CN103076744A - Linear quadric form control method for non-minimum realization of state space in chemical process - Google Patents

Linear quadric form control method for non-minimum realization of state space in chemical process Download PDF

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CN103076744A
CN103076744A CN2013100181074A CN201310018107A CN103076744A CN 103076744 A CN103076744 A CN 103076744A CN 2013100181074 A CN2013100181074 A CN 2013100181074A CN 201310018107 A CN201310018107 A CN 201310018107A CN 103076744 A CN103076744 A CN 103076744A
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chemical process
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CN103076744B (en
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张日东
陈霄
郑松
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Hangzhou Dianzi University
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Abstract

The invention relates to a linear quadric form control method for non-minimum realization of a state space in a chemical process. At present, parameter control completely depends on the experience of technicists by adopting the conventional simple control means, but the control effect is poor. The method provided by the invention comprises steps of by means of data acquisition, procedure processing, predicting mechanism, data drive, optimizing and the like, establishing procedure model on the basis of actual data in the chemical process, digging out basic procedure characteristics; and then establishing a linear quadric form control loop on the basis of the transfer function procedure model; and finally, through calculating parameters of a linear quadric form controller, implementing linear quadric form control on a procedure object. According to the linear quadric form control method, errors between the ideal technological parameter and the real technological parameter can be reduced effectively, the fault of the traditional controller is further made up, and meanwhile a control device is in an optimal state to ensure that the technological parameter in the production process is strictly controlled.

Description

The non-Minimal Realization state space of chemical process 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 Linear-Quadratic Problem control method.
Background technology
Chemical process is the important component part of China's process flow industry process, its control whether effective directly to follow-up process treatment process and reduce full-range energy consumption and all be of great importance.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 market is more and more higher to the quality requirements of petrochemicals, and the development of production Technology, the technological process more complex though traditional control method has satisfied certain requirement, is difficult to further promote the control level.Simple 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, has formed the requirement that develops into the senior stages such as complex control, advanced control from routine control.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 deficiency for existing Chemical Processing Systems control technology, and the non-Minimal Realization state space of a kind of chemical process 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 transport function process model based on the chemical process real data, excavates basic process characteristic; Then set up the Linear-Quadratic Problem control loop based on this transport function process model; By calculating the parameter of linear quadratic type controller, process object is implemented Linear-Quadratic Problem control 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 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 real data to set up the transport function process model, concrete grammar is:
Step (1). the input of operation chemical process makes it have individual step to change, and is exported in real time by recorder record chemical process, with the real-time output valve of chemical process Response curve convert Dimensionless Form to
Figure 2013100181074100002DEST_PATH_IMAGE004
:
Figure 2013100181074100002DEST_PATH_IMAGE006
Wherein,
Figure 2013100181074100002DEST_PATH_IMAGE008
That chemical process is exported in real time
Figure 435019DEST_PATH_IMAGE002
Steady-state value.
Step (2). choose two calculation levels,
Figure 2013100181074100002DEST_PATH_IMAGE010
, according to the needed parameter of following computing formula calculation of transfer function process model
Figure 2013100181074100002DEST_PATH_IMAGE012
:
Figure 2013100181074100002DEST_PATH_IMAGE014
Wherein,
Figure 2013100181074100002DEST_PATH_IMAGE016
Step amplitude of variation for the chemical process input.
Step (3). the parameter that step (2) is obtained is converted into the transport function process model of Laplce's form:
Figure 2013100181074100002DEST_PATH_IMAGE018
Wherein,
Figure 2013100181074100002DEST_PATH_IMAGE020
Be the Laplace transform operator, Be the time constant of model,
Figure 2013100181074100002DEST_PATH_IMAGE024
Be the time lag of transport function process model,
Figure 2013100181074100002DEST_PATH_IMAGE026
The Laplace transform of the output valve of expression process model, The Laplace transform of the input of expression process model.
(2) design non-Minimal Realization state space linear quadratic type controller based on this transport function process model, concrete grammar is:
A. above-mentioned transport function process model is passed through the sampling period
Figure 2013100181074100002DEST_PATH_IMAGE032
Be converted into discrete input/output model:
Figure 2013100181074100002DEST_PATH_IMAGE034
Wherein With
Figure 2013100181074100002DEST_PATH_IMAGE038
Respectively output and the input variable of discrete input/output model,
Figure 2013100181074100002DEST_PATH_IMAGE040
With
Figure 2013100181074100002DEST_PATH_IMAGE042
Be respectively
Figure 448587DEST_PATH_IMAGE036
With
Figure 884247DEST_PATH_IMAGE038
The coefficient polynomial expression;
Figure 2013100181074100002DEST_PATH_IMAGE044
Wherein
Figure 2013100181074100002DEST_PATH_IMAGE046
Corresponding coefficient,
Figure 2013100181074100002DEST_PATH_IMAGE048
For after move The step operator,
Figure 2013100181074100002DEST_PATH_IMAGE052
It is the discrete input/output model order that obtains;
B. above-mentioned discrete input/output model is passed through backward shift operator
Figure 2013100181074100002DEST_PATH_IMAGE054
Be processed into state space form:
Figure 2013100181074100002DEST_PATH_IMAGE056
Figure 2013100181074100002DEST_PATH_IMAGE058
。
Wherein,
Figure 2013100181074100002DEST_PATH_IMAGE060
,
Figure 2013100181074100002DEST_PATH_IMAGE062
Respectively
Figure 2013100181074100002DEST_PATH_IMAGE064
State variable value and output variable value constantly,
Figure 2013100181074100002DEST_PATH_IMAGE066
Be
Figure 2013100181074100002DEST_PATH_IMAGE068
Input incremental variable value constantly,
Figure 2013100181074100002DEST_PATH_IMAGE070
, Be respectively
Figure 2013100181074100002DEST_PATH_IMAGE074
Output variable increment and input variable increment size constantly,
Figure 2013100181074100002DEST_PATH_IMAGE076
, ,
Figure 2013100181074100002DEST_PATH_IMAGE080
Be respectively corresponding state matrix, input matrix and output matrix, For getting the transposition symbol.
Figure 2013100181074100002DEST_PATH_IMAGE084
Figure 2013100181074100002DEST_PATH_IMAGE088
；
C. the vector form that defines non-Minimal Realization state space linear quadratic type controller objective function is:
Figure 2013100181074100002DEST_PATH_IMAGE090
Wherein,
Figure 2013100181074100002DEST_PATH_IMAGE092
Be objective function, With
Figure 2013100181074100002DEST_PATH_IMAGE096
Be respectively the weighting matrix of state variable and output variable.
D. the parameter of computing controller, specifically:
Figure 2013100181074100002DEST_PATH_IMAGE098
Wherein
Figure 2013100181074100002DEST_PATH_IMAGE100
Be
Figure 698970DEST_PATH_IMAGE068
State variable value constantly,
Figure 2013100181074100002DEST_PATH_IMAGE102
Be controller feedback factor vector.
The non-Minimal Realization state space of a kind of chemical process 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 Oxygen Content in Delayed Coking Furnace process control as example:
Here described as an example with the Oxygen Content in Delayed Coking Furnace process control.This process is the process of a complexity, and oxygen content not only is subject to the impact of air intake flow, also is subjected to furnace pressure, the impact of fuel quantity flow simultaneously.Regulating measure adopts the air intake flow, and remaining affects as uncertain factor.
(1) set up the transport function process model, concrete grammar is:
The first step: the intake air door of operation Oxygen Content in Delayed Coking Furnace process makes its input have individual step to change, utilize data acquisition unit to gather Oxygen Content in Delayed Coking Furnace process input data (air intake flow) and output data (Oxygen Content in Delayed Coking Furnace), exported in real time by the recorder recording process, with the real-time output valve of process
Figure 852871DEST_PATH_IMAGE002
Response convert Dimensionless Form to
Figure 673059DEST_PATH_IMAGE004
:
Figure 25543DEST_PATH_IMAGE006
Wherein,
Figure 404309DEST_PATH_IMAGE008
That process is exported in real time
Figure 779927DEST_PATH_IMAGE002
Steady-state value.
Second step: choose two calculation levels,
Figure 200544DEST_PATH_IMAGE010
, according to the needed parameter of following computing formula calculation of transfer function process model
Figure 548480DEST_PATH_IMAGE012
:
Figure 832569DEST_PATH_IMAGE014
Wherein,
Figure 695482DEST_PATH_IMAGE016
Step amplitude of variation for the process input.
The 3rd step: the parameter that second step is obtained is converted into the transport function process model of Laplce's form:
Figure 795156DEST_PATH_IMAGE018
Wherein,
Figure 519571DEST_PATH_IMAGE020
Be the Laplace transform operator,
Figure 741605DEST_PATH_IMAGE022
Be the time constant of model,
Figure 528034DEST_PATH_IMAGE024
Be the time lag of transport function process model,
Figure 165819DEST_PATH_IMAGE026
The Laplace transform of the output valve of expression process model, The Laplace transform of the input of expression process model.
(2) the non-Minimal Realization state space of design Oxygen Content in Delayed Coking Furnace process linear quadratic type controller, concrete grammar is:
A. the transport function process model that obtains in above-mentioned is passed through the sampling period
Figure 889428DEST_PATH_IMAGE032
Being converted into discrete input/output model is:
Figure 829440DEST_PATH_IMAGE034
Wherein
Figure 740895DEST_PATH_IMAGE036
With
Figure 740950DEST_PATH_IMAGE038
Respectively output and the input variable of discrete input/output model,
Figure 65752DEST_PATH_IMAGE040
With
Figure 543875DEST_PATH_IMAGE042
Be respectively
Figure 575417DEST_PATH_IMAGE036
With
Figure 746373DEST_PATH_IMAGE038
The coefficient polynomial expression;
Figure 168258DEST_PATH_IMAGE044
Wherein
Figure 715652DEST_PATH_IMAGE046
Corresponding coefficient,
Figure 664016DEST_PATH_IMAGE048
For after move The step operator, It is the discrete input/output model order that obtains;
B. above-mentioned discrete input/output model is passed through backward shift operator
Figure 548030DEST_PATH_IMAGE054
Be processed into state space form:
Figure 693021DEST_PATH_IMAGE058
。
Wherein,
Figure 214132DEST_PATH_IMAGE060
,
Figure 463848DEST_PATH_IMAGE062
Respectively
Figure 885339DEST_PATH_IMAGE064
State variable and output variable value constantly,
Figure 132781DEST_PATH_IMAGE066
Be
Figure 141188DEST_PATH_IMAGE068
Input incremental variable value constantly,
Figure 132278DEST_PATH_IMAGE070
,
Figure 972058DEST_PATH_IMAGE072
Be respectively
Figure 826620DEST_PATH_IMAGE074
Output variable increment and input variable increment size constantly, ,
Figure 913841DEST_PATH_IMAGE078
,
Figure 545811DEST_PATH_IMAGE080
Be respectively corresponding state matrix, input matrix and output matrix,
Figure 633590DEST_PATH_IMAGE082
For getting the transposition symbol.
Figure 616590DEST_PATH_IMAGE084
Figure 11799DEST_PATH_IMAGE086
Figure 498275DEST_PATH_IMAGE088
；
C. the vector form that defines non-Minimal Realization state space linear quadratic type controller objective function is:
Figure 524000DEST_PATH_IMAGE090
Wherein, Be objective function,
Figure 98256DEST_PATH_IMAGE094
With
Figure 704817DEST_PATH_IMAGE096
Be respectively the weighting matrix of state variable and output variable.
D. the parameter of computing controller, specifically:
Wherein
Figure 390194DEST_PATH_IMAGE102
Be controller feedback factor vector.

Claims (1)

1. the non-Minimal Realization state space of chemical process Linear-Quadratic Problem control method is characterized in that the concrete steps of the method are:
I. utilize the chemical process real data to set up the transport function process model, concrete grammar is:
Step (1). the input of operation chemical process makes it have individual step to change, and is exported in real time by recorder record chemical process, with the real-time output valve of chemical process
Figure 2013100181074100001DEST_PATH_IMAGE002
Response curve convert Dimensionless Form to
Figure 2013100181074100001DEST_PATH_IMAGE004
:
Figure 2013100181074100001DEST_PATH_IMAGE006
Wherein,
Figure 2013100181074100001DEST_PATH_IMAGE008
That chemical process is exported in real time
Figure 612878DEST_PATH_IMAGE002
Steady-state value;
Step (2). choose two calculation levels,
Figure 2013100181074100001DEST_PATH_IMAGE010
, according to the needed parameter of following computing formula calculation of transfer function process model
Figure DEST_PATH_IMAGE012
:
Figure 2013100181074100001DEST_PATH_IMAGE014
Wherein,
Figure 2013100181074100001DEST_PATH_IMAGE016
Step amplitude of variation for the chemical process input;
Step (3). the parameter that step (2) is obtained is converted into the transport function process model of Laplce's form:
Wherein,
Figure DEST_PATH_IMAGE020
Be the Laplace transform operator,
Figure DEST_PATH_IMAGE022
Be the time constant of model,
Figure DEST_PATH_IMAGE024
Be the time lag of transport function process model, The Laplace transform of the output valve of expression process model,
Figure DEST_PATH_IMAGE028
The Laplace transform of the input of expression process model;
Figure DEST_PATH_IMAGE030
II. design non-Minimal Realization state space linear quadratic type controller based on this transport function process model, concrete grammar is:
A. above-mentioned transport function process model is passed through the sampling period
Figure DEST_PATH_IMAGE032
Be converted into discrete input/output model:
Wherein
Figure DEST_PATH_IMAGE036
With
Figure DEST_PATH_IMAGE038
Respectively output and the input variable of discrete input/output model, With Be respectively
Figure 219088DEST_PATH_IMAGE036
With
Figure 258719DEST_PATH_IMAGE038
The coefficient polynomial expression;
Figure DEST_PATH_IMAGE044
Wherein
Figure DEST_PATH_IMAGE046
Corresponding coefficient,
Figure DEST_PATH_IMAGE048
For after move
Figure DEST_PATH_IMAGE050
The step operator,
Figure DEST_PATH_IMAGE052
It is the discrete input/output model order that obtains;
B. above-mentioned discrete input/output model is passed through backward shift operator
Figure DEST_PATH_IMAGE054
Be processed into state space form:
Figure DEST_PATH_IMAGE056
Figure DEST_PATH_IMAGE058
；
Wherein,
Figure DEST_PATH_IMAGE060
,
Figure DEST_PATH_IMAGE062
Respectively
Figure DEST_PATH_IMAGE064
State variable value and output variable value constantly,
Figure DEST_PATH_IMAGE066
Be Input incremental variable value constantly,
Figure DEST_PATH_IMAGE070
,
Figure DEST_PATH_IMAGE072
Be respectively
Figure DEST_PATH_IMAGE074
Output variable increment and input variable increment size constantly,
Figure DEST_PATH_IMAGE076
,
Figure DEST_PATH_IMAGE078
,
Figure DEST_PATH_IMAGE080
Be respectively corresponding state matrix, input matrix and output matrix,
Figure DEST_PATH_IMAGE082
For getting the transposition symbol;
Figure DEST_PATH_IMAGE084
Figure DEST_PATH_IMAGE086
Figure DEST_PATH_IMAGE088
；
C. the vector form that defines non-Minimal Realization state space linear quadratic type controller objective function is:
Figure DEST_PATH_IMAGE090
Wherein,
Figure DEST_PATH_IMAGE092
Be objective function, With
Figure DEST_PATH_IMAGE096
Be respectively the weighting matrix of state variable and output variable;
D. the parameter of computing controller,
Figure DEST_PATH_IMAGE098
, wherein
Figure DEST_PATH_IMAGE100
Be
Figure 933633DEST_PATH_IMAGE068
State variable value constantly,
Figure DEST_PATH_IMAGE102
Be controller feedback factor vector.
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CN102053562A (en) * 2011-01-05 2011-05-11 杭州电子科技大学 Cracking furnace exit temperature hybrid control method
CN102880046A (en) * 2012-09-24 2013-01-16 杭州电子科技大学 Chemical multi-variable process decoupling prediction function control method
CN102880047A (en) * 2012-09-24 2013-01-16 杭州电子科技大学 Adjoint matrix decoupling prediction control method for oil refining industrial heating furnace temperature process

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101709867A (en) * 2009-12-18 2010-05-19 杭州电子科技大学 Hybrid control method for drum water level system of coal-fired boiler
CN101872182A (en) * 2010-05-21 2010-10-27 杭州电子科技大学 Batch process monitoring method based on recursive non-linear partial least square
CN101872432A (en) * 2010-05-21 2010-10-27 杭州电子科技大学 Ant colony optimization method by introducing curiosity factor
CN102053562A (en) * 2011-01-05 2011-05-11 杭州电子科技大学 Cracking furnace exit temperature hybrid control method
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