IMAGE010
Conversion, transfer function matrix, input vector Conversion;
Figure 2013100181089100002DEST_PATH_IMAGE012
，
Figure 2013100181089100002DEST_PATH_IMAGE014
，
Figure 2013100181089100002DEST_PATH_IMAGE016
Figure 2013100181089100002DEST_PATH_IMAGE018
,
Figure 2013100181089100002DEST_PATH_IMAGE020
, ,
Figure 2013100181089100002DEST_PATH_IMAGE024
Each return transfer function of expression process,
Figure 2013100181089100002DEST_PATH_IMAGE026
With
Figure 2013100181089100002DEST_PATH_IMAGE028
Be respectively
Figure 2013100181089100002DEST_PATH_IMAGE030
Individual input, output variable
Figure 952555DEST_PATH_IMAGE010
Conversion, ,
Figure 384673DEST_PATH_IMAGE010
Be the discrete transform operator of computer control system,
Figure 2013100181089100002DEST_PATH_IMAGE034
For
Figure 229263DEST_PATH_IMAGE010
Inverse,
Figure 2013100181089100002DEST_PATH_IMAGE036
Be the input/output variable number of process, described inputoutput data is the data of storing in the data acquisition unit;
Further above-mentioned equation being chosen adjoint matrix decoupling zero battle array is:
Figure 2013100181089100002DEST_PATH_IMAGE038
Wherein, Adjoint matrix decoupling zero battle array, For
Figure 649487DEST_PATH_IMAGE006
Adjoint matrix.
Above-mentioned adjoint matrix decoupling zero battle array and process input and output model combination are obtained:
Figure 2013100181089100002DEST_PATH_IMAGE044
Wherein, The decoupling zero process model that obtains, For
Figure 382957DEST_PATH_IMAGE006
Determinant,
Figure 2013100181089100002DEST_PATH_IMAGE050
For with
Figure 353187DEST_PATH_IMAGE006
Determinant be the diagonal matrix of element.
Figure 2013100181089100002DEST_PATH_IMAGE052
Above-mentioned decoupling zero process model is processed into
Figure 927650DEST_PATH_IMAGE036
The discrete equation form of individual single argument process:
Figure 2013100181089100002DEST_PATH_IMAGE054
Wherein With
Figure 2013100181089100002DEST_PATH_IMAGE058
Respectively The output of individual process and input variable, ,
Figure 2013100181089100002DEST_PATH_IMAGE062
With
Figure 2013100181089100002DEST_PATH_IMAGE064
Be respectively
Figure 801857DEST_PATH_IMAGE056
With The matrix of coefficients polynomial expression;
Figure 2013100181089100002DEST_PATH_IMAGE068
Wherein Corresponding coefficient, For after move
Figure 2013100181089100002DEST_PATH_IMAGE074
The step operator,
Figure 2013100181089100002DEST_PATH_IMAGE076
It is the model order that obtains;
The discrete equation model of above-mentioned single argument process is passed through backward shift operator
Figure 2013100181089100002DEST_PATH_IMAGE078
Be processed into state space form:
Figure 2013100181089100002DEST_PATH_IMAGE082
。
Wherein,
Figure 2013100181089100002DEST_PATH_IMAGE084
,
Figure 2013100181089100002DEST_PATH_IMAGE086
Respectively
Figure 2013100181089100002DEST_PATH_IMAGE088
Variate-value constantly,
Figure 2013100181089100002DEST_PATH_IMAGE090
Be
Figure 2013100181089100002DEST_PATH_IMAGE092
Input incremental variable value constantly,
Figure 2013100181089100002DEST_PATH_IMAGE094
,
Figure 2013100181089100002DEST_PATH_IMAGE096
Be respectively
Figure 2013100181089100002DEST_PATH_IMAGE098
Output variable increment and input variable increment size constantly,
Figure 2013100181089100002DEST_PATH_IMAGE100
,
Figure 2013100181089100002DEST_PATH_IMAGE102
,
Figure 2013100181089100002DEST_PATH_IMAGE104
Be respectively corresponding state matrix, input matrix and output matrix,
Figure 2013100181089100002DEST_PATH_IMAGE106
For getting the transposition symbol.
Figure 2013100181089100002DEST_PATH_IMAGE108
Figure 2013100181089100002DEST_PATH_IMAGE112
(2) based on this decoupling zero state-space model design Linear-Quadratic Problem controller, concrete grammar is:
A. the objective function that defines this linear quadratic type controller is:
Figure 2013100181089100002DEST_PATH_IMAGE114
Wherein,
Figure 2013100181089100002DEST_PATH_IMAGE116
Be objective function,
Figure 2013100181089100002DEST_PATH_IMAGE118
With
Figure 2013100181089100002DEST_PATH_IMAGE120
Be respectively the weighting matrix of state variable and output variable.
B. calculate the parameter of linear quadratic type controller, specifically:
Figure 2013100181089100002DEST_PATH_IMAGE122
Wherein
Figure 2013100181089100002DEST_PATH_IMAGE124
Be
Figure 590427DEST_PATH_IMAGE092
Variate-value constantly,
Figure 2013100181089100002DEST_PATH_IMAGE126
Be controller feedback factor vector.
The non-Minimal Realization state space of a kind of chemical process decoupling zero Linear-Quadratic Problem control method that the present invention proposes has remedied the deficiency of traditional control, and has effectively made things convenient for controller's design, guarantees the lifting of control performance, satisfies simultaneously given production performance index.
The control technology that the present invention proposes can effectively reduce the error between ideal technology parameter and the actual process parameter, further remedied the deficiency of traditional controller, guarantee that simultaneously control device operates in optimum condition, make the technological parameter of production run reach strict control.
Embodiment
Take the process control of coking heater furnace pressure as example:
Here described as an example with the process control of coking heater furnace pressure.This process is the process of a Multivariable Coupling, and furnace pressure not only is subject to the impact of stack damper aperture, also is subjected to fuel quantity, the impact of air intake flow simultaneously.Regulating measure adopts the stack damper aperture, and remaining affects as uncertain factor.
(1) set up the decoupling zero state-space model, concrete grammar is:
At first utilize data acquisition unit to gather chemical process input data (stack damper aperture) and output data (heating furnace furnace pressure), it is as follows to set up input/output model:
Figure DEST_PATH_IMAGE128
Wherein,
Figure DEST_PATH_IMAGE130
,
Figure DEST_PATH_IMAGE132
,
Figure 268664DEST_PATH_IMAGE022
,
Figure DEST_PATH_IMAGE134
The transport function equation of expression heating furnace furnace pressure process,
Figure DEST_PATH_IMAGE136
Be respectively stack damper aperture, heating-furnace gun pressure force data
Figure DEST_PATH_IMAGE138
Conversion;
Then define three variablees ,
Figure DEST_PATH_IMAGE142
,
Figure DEST_PATH_IMAGE144
As follows:
Figure DEST_PATH_IMAGE146
Input data and the output data of above process are expressed as:
Figure 781422DEST_PATH_IMAGE002
Further above-mentioned equation being chosen adjoint matrix decoupling zero battle array is:
Wherein,
Figure 4779DEST_PATH_IMAGE040
Adjoint matrix decoupling zero battle array,
Figure 285326DEST_PATH_IMAGE042
For
Figure 657401DEST_PATH_IMAGE006
Adjoint matrix.
The said process model is launched to obtain:
Figure 790442DEST_PATH_IMAGE044
Wherein,
Figure 906166DEST_PATH_IMAGE046
The decoupling zero process model that obtains,
Figure 309728DEST_PATH_IMAGE048
For Determinant,
Figure 410725DEST_PATH_IMAGE050
For with
Figure 64560DEST_PATH_IMAGE006
Determinant be the diagonal matrix of element.
Figure 821163DEST_PATH_IMAGE052
Above-mentioned decoupling zero process model is processed into The discrete representation mode of individual single argument process:
Figure 141210DEST_PATH_IMAGE054
Wherein,
Figure 598736DEST_PATH_IMAGE056
,
Figure DEST_PATH_IMAGE148
Respectively
Figure 475425DEST_PATH_IMAGE030
The output of individual process, input variable,
Figure 861669DEST_PATH_IMAGE062
,
Figure 191020DEST_PATH_IMAGE064
Be respectively
Figure 452237DEST_PATH_IMAGE056
,
Figure 767722DEST_PATH_IMAGE148
The matrix of coefficients polynomial expression,
Figure 761086DEST_PATH_IMAGE076
The model order that obtains,
Figure 577732DEST_PATH_IMAGE070
Corresponding coefficient,
Figure 377061DEST_PATH_IMAGE072
For after move
Figure 962763DEST_PATH_IMAGE074
The step operator.
Figure 956389DEST_PATH_IMAGE068
The discrete equation model of above-mentioned single argument process is passed through backward shift operator
Figure 260331DEST_PATH_IMAGE078
Be processed into state space form:
Figure 535455DEST_PATH_IMAGE080
Wherein, ,
Figure 638726DEST_PATH_IMAGE086
Respectively
Figure 928500DEST_PATH_IMAGE088
Variate-value constantly,
Figure 69631DEST_PATH_IMAGE090
Be
Figure 629925DEST_PATH_IMAGE092
Input incremental variable value constantly, ,
Figure 414527DEST_PATH_IMAGE096
Be respectively Output variable increment and input variable increment size constantly,
Figure 275615DEST_PATH_IMAGE100
,
Figure 280481DEST_PATH_IMAGE102
,
Figure 780732DEST_PATH_IMAGE104
Be respectively corresponding state matrix, input matrix and output matrix,
Figure 201349DEST_PATH_IMAGE106
For getting the transposition symbol.
。
Figure 410537DEST_PATH_IMAGE108
Figure 398085DEST_PATH_IMAGE110
(2) design furnace pressure state-space model linear quadratic type controller, concrete grammar is:
The first step: the objective function that defines this linear quadratic type controller is:
Figure 309988DEST_PATH_IMAGE114
Wherein,
Figure 594339DEST_PATH_IMAGE116
Be objective function,
Figure 131500DEST_PATH_IMAGE118
With
Figure 221815DEST_PATH_IMAGE120
Be respectively the weighting matrix of state variable and output variable.
Second step: calculate the parameter of linear quadratic type controller, specifically:
Figure 403398DEST_PATH_IMAGE122
Wherein
Figure 153923DEST_PATH_IMAGE126
Be controller feedback factor vector.

Claims (1)

1. the non-Minimal Realization state space of chemical process decoupling zero Linear-Quadratic Problem control method is characterized in that the concrete steps of the method are:
I. utilize the chemical process model to set up the decoupling zero state-space model, concrete grammar is:
At first gather the inputoutput data of chemical process, it is as follows to utilize these data to set up input/output model:
Figure 2013100181089100001DEST_PATH_IMAGE002
Wherein
Figure 2013100181089100001DEST_PATH_IMAGE004
, ,
Figure DEST_PATH_IMAGE008
Be respectively output vector
Figure DEST_PATH_IMAGE010
Conversion, transfer function matrix, input vector Conversion;
Figure DEST_PATH_IMAGE012
， ，
Figure DEST_PATH_IMAGE016
Figure DEST_PATH_IMAGE018
,
Figure DEST_PATH_IMAGE020
, ,
Figure DEST_PATH_IMAGE024
Each return transfer function of expression process,
Figure DEST_PATH_IMAGE026
With
Figure DEST_PATH_IMAGE028
Be respectively
Figure DEST_PATH_IMAGE030
Individual input, output variable
Figure 889449DEST_PATH_IMAGE010
Conversion,
Figure DEST_PATH_IMAGE032
, Be the discrete transform operator of computer control system, For
Figure 299494DEST_PATH_IMAGE010
Inverse,
Figure DEST_PATH_IMAGE036
Be the input/output variable number of process, described inputoutput data is the data of storing in the data acquisition unit;
Further above-mentioned equation being chosen adjoint matrix decoupling zero battle array is:
Wherein, Adjoint matrix decoupling zero battle array,
Figure DEST_PATH_IMAGE042
For
Figure 437345DEST_PATH_IMAGE006
Adjoint matrix;
Above-mentioned adjoint matrix decoupling zero battle array and process input and output model combination are obtained:
Wherein,
Figure DEST_PATH_IMAGE046
The decoupling zero process model that obtains, For
Figure 516070DEST_PATH_IMAGE006
Determinant,
Figure DEST_PATH_IMAGE050
For with
Figure 531300DEST_PATH_IMAGE006
Determinant be the diagonal matrix of element;
Above-mentioned decoupling zero process model is processed into The discrete equation form of individual single argument process:
Figure DEST_PATH_IMAGE054
Wherein With Respectively The output of individual process and input variable,
Figure DEST_PATH_IMAGE060
,
Figure DEST_PATH_IMAGE062
With Be respectively
Figure 609218DEST_PATH_IMAGE056
With
Figure DEST_PATH_IMAGE066
The matrix of coefficients polynomial expression;
Figure DEST_PATH_IMAGE068
Wherein
Figure DEST_PATH_IMAGE070
Corresponding coefficient,
Figure DEST_PATH_IMAGE072
For after move
Figure DEST_PATH_IMAGE074
The step operator,
Figure DEST_PATH_IMAGE076
It is the model order that obtains;
The discrete equation model of above-mentioned single argument process is passed through backward shift operator
Figure DEST_PATH_IMAGE078
Be processed into state space form:
Figure DEST_PATH_IMAGE080
Figure DEST_PATH_IMAGE082
；
Wherein,
Figure DEST_PATH_IMAGE084
,
Figure DEST_PATH_IMAGE086
Respectively
Figure DEST_PATH_IMAGE088
Variate-value constantly,
Figure DEST_PATH_IMAGE090
Be
Figure DEST_PATH_IMAGE092
Input incremental variable value constantly,
Figure DEST_PATH_IMAGE094
,
Figure DEST_PATH_IMAGE096
Be respectively
Figure DEST_PATH_IMAGE098
Output variable increment and input variable increment size constantly,
Figure DEST_PATH_IMAGE100
,
Figure DEST_PATH_IMAGE102
,
Figure DEST_PATH_IMAGE104
Be respectively corresponding state matrix, input matrix and output matrix,
Figure DEST_PATH_IMAGE106
For getting the transposition symbol;
Figure DEST_PATH_IMAGE110
Figure DEST_PATH_IMAGE112
II. based on this decoupling zero state-space model design Linear-Quadratic Problem controller, concrete grammar is:
A. the objective function that defines this linear quadratic type controller is:
Figure DEST_PATH_IMAGE114
Wherein,
Figure DEST_PATH_IMAGE116
Be objective function,
Figure DEST_PATH_IMAGE118
With
Figure DEST_PATH_IMAGE120
Be respectively the weighting matrix of state variable and output variable;
B. calculate the parameter of linear quadratic type controller,
Figure DEST_PATH_IMAGE122
, wherein
Figure DEST_PATH_IMAGE124
Be
Figure 53188DEST_PATH_IMAGE092
Variate-value constantly,
Figure DEST_PATH_IMAGE126
Be controller feedback factor vector.
CN 201310018108 2013-01-18 2013-01-18 Chemical process decoupling non-minimal realization state space linear quadric form control method Pending CN103064293A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104317194A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Temperature control method for non-minimal state space model predictive control optimization
CN105353619A (en) * 2015-11-26 2016-02-24 杭州电子科技大学 Rolling time domain tracking control method for batch injection molding process
CN113534661A (en) * 2021-06-03 2021-10-22 太原理工大学 Resistance furnace temperature control method based on Kalman filtering and non-minimum state space

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104317194A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Temperature control method for non-minimal state space model predictive control optimization
CN105353619A (en) * 2015-11-26 2016-02-24 杭州电子科技大学 Rolling time domain tracking control method for batch injection molding process
CN105353619B (en) * 2015-11-26 2018-12-21 杭州电子科技大学 A kind of rolling time horizon tracking and controlling method of batch injection moulding process
CN113534661A (en) * 2021-06-03 2021-10-22 太原理工大学 Resistance furnace temperature control method based on Kalman filtering and non-minimum state space

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Application publication date: 20130424