CN104749954A - 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 PDF

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CN104749954A
CN104749954A CN201510065397.7A CN201510065397A CN104749954A CN 104749954 A CN104749954 A CN 104749954A CN 201510065397 A CN201510065397 A CN 201510065397A CN 104749954 A CN104749954 A CN 104749954A
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module
model
control
control system
ash bin
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CN104749954B (en
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靳其兵
刘立业
曹丽婷
王琪
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Beijing University of Chemical Technology
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Beijing University of Chemical Technology
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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

Based on intelligent ash bin control system device and the control method of internal model control strategy
Technical field
The present invention the present invention relates to industrial process control field, particularly relates to the Control System Design for complicated chemical industrial process object.
Background technology
In chemical industry actual production process, there is a large amount of complex process objects.Therefore, the control problem of complicated chemical industry process object is of great significance for production practices.In single variable control system, often there is the factors such as large dead time, non-linear, strong jamming; And in multi-variable system, often there is the factors such as large coupling, strong jamming, instability.Traditional control method is the inputoutput data according to control object, by discrimination method, picks out the parameter of model, then carrys out CONTROLLER DESIGN according to the model parameter picked out.This design process is more loaded down with trivial details, and all can produce new error in identification and CONTROLLER DESIGN process, causes the robustness of control system poor.For production practices, traditional control method design process is complicated, and control accuracy is not high, and productivity effect is low, can not meet the requirement of enterprise.
Summary 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 the Control System Design method for complicated chemical industry process object, improve applicability and the robustness of control system.
Based on an intelligent ash bin control device for internal model control strategy, it is characterized in that, comprising: internal model control module, Matching Model recognition module, ash bin controller module;
Described internal model control module comprises three parts, the internal model module of controlled device, filter module and control module.The internal model module of controlled device is for estimating the impact that performance variable exports control system, and filter module is used for making control system have robust stability, and control module is used for the predicted value of calculating operation variable, realizes the tracking characteristics of control system;
Described Matching Model recognition module comprises for the inputoutput data according to object, estimates the parameter of object model, then utilizes the Matching Model of model as internal model control of institute's identification;
The structure of described ash bin controller module comprises category of model module and attitude conirol module, wherein category of model module adopts support vector machine technology, the Matching Model of recognition module is carried out the positive and negative model sample of pre-service, and extract eigenwert, sorter is trained, after the support vector finding out often kind of classification, the sorter trained is utilized to carry out the detection and indentification of model; Attitude conirol module comprises control strategy and selects module and automatic adjusting module two parts.
Further, the mode that described attitude conirol module adopts Off-line control device storehouse to combine with parameter tuning, namely the controller architecture form in Off-line control device storehouse is selected according to the classification results of model classifiers, then carry out parameter tuning according to control system performance index, realize tracing control.
Further, described attitude conirol module adopts the classification results according to model classifiers, and the version of default control, then according to the requirement of control system performance, sets optimizing index, carries out online optimizing and adjusts.
Other features that the present invention possesses and effect will elaborate in the following description further.Object of the present invention and other advantages pass through instructions, claims, and the structure pointed out in accompanying drawing realizes and obtains.
Below by accompanying drawing, in conjunction with specific embodiments, the invention will be further described.
Accompanying drawing explanation
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 process flow diagram;
Fig. 4 is intelligent ash bin internal mode controller structure;
Fig. 5 is SVM model classifiers training assorting 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 that the frequency of JSTH system model and identification model contrasts; (black circle is Matching Model, and black line is realistic model)
Figure 11 is the response of JSTH control system setting value tracking;
Embodiment
Ash bin controller module comprises category of model module and attitude conirol module, wherein category of model module adopts support vector machine (SVM) technology, the Matching Model of recognition module is carried out the positive and negative model sample of pre-service, and extract eigenwert, sorter is trained, after the support vector finding out often kind of classification, the sorter trained is utilized to carry out the detection and indentification of model; Attitude conirol module comprises control strategy and selects module and automatic adjusting module two parts.At this, adopt two kinds of structural design schemes to realize controling parameters and to adjust the setting function of module.A kind of scheme is the mode adopting Off-line control device storehouse to combine with parameter tuning, namely the controller architecture form in Off-line control device storehouse is selected according to the classification results of model classifiers, then carry out parameter tuning according to control system performance index, realize tracing control.Another scheme adopts the classification results according to model classifiers, and the version of default control, then according to the requirement of control system performance, sets optimizing index, carries out online optimizing and adjusts.
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.It should be noted that instructions only for instruction and explanation of the present invention, be not intended to limit the present invention.
Title of the present invention is " the intelligent ash bin control device based on internal model control strategy ", and " ash bin " wherein refers to the unknown parameters of controlled device, and internal dynamic change is unknown, but the situation that structure is known; So-called " intelligence " is mainly reflected in the classification results according to model classifiers, the version of selection control, achieve and operation unmanned from the overall process recognizing control, strengthen integrated level and the automaticity of control system, improve the adaptive faculty of control system, enhance the robustness of control system.Fig. 1 is intelligent ash bin control system schematic diagram, illustrates Basic Design process of the present invention and thought.
1. system architecture
Realize system of the present invention to be made up of three parts: internal model control module, Matching Model recognition module, ash bin controller module.As shown in Figure 2, be the structured flowchart of intelligent ash bin internal model control system.In the drawings, r represents the input quantity of system; E represents error; U is controlled quentity controlled variable; IGB-IMC is intelligent ash bin internal mode controller, with G iGB-IMCrepresent; G is controlled device; G mfor identification system institute of institute identification Matching Model out; D is Observable interference volume; y pfor the output of system; y mfor the output of Matching Model.First, recognition module is according to the data of controlled device input and output, and estimation model parameter, picks out effective Matching Model G m; Then model classifiers is classified according to the effective Matching Model picked out; After having classified, parameter tuning module, according to the requirement of classification results and control system performance, adopts corresponding controller architecture and controller parameters setting.By the basic theories of internal model control, can learn:
The input/output relation transport function of system is such as formula shown in (1) and formula (2), and formula (1) is the transport function from input end to output terminal, and formula (2) is the relational expression from interference end to output terminal.
y r = G IGB - IMC G 1 + G IGB - IMC ( G - G m ) - - - ( 1 )
y d = 1 - G IGB - IMC G m 1 + G IGB - IMC ( G - G m ) - - - ( 2 )
The closed loop response of system can be obtained such as formula shown in (3) by formula (1) and formula (2):
y = G IGB - IMC Gr 1 + G IGB - IMC ( G - G m ) + 1 - G IGB - IMC G m 1 + G IGB - IMC ( G - G m ) d - - - ( 3 )
2. stability analysis
As Model Matching (G=G m) and when not having external disturbance, the output of Matching Model and the input of controlled device are suitable, then the stability of system determined by controlled device and controller; If stablize during controlled device, then the stable of system is correlated with as controller.The maximum characteristics and advantages of internal model control that Here it is.
For Stepped Impedance Resonators and disturbance, if unmatched models, as long as the steady-state gain of controller equals the inverse of model steady-state gain, then whole closed-loop system is still stable.Formula (4) in unmatched models situation, the condition of system stability demand fulfillment:
e = r - y = 1 - G IGB - IMC G m 1 + G IGB - IMC ( G - G m ) [ r - d ] - - - ( 4 )
From final-value theorem, if hypothesis stablizes deviation e (∞)=0, then wherein G iGB-IMC(0) be the steady-state gain of controller, for the steady-state gain of Matching Model.
3. Matching Model recognition module
Matching Model recognition module comprises three ingredients, the criterion of controlled device inputoutput data, plant model collection and model parameter estimation.As shown in Figure 3, be the process flow diagram of recognition module.
According to the priori of controlled device, design identification experiment, and then the inputoutput data of controlled device required for producing; And according to the priori of given controlled device, select suitable Models Sets and parameter estimation criterion; Then carry out the identification of model, i.e. identification part then.Obtain estimation model G mafter, then verify; If by checking, then return initial part, redesign identification experiment, obtain data or reselect Models Sets and the parameter estimation criterion of suitable controlled device; If estimation model by checking, then exports identification model.
The estimation model G picked out m, classify for the model classifiers of ash bin controller module, then select suitable controller, finally carry out attitude conirol.
4. ash bin controller module
Two modules are comprised, category of model module and attitude conirol module at ash bin controller module.As shown in Figure 4, controller module carries out monitoring, identify and classifying according to the input and output amount of system, systematic error and the Matching Model by identification gained, and then selects corresponding controller form and the parameter of setting controller.
Category of model module adopts can support vector machine (SVM) technology, to the model G of institute's identification mclassify.Controlled device is divided into five classes by the present invention, as shown in table 1.
Table 1 Matching Model is classified
SVM trains assorting process to adopt the method for comparison between two, and its process as shown in Figure 5.During training classification, first judge sorter " 1 to 5 ", if be judged as 5, select left-hand branch, then judge sorter " 2 to 5 " this, if be still 5, then still select left-hand branch, judge always, just can obtain classification results.As shown in Figure 5, this is a directed acyclic graph to training assorting process, and classification speed is fast, do not classify overlapping and unclassified phenomenon, and can improve effect by adjusting " choosing of root node " and exporting " degree of confidence ".
Model classifiers workflow as shown in Figure 6.First, to train SVM classifier.Prepare positive and negative model sample and pre-service is carried out to it, should ensure to distinguish, make data volume minimum again, being namely conducive to calculating; After extraction eigenwert, train sorter, training classifier process is carried out as stated above; The result of training can find out the support vector of every class.Comprise the Lagrange coefficient that each support vector is corresponding, the parameter etc. of sorter; Finally, the sorter trained is utilized to carry out the detection and indentification of model.
Attitude conirol module comprises control strategy and selects module and automatic adjusting module two parts, for the method for controling parameters of adjusting according to the selection of category of model result, realizes ash bin and controls.At this, adopt two kinds of structural design schemes to realize controling parameters and to adjust the setting function of module.A kind of scheme is the mode adopting Off-line control device storehouse to combine with parameter tuning, namely the controller architecture form in Off-line control device storehouse is selected according to the classification results of model classifiers, then parameter tuning is carried out according to control system performance index, realize tracing control, its control structure as shown in Figure 7.Another scheme adopts the classification results according to model classifiers, and the version of default control, then according to the requirement of control system performance, sets optimizing index, and carry out online optimizing and adjust, its control structure as shown in Figure 8.
When control system is single-variable system, the form of selected controller is shown below:
Wherein, s is Laplce's domain variable, b, a 2, a 1, L is the parameter needing to adjust, and is real number.
When control system is multi-variable system, selected controller form is shown below:
Wherein, each element g ijexpression be
Wherein, s is Laplce's domain variable, b ij, a ij2, a ij1, L ijfor needing the parameter of adjusting, be real number.M is line number, and n is columns, as m=n, is the controller architecture form of method, system; As m > n, it is the controller architecture form of fat shape system; As m < n, it is the controller architecture form of thin shape system.
For water jacket agitating heating device (Jacketed Stirred Tank Heater, JSTH) control system, JSTH is extensively present in the middle of commercial production.JSTH control system as shown in Figure 9.Owing to there is the strongly disturbing situation of large dead time and outside in actual production, performance and the quality of control system are had a strong impact on.Therefore, for actual production, it is very important for carrying out effective and stable control to product temperature.The model of JSTH system is shown below:
G ( s ) = 0.96 120 s + 1 e - 54 s
The Matching Model obtained after recognition module identification is as follows:
G m ( s ) = 1.15 132 s + 1 e - 59 s
In order to analyze the difference between gained Matching Model and realistic model, adopt the method for frequency-domain analysis, as shown in Figure 10.As seen from Figure 10, the effective Matching Model of gained is similar in medium and low frequency characteristic to realistic model, larger at medium-high frequency characteristic deviation.After obtaining effective Matching Model by recognition module, then classified by model classifiers.After classification, gained type is 1 type, i.e. FOPDT system.Select scheme two, the structure of static controller also adopts NPSO searching algorithm (quote and save from the research and apply 5.3.1 of Cheng Zhijin-multivariate modular system discrimination method) according to the requirement of control system performance, carry out online optimizing to adjust, gained controller is shown below:
G c ( s ) = 0.8696 &times; ( 131.5971 s + 1 ) 26.0134 s + 1
Add the step response that amplitude is 1 when 50s, and add the step response that amplitude is-1 when 1000s, with the tracking power of test control system.The output response of gained system and controller export as shown in figure 11.As seen from Figure 11, control system has good tracking power and has good performance index, meets the requirement of control system.
Result shows, and present system structure is simple, convenient operation.Can find out that bright the proposed intelligent ash bin Control System Design method based on internal model control strategy of this law is effective and feasible by simulation curve, have and follow the tracks of very well and jamproof ability, even when unmatched models, also can good control system, there is stronger robustness.

Claims (3)

1. based on an intelligent ash bin control device for internal model control strategy, it is characterized in that, comprising: internal model control module, Matching Model recognition module, ash bin controller module;
Described internal model control module comprises three parts, the internal model module of controlled device, filter module and control module.The internal model module of controlled device is for estimating the impact that performance variable exports control system, and control module is used for the predicted value of calculating operation variable, realizes the tracking characteristics of control system;
Described Matching Model recognition module comprises for the inputoutput data according to object, estimates the parameter of object model, then utilizes the Matching Model of model as internal model control of institute's identification;
The structure of described ash bin controller module comprises category of model module and attitude conirol module, wherein category of model module adopts support vector machine technology, the Matching Model of recognition module is carried out the positive and negative model sample of pre-service, and extract eigenwert, sorter is trained, after the support vector finding out often kind of classification, the sorter trained is utilized to carry out the detection and indentification of model; Attitude conirol module comprises control strategy and selects module and automatic adjusting module two parts.
2. as claimed in claim 1 based on the intelligent ash bin control system device of internal model control strategy, it is characterized in that: the mode that described attitude conirol module adopts Off-line control device storehouse to combine with parameter tuning, namely the controller architecture form in Off-line control device storehouse is selected according to the classification results of model classifiers, then carry out parameter tuning according to control system performance index, realize tracing control.
3. as claimed in claim 1 based on the intelligent ash bin control system device of internal model control strategy, it is characterized in that: described attitude conirol module adopts the classification results according to model classifiers, the version of default control, then according to the requirement of control system performance, set optimizing index, carry out online optimizing and adjust.
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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
CN111546336A (en) * 2020-04-29 2020-08-18 北京航空航天大学 Ash box model parameter identification method and system for robot system

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