CN102011220B - Fuzzy-controller-based autolevelling control system and control method - Google Patents
Fuzzy-controller-based autolevelling control system and control method Download PDFInfo
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- CN102011220B CN102011220B CN 201010531852 CN201010531852A CN102011220B CN 102011220 B CN102011220 B CN 102011220B CN 201010531852 CN201010531852 CN 201010531852 CN 201010531852 A CN201010531852 A CN 201010531852A CN 102011220 B CN102011220 B CN 102011220B
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Abstract
The invention discloses a fuzzy-controller-based autolevelling control system and a fuzzy-controller-based autolevelling control method. A linear density value is set in a controller, a cotton sliver is placed in a feeding detection point, and a detection mechanism detects the linear density of the cotton silver, obtains the actual linear density value of the fed cotton silver and sends the actual linear density value of the cotton silver to a controller; and the detected cotton silver is delivered to a drawing mechanism, the controller compares the actual linear density value with the set linear density value and controls the drawing mechanism to draw the cotton silver according to an initial drawing ratio and draw a following cotton silver according to a regulated drawing ratio. In the invention, the autolevelling control is realized by a fuzzy control technique, and through the analysis of the mathematical model of the autolevelling system, the fuzzy controller of the system is built and a simulation model is built in simulink; and according to a simulation waveform, the system and the method can improve product quality obviously.
Description
Technical field
The invention belongs to Signal and Information Processing and textile technology field, be specifically related to a kind of autolevelling control system based on fuzzy controller, the invention still further relates to the method for utilizing this system to control.
Background technology
Auto-leveling system is a kind of self-checking device that is used for controlling the sliver plucked.Autoleveller is controlled the quantitative of output sliver automatically, is to adopt the way of automatically regulating drafting multiple.It adds high draft when sliver is too thick, then reduce drawing-off when too thin, makes at last output sliver keep all the time the thickness of certain limit and quantitatively, and it has extremely important status in Modern Textile Industry.This system is a strong disturbance (the input cotton stripline density constantly changes), large definite value (line density of output sliver the is certain value) control system that postpones (neat and well spaced point is to the time-delay of test point).
In traditional autoleveller control, be to adopt PID control mostly, this control procedure is comparatively complicated.Although traditional PID is controlled in the industrial production and is used widely, for large time delay, nonlinear complication system, conventional PID control is difficult to guarantee that its control effect is in optimum state.And fuzzy control does not need the mathematical models of control object, and it is a kind of rule-based control, according to operating personnel's control experience and expert's knowledge, can controlled amount by just tabling look-up, and realize simply, control effective.
Fuzzy control is a kind of typical case of Based Intelligent Control and form early, as a branch of Based Intelligent Control, since the control that is applied to boiler and steam engine of the Mandani success of Britain in 1974, has particularly obtained in recent years development at full speed.Fuzzy control is the product that fuzzy mathematics and control theory combine, it has taked human thinking to have the characteristics of ambiguity, by instruments such as the membership function in the use fuzzy mathematics, fuzzy relation, fuzzy reasonings, controlled form is controlled, Mathematical Modeling, system robustness that it does not need to set up controlled device are strong, and fuzzy control method is easy to grasp.Therefore, it is specially adapted to those complex industrial control system that is difficult to the mathematical models of procurement process and becomes time lag, non-linear, large time delay when having, and has stronger robustness and antijamming capability.
Summary of the invention
The purpose of this invention is to provide a kind of autolevelling control system based on fuzzy controller, solved existing PID control system for large time delay, nonlinear complication system, be difficult to guarantee that its control effect is in the problem of optimum state, control by counter extensioin mechanism roller speed, improve the situation of sliver plucked, improve the quality of products.
Another object of the present invention provides a kind of method of utilizing said system to control.
The technical solution used in the present invention is, a kind of autolevelling control system based on fuzzy control, comprise the feeding test point and the drafter that connect successively, also be connected with testing agency and controller in turn on the feeding test point, the output of controller is connected with drafter by executing agency.
Another technical scheme that the present invention adopts is, adopt a kind of autolevelling control system based on fuzzy control, its structure is: comprise the feeding test point and the drafter that connect successively, also be connected with testing agency and controller in turn on the feeding test point, the output of controller is connected with drafter by executing agency
Specifically implement according to following steps:
Step 1: set the line density value in controller, sliver is put into the feeding test point, testing agency is detected cotton stripline density, obtains feeding the actual line density value of sliver, and the actual line density value of sliver is passed to controller;
Step 2: the sliver after the detection of upper step is delivered to drafter, controller compares the actual line density value that step 1 obtains with setting line density value, obtain the adjusted value of draw ratio according to fuzzy control rule, the adjusted value of the draw ratio that obtains is passed to executing agency, draw ratio after executing agency is adjusted according to the adjusted value of draw ratio, then control drafter and according to the draw ratio after adjusting sliver is carried out drawing-off, so that the line density value of sliver is identical with setting line density value, obtain the sliver after neat and well spaced, finish the autoleveller control based on fuzzy control.
Characteristics of the present invention also are,
Its middle controller obtains the adjusted value of draw ratio according to fuzzy control rule, specifically implement according to following steps:
A: the input variable of determining fuzzy controller is error exact value e and error rate exact value ec, input variable is the actual line density value and deviation and the change of error amount of setting the line density value of input sliver, the change of error amount is to obtain by the differentiate to deviation, and output variable is the adjustment amount U of draw ratio;
B: rule of thumb the rule with the autoleveller input/output model obtains following fuzzy control conditional statement:
If cotton layer thickness is little a lot, and the trend that further reduces is arranged, the fuzzy controller output voltage strengthens so;
If cotton layer thickness is little a lot, and the trend of further increase is arranged, the fuzzy controller output voltage is got intermediate value so;
If cotton layer thickness is large a lot, and the trend of further increase is arranged, the fuzzy controller output voltage reduces so;
If cotton layer thickness is large a lot, and the trend that further reduces is arranged, the fuzzy controller output voltage is got intermediate value so;
According to above rule and the controlled rule of Artificial Control experience, in the FIS editing machine in MATIAB rule is edited;
C: the error exact value e that obtains in the steps A and error rate exact value ec are quantized to fuzzy domain become error fuzzy value E and error rate fuzzy value EC, be about to deviation and change of error amount and quantize to [6,6] domain section, the fuzzy subset of definition input, output fuzzy variable; The control law of the fuzzy controller that is obtained by step B is determined the shape of fuzzy subset's membership function curve, utilizes the fuzzy control tool box among the matlab that fuzzy controller is carried out emulation;
D: in simulink, set up the system simulation model of autolevelling control system fuzzy control, the fuzzy controller that obtains among the step C is embedded in the simulation model.
The invention has the beneficial effects as follows, at first to existing neat and well spaced algorithm summarize, research and emulation; Then propose FUZZY ALGORITHMS FOR CONTROL, this comprises the design of membership function, the design of inference rule and decision table, and the selection of quantizing factor; At last, institute's algorithm is carried out Matlab emulation, according to simulation result algorithm is revised, then continue emulation.
Fuzzy control is a kind of experienced control mode that is based upon, and need not to know in advance the mathematical models by the control object, effectively controls so can be difficult to ask for the object that maybe can't ask for to those Mathematical Modelings.People's manual control decision can be described with language, is summarized as a series of conditional statements, i.e. control law.Fuzzy control method is applicable to nonlinear Control, can receive satisfied effect for the system that is difficult to accurately to set up Mathematical Modeling such as this class of autoleveller.Simultaneously, drafting of sliver has again its specific (special) requirements, for example adds high draft when sliver is too thick, then reduces drawing-off when too thin, automatically regulates the drafting multiple of drafting system, makes the satisfactory numerical value of quantitative maintenance that spins sliver.And fuzzy control is carried out reasoning with the input that is changed to of deviation and deviation according to fuzzy rule, draws the control variables of system, this just and the drafting of sliver specific (special) requirements similar characteristics are arranged.Therefore adopt fuzzy control to realize autoleveller at drawing frame, its performance is better than the control method of traditional autoleveller.
Description of drawings
Fig. 1 is the structural representation that the present invention is based on the autolevelling control system of fuzzy controller;
Fig. 2 is the two-dimensional fuzzy controller basic structure schematic diagram that adopts in the control method of the present invention;
Fig. 3 is the membership function of embodiment of the invention autolevelling control system step-up error exact value e in matlab;
Fig. 4 is the membership function of embodiment of the invention autolevelling control system step-up error rate of change exact value ec in matlab;
Fig. 5 is embodiment of the invention autolevelling control system arranges controlled quentity controlled variable U in matlab membership function;
Fig. 6 is the control law that embodiment of the invention autolevelling control system arranges in matlab;
Fig. 7 is the fuzzy control rule table that embodiment of the invention autolevelling control system arranges in matlab;
Fig. 8 is the simulation result of autolevelling control system in simulink in the embodiment of the invention.
Among the figure, 1. feed test point, 2. drafter, 3. testing agency, 4. executing agency, 5. controller.
The specific embodiment
The present invention is described in detail below in conjunction with the drawings and specific embodiments.
The present invention is based on the autolevelling control system of fuzzy controller, its structure as shown in Figure 1, comprise the feeding test point 1 and the drafter 2 that connect successively, also be connected with testing agency 3 and controller 5 in turn on the feeding test point 1, the output of controller 5 is connected with drafter 2 by executing agency 4.
The present invention is based on the auto-leveling control method of fuzzy controller, specifically implement according to following steps:
Step 1: in controller 5 interior setting line density values, sliver is put into feeding test point 1,3 pairs of cotton stripline densities of testing agency detect, and obtain feeding the actual line density value of sliver, and the actual line density value of sliver is passed to controller 5;
Step 2: the sliver after the detection of upper step is delivered to drafter 2, controller 5 compares the actual line density value that step 1 obtains with setting line density value, obtain the adjusted value of draw ratio according to fuzzy control rule, the adjusted value of the draw ratio that obtains is passed to executing agency 4, draw ratio after executing agency 4 is adjusted according to the adjusted value of draw ratio, then control drafter 2 and according to the draw ratio after adjusting sliver is carried out drawing-off, so that the line density value of sliver is identical with setting line density value, obtain the sliver after neat and well spaced, finish the autoleveller control based on fuzzy control.
A: the input variable of determining fuzzy controller is exact value e and the error rate exact value ec of error, input variable is the actual line density value and deviation and the change of error amount of setting the line density value of input sliver, the change of error amount is to obtain by the differentiate to deviation, and output variable is controlled quentity controlled variable U (U is the adjustment amount of draw ratio);
B: rule of thumb the rule with the autoleveller input/output model obtains following fuzzy control conditional statement:
If cotton layer thickness is little a lot, and the trend that further reduces is arranged, the fuzzy controller output voltage strengthens so;
If cotton layer thickness is little a lot, and the trend of further increase is arranged, the fuzzy controller output voltage is got intermediate value so;
If cotton layer thickness is large a lot, and the trend of further increase is arranged, the fuzzy controller output voltage reduces so;
If cotton layer thickness is large a lot, and the trend that further reduces is arranged, the fuzzy controller output voltage is got intermediate value so.
According to above rule and the controlled rule of Artificial Control experience, in the FIS editing machine in MATLAB rule is edited.
C: the error exact value e that obtains in the steps A and error rate exact value ec are quantized to fuzzy domain become error fuzzy value E and error rate fuzzy value EC, be about to deviation and change of error amount and quantize to [6,6] domain section, the fuzzy subset of definition input, output fuzzy variable; The control law of the fuzzy controller that is obtained by step B is determined the shape of fuzzy subset's membership function curve, utilizes the fuzzy control tool box among the matlab that fuzzy controller is carried out emulation, and the basic structure of two-dimensional fuzzy controller as shown in Figure 2;
D: in simulink, set up the system simulation model of autolevelling control system fuzzy control, the fuzzy controller that obtains among the step C is embedded in the simulation model.
Embodiment
Step 1: in controller 5 interior setting line density values, sliver is put into feeding test point 1,3 pairs of cotton stripline densities of testing agency detect, and obtain feeding the actual line density value of sliver, and the actual line density value of sliver is passed to controller 5;
Step 2: the sliver after the detection of upper step is delivered to drafter 2, controller 5 compares the actual line density value that step 1 obtains with setting line density value, obtain the adjusted value of draw ratio according to fuzzy control rule, the adjusted value of the draw ratio that obtains is passed to executing agency 4, draw ratio after executing agency 4 is adjusted according to the adjusted value of draw ratio, then control drafter 2 and according to the draw ratio after adjusting sliver is carried out drawing-off, so that the line density value of sliver is identical with setting line density value, obtain the sliver after neat and well spaced, finish the autoleveller control based on fuzzy control.
A: the input variable of determining fuzzy controller is exact value e and the error rate exact value ec of error, input variable is the actual line density value and deviation and the change of error amount of setting the line density value of input sliver, the change of error amount is to obtain by the differentiate to deviation, output variable is controlled quentity controlled variable U (U is the adjustment amount of draw ratio), as shown in Figure 3, membership function for the present embodiment autolevelling control system step-up error exact value e in matlab, as shown in Figure 4, membership function for the present embodiment autolevelling control system step-up error rate of change exact value ec in matlab, the membership function of controlled quentity controlled variable U is set in matlab for the present embodiment autolevelling control system as shown in Figure 5;
B: rule of thumb the rule with the autoleveller input/output model obtains following fuzzy control conditional statement:
If cotton layer thickness is little a lot, and the trend that further reduces is arranged, the fuzzy controller output voltage strengthens so;
If cotton layer thickness is little a lot, and the trend of further increase is arranged, the fuzzy controller output voltage is got intermediate value so;
If cotton layer thickness is large a lot, and the trend of further increase is arranged, the fuzzy controller output voltage reduces so;
If cotton layer thickness is large a lot, and the trend that further reduces is arranged, the fuzzy controller output voltage is got intermediate value so.
Such as Figure 6 and Figure 7, obtain 56 control laws according to above rule and Artificial Control experience, in the FIS editing machine in MATLAB rule is edited.
C: the error exact value e that obtains in the steps A and error rate exact value ec are quantized to fuzzy domain become error fuzzy value E and error rate fuzzy value EC, be about to deviation and change of error amount and quantize to [6,6] domain section, the fuzzy subset of definition input, output fuzzy variable; The control law of the fuzzy controller that is obtained by step B is determined the shape of fuzzy subset's membership function curve, utilizes the fuzzy control tool box among the matlab that fuzzy controller is carried out emulation;
D: the system simulation model of in simulink, setting up the autolevelling control system fuzzy control, the fuzzy controller that obtains among the step C is embedded in the simulation model, the quantizing factor of the variable quantity of error originated from input and error is 10, the quantizing factor of output controlled quentity controlled variable is 0.14, as shown in Figure 8, random signal through over-sampling is curve b, and the control law that draws according to this calculated signals is curve a, at last curve c during the signal of output.Can find out, the curve of output c amplitude of controlled quentity controlled variable U is significantly less than input curve b after fuzzy controller is regulated, and supposes that this random signal is exactly every Mick weight of sliver, and fuzzy controller improves significantly to it.
Claims (1)
1. auto-leveling control method based on fuzzy control, it is characterized in that, adopt a kind of autolevelling control system based on fuzzy control, its structure is: comprise the feeding test point (1) and the drafter (2) that connect successively, also be connected with testing agency (3) and controller (5) on the described feeding test point (1) in turn, the output of controller (5) is connected with drafter (2) by executing agency (4)
Specifically implement according to following steps:
Step 1: in controller (5), set the line density value, sliver is put into feeding test point (1), testing agency (3) is detected cotton stripline density, obtains feeding the actual line density value of sliver, and the actual line density value of sliver is passed to controller (5);
Step 2: the sliver after the detection of upper step is delivered to drafter (2), controller (5) compares the actual line density value that step 1 obtains with setting line density value, obtain the adjusted value of draw ratio according to fuzzy control rule, the adjusted value of the draw ratio that obtains is passed to executing agency (4), draw ratio after executing agency (4) is adjusted according to the adjusted value of draw ratio, then control drafter (2) and according to the draw ratio after adjusting sliver is carried out drawing-off, so that the line density value of sliver is identical with setting line density value, obtain the sliver after neat and well spaced, finish the autoleveller control based on fuzzy control;
Described controller (5) obtains the adjusted value of draw ratio according to fuzzy control rule, specifically implement according to following steps:
A: the input variable of determining fuzzy controller is namely inputted the actual line density value of sliver and error exact value e and the error rate exact value ec of setting line density value, error rate exact value ec obtains by the differentiate to error exact value e, and output variable is the adjustment amount U of draw ratio;
B: rule of thumb the rule with the autoleveller input/output model obtains following fuzzy control conditional statement:
If cotton layer thickness is little a lot, and the trend that further reduces is arranged, the adjustment amount of fuzzy controller draw ratio strengthens so;
If cotton layer thickness is little a lot, and the trend of further increase is arranged, the adjustment of fuzzy controller draw ratio measures intermediate value so;
If cotton layer thickness is large a lot, and the trend of further increase is arranged, the adjustment amount of fuzzy controller draw ratio reduces so;
If cotton layer thickness is large a lot, and the trend that further reduces is arranged, the adjustment of fuzzy controller draw ratio measures intermediate value so;
According to above rule and the controlled rule of Artificial Control experience, in the FIS editing machine in MATLAB rule is edited;
C: the error exact value e that obtains in the steps A and error rate exact value ec are quantized to fuzzy domain become error fuzzy value E and error rate fuzzy value EC, be about to deviation and change of error amount and quantize to [6,6] domain section, the fuzzy subset of definition input, output fuzzy variable; The control law of the fuzzy controller that is obtained by step B is determined the shape of fuzzy subset's membership function curve, utilizes the fuzzy control tool box among the matlab that fuzzy controller is carried out emulation;
D: in simulink, set up the system simulation model of autolevelling control system fuzzy control, the fuzzy controller that obtains among the step C is embedded in the simulation model.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1356417A (en) * | 2001-12-24 | 2002-07-03 | 青岛大学 | Self-regulator of uniformity for carding machine |
| CN1920707A (en) * | 2006-09-14 | 2007-02-28 | 上海交通大学 | Delayed setting method for self-regulated uniformity open-circuit controller |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1356417A (en) * | 2001-12-24 | 2002-07-03 | 青岛大学 | Self-regulator of uniformity for carding machine |
| CN1920707A (en) * | 2006-09-14 | 2007-02-28 | 上海交通大学 | Delayed setting method for self-regulated uniformity open-circuit controller |
Non-Patent Citations (6)
| Title |
|---|
| 姚杰等.并条机智能型自调匀整装置的研究与开发.《上海纺织科技》.2004,第32卷(第6期),第21-22页. * |
| 姚杰等.新型开环自调匀整装置的分析与设计.《纺织学报》.2005,第26卷(第3期),第44-45,53页. * |
| 朱耀麟等.并条机开环自调匀整控制***中实时延时控制算法研究.《青岛大学学报（工程技术版）》.2009,第24卷(第2期),第37-40页. * |
| 熊磊.梳棉机混合环自调匀整设计与研究.《青岛大学硕士学位论文》.2007,第2节. * |
| 邹泽明等.一种高速并条机自调匀整的新型控制***研究.《青岛大学学报（工程技术版）》.2004,第19卷(第3期),第27-31页. * |
| 郜迎君等.基于模糊控制的并条机自调匀整控制***的研究.《中原工学院学报》.2005,第16卷(第4期), * |
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