CN110069015A - A kind of method of Distributed Predictive function control under non-minimumization state-space model - Google Patents

A kind of method of Distributed Predictive function control under non-minimumization state-space model Download PDF

Info

Publication number
CN110069015A
CN110069015A CN201910431800.1A CN201910431800A CN110069015A CN 110069015 A CN110069015 A CN 110069015A CN 201910431800 A CN201910431800 A CN 201910431800A CN 110069015 A CN110069015 A CN 110069015A
Authority
CN
China
Prior art keywords
subsystem
moment
input
increment
state
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201910431800.1A
Other languages
Chinese (zh)
Other versions
CN110069015B (en
Inventor
张日东
吴胜
欧丹林
蒋超
高福荣
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang Bang Ye Science And Technology Co Ltd
Hangzhou Dianzi University
Hangzhou Electronic Science and Technology University
Original Assignee
Zhejiang Bang Ye Science And Technology Co Ltd
Hangzhou Electronic Science and Technology University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang Bang Ye Science And Technology Co Ltd, Hangzhou Electronic Science and Technology University filed Critical Zhejiang Bang Ye Science And Technology Co Ltd
Priority to CN201910431800.1A priority Critical patent/CN110069015B/en
Publication of CN110069015A publication Critical patent/CN110069015A/en
Application granted granted Critical
Publication of CN110069015B publication Critical patent/CN110069015B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses the Distributed Predictive function control methods under a kind of non-minimumization state-space model, include the following steps: step 1, establish Distributed Predictive function control non-minimumization state-space model；Distributed Predictive function controls controller under step 2, design non-minimumization state-space model.The present invention passes through the means such as data acquisition, model foundation, prediction mechanism, optimization, establish the Distributed Predictive function control method under one kind non-minimumization state-space model, using this method under the premise of guaranteeing compared with high control precision and stability, deficiency of traditional anger Distributed Predictive function control method in the multivariable process control containing Nonself-regulating plant can be effectively made up, and meets the needs of actual industrial process.

Description

A kind of method of Distributed Predictive function control under non-minimumization state-space model
Technical field
The invention belongs to field of automation technology, the Distributed Predictive letter being related under one kind non-minimumization state-space model The design method of number control.
Background technique
Distributed Predictive function controls (DPFC) although control effect is preferable, the rapidity of system should not enough, Especially in the case where introducing interference, restores stable overlong time again, may not reached requirement in some industrial process, Interfere simultaneously introducing, and in the lesser situation of setting value, output cannot perfect tracking fixed valure, there are small Deviation, although deviation very little, is implicitly present in.During actual industrial production, with the control precision and peace to product The requirement of full operation is higher and higher, and also it is necessary to eliminate for some small deviations.Therefore, for Distributed Predictive function control into The certain improvement of row is necessary.
Summary of the invention
The advantages of providing state-space method based on non-minimum state space (NMSS) Model Predictive Control, such as analysis are held Easily, structure designs simple etc..For the fact that, the invention proposes a kind of new non-minimum state-space models of extension (EMSMS), the influence it considers output error, measurement output and input to Model Predictive Control controller design, extension Non-minimum state-space model PREDICTIVE CONTROL maintain the good advantage of state-space model, and prevent tracking error into one Step increases, and can control error well.Here in conjunction with extension non-minimum state spatial model and Distributed Predictive function Control had both maintained Distributed Predictive function and has controlled preferable control effect, while having improved the fast of system to a certain extent Speed.
The technical scheme is that establishing one by means such as data acquisition, model foundation, prediction mechanism, optimizations Distributed Predictive function control method under kind non-minimumization state-space model is being guaranteed using this method compared with high control precision Under the premise of stability, traditional anger Distributed Predictive function control method can be effectively made up containing the changeable of Nonself-regulating plant The deficiency in process control is measured, and meets the needs of actual industrial process.
The step of the method for the present invention includes:
Step 1, Distributed Predictive function control the foundation of non-minimumization state-space model, comprise the concrete steps that:
1.1, the multivariable N input N output large scale system in industrial process is regarded as and purely retarded is added by N number of one order inertia (FOPDT) the N input N output multivariable one order inertia of the subsystem composition of model, available following form adds purely retarded (FOPDT) model system:
Wherein, K11,…,Kij,…,KnnFor the steady-state gain that j-th of multivariable process object input exports i-th, i =1 ..., n；J=1 ..., n, T11,…,Tij,…,TnnThe time that i-th is exported for j-th of multivariable process object input Constant, τ11,…,τij,…,τnnThe lag time that i-th is exported for j-th of multivariable process object input.
1.2, transmission function of the input of j-th of object to the output of i-th of object are as follows:
1.3, in sampling time TsUnder the conditions of, discretization is carried out to step 1.2, available:
Wherein, k indicates moment, d=τij/TsInteger part, αij=e-Ts/Tij, yi(k),yiIt (k-1) is respectively i-th The output at k moment and k-1 moment of a subsystem, uiIt (k-d-1) is input of i-th of subsystem at the k-d-1 moment, uj It (k-d-1) is input of j-th of subsystem at the k-d-1 moment,Reflect other subsystems Input the influence exported to i-th of subsystem.
1.4, it to the first-order difference after the model orientation of discretization, obtains:
Wherein, Δ yi(k),Δyi(k-1) be respectively i-th of subsystem the output increment at k moment and k-1 moment, ΔuiIt (k-d-1) is input increment of i-th of subsystem at the k-d-1 moment, Δ ujIt (k-d-1) is j-th of subsystem in k-d-1 The input increment at moment,The input of other subsystems is reflected to export i-th of subsystem Influence increment.
1.5, by step 1.3 and step 1.4；It can obtain:
Δxi,j(k+1)=Ai,j,mΔxi,j(k)+Bi,j,mΔui(k)+Di,j,mΔuj(k)
Δyi(k+1)=Ci,j,mΔxi,j(k+1)
Wherein, Δ xi,j(k)=[Δ yi(k),Δui(k-1),…,Δui(k-d), Δ uj(k-1),…,Δuj(k-d)]T, Δui(k-1),,Δui(k-d), Δ uj(k-1),…,Δuj(k-d) k-1 is respectively indicated ..., k-d, the input increment at moment, Output increment；Δxi,j(k+1)=[Δ yi(k+1),Δui(k) ..., Δ ui(k-d+1), Δ uj(k),…,Δuj(k-d+1) ]T, Δ yi(k+1),Δui(k-d+1), Δ uj(k-d+1) respectively indicate i-th of subsystem the k+1 moment output increment, Input increment, j-th subsystem input increment at k-d+1 moment of i-th of subsystem at the k-d+1 moment, T representing matrix Transposition symbol.
Bi,j,m=[0 1000 ... 0]T；
Ci,j,m=[1 00 ... 000 0]；
Di,j,m=[0 ... 0010 ... 0]T；
1.6, according to step 1.5, non-minimum state-space model can be obtained:
zi(k+1)=Azi(k)+BΔui(k)+DΔuj(k)+CΔri(k+1)
Wherein:ei(k)=yi(k)-ri(k)
ei(k+1)=ei(k)+Ci,j,mAi,j,mΔxi,j(k)+Ci,j,mBi,j,mΔui(k)+Di,j,mBi,j,mΔuj(k)-Δri (k+1)
riIt (k) is the reference rail at k moment Mark, Δ riIt (k+1) is the reference locus increment at k+1 moment, ei(k), eiIt (k+1) is respectively k, etching system output and reference when k+1 The error of track.
The design of Distributed Predictive function control controller, specific steps under step 2, non-minimumization state-space model It is:
2.1, it according to step 1.6, can obtain:
Zi=Gzi(k)+SΔUi,j+ΨΔRi
Wherein, P indicates optimization time domain, and M indicates control time domain
ΔUi,j=[Δ ui(k) Δui(k+1) … Δui(k+M-1),Δuj(k) Δuj(k+1) … Δuj(k+M- 1)]T
ΔRi=[Δ ri(k+1) Δri(k+2) … Δri(k+P)]T
ri(k+ii)=λiiyi(k)+(1-λii)ci(k), ii=1,2 ..., P
λ indicates the softening factor, ci(k) setting value at i-th of subsystem k moment, Δ r are indicatedi(k+1), Δ ri(k+ 2) ..., Δ ri(k+P) k+1 is respectively indicated ..., k+P moment reference locus increment.
2.2, i-th of subsystem objectives function is taken:
Ji=Zi TQi,mZi+ΔUi,j TRi,j,mΔUi,j
Wherein, Qi,m=block diag (Qi,1,Qi,2,…,Qi,P-1,Qi,P)
ri,1,…,ri,M,rj,1,…,rj,MI-th, j subsystem is respectively indicated from the reference locus at 1 ..., k moment Value.
2.3, it can be obtained by step 2.2:
ΔUi,j=-(STQi,mS+Ri,j,m)-1STQi,m(Gzi(k)+ΨΔRi)
ui(k)=[1 0 ... 0] Δ Ui,j+ui(k-1)
Wherein, ui(k), ui(k-1) control amount at i-th of subsystem k, k-1 moment is respectively indicated.
2.4, the controlling increment Δ U of i-th of subsystem is utilizedi,j, obtain the practical control amount u of i-th of subsystemi(k)= [1 0 … 0]ΔUi,j+ui(k-1) i-th of subsystem is acted on；Then the 1st ..., n son can be similarly found out respectively The control amount of system.
Specific embodiment
The invention will be further described below.
By taking general predictive control as an example:
Drum Water Level Control System for Boiler is a typical multivariable complex object, and regulating measure is using control water-supply valve Valve opening.
The foundation of step 1, Drum Water Level Control System for Boiler model, comprises the concrete steps that:
1.1, the multivariable N input N output large scale system in Drum Water Level Control System for Boiler is regarded as by N number of single order Inertia adds the subsystem of purely retarded (FOPDT) model to form, and the N input N output multivariable single order of available following form is used Property adds purely retarded (FOPDT) model system:
Wherein, K11,…,Kij,…,KnnThe stable state that i-th is exported for j-th of Drum Water Level Control System for Boiler input Gain, i=1 ..., n；J=1 ..., n, T11,…,Tij,…,TnnIt is j-th of Drum Water Level Control System for Boiler input to i-th The time constant of a output, τ11,…,τij,…,τnnI-th is exported for j-th of Drum Water Level Control System for Boiler input Lag time.
1.2, in Drum Water Level Control System for Boiler the input of j-th of object to the transmission function of the output of i-th of object Are as follows:
1.3, in sampling time TsUnder the conditions of, discretization is carried out to step 1.2, available:
Wherein, k indicates moment, d=τij/TsInteger part, αij=e-Ts/Tij, yi(k),yiIt (k-1) is respectively i-th The steam water-level at k moment and k-1 moment of a subsystem, uiIt (k-d-1) is valve of i-th of subsystem at the k-d-1 moment Door aperture, ujIt (k-d-1) is valve opening of j-th of subsystem at the k-d-1 moment,Reflection Other subsystems input the influence to i-th of subsystem steam water-level.
1.4, it to the first-order difference after the model orientation of discretization, obtains:
Wherein, Δ yi(k),Δyi(k-1) be respectively i-th of subsystem the steam water-level at k moment and k-1 moment Increment, Δ uiIt (k-d-1) is valve opening increment of i-th of subsystem at the k-d-1 moment, Δ ujIt (k-d-1) is j-th of subsystem The valve opening increment united at the k-d-1 moment,The input of other subsystems is reflected to i-th The influence increment of a subsystem steam water-level.
1.5, by step 1.3 and step 1.4；It can obtain:
Δxi,j(k+1)=Ai,j,mΔxi,j(k)+Bi,j,mΔui(k)+Di,j,mΔuj(k)
Δyi(k+1)=Ci,j,mΔxi,j(k+1)
Wherein, Δ xi,j(k)=[Δ yi(k),Δui(k-1),…,Δui(k-d), Δ uj(k-1),…,Δuj(k-d)]T,
Δui(k-1),…,Δui(k-d), Δ uj(k-1),…,Δuj(k-d) k-1 is respectively indicated ..., k-d, the moment Valve opening increment, steam water-level increment；Δxi,j(k+1)=[Δ yi(k+1),Δui(k),…,Δui(k-d+1), Δ uj (k),…,Δuj(k-d+1)]T, Δ yi(k+1),Δui(k-d+1), Δ uj(k-d+1) respectively indicate i-th of subsystem in k+ The steam water-level increment at 1 moment, i-th of subsystem are in the valve opening increment at k-d+1 moment, j-th of subsystem in k-d+1 The valve opening increment at quarter, the transposition symbol of T representing matrix.
Bi,j,m=[0 1000 ... 0]T；
Ci,j,m=[1 00 ... 000 0]；
Di,j,m=[0 ... 0010 ... 0]T；
1.6, according to step 1.5, non-minimum state-space model can be obtained:
zi(k+1)=Azi(k)+BΔui(k)+DΔuj(k)+CΔri(k+1)
Wherein:ei(k)=yi(k)-ri(k)
ei(k+1)=ei(k)+Ci,j,mAi,j,mΔxi,j(k)+Ci,j,mBi,j,mΔui(k)+Di,j,mBi,j,mΔuj(k)-Δri (k+1)
riIt (k) is the reference rail at k moment Mark, Δ riIt (k+1) is the reference locus increment at k+1 moment, ei(k), ei(k+1) be respectively k, when k+1 etching system steam water-level with The error of reference locus.
The design of step 2, Drum Water Level Control System for Boiler controller, comprises the concrete steps that:
2.1, it according to step 1.6, can obtain:
Zi=Gzi(k)+SΔUi,j+ΨΔRi
Wherein, P indicates optimization time domain, and M indicates control time domain
ΔUi,j=[Δ ui(k) Δui(k+1) … ui(k+M-1),Δuj(k) Δuj(k+1) … Δuj(k+M- 1)]T
ΔRi=[Δ ri(k+1) Δri(k+2) … Δri(k+P)]T
ri(k+ii)=λiiyi(k)+(1-λii)ci(k), ii=1,2 ..., P
λ indicates the softening factor, ci(k) setting value at i-th of subsystem k moment, Δ r are indicatedi(k+1), Δ ri(k+ 2) ..., Δ ri(k+P) k+1 is respectively indicated ..., k+P moment reference locus increment.
2.2, i-th of subsystem objectives function is taken:
Ji=Zi TQi,mZi+ΔUi,j TRi,j,mΔUi,j
Wherein, Qi,m=block diag (Qi,1,Qi,2,…,Qi,P-1,Qi,P)
ri,1,…,ri,M,rj,1,…,rj,MI-th, j subsystem is respectively indicated from the reference locus at 1 ..., k moment Value.
2.3, it can be obtained by step 2.2:
ΔUi,j=-(STQi,mS+Ri,j,m)-1STQi,m(Gzi(k)+ΨΔRi)
ui(k)=[1 0 ... 0] Δ Ui,j+ui(k-1)
Wherein, ui(k), ui(k-1) valve opening at i-th of subsystem k, k-1 moment is respectively indicated.
2.4, the valve opening increment Delta U of i-th of subsystem is utilizedi,j, obtain the practical valve opening of i-th of subsystem ui(k)=[1 0 ... 0] Δ Ui,j+ui(k-1) i-th of subsystem is acted on；Then can similarly find out respectively the 1st ..., The valve opening of n subsystem.

Claims (3)

1. the Distributed Predictive function control method under a kind of non-minimumization state-space model, includes the following steps:
Step 1 establishes Distributed Predictive function control non-minimumization state-space model；
Distributed Predictive function controls controller under step 2, design non-minimumization state-space model.
2. the Distributed Predictive function control method under non-minimumization state-space model as described in claim 1, feature It is:
Step 1 is specific as follows:
1.1, the multivariable N input N output large scale system in industrial process is regarded as and purely retarded is added by N number of one order inertia (FOPDT) the N input N output multivariable one order inertia of the subsystem composition of model, available following form adds purely retarded (FOPDT) model system:
Wherein, K11,…,Kij,…,KnnFor the steady-state gain that j-th of multivariable process object input exports i-th, i= 1,…,n；J=1 ..., n, T11,…,Tij,…,TnnThe time exported for j-th of multivariable process object input to i-th is normal Number, τ11,…,τij,…,τnnThe lag time that i-th is exported for j-th of multivariable process object input；
1.2, transmission function of the input of j-th of object to the output of i-th of object are as follows:
1.3, in sampling time TsUnder the conditions of, discretization is carried out to step 1.2, available:
Wherein, k indicates moment, d=τij/TsInteger part,yi(k),yiIt (k-1) is respectively i-th of subsystem The output at k moment and k-1 moment, uiIt (k-d-1) is input of i-th of subsystem at the k-d-1 moment, uj(k-d-1) it is Input of j-th of subsystem at the k-d-1 moment,The input of other subsystems is reflected to i-th The influence of a subsystem output；
1.4, it to the first-order difference after the model orientation of discretization, obtains:
Wherein, Δ yi(k),Δyi(k-1) be respectively i-th of subsystem the output increment at k moment and k-1 moment, Δ ui It (k-d-1) is input increment of i-th of subsystem at the k-d-1 moment, Δ ujIt (k-d-1) is j-th of subsystem at the k-d-1 moment Input increment,It reflects other subsystems and inputs the shadow exported to i-th of subsystem Ring increment；
1.5, by step 1.3 and step 1.4；It can obtain:
Δxi,j(k+1)=Ai,j,mΔxi,j(k)+Bi,j,mΔui(k)+Di,j,mΔuj(k)
Δyi(k+1)=Ci,j,mΔxi,j(k+1)
Wherein, Δ xi,j(k)=[Δ yi(k),Δui(k-1),…,Δui(k-d), Δ uj(k-1),…,Δuj(k-d)]T, Δ ui (k-1),…,Δui(k-d), Δ uj(k-1),…,Δuj(k-d) k-1 is respectively indicated ..., k-d is the input increment at moment, defeated Increment out；Δxi,j(k+1)=[Δ yi(k+1),Δui(k),…,Δui(k-d+1), Δ uj(k),…,Δuj(k-d+1)]T, Δyi(k+1),Δui(k-d+1), Δ uj(k-d+1) respectively indicate i-th of subsystem the k+1 moment output increment, i-th Input increment, j-th subsystem input increment at k-d+1 moment of a subsystem at the k-d+1 moment, T representing matrix turn Set symbol；
Bi,j,m=[0 1000 ... 0]T；
Ci,j,m=[1 00 ... 000 0]；
Di,j,m=[0 ... 0010 ... 0]T；
1.6, according to step 1.5, non-minimum state-space model can be obtained:
zi(k+1)=Azi(k)+BΔui(k)+DΔuj(k)+CΔri(k+1)
Wherein:ei(k)=yi(k)-ri(k)ei(k+1)=ei(k)+Ci,j, mAi,j,mΔxi,j(k)+Ci,j,mBi,j,mΔui(k)+Di,j,mBi,j,mΔuj(k)-Δri(k+1)riIt (k) is the reference locus at k moment, Δ ri It (k+1) is the reference locus increment at k+1 moment, ei(k), eiIt (k+1) is respectively k, etching system output and reference locus when k+1 Error.
3. the Distributed Predictive function control method under non-minimumization state-space model as claimed in claim 2, feature Be: step 2 is specific as follows:
2.1, it according to step 1.6, can obtain:
Zi=Gzi(k)+SΔUi,j+ΨΔRi
Wherein, P indicates optimization time domain, and M indicates control time domain
ΔUi,j=[Δ ui(k) Δui(k+1) … Δui(k+M-1),Δuj(k) Δuj(k+1) … Δuj(k+M-1)]T
ΔRi=[Δ ri(k+1) Δri(k+2) … Δri(k+P)]T
ri(k+ii)=λiiyi(k)+(1-λii)ci(k), ii=1,2 ..., P
λ indicates the softening factor, ci(k) setting value at i-th of subsystem k moment, Δ r are indicatedi(k+1), Δ ri(k+2) ..., Δ ri (k+P) k+1 is respectively indicated ..., k+P moment reference locus increment；
2.2, i-th of subsystem objectives function is taken:
Ji=Zi TQi,mZi+ΔUi,j TRi,j,mΔUi,j
Wherein, Qi,m=block diag (Qi,1,Qi,2,…,Qi,P-1,Qi,P)
ri,1,…,ri,M,rj,1,…,rj,MRespectively indicate value of i-th, the j subsystem from the reference locus at 1 ..., k moment；
2.3, it can be obtained by step 2.2:
ΔUi,j=-(STQi,mS+Ri,j,m)-1STQi,m(Gzi(k)+ΨΔRi)
ui(k)=[1 0 ... 0] Δ Ui,j+ui(k-1)
Wherein, ui(k), ui(k-1) control amount at i-th of subsystem k, k-1 moment is respectively indicated；
2.4, the controlling increment Δ U of i-th of subsystem is utilizedi,j, obtain the practical control amount u of i-th of subsystemi(k)=[1 0 … 0]ΔUi,j+ui(k-1) i-th of subsystem is acted on；Then the control of the 1st ..., n subsystem is similarly found out respectively Amount.
CN201910431800.1A 2019-05-22 2019-05-22 Distributed prediction function control method under non-minimized state space model Active CN110069015B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910431800.1A CN110069015B (en) 2019-05-22 2019-05-22 Distributed prediction function control method under non-minimized state space model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910431800.1A CN110069015B (en) 2019-05-22 2019-05-22 Distributed prediction function control method under non-minimized state space model

Publications (2)

Publication Number Publication Date
CN110069015A true CN110069015A (en) 2019-07-30
CN110069015B CN110069015B (en) 2021-12-28

Family

ID=67371270

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910431800.1A Active CN110069015B (en) 2019-05-22 2019-05-22 Distributed prediction function control method under non-minimized state space model

Country Status (1)

Country Link
CN (1) CN110069015B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112947323A (en) * 2021-01-26 2021-06-11 杭州电子科技大学 Chemical process distributed control method for explicit model predictive control optimization

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120053751A1 (en) * 2009-02-03 2012-03-01 Dong Energy Power A/S Distributed electrical power production system and method of control thereof
CN102520616A (en) * 2011-12-30 2012-06-27 杭州电子科技大学 Partial decoupling unminimized model prediction function control method in oil refining industrial process
KR20130081965A (en) * 2012-01-10 2013-07-18 주식회사 에어로네트 Mathod for smart grid control and monitering of wind power turbin based on day-ahead wind distribution model
CN104317321A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Coking furnace hearth pressure control method based on state-space predictive functional control optimization
CN105159095A (en) * 2015-10-10 2015-12-16 杭州电子科技大学 Multivariable process distillation column model prediction control optimization PID control method
CN106338915A (en) * 2016-10-31 2017-01-18 浙江邦业科技股份有限公司 Extended state space predictive function control based integral object control method
CN106444362A (en) * 2016-12-06 2017-02-22 杭州电子科技大学 Distributed PID (Proportion Integration Differentiation) predictive function control method for furnace box temperature of waste plastic cracking furnace
CN106786677A (en) * 2017-03-29 2017-05-31 温州大学 A kind of interconnected electric power system distributed dynamic matrix frequency control method of meter and wind-powered electricity generation
CN106842958A (en) * 2017-03-20 2017-06-13 福州大学 A kind of distributed model predictive control method of back-to-back converter
CN108089442A (en) * 2017-12-12 2018-05-29 浙江大学宁波理工学院 A kind of PI controller parameter automatic setting methods based on Predictive function control and fuzzy control
CN109085755A (en) * 2018-08-14 2018-12-25 中国电力科学研究院有限公司 The advanced control method for frequency of wind-powered electricity generation group system and system based on distributed model predictive control
CN109491248A (en) * 2018-11-20 2019-03-19 中南大学 Magnetic levitation ball position prediction control method based on RBF-ARX model and laguerre function
CN109544922A (en) * 2018-11-27 2019-03-29 华南理工大学 A kind of traffic network Distributed Predictive Control method based on region division

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120053751A1 (en) * 2009-02-03 2012-03-01 Dong Energy Power A/S Distributed electrical power production system and method of control thereof
CN102520616A (en) * 2011-12-30 2012-06-27 杭州电子科技大学 Partial decoupling unminimized model prediction function control method in oil refining industrial process
KR20130081965A (en) * 2012-01-10 2013-07-18 주식회사 에어로네트 Mathod for smart grid control and monitering of wind power turbin based on day-ahead wind distribution model
CN104317321A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Coking furnace hearth pressure control method based on state-space predictive functional control optimization
CN105159095A (en) * 2015-10-10 2015-12-16 杭州电子科技大学 Multivariable process distillation column model prediction control optimization PID control method
CN106338915A (en) * 2016-10-31 2017-01-18 浙江邦业科技股份有限公司 Extended state space predictive function control based integral object control method
CN106444362A (en) * 2016-12-06 2017-02-22 杭州电子科技大学 Distributed PID (Proportion Integration Differentiation) predictive function control method for furnace box temperature of waste plastic cracking furnace
CN106842958A (en) * 2017-03-20 2017-06-13 福州大学 A kind of distributed model predictive control method of back-to-back converter
CN106786677A (en) * 2017-03-29 2017-05-31 温州大学 A kind of interconnected electric power system distributed dynamic matrix frequency control method of meter and wind-powered electricity generation
CN108089442A (en) * 2017-12-12 2018-05-29 浙江大学宁波理工学院 A kind of PI controller parameter automatic setting methods based on Predictive function control and fuzzy control
CN109085755A (en) * 2018-08-14 2018-12-25 中国电力科学研究院有限公司 The advanced control method for frequency of wind-powered electricity generation group system and system based on distributed model predictive control
CN109491248A (en) * 2018-11-20 2019-03-19 中南大学 Magnetic levitation ball position prediction control method based on RBF-ARX model and laguerre function
CN109544922A (en) * 2018-11-27 2019-03-29 华南理工大学 A kind of traffic network Distributed Predictive Control method based on region division

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
DAWEI WANG 等: "《Design of distributed PID-type dynamic matrix controller for fractional-order systems》", 《INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE》 *
JIANMING ZHANG: "《A non-minimal state space formulation based predictive functional control design for inverse-response processes》", 《CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS》 *
RIDONG ZHANG 等: "《Design of fractional order modeling based extended non-minimal state space MPC for temperature in an industrial electric heating furnace》", 《JOURNAL OF PROCESS CONTROL》 *
张运诗 等: "《分布式预测函数控制优化算法》", 《自动化仪表》 *
霍爱清 等: "《增广非最小状态空间法的稳定平台预测控制研究》", 《计算机工程与应用》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112947323A (en) * 2021-01-26 2021-06-11 杭州电子科技大学 Chemical process distributed control method for explicit model predictive control optimization
CN112947323B (en) * 2021-01-26 2022-03-18 杭州电子科技大学 Chemical process distributed control method for explicit model predictive control optimization

Also Published As

Publication number Publication date
CN110069015B (en) 2021-12-28

Similar Documents

Publication Publication Date Title
CN109581870B (en) Dynamic matrix control method for temperature in energetic material reaction kettle
CN101751051B (en) Cement decomposing furnace temperature control method based on constraint smith GPC
CN100465825C (en) Variable structural nonlinear model predictor controller
CN110285403A (en) Main Steam Temperature Control method based on controlled parameter prediction
CN108490779B (en) Rare earth extraction process decoupling control method
CN110069015A (en) A kind of method of Distributed Predictive function control under non-minimumization state-space model
CN108664009A (en) Divided stages based on correlation analysis and fault detection method
CN106200379B (en) A kind of distributed dynamic matrix majorization method of Nonself-regulating plant
CN113515893B (en) Method for establishing real-time prediction model of rare earth extraction process, prediction method and prediction device
CN117270483A (en) Full-flow dynamic optimization control method and device for chemical production device and electronic equipment
CN109782588A (en) The tight format non-model control method of the different factor of MIMO
CN106706491B (en) Intelligent detection method for membrane bioreactor-MBR water permeability
CN1573624B (en) Apparatus and method for batch property estimation
Teng et al. Explicit distributed model predictive control design for chemical processes under constraints and uncertainty
CN114326395B (en) Intelligent generator set control model online updating method based on working condition discrimination
CN109034366B (en) Application of ELM integrated model based on multiple activation functions in chemical engineering modeling
CN109870986A (en) A kind of stirred autoclave On-Line Control Method based on neural network and data-driven
CN113076524B (en) Water turbine axis detection data optimization processing method based on ant colony algorithm
CN104898587A (en) Industrial process modeling system and method based on parallel diffusion-type intelligent search algorithm
CN111896038B (en) Semiconductor process data correction method based on correlation entropy and shallow neural network
CN110631003B (en) Reheated steam temperature adjusting method based on hierarchical scheduling multi-model predictive control
CN107045360B (en) One kind four holds high water tank distributions feedback
Pei et al. Iterative Learning Control for Tricalcium Neutralization Process with Initial Disturbance
Li et al. Supervisory predictive control of weighted least square support vector machine based on Cauchy distribution
Liu et al. Analysis of operational performance reliability for CNC equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant