CN104749954B - Intelligent ash bin control system device based on IMC (Internal Model Control) strategy and control method - Google Patents
Intelligent ash bin control system device based on IMC (Internal Model Control) strategy and control method Download PDFInfo
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Abstract
The invention relates to an intelligent ash bin control system device based on an IMC (Internal Model Control) strategy, which aims at a control system of an object in a complicate chemical industrial engineering and aims to simplify a traditional complicate designing process to meet the actual production requirements of enterprises. According to the intelligent ash bin control system device based on the IMC strategy provided by the inventions, the IMC strategy is applied to integrally integrate an identification module and a control module. The intelligent ash bin control system device mainly comprises the identification module and the control module; the control module further comprises a model classifier module and a parameters setting module; the intelligent classification of effective matching modules can be realized by the model classifier module by virtue of an SVM (Space Vector Modulation) technology; the parameter setting module further comprises two schemes: the first scheme is to adopt a way that an offline controller library is combined with the parameter setting module; and the second scheme is to adopt a way that a preset control structural way and an online parameter optimization and setting way. The intelligent ash bin control system device provided by the invention has the advantages of being simple in structure and simple in design process, and the precision and production benefit of the control system can be improved.
Description
Technical field
Invention is related to industrial process control field, in particular with the control of complicated chemical industrial process object
System design processed.
Background technology
In chemical industry actual production process, substantial amounts of complex process object is there is.Therefore, complicated chemical industry process object
Control problem is of great significance for production practices.In single variable control system, large dead time, non-thread are there is often
The factors such as property, strong jamming；And in multi-variable system, the factor such as big coupling, strong jamming, unstable is there is often.Traditional
Control method is the inputoutput data according to control object, by discrimination method, picks out the parameter of model, and then basis is distinguished
The model parameter known designs controller.This design process is comparatively laborious, and during recognizing and designing controller
New error will be produced, causes the robustness of control system poor.For production practices, traditional control method sets
Meter process is complicated, and control accuracy is not high, and productivity effect is low, it is impossible to meet the requirement of enterprise.
The content of the invention
In view of this, the present invention devises a kind of intelligent ash bin control system device based on internal model control strategy.
Simplify for the Control System Design method of complicated chemical industry process object, improve the suitability and the robustness of control system.
A kind of intelligent ash bin control device based on internal model control strategy, it is characterised in that include：Internal model control module,
Matching Model identification module, ash bin controller module；
Described internal model control module includes three parts, the internal model module of controlled device, filter module and control
Molding block.The internal model module of controlled device is used to estimate the impact that performance variable exports control system, filter module
There is robust stability for making control system, control module is used to calculate the predictive value of performance variable, realizes control system
Tracking characteristics；
Described Matching Model identification module is included for according to the inputoutput data of object, estimating object model
Parameter, then by the use of the model for being recognized as the Matching Model of internal model control；
The structure of described ash bin controller module includes category of model module and attitude conirol module, its middle mold
Type sort module adopts support vector machine technology, and the Matching Model of identification module is carried out into the positive and negative model sample of pretreatment, and carries
Eigenvalue is taken, grader is trained, after the supporting vector for finding out every kind of classification, using the grader for training mould is carried out
The detection of type and identification；Attitude conirol module includes two parts of control strategy selecting module and automatic adjusting module.
Further, side of the described attitude conirol module using Off-line control device storehouse in combination with parameter tuning
Formula, i.e., select the controller architecture form in Off-line control device storehouse, then according to control according to the classification results of model classifiers
System performance index carries out parameter tuning, realizes tracing control.
Further, described attitude conirol module is using the classification results according to model classifiers, default control
Version, then according to the requirement of control system performance, set optimizing index, carry out online optimizing and adjust.
Other features and effect that the present invention possesses will be further elaborated in the following description.The mesh of the present invention
And other advantages by description, the structure pointed out in claims, and accompanying drawing is realizing and obtain.
The invention will be further described to combine specific embodiment below by accompanying drawing.
Description of the drawings
Fig. 1 is intelligent ash bin control system schematic diagram；
Fig. 2 is intelligent ash bin internal model control system structured flowchart；
Fig. 3 is Matching Model identification flow chart；
Fig. 4 is intelligent ash bin internal mode controller structure；
Fig. 5 is SVM model classifiers training categorizing process；
Fig. 6 is model classifiers workflow diagram；
Fig. 7 is attitude conirol device organization plan one；
Fig. 8 is attitude conirol device organization plan two；
Fig. 9 is water jacket agitating heating device schematic diagram；
Figure 10 is the frequency contrast of JSTH system modeies and identification model；(black circle is Matching Model, and black line is actual mould
Type)
Figure 11 is the response of JSTH control systems setting value tracking；
Specific embodiment
Ash bin controller module includes category of model module and attitude conirol module, and wherein category of model module is adopted
With support vector machine (SVM) technology, the Matching Model of identification module is carried out into the positive and negative model sample of pretreatment, and extract feature
Value, is trained to grader, after the supporting vector for finding out every kind of classification, using the grader for training the inspection of model is carried out
Survey and identification；Attitude conirol module includes two parts of control strategy selecting module and automatic adjusting module.Here, adopting
Realize that control parameter is adjusted the setting function of module with two kinds of structural design schemes.A kind of scheme be using Off-line control device storehouse with
The mode that parameter tuning combines, i.e., select the controller architecture in Off-line control device storehouse according to the classification results of model classifiers
Form, then carries out parameter tuning according to control system performance indications, realizes tracing control.Another scheme is using according to mould
The classification results of type grader, the version of default control, then according to the requirement of control system performance, sets optimizing and refers to
Mark, carries out online optimizing and adjusts.
Below in conjunction with the accompanying drawings the present invention is described in detail with specific embodiment.It should be noted that description is only used for
The bright and explanation present invention, is not intended to limit the present invention.
The present invention title be " based on internal model control strategy intelligent ash bin control device ", " ash bin " therein refer to by
The unknown parameters of control object, internal dynamic change is unknown, but situation known to structure；So-called " intelligence " is mainly reflected in root
According to the classification results of model classifiers, the version of selection control, realize from recognize control overall process nobody drive
Sail and operate, strengthen the integrated level and automaticity of control system, improve the adaptability of control system, enhance control
The robustness of system processed.Fig. 1 is intelligent ash bin control system schematic diagram, illustrates the Basic Design process and thought of the present invention.
1. system structure
The system for realizing the present invention is made up of three parts：Internal model control module, Matching Model identification module, ash bin control
Device module.As shown in Fig. 2 for the structured flowchart of intelligent ash bin internal model control system.In figure, r represents the input quantity of system；e
Represent error；U is controlled quentity controlled variable；IGB-IMC is intelligent ash bin internal mode controller, with GIGB-IMCRepresent；G is controlled device；GmFor institute
Identification system picks out the Matching Model come；D is Observable interference volume；ypFor the output of system；ymFor the defeated of Matching Model
Go out.First, data of the module according to controlled device input and output are recognized, model parameter is estimated, is picked out and is effectively matched model Gm；
Then model classifiers are classified according to the model that is effectively matched for picking out；After the completion of classification, parameter tuning module according to point
The requirement of class result and control system performance, using corresponding controller architecture and controller parameters setting.By internal model control
Basic theories, can learn：
Shown in the input/output relation transmission function such as formula (1) and formula (2) of system, formula (1) is from input to output
The transmission function at end, formula (2) is the relational expression from interference end to outfan.
Can be obtained by formula (1) and formula (2) shown in the closed loop response such as formula (3) of system：
2. stability analyses
As Model Matching (G=Gm) and when not having external disturbance, the output of Matching Model is phase with the input of controlled device
When, then the stability of system is determined by controlled device with controller；If stablizing during controlled device, system it is stable as
Controller is related.Here it is the maximum feature and advantage of internal model control.
For Stepped Impedance Resonators and disturbance, if unmatched models, as long as the steady-state gain of controller is equal to model steady-state gain
Inverse, then whole closed loop system is still stable.In the case of formula (4) is for unmatched models, system stability needs what is met
Condition：
From final-value theorem, if assuming stable deviation e (∞)=0,Wherein
GIGB-IMC(0) it is the steady-state gain of controller,For the steady-state gain of Matching Model.
3. Matching Model recognizes module
Matching Model identification module includes three ingredients, controlled device inputoutput data, plant model collection
With the criterion of model parameter estimation.As shown in figure 3, the flow chart to recognize module.
According to the priori of controlled device, design identification experiment, and then the input of the controlled device required for producing is defeated
Go out data；And according to the priori of the controlled device given, select suitable Models Sets and parameter estimation criterion；Then, then
The identification of model is carried out, that is, recognizes part.Obtain estimating model GmAfterwards, then verified；If not over checking, returning just
Initial portion, redesigns identification experiment, obtains data or reselects the Models Sets and parameter estimation criterion of suitable controlled device；
If estimating, model, by checking, exports identification model.
The estimation model G for being picked outm, the model classifiers for ash bin controller module are classified, are then selected
Suitable controller, is finally controlled device parameter tuning.
4. ash bin controller module
Two modules, category of model module and attitude conirol module are included in ash bin controller module.Such as Fig. 4 institutes
Show, controller module is monitored, recognizes according to the input and output amount of system, systematic error and the Matching Model by obtained by identification
And classification, the then parameter of the corresponding controller form of reselection and setting controller.
Category of model module using can support vector machine (SVM) technology, to the model G for being recognizedmClassified.The present invention
Controlled device is divided into into five classes, as shown in table 1.
The Matching Model of table 1 is classified
Method of the SVM training categorizing processes using comparing two-by-two, its process is as shown in Figure 5.During training classification, first judge to divide
Class device " 1 pair 5 ", if being judged as 5, selects left-hand branch, then judge grader " 2 pairs 5 " this, if being still 5, still select left side
Branch, judges always, it is possible to obtain classification results.Training categorizing process as shown in figure 5, this is a directed acyclic graph, point
Class speed is fast, do not classify overlap and unclassified phenomenon, it is possible to by adjusting " selection of root node " and output " confidence
Degree " carrys out improvement.
It is as shown in Figure 6 in the workflow of model classifiers.First, SVM classifier is trained.Prepare positive negative norm
Pattern sheet simultaneously carries out pretreatment to it, should ensure to distinguish, makes data volume minimum again, that is, be conducive to calculating；It is special extracting
After value indicative, grader is trained, training grader process is carried out as stated above；The result of training can find out every class
Supporting vector.Including the corresponding Lagrange coefficient of each supporting vector, parameter of grader etc.；Finally, using training
Grader carries out the detection and identification of model.
Attitude conirol module includes two parts of control strategy selecting module and automatic adjusting module, for basis
Category of model result selects the method for control parameter of adjusting, and realizes ash bin control.Here, being realized using two kinds of structural design schemes
Control parameter is adjusted the setting function of module.A kind of scheme be by the way of Off-line control device storehouse is in combination with parameter tuning,
The controller architecture form in Off-line control device storehouse is selected according to the classification results of model classifiers, then according to control system
Performance indications carry out parameter tuning, realize tracing control, and its control structure is as shown in Figure 7.Another scheme is using according to mould
The classification results of type grader, the version of default control, then according to the requirement of control system performance, sets optimizing and refers to
Mark, carries out online optimizing and adjusts, and its control structure is as shown in Figure 8.
When control system is single-variable system, the form of selected controller is shown below：
Wherein, s be Laplce's domain variable, b,a2, a1, L is the parameter for needing to adjust, and is real number.
When control system is multi-variable system, selected controller form is shown below：
Wherein, each element gijExpression be
Wherein, s be Laplce's domain variable, bij,aij2, aij1, LijFor the parameter for needing to adjust, real number is.M is
Line number, n is columns, is the controller architecture form of method, system as m=n；It is the controller knot of fat shape system as m ＞ n
Configuration formula；It is the controller architecture form of thin shape system as m ＜ n.
By taking water jacket agitating heating device (Jacketed Stirred Tank Heater, JSTH) control system as an example, JSTH
It is widely present in the middle of commercial production.JSTH control systems are as shown in Figure 9.Due to there is large dead time and outer in actual production
The strongly disturbing situation in portion, has had a strong impact on the performance and quality of control system.Therefore, for actual production, to product temperature
It is highly important that degree carries out effective and stable control.The model of JSTH systems is shown below：
Resulting Matching Model is as follows after through recognizing module identification：
In order to analyze the difference between gained Matching Model and realistic model, using the method for frequency-domain analysiss, such as Figure 10 institutes
Show.As seen from Figure 10, to be effectively matched model similar in middle low frequency characteristic to realistic model for gained, medium-high frequency characteristic deviation compared with
Greatly.Obtained after effective Matching Model by identification module, then classified by model classifiers.Gained type is 1 after classification
Type, i.e. FOPDT systems.From scheme two, the structure of static controller simultaneously (is quoted from Cheng Zhijin-many using NPSO searching algorithms
The research of variable modular system discrimination method with using 5.3.1 section) according to the requirement of control system performance, carry out online optimizing whole
Fixed, gained controller is shown below：
The step response that amplitude is 1 is added in 50s, and the step response that amplitude is -1 is added in 1000s, to test
The ability of tracking of control system.The output response and controller output of gained system is as shown in figure 11.As seen from Figure 11, control system
System has good ability of tracking and with good performance indications, meets the requirement of control system.
As a result show, present system simple structure, be easy to operation.By simulation curve it can be seen that this law is bright is proposed
The intelligent ash bin Control System Design method based on internal model control strategy it is effective and feasible, with very well tracking and anti-interference
Ability, even in the case of unmatched models, also can control system well, with stronger robustness.
Claims (3)
1. a kind of intelligent ash bin control device based on internal model control strategy, it is characterised in that include：Internal model control module,
With Model Distinguish module, ash bin controller module；
Described internal model control module includes three parts, the internal model module of controlled device, filter module and control mould
Block；The internal model module of controlled device is used to estimate the impact that performance variable exports control system that control module to be based on
The predictive value of performance variable is calculated, the tracking characteristics of control system are realized；
Described Matching Model identification module is included for according to the inputoutput data of object, estimating the ginseng of object model
Number, then by the use of the model for being recognized as the Matching Model of internal model control；
The structure of described ash bin controller module includes category of model module and attitude conirol module, wherein model point
Generic module adopts support vector machine technology, and the positive and negative model sample of the Matching Model of identification module is carried out into pretreatment, and extracts
Eigenvalue, is trained to grader, after the supporting vector for finding out every kind of classification, using the grader for training model is carried out
Detection with identification；Attitude conirol module includes two parts of control strategy selecting module and automatic adjusting module.
2. the intelligent ash bin control device of internal model control strategy is based on as claimed in claim 1, it is characterised in that：Described control
Device parameter tuning module processed by the way of Off-line control device storehouse is in combination with parameter tuning, i.e., according to the classification of model classifiers
As a result the controller architecture form in Off-line control device storehouse is selected, then parameter tuning is carried out according to control system performance indications,
Realize tracing control.
3. the intelligent ash bin control device of internal model control strategy is based on as claimed in claim 1, it is characterised in that：Described control
Device parameter tuning module processed is using the classification results according to model classifiers, the version of prescription controller, then according to control
The requirement of systematic function processed, sets optimizing index, carries out online optimizing and adjusts.
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| CN111360252A (en) * | 2020-03-05 | 2020-07-03 | 西安理工大学 | Model identification and internal model control system based on 3D printer |
| CN111546336B (en) * | 2020-04-29 | 2021-01-29 | 北京航空航天大学 | Ash box model parameter identification method and system for robot system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6697767B2 (en) * | 2000-10-18 | 2004-02-24 | The National University Of Singapore | Robust process identification and auto-tuning control |
| CN1851572A (en) * | 2006-05-18 | 2006-10-25 | 上海交通大学 | Method for designing inner mould self-setting digital controller |
| CN102393627A (en) * | 2011-10-21 | 2012-03-28 | 靳其兵 | System and method for optimizing and adjusting controller parameters in distributed control system |
| CA2897189A1 (en) * | 2013-01-14 | 2014-07-17 | The Regents Of The University Of California | Model-based personalization scheme of an artificial pancreas for type i diabetes applications |
-
2015
- 2015-02-08 CN CN201510065397.7A patent/CN104749954B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6697767B2 (en) * | 2000-10-18 | 2004-02-24 | The National University Of Singapore | Robust process identification and auto-tuning control |
| CN1851572A (en) * | 2006-05-18 | 2006-10-25 | 上海交通大学 | Method for designing inner mould self-setting digital controller |
| CN102393627A (en) * | 2011-10-21 | 2012-03-28 | 靳其兵 | System and method for optimizing and adjusting controller parameters in distributed control system |
| CA2897189A1 (en) * | 2013-01-14 | 2014-07-17 | The Regents Of The University Of California | Model-based personalization scheme of an artificial pancreas for type i diabetes applications |
Non-Patent Citations (4)
| Title |
|---|
| 一种基于支持向量机的内模控制方法;王定成 等;《控制理论与应用》;20040229;第21卷(第1期);全文 * |
| 基于惯量辨识的测试转台自适应内模控制;朱海荣 等;《控制理论与应用》;20130228;第30卷(第2期);全文 * |
| 船舶航向最小二乘支持向量机内模控制;宋佳 等;《电机与控制学报》;20091130;第13卷(第S1期);全文 * |
| 良好非线性模型辨识及其内模控制;刘陆洲 等;《电机与控制学报》;20090731;第13卷(第4期);全文 * |
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