CN106019943A - Fuzzy control method for intelligent shearing machine and control system thereof - Google Patents

Fuzzy control method for intelligent shearing machine and control system thereof Download PDF

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CN106019943A
CN106019943A CN201610516896.8A CN201610516896A CN106019943A CN 106019943 A CN106019943 A CN 106019943A CN 201610516896 A CN201610516896 A CN 201610516896A CN 106019943 A CN106019943 A CN 106019943A
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fuzzy
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error
fuzzy control
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尹智勇
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Shanghai Yin Science and Technology Co Ltd
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    • 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

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Abstract

The invention relates to a fuzzy control method for an intelligent shearing machine and a control system thereof. The fuzzy control method comprises the following steps of acquiring the detected actual output value Yk of the shearing cutter path of a shearing machine; calculating the error E of a control target and the error change rate EC thereof according to the standard value Yset of the shearing cutter path of the shearing machine and the actual output value Yk of the shearing cutter path of the shearing machine; setting the quantization domain of a fuzzy controller; calculating the quantization factor Ke of the error E and the quantization factor Kec of the error change rate EC; formulating a fuzzy control rule table Fuzzy table; calculating an output adjustment increment delta U according to the current error E, the quantization factor Ke of the error E, the error change rate EC and the quantization factor Kec of the error change rate EC; updating the output control quantity Uk within the current period, wherein Uk=Uk-1+ delta U and Uk-1 represents the output control quantity within the last period; outputting the output control quantity Uk to an actuating mechanism, and conducting the fuzzy control for the shearing cutter path of the shearing machine. According to the technical scheme of the invention, the adjustment of fuzzy control is simplified, so that the versatility of fuzzy control is improved.

Description

Fuzzy control method and control system of intelligent shearing machine
Technical Field
The invention relates to the technical field of fuzzy control of shearing machines, in particular to a fuzzy control method and a fuzzy control system of an intelligent shearing machine.
Background
Fuzzy Logic Control (Fuzzy Logic Control), which is called Fuzzy Control for short, is a computer numerical Control technology based on Fuzzy set theory, Fuzzy linguistic variables and Fuzzy Logic reasoning, and is essentially a nonlinear Control belonging to the field of intelligent Control.
The fuzzy control has a wide application basis in common system control, and has the advantages of no dependence on a specific object model and wide application range. However, in practical application, it is found that the static characteristics of the discretized fuzzy controller are not ideal, and particularly near a deviation zero value, the problem of repeated oscillation adjustment of output is easy to occur, and a certain static difference exists between the output and a set value.
Generally, a common improvement method is to switch to the conventional PID to improve the steady-state characteristic after roughly adjusting to a region near a steady-state region by using fuzzy control, but such a method depends on the magnitude of the steady-state oscillation amplitude, needs to repeatedly adjust PID parameters, and has low universality and a long actual debugging period.
As far as the prior art is concerned, the application of fuzzy control technology in cloth shears has been rarely reported. Therefore, how to apply the fuzzy control technology to the cloth shearing machine is still to be studied by those skilled in the art.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a method and a control system for fuzzy control of a shearing knife of a shearing machine by adopting a fuzzy controller, so that the control system of the shearing machine can simplify the adjustment of fuzzy control and improve the universality of the control system.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a fuzzy control method of an intelligent cutting machine comprises the following steps:
obtaining the actual output value Y of the shearing path of the shearing machinek；
According to the standard value Y of the shearing path of the shearing machinesetAnd the actual output value YkCalculating the error E and the error change rate EC of the shearing path of the shearing machine;
setting the quantization discourse domain of the fuzzy controller, and calculating the quantization factor K of the error EeAnd a quantization factor K of said error rate of change ECec；
Making fuzzy control rule table fuzzy, according to current error E and its quantization factor KeError rate of change EC and quantization factor K thereofecCalculating an output adjustment increment delta U;
updating the output control quantity U of the current periodkWherein, Uk＝Uk_1+ΔU，Uk_1The output control quantity of the previous period;
outputting the control quantity UkAnd outputting the data to a shearing executing mechanism of the shearing machine, and carrying out fuzzy control on a shearing path of the shearing machine.
Wherein, the preferable technical scheme is that before the step of calculating the output adjustment increment Δ U, the method further comprises:
judging whether the current error E is within a coarse adjustment range of fuzzy control, and calculating the output adjustment increment delta U by the following steps when the error E is judged to be within the coarse adjustment range of the fuzzy control:
calculating an output adjustment delta reference value deltaUref；
According to the current error E and the quantization factor KeError rate of change EC and quantization factor K thereofecSearching the fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
according to Δ U ═ FuzzyU ×. k ×. Δ UrefCalculating to obtain the value of the output adjustment increment delta U; wherein k is an accelerative scaling factor greater than 1.
Preferably, before the step of calculating the output adjustment increment Δ U, the method further includes:
judging whether the current error E is in a fine adjustment range of fuzzy control or not, and calculating the output adjustment increment delta U by the following steps when the error E is judged to be in the fine adjustment range of the fuzzy control;
calculating an output adjustment delta reference value Δ Uref；
According to the current error E and the quantization factor KeError rate of change EC and quantization factor K thereofecSearching the fuzzy control rule table fuzzy Table, and acquiring a current fuzzy control variable fuzzy U;
according to Δ U ═ FuzzyU @ Δ UrefAnd calculating to obtain the value of the output adjustment increment delta U.
Further preferably, the calculating the output adjustment increment reference value Δ Uref includes:
searching the fuzzy control rule table fuzzy Table to obtain the output maximum quantized value fuzzy Umax；
Determining the system settling time TcAnd a system delay time Td；
Determination of the System tolerance EmaxAnd its corresponding minimum output adjustment incremental value DeltaUmin；
According to Δ Uref≤ΔUmin/FuzzyUmax*Tc/TdAnd calculating to obtain the output adjustment increment reference value delta Uref。
The preferable technical scheme is that the method further comprises the following steps:
adjusting fuzzy control of a shearing path of the shearing machine by modifying or presetting at least one of the following parameters;
wherein the parameters include: the system adjusts the time TcSystem delay time TdOutputting the adjustment increment reference value delta UrefError E, error rate of change EC.
Preferably, the fuzzy controller further comprises:
an input interface unit for acquiring the actual output value Y of the shearing path of the shearing machinek；
A first calculating unit for calculating a standard value Y of the shearing path of the shearing machinesetAnd the actual output value YkCalculating the error E and the error change rate EC of the shearing path of the shearing machine;
a second calculation unit for setting the quantization domain of the fuzzy controller and calculating the quantization factor K of the error EeAnd a quantization factor K of said error rate of change ECec；
A third calculating unit for formulating fuzzy control rule table according to current error E and its quantization factor Ke, error change rate EC and its quantization factor KecCalculating an output adjustment increment delta U;
a fourth calculating unit for updating the output control amount U of the current cyclekWherein, Uk＝Uk_1+ΔU，Uk_1The output control quantity of the previous period;
an output interface unit for outputting the control quantity UkAnd outputting the data to an actuating mechanism to perform fuzzy control on the shearing path of the shearing machine.
Further preferably, the third calculating unit includes:
a search subunit for searching the quantization factor K according to the current error EeError rate of change EC and quantization factor K thereofecSearching a fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
the judging subunit is used for judging whether the current error E is within a coarse/fine adjustment range of fuzzy control;
a first calculating subunit, connected to the searching subunit, for calculating an output adjustment increment reference value Δ Uref；
A second calculating subunit, connected to the searching subunit, the judging subunit and the first calculating subunit, and configured to select Δ U ═ FuzzyU ×. k ×. Δ U according to a judgment result of the judging subunitrefOr Δ U ═ FuzzyU · Δ Uref，
Calculating to obtain the value of the output adjustment increment delta U; wherein k is an accelerative scaling factor greater than 1.
Further preferably, the first calculating subunit includes:
a searching submodule for searching fuzzy control rule table fuzzy Table and obtaining output maximum quantized value fuzzy Umax；
A first determination submodule for determining a system control time TcAnd a system delay time Td；
A second determination submodule for determining a system tolerance EmaxAnd its corresponding minimum output adjustment incremental value DeltaUmin；
A calculation submodule connected to the search submodule, the first measurement submodule and the second measurement submodule and used for calculating the difference between the first measurement submodule and the second measurement submodule according to the condition that the value of the first measurement submodule is larger than or equal to the value of the second measurement submodulemin/FuzzyUmax*Tc/TdAnd calculating to obtain an output adjustment increment reference value delta Uref。
Another objective of the embodiments of the present invention is to provide a fuzzy control system for an intelligent shearing machine, so as to simplify adjustment of fuzzy control and improve universality.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a fuzzy control system of an intelligent shearing machine comprises any one of the fuzzy controller, a shearing knife actuating mechanism of the shearing machine, a shearing path of the shearing machine and a measuring device; wherein,
the fuzzy controller is used for outputting the control quantity UkThe shearing actuating mechanism is output to the shearing machine and used for controlling the quantity U according to the outputkCarrying out fuzzy control on the shearing path of the shearing machine;
the measuring device is used for detecting the actual output value Y of the shearing path of the shearing machinekAnd feeding back the detection data to the fuzzy controller.
The invention has the advantages and beneficial effects that:
compared with the prior art, the fuzzy control method and the control system of the intelligent shearing machine have the following advantages:
the fuzzy control method and the control system of the intelligent shearing machine of the invention carry out incremental adjustment by utilizing fuzzy control output, combine variable step length adjustment with fuzzy control, have strong universality and short adjustment period, do not need to modify programs aiming at different systems, and measure or estimate the quantization factor K of the error E through simple experimentseAnd a quantization factor K of the error rate of change ECecRelatively accurate output control U can be obtainedkThe adjustment of the fuzzy control can be simplified and the versatility can be improved.
In addition, the fuzzy control method and the control system of the intelligent shearing machine adopt a rapid determination mode of key parameters, and can rapidly and intuitively realize the adjustment of corresponding parameters by matching with a monitoring device.
Drawings
Fig. 1 is a schematic block diagram of a fuzzy control system of the intelligent shearing machine of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, an embodiment of a fuzzy control method of an intelligent shearing machine according to the present invention is:
in order to simplify the adjustment of the fuzzy control of the intelligent shearing machine and improve the universality thereof, the embodiment provides a fuzzy control method, which comprises the following steps:
s102: obtaining actual output value Y of shearing path of shearing machinek；
S104: according to the standard value Y of the shearing path of the shearing machinesetAnd the actual output value YkCalculating the error E and the error change rate EC of the shearing path of the shearing machine;
s106: setting the quantization discourse domain of the fuzzy controller of the intelligent shearing machine, and calculating the quantization factor K of the error EeAnd a quantization factor K of the error rate of change ECec；
S108: making fuzzy control rule table fuzzy, according to current error E and its quantization factor KeError rate of change EC and quantization factor K thereofecCalculating an output adjustment increment delta U;
s110: according to Uk＝Uk_1+ Δ U, the control amount U of the output of the previous cyclek_1Updating the output control quantity U of the current periodk；
S112: outputting the control quantity UkAnd outputting the data to a shearing knife executing mechanism of the shearing machine, and carrying out fuzzy control on the shearing path of the shearing machine.
As an alternative embodiment, the above-mentioned paste control method may further include: judging whether the current error E is within a coarse adjustment range of the fuzzy control, and when judging that the error E is within the coarse adjustment range of the fuzzy control, in S108, calculating the output adjustment increment delta U by the following steps:
s108 a: calculating an output adjustment delta reference value Δ Uref；
S108 b: according to the current error E and the quantization factor KeError rate of change EC and quantization factor K thereofecSearching a fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
s108 c: according to Δ U ═ FuzzyU ×. k ×. Δ UrefThe value of the output adjustment increment Δ U is calculated. And k is an acceleration regulation scaling factor larger than 1, and the selectable value range is 5-20.
Wherein, the error E in the coarse tuning range of the fuzzy control may refer to: the error E is outside the quantization domain of the fuzzy control. For example, the universe of fuzzy control errors is [ -3, 3], and the absolute value of the actual error is greater than 6, at which time the error E is within the coarse tuning range of the fuzzy control. The coarse adjustment range can be set according to the shearing path and the control condition of the actual shearing machine.
In the above embodiment, the fuzzy control method performs incremental adjustment by using fuzzy control output, combines variable step length adjustment with fuzzy control, has strong universality and short adjustment period, does not need to modify programs for different systems, and determines the error E, the error change rate EC and the adjustment reference value Δ U through simple experimentsrefAnd accelerating the adjustment of the scale factor k to obtain a relatively accurate scale factorOutput control amount U ofkThe adjustment of the fuzzy control can be simplified and the versatility can be improved. In addition, the fuzzy control method and the control system of the intelligent shearing machine adopt a rapid determination mode of key parameters, and can rapidly and intuitively realize the adjustment of corresponding parameters by matching with a monitoring device.
As another optional implementation, the fuzzy control method of the intelligent shearing machine may further include: judging whether the current error E is within a fine tuning range of the fuzzy control, and when the error E is judged to be within the fine tuning range of the fuzzy control, in S108, calculating the output adjustment increment delta U by the following steps:
S108A: calculating an output adjustment delta reference value Δ Uref；
S108B: according to the current error E and the quantization factor KeError rate of change EC and quantization factor K thereofecSearching a fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
S108C: according to Δ U ═ FuzzyU @ Δ UrefAnd calculating to obtain the value of the output adjustment increment delta U.
Wherein, the error E in the fine tuning range of the fuzzy control may refer to: the case where the error E is near the quantization domain of the fuzzy control. For example, the fuzzy control error argument is [ -3, 3] and the actual error is [ -6, 6], when the error E is within the fine tuning range of the fuzzy control. The fine tuning range can be set according to the shearing path and the control condition of the actual shearing machine.
It should be noted that "determining whether the error E is within the coarse adjustment range" and "determining whether the error E is within the fine adjustment range" may be the same determining operation, that is, when the error E is not within the coarse adjustment range, the error E may be set within the fine adjustment range by default.
Optionally, in the two embodiments, the following steps may be adopted to calculate the output adjustment increment reference value Δ Uref:
1) finding fuzzy controlsMaking a rule table fuzzy Table, and acquiring an output maximum quantized value fuzzy Umax；
2) Determining the system settling time TcAnd a system delay time Td；
3) Determination of the System tolerance EmaxAnd the corresponding minimum output adjustment increment value delta Umin;
4) according to Δ Uref≤ΔUmin/FuzzyUmax*Tc/TdAnd calculating to obtain an output adjustment increment reference value delta Uref。
In the above embodiment, the output control amount is calculated using the incremental control method, that is: u shapek＝Uk_1+ Δ U, where the Δ U adjustment reference Δ Uref is set by the user according to experimental data or is obtained by direct calculation, and the Δ U theoretical value is dynamically obtained by the fuzzy controller according to the current error E and the error change rate EC, and the corresponding calculation formula is as follows:
1) if the error E is far out of the quantization range of the fuzzy control, such as the quantization range of the fuzzy control error is [ -3, 3]And the actual error quantization absolute value is greater than 6, then at this point: Δ U ═ FuzzyU × k ×. Δ Uref
Wherein k is an acceleration regulation scale factor larger than 1, and the value range is generally 5-20.
2) If the error E is near the quantization range of the fuzzy control, e.g., the fuzzy control error universe is [ -3, 3] and the actual error is [ -6, 6], then, at this time:
ΔU＝FuzzyU*ΔUref
it is to be noted that Δ U can be determined here by the following methodrefThe size of (2):
i)ΔUrefproportional to the control regulation period TcAnd system delay TdThe ratio of (a) to (b).
ii)ΔUrefThe size of (A) can satisfy: adjustment increase in system delay timeThe response caused by the quantity Sum (Δ U) is the minimum adjustment increment (set to Δ U) allowed within the allowable deviation of the systemmin) That is to say Sum (delta U) is less than or equal to delta UminBecause:
Sum(ΔU)≤Td/Tc*ΔUref*FuzzyUmax
wherein, the FuzzyUmaxRefers to the maximum U value of the output of the fuzzy controller, such as when the output of the fuzzy controller has a quantization range of [ -6, 6]When it is, then FuzzyUmaxIf the value is 6, then:
ΔUref≤ΔUmin/FuzzyUmax*Tc/Td
thus, the system delay T can be satisfieddThe deviation caused by the adjustment increment in the time range is smaller than the allowable deviation of the system.
In an optional embodiment, the fuzzy control method may further include:
s114: adjusting fuzzy control of a shearing path of the shearing machine by modifying or presetting at least one of the following parameters;
wherein the parameters include: the system adjusts the time TcSystem delay time TdOutputting the adjustment increment reference value delta UrefError E, error rate of change EC.
The above embodiments describe a general flow of the fuzzy control method, and the fuzzy control method is further described with reference to fig. 1 and an example, where the fuzzy control method includes the following steps:
s202: measuring system delay time Td。
S204: minimum output adjustment increment value delta U corresponding to allowable deviation of measuring systemmin。
For example, when the allowable deviation of the system is 1, the minimum value of the corresponding adjustment increment when the response of the system increases or decreases by 1 is the minimum valueΔUmin。
S206: adjusting the incremental value Δ U based on the minimum outputminCalculating an adjustment incremental reference value (adjustment reference) DeltaUrefThe value is obtained.
In this step, according to Δ Uref≤ΔUmin/FuzzyUmax*Tc/TdThe Δ Uref value can be calculated.
S208: and determining fuzzy fundamental domains of the error E and the error change rate EC.
Here, the fuzzy fundamental domains of the error E and the error rate of change EC are determined according to the experimental data, the corresponding error fundamental domain is generally 3 to 5 times of the allowable deviation, and the domain of the error rate of change EC can be obtained according to the experimental data within the range of the error E domain by the following formula:
Ek＝Yset-Yk
EC＝Ek-Ek_1
Ek_1＝Ek
wherein E iskRepresenting the current error value, YsetIndicating a control target value, YkRepresenting the actual output value, Ek_1Representing the error value of the previous cycle. Here, the current error value E is calculatedkThe error value E of the previous cyclek_1Updated to the current error value Ek。
S210: setting the quantization discourse domain of the fuzzy controller, calculating the error E and the quantization factor K of the error change rate ECe、Kec。
For example: when the error of the fuzzy controller and the quantization domain of the error change rate are [ -6, 6]And the basic universe of error is [ -3, 3]The error change rate is in the range of-0.1, 0.1]Then, the quantization factor corresponding to the error E is Ke 6/3 2, and the quantization factor of the error rate of change EC is Kec＝6/0.1＝60。
S212: and (4) formulating a fuzzy control rule table fuzzy variable according to the error E, the error change rate EC and the output quantization domain [ -6, 6] and the membership function in the fuzzy rule.
For example: and acquiring a corresponding discretization fuzzy control rule table fuzzy ytable by using a fuzzy tool of the matlab tool according to the following fuzzy control rule table.
TABLE 1 fuzzy control rules Table
When the error E, the error change rate EC and the output quantization domain are [ -6, 6], and the membership function in the fuzzy rule is a trigonometric function, the corresponding discretized fuzzy control rule table FuzzyTable can be shown as follows:
table 2 discretized fuzzy control rule table FuzzyTable
S214: and searching a fuzzy control rule table fuzzy table according to the current error E and the error change rate EC to obtain the current fuzzy U.
FuzzyU＝Fuzzytable(i，j)
Wherein, the value of i can be determined by adopting the following rules:
when E is Ke < -6, i is 1;
when E is Ke<-5 and E.KeWhen the value is more than or equal to-6, i is 2;
……
and so on.
Similar to the above, the j value can be determined using the following rule:
when EC X Kec<At-6 time，j＝1；
When EC Kec < -5EC Kec ≧ 6, j ═ 2;
……
and so on.
S216: according to the acquired FuzzyU and delta UrefAn output adjustment delta au value is calculated.
The output adjustment increment delta U value can be calculated according to the following principle in the step:
1) if the error is far beyond the quantization range of the fuzzy control, for example, the fuzzy control error range is [ -3, 3]And the actual absolute value of the error is greater than 6, at this time: Δ U ═ FuzzyU × k ×. Δ Uref. And k is an acceleration regulation scaling factor larger than 1, and the selectable value range is 5-20.
2) If the error is near the quantization universe of universe]And the actual error is [ -6, 6 [)]At this time: Δ U ═ FuzzyU · Δ Uref。
S218: calculating the final output control quantity U according to the calculated output adjustment increment delta Uk。
Calculating the final output control quantity U according to the following formulak：
Uk＝Uk_1+ΔU
Uk_1＝Uk
Here, the current output control amount U is calculated through the above stepskFurther controlling the output of the last period of the fuzzy controller by the control value Uk_1And updating the output control quantity Uk of the current period.
The above are embodiments of the fuzzy control method, and the following describes a fuzzy controller using the above fuzzy control method with reference to fig. 1.
Intelligent shear embodiment:
in this embodiment, in order to simplify the adjustment of the fuzzy control of the intelligent shearing machine and improve the versatility thereof, a fuzzy controller is provided, which includes the following units:
an input interface unit for acquiring the actual output value Y of the shearing path of the shearing machinek
The first calculation unit is used for calculating the error E and the error change rate EC of the shearing path of the shearing machine according to the standard value Yset and the actual output value Yk of the shearing path of the shearing machine;
a second calculation unit for setting the quantization domain of the fuzzy controller and calculating the quantization factor K of the error EeAnd a quantization factor K of the error rate of change ECec；
A third calculating unit for formulating fuzzy control rule table fuzzy control rule according to current error E and quantization factor K thereofeError rate of change EC and quantization factor K thereofecCalculating an output adjustment increment delta U;
a fourth calculation unit for calculating a value according to Uk＝Uk_1+ Δ U, the control amount U of the output of the previous cyclek_1Updating the output control quantity U of the current periodk；
An output interface unit for outputting the control quantity UkAnd outputting the data to an actuating mechanism to perform fuzzy control on the shearing path of the shearing machine.
In the above units, the input interface unit and the output interface unit can be respectively connected with the first, second, third and fourth calculating units, and the output interface unit outputs the calculating results of each unit, or selectively outputs the calculating results designated by the user according to the setting of the user. The first, second, third and fourth computing units can call data mutually according to computing requirements.
In an alternative embodiment, the third computing unit may include the following sub-units:
a search subunit for searching the error rate EC and the quantization factor K according to the current error E and the quantization factor KeecSearching a fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
the judging subunit is used for judging whether the current error E is within a coarse/fine adjustment range of fuzzy control;
the first calculating subunit is connected with the searching subunit and used for calculating an output adjustment increment reference value delta Uref;
a second calculating subunit, connected to the searching subunit, the judging subunit and the first calculating subunit, and configured to select Δ U ═ FuzzyU ×. k ×. Δ U according to a judgment result of the judging subunitrefOr Δ U ═ FuzzyU · Δ UrefCalculating to obtain the value of the output adjustment increment delta U; wherein k is an accelerative scaling factor greater than 1.
In the above embodiment, the first calculating subunit includes:
the searching submodule is used for searching the fuzzy control rule table fuzzy table and acquiring an output maximum quantized value fuzzy Umax;
a first measuring submodule for measuring a system adjustment time Tc and a system delay time Td;
a second measuring submodule for measuring the system allowable deviation Emax and the corresponding minimum output adjustment increment value DeltaUmin；
A calculation submodule connected with the search submodule, the first measurement submodule and the second measurement submodule and used for measuring the difference between the delta U and the delta Uref≤ΔUmin/FuzzyUmax*Tc/TdAnd calculating to obtain an output adjustment increment reference value delta Uref.
Since the present apparatus embodiment is used for implementing the foregoing method embodiment, and both have the same inventive concept, related embodiments may refer to the foregoing method embodiment, and are not described herein again.
Intelligent shear null system embodiment:
here, in order to implement the fuzzy control method, a fuzzy control system of an intelligent shearing machine is proposed, and as shown in fig. 1, the fuzzy control system includes: the fuzzy controller, the shearing-to-executing mechanism of the intelligent machine, the shearing path of the shearing machine and the measuring device in any embodiment. Wherein:
the fuzzy controller is used for outputting the control quantity UkOutput to the actuating mechanism, and the actuating mechanism is used for outputting the control quantity U according to the outputkCarrying out fuzzy control on the shearing path of the shearing machine;
the measuring device is used for detecting the actual output value Yk of the shearing path of the shearing machine and feeding back the detection data to the fuzzy controller.
As can be seen from the foregoing embodiments, the fuzzy control method and the control system of the intelligent shearing machine according to the embodiments of the present invention have the following advantages:
the fuzzy control method and the control system of the intelligent shearing machine of the invention utilize the fuzzy control output to carry out incremental adjustment, combine variable step length adjustment with fuzzy control, have strong universality and short adjustment period, do not need to modify programs aiming at different systems, and measure or estimate the system delay and input and output proportional parameters (such as the quantization factor K of the error E) through simple experimentseAnd a quantization factor K of the error rate of change ECec) Relatively accurate adjustment parameter (output control quantity U) can be obtainedk)。
In addition, the fuzzy control method and the control system of the intelligent shearing machine adopt a rapid determination mode of key parameters, and can rapidly and intuitively realize the adjustment of corresponding parameters by matching with a monitoring device. Adjusting time T for a system via a monitoring interfacecSystem delay time TdOutputting the adjustment increment reference value delta UrefThe basic domains of error E and error change rate EC are set, and thenThe flexibility and practicality of the step enhancement system.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software. The storage device is a nonvolatile memory, such as: ROM/RAM, flash memory, magnetic disk, optical disk, etc.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the technical principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (9)

1. A fuzzy control method of an intelligent cutting machine is characterized by comprising the following steps:
obtaining the actual output value Y of the shearing path of the shearing machinek；
According to the standard value Y of the shearing path of the shearing machinesetAnd the actual output value YkCalculating the error E and the error change rate EC of the shearing path of the shearing machine;
setting the quantization discourse domain of the fuzzy controller, and calculating the quantization factor K of the error EeAnd a quantization factor K of said error rate of change ECec；
Making fuzzy control rule table fuzzy, according to current error E and its quantization factor KeError rate of change EC and quantization factor K thereofecCalculating an output adjustment increment delta U;
updating the output control quantity U of the current periodkWherein, Uk＝Uk_1+ΔU，Uk_1The output control quantity of the previous period;
outputting the control quantity UkAnd outputting the data to a shearing knife executing mechanism of the shearing machine, and carrying out fuzzy control on the shearing path of the shearing machine.
2. The fuzzy control method of a smart cutter as set forth in claim 1, wherein said step of calculating the output adjustment increment Δ U is preceded by the step of:
judging whether the current error E is within a coarse adjustment range of fuzzy control, and calculating the output adjustment increment delta U by the following steps when the error E is judged to be within the coarse adjustment range of the fuzzy control:
calculating an output adjustment delta reference value Δ Uref；
According to the current error E and the quantization factor KeError rate of change EC and quantization factor K thereofecSearching the fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
according to Δ U ═ FuzzyU ×. k ×. Δ UrefCalculating to obtain the value of the output adjustment increment delta U; wherein k is an accelerative scaling factor greater than 1.
3. The fuzzy control method of a smart cutter as set forth in claim 1, wherein said step of calculating the output adjustment increment Δ U is preceded by the step of:
judging whether the current error E is in a fine adjustment range of fuzzy control or not, and calculating the output adjustment increment delta U by the following steps when the error E is judged to be in the fine adjustment range of the fuzzy control;
calculating an output adjustment delta reference valueΔUref；
According to the current error E and the quantization factor KeError rate of change EC and quantization factor K thereofecSearching the fuzzy control rule table fuzzy Table, and acquiring a current fuzzy control variable fuzzy U;
according to Δ U ═ FuzzyU @ Δ UrefAnd calculating to obtain the value of the output adjustment increment delta U.
4. The fuzzy control method of the smart cutter according to claim 2 or 3, wherein the calculating the output adjustment increment reference value Δ Uref includes:
searching the fuzzy control rule table fuzzy Table to obtain the output maximum quantized value fuzzy Umax；
Determining the system settling time TcAnd a system delay time Td；
Determination of the System tolerance EmaxAnd its corresponding minimum output adjustment incremental value DeltaUmin；
According to Δ Uref≤ΔUmin/FuzzyUmax*Tc/TdAnd calculating to obtain the output adjustment increment reference value delta Uref。
5. The fuzzy control method of the smart cutter as set forth in claim 4, further comprising:
adjusting fuzzy control of a shearing path of the shearing machine by modifying or presetting at least one of the following parameters;
wherein the parameters include: the system adjusts the time TcSystem delay time TdOutputting the adjustment increment reference value delta UrefError E, error rate of change EC.
6. The fuzzy control method of the smart cutter according to claim 1, wherein the fuzzy controller comprises:
input interface unit for acquiring shearing path of detected shearing machineActual output value Yk；
A first calculating unit for calculating a standard value Y of the shearing path of the shearing machinesetAnd the actual output value YkCalculating the error E and the error change rate EC of the shearing path of the shearing machine;
a second calculation unit for setting the quantization domain of the fuzzy controller and calculating the quantization factor K of the error EeAnd a quantization factor K of said error rate of change ECec；
A third calculating unit for formulating fuzzy control rule table according to current error E and its quantization factor Ke, error change rate EC and its quantization factor KecCalculating an output adjustment increment delta U;
a fourth calculating unit for updating the output control amount U of the current cyclekWherein, Uk＝Uk_1+ΔU，Uk_1The output control quantity of the previous period;
an output interface unit for outputting the control quantity UkAnd outputting the data to an actuating mechanism to perform fuzzy control on the shearing path of the shearing machine.
7. The fuzzy control method of the smart cutter as set forth in claim 6, wherein the third calculation unit comprises:
a search subunit for searching the quantization factor K according to the current error EeError rate of change EC and quantization factor K thereofecSearching a fuzzy control rule table fuzzy table to obtain a current fuzzy control variable fuzzy U;
the judging subunit is used for judging whether the current error E is within a coarse/fine adjustment range of fuzzy control;
a first calculating subunit, connected to the searching subunit, for calculating an output adjustment increment reference value Δ Uref；
A second calculating subunit, connected to the searching subunit, the judging subunit and the first calculating subunit, and configured to select Δ U ═ FuzzyU ×. k ×. Δ U according to a judgment result of the judging subunitrefOr Δ U ═ FuzzyU · Δ Uref，
Calculating to obtain the value of the output adjustment increment delta U; wherein k is an accelerative scaling factor greater than 1.
8. The fuzzy control method of the smart cutter as set forth in claim 7, wherein the first calculating sub-unit comprises:
a searching submodule for searching fuzzy control rule table fuzzy Table and obtaining output maximum quantized value fuzzy Umax；
A first determination submodule for determining a system control time TcAnd a system delay time Td；
A second determination submodule for determining a system tolerance EmaxAnd its corresponding minimum output adjustment incremental value DeltaUmin；
A calculation submodule connected to the search submodule, the first measurement submodule and the second measurement submodule and used for calculating the difference between the first measurement submodule and the second measurement submodule according to the condition that the value of the first measurement submodule is larger than or equal to the value of the second measurement submodulemin/FuzzyUmax*Tc/TdAnd calculating to obtain an output adjustment increment reference value delta Uref。
9. A fuzzy control system for an intelligent shearing machine, comprising: the fuzzy controller of any one of claims 6 to 8, a shear blade actuator of a shear, a shear path of a shear, and a measuring device; wherein,
the fuzzy controller is used for outputting the control quantity UkThe shearing actuating mechanism is output to the shearing machine and used for controlling the quantity U according to the outputkCarrying out fuzzy control on the shearing path of the shearing machine;
the measuring device is used for detecting the actual output value Y of the shearing path of the shearing machinekAnd feeding back the detection data to the fuzzy controller.
CN201610516896.8A 2016-07-02 2016-07-02 Fuzzy control method for intelligent shearing machine and control system thereof Pending CN106019943A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109709808A (en) * 2018-12-29 2019-05-03 江苏华宏科技股份有限公司 Scrap shearing machine shears oil cylinder stroke Fuzzy control system and method
CN109856974A (en) * 2019-03-01 2019-06-07 烟台大学 A kind of four factor intelligent control methods based on controlled volume variation

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020173901A1 (en) * 2001-04-20 2002-11-21 Unisia Jecs Corporation Control apparatus and control method
CN104216292A (en) * 2014-09-16 2014-12-17 湖南三一智能控制设备有限公司 Fuzzy controller, fuzzy control method and fuzzy control system
CN104328643A (en) * 2014-09-30 2015-02-04 江苏和鹰机电科技有限公司 Automatic shearing machine and machine head control method of automatic shearing machine
CN104532536A (en) * 2014-09-30 2015-04-22 上海和鹰机电科技股份有限公司 Automatic cutting machine and automatic cutting method thereof
CN104818603A (en) * 2015-03-27 2015-08-05 上海和鹰机电科技股份有限公司 Intelligent cutting method and system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020173901A1 (en) * 2001-04-20 2002-11-21 Unisia Jecs Corporation Control apparatus and control method
CN104216292A (en) * 2014-09-16 2014-12-17 湖南三一智能控制设备有限公司 Fuzzy controller, fuzzy control method and fuzzy control system
CN104328643A (en) * 2014-09-30 2015-02-04 江苏和鹰机电科技有限公司 Automatic shearing machine and machine head control method of automatic shearing machine
CN104532536A (en) * 2014-09-30 2015-04-22 上海和鹰机电科技股份有限公司 Automatic cutting machine and automatic cutting method thereof
CN104818603A (en) * 2015-03-27 2015-08-05 上海和鹰机电科技股份有限公司 Intelligent cutting method and system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
崔宝侠，等: "基于模糊控制对剪切线位置控制方式的改进", 《沈阳工业大学学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109709808A (en) * 2018-12-29 2019-05-03 江苏华宏科技股份有限公司 Scrap shearing machine shears oil cylinder stroke Fuzzy control system and method
CN109856974A (en) * 2019-03-01 2019-06-07 烟台大学 A kind of four factor intelligent control methods based on controlled volume variation

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