CN107390528A - A kind of adaptive fuzzy control method of weld joint tracking application - Google Patents

A kind of adaptive fuzzy control method of weld joint tracking application Download PDF

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CN107390528A
CN107390528A CN201710731424.9A CN201710731424A CN107390528A CN 107390528 A CN107390528 A CN 107390528A CN 201710731424 A CN201710731424 A CN 201710731424A CN 107390528 A CN107390528 A CN 107390528A
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fuzzy
msub
mrow
deviation
controller
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邹焱飚
王研博
周卫林
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South China University of Technology SCUT
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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Abstract

The invention discloses a kind of adaptive fuzzy control method of weld joint tracking application, including step：1) the deviation e and deviation variation rate that will be obtainedIt is blurred, obtains fuzzy variable deviation E and deviation variation rate EC；2) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, establish fuzzy rule；3) by the fuzzy variable deviation E and deviation variation rate E of inputCIt is input in indistinct logic computer and obtains fuzzy output amount U；4) maximum value process defuzzification is used, fuzzy output amount U is converted into exact value u；5) controller scale factor k is introducedi(i=1,2 ..., n) and controller quantizing factor ku, fuzzy domain, the fuzzy rule function of input and output are set；6) real-time update is carried out to fuzzy rule function according to the change of input/output amount, calculates and export the bias voltage value of controlled device.The present invention to fuzzy controller by carrying out parameter adjustment in real time in control process, so as to solve the problems, such as that unstable control process, bad dynamic performance, control process are coarse.

Description

A kind of adaptive fuzzy control method of weld joint tracking application
Technical field
Invention is related to Weld Seam Tracking Control field, more particularly to a kind of adaptive fuzzy control method of weld bead feature points.
Background technology
After the displacement of welding robot needs movement has been obtained, because the mathematical modeling of robot is unknown, therefore, These discrete deviations can not be transmitted directly to robot controller.Simultaneously because mechanical system is compared with vision detection system In the presence of certain hysteresis quality, if without effective control algolithm, welding gun end is easily caused to occur periodically in welding process Chattering phenomenon, due to the influence of the accidental errors such as non-linear and time variation in controlled process be present, and traditional fuzzy control Middle fuzzy rule base and membership function are changeless, so that control process is unstable, bad dynamic performance, are controlled Journey is coarse, in order to solve this problem, it is necessary to carry out parameter adjustment to fuzzy controller in real time in control process.Therefore, Adaptive fuzzy controller is used herein, and changing for controlled device is adapted to by being continuously updated model parameter in control process Become, therefore, accurately carry out the Adaptive Fuzzy Control of weld bead feature points in real time, be the main contents of the present invention.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide one kind calculate it is simple and convenient, meet weld joint tracking It is required that Adaptive Fuzzy Control computational methods.
Above-mentioned purpose is achieved through the following technical solutions：
A kind of adaptive fuzzy control method of weld joint tracking application, including step：
1) the deviation e and deviation variation rate that will be obtainedBe blurred, obtain fuzzy variable deviation E and partially Poor rate of change EC；
2) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, establish fuzzy rule；
3) by the fuzzy variable deviation E and deviation variation rate E of inputCIt is input in indistinct logic computer and makes inferences to obtain mould Paste output quantity U；
4) maximum value process defuzzification is used, fuzzy output amount U is converted into exact value u；
5) controller scale factor k is introducedi(i=1,2 ..., n) and controller quantizing factor ku, the moulds of input and output is set Paste domain, fuzzy rule function；
6) real-time update is carried out to fuzzy rule function according to the change of input/output amount, calculates and export controlled device Bias voltage value.
Further, described step 1) specifically includes：
According to obtained deviation e and deviation variation rateSelect a two-dimensional fuzzy controller, by deviation e and Deviation variation rate ecFuzzy inputing method module, gauss of distribution function is selected to export fuzzy become after blurring as membership function Measure E and EC。
Further, described step 2) specifically includes：
21) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, it is fuzzy to establish following 42 Rule：
If E=NB and EC=NB then U=NB
Or if E=NB and EC=NM then U=NB
or…
Or if E=PB and EC=PM then U=PB
Or if E=PB and EC=PB then U=PB
22) implication relation of whole fuzzy rule base is：
Further, described step 3) specifically includes：
31) by the fuzzy variable deviation E and deviation variation rate E of inputCIt is input in indistinct logic computer, fuzzy reasoning expression Formula is：
32) deviation e and deviation variation rate ec4 fuzzy rules can be triggered, for each fuzzy rule, what is be calculated is every The output quantity U of rulei(i=1,2,3,4) it is：
U1=μNB(e)∧μNB(ec)∧μNB(u)
U1=μNB(e)∧μNM(ec)∧μNB(u)
U1=μNS(e)∧μNB(ec)∧μNB(u)
U1=μNS(e)∧μNM(ec)∧μNS(u)；
33) calculating total fuzzy output amount is：
Further, fuzzy output amount U is converted to exact value u by described step 4) using maximum membership degree method, because logical Fuzzy output amount includes multiple domains in the case of often, and to obtain accurate output valve, exact value is obtained using maximum value process, Its expression formula is：
U=sign (xi)max(|xi|)
Wherein, xiRepresent the centrifugal pump on input domain, sign (xi) it is sign function, expression takes xiSymbol it is (positive and negative Number).
Further, in described step 5), the controller quantizing factor ki(i=1,2 ..., n) and controller ratio Factor kuExpression formula be：
Wherein：ki(i=1,2 ..., n) represents the quantizing factor of controller, niRepresent the fuzzy domain subset of input, kuRepresent The scale factor of controller, u represent fuzzy output, and l represents the fuzzy domain subset of output.
Further, in described step 5), the size of the fuzzy domain is
Xi(xi)=[- α (xi)xi,α(xi)xi],
α(xi) be domain updating factor, its value is；
Wherein：λi∈ (0,1) represents weight coefficient, kci>0 representsCoefficient correlation.
Further, in described step 5), the fuzzy rule function by fuzzy controller fuzzy variable deviation E, Deviation variation rate ECGained is designed with the fuzzy domain of control output：
In formula, N be fuzzy system quantification gradation, i.e. E={ 0, ± 1 ..., ± N }, weight coefficient α and absolute value of the bias | E | linear function, take 0.1≤α0≤ 0.5,0.5≤αs≤ 1, so control error smaller, while the control process time It is shorter.
Further, described step 6) specifically includes：
61) time-varying characteristics of controlled device in automatic Arc Welding are directed to, set the renewal of adaptive fuzzy controller to join Number：
In formula, 0<α1<1 is deviation weight coefficient, and K, A are initial subset parameters, chooses K=0.8004, A= 0.4998, the quantification gradation N=3 of error, weight parameter αs=0.8, α0=0.5；
62) following real-time update is carried out to fuzzy rule function：
63) deviation of weld bead feature points is transformed into bias voltage, is sent to robot controller in real time and carries out automatically Weld job.
Compared with prior art, the present invention uses adaptive fuzzy controller, in real time to fuzzy control in control process Device carries out parameter adjustment, i.e., adapts to the change of controlled device by being continuously updated model parameter in control process, realizes The Adaptive Fuzzy Control of weld bead feature points is accurately carried out in real time, and control process is unstable so as to solving in the prior art, moves State degradation, the problem of control process is coarse.
Brief description of the drawings
Fig. 1 is the fuzzy output before and after fuzzy reasoning.
Fig. 2 is the schematic diagram that fuzzy domain changes in real time.
Fig. 3 is the schematic flow sheet of the adaptive fuzzy control method of weld joint tracking application.
Embodiment
The goal of the invention of the present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings, embodiment is not It can repeat one by one herein, but therefore embodiments of the present invention are not defined in following examples.
A kind of adaptive fuzzy control method of weld joint tracking application, including step：
1) the deviation e and deviation variation rate that will be obtainedBe blurred, obtain fuzzy variable deviation E and partially Poor rate of change EC；
2) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, establish fuzzy rule；
3) by the fuzzy variable deviation E and deviation variation rate E of inputCIt is input in indistinct logic computer and makes inferences to obtain mould Paste output quantity U；
4) maximum value process defuzzification is used, fuzzy output amount U is converted into exact value u；
5) controller scale factor k is introducedi(i=1,2 ..., n) and controller quantizing factor ku, the moulds of input and output is set Paste domain, fuzzy rule function；
6) real-time update is carried out to fuzzy rule function according to the change of input/output amount, calculates and export controlled device Bias voltage value.
Specifically, described step 1) specifically includes：
According to obtained deviation e and deviation variation rateSelect a two-dimensional fuzzy controller, by deviation e and Deviation variation rate ecFuzzy inputing method module (D/F), gauss of distribution function is selected as membership function, output mould after blurring Paste variable E and EC。
Specifically, described step 2) specifically includes：
21) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, it is fuzzy to establish following 42 Rule：
If E=NB and EC=NB then U=NB
Or if E=NB and EC=NM then U=NB
or…
Or if E=PB and EC=PM then U=PB
Or if E=PB and EC=PB then U=PB；
22) implication relation of whole fuzzy rule base is：
Specifically, described step 3) specifically includes：
31) by the fuzzy variable deviation E and deviation variation rate E of inputCIt is input in indistinct logic computer, fuzzy reasoning expression Formula is：
32) deviation e and deviation variation rate ec4 fuzzy rules can be triggered, for each fuzzy rule, what is be calculated is every The output quantity U of rulei(i=1,2,3,4) it is：
U1=μNB(e)∧μNB(ec)∧μNB(u)
U1=μNB(e)∧μNM(ec)∧μNB(u)
U1=μNS(e)∧μNB(ec)∧μNB(u)
U1=μNS(e)∧μNM(ec)∧μNS(u)；
33) calculating total fuzzy output amount is：
Fuzzy output before and after fuzzy reasoning is as shown in Figure 1.
Specifically, fuzzy output amount U is converted to exact value u by described step 4) using maximum membership degree method, because logical Fuzzy output amount includes multiple domains in the case of often, and to obtain accurate output valve, exact value is obtained using maximum value process, Its expression formula is：
U=sign (xi)max(|xi|)
Wherein, xiRepresent the centrifugal pump on input domain, sign (xi) it is sign function, expression takes xiSymbol it is (positive and negative Number).
Specifically, in described step 5), the controller quantizing factor ki(i=1,2 ..., n) and controller ratio Factor kuExpression formula be：
Wherein：ki(i=1,2 ..., n) represents the quantizing factor of controller, niRepresent the fuzzy domain subset of input, kuRepresent The scale factor of controller, u represent fuzzy output, and l represents the fuzzy domain subset of output.By change scale factor and quantify because The size of son can change fuzzy rule function in real time, so as to change the size of the fuzzy domain of input and output.Fuzzy domain The schematic diagram changed in real time is as shown in Figure 2.
Specifically, in described step 5), the size of the fuzzy domain is
Xi(xi)=[- α (xi)xi,α(xi)xi],
Wherein α (xi) be domain updating factor, its value is；
Wherein：λi∈ (0,1) represents weight coefficient, kci>0 representsCoefficient correlation.
Specifically, in described step 5), the fuzzy rule function by fuzzy controller fuzzy variable deviation E, Deviation variation rate ECGained is designed with the fuzzy domain of control output：
In formula, N be fuzzy system quantification gradation, i.e. E={ 0, ± 1 ..., ± N }, weight coefficient α and absolute value of the bias | E | linear function, take 0.1≤α0≤ 0.5,0.5≤αs≤ 1, so control error smaller, while the control process time It is shorter.
Specifically, described step 6) specifically includes：
61) time-varying characteristics of controlled device in automatic Arc Welding are directed to, set the renewal of adaptive fuzzy controller to join Number：
In formula, 0<α1<1 is deviation weight coefficient, and K, A are initial subset parameters, chooses K=0.8004, A= 0.4998, the quantification gradation N=3 of error, weight parameter αs=0.8, α0=0.5；
62) following real-time update is carried out to fuzzy rule function：
63) deviation of weld bead feature points is transformed into bias voltage, is sent to robot controller in real time and carries out automatically Weld job.
So, the deviation of weld bead feature points has been transformed into bias voltage, can be sent to robot controller progress Automatic welding operation, the process of whole adaptive fuzzy control method is as shown in Figure 3.
The above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not to the present invention Embodiment restriction.For those of ordinary skill in the field, can also make on the basis of the above description Other various forms of changes or variation.There is no necessity and possibility to exhaust all the enbodiments.It is all the present invention All any modification, equivalent and improvement made within spirit and principle etc., should be included in the protection of the claims in the present invention Within the scope of.

Claims (9)

1. a kind of adaptive fuzzy control method of weld joint tracking application, it is characterised in that including step：
1) the deviation e and deviation variation rate that will be obtainedIt is blurred, obtains fuzzy variable deviation E and deviation becomes Rate EC；
2) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, establish fuzzy rule；
3) by the fuzzy variable deviation E and deviation variation rate E of inputCBe input in indistinct logic computer make inferences to obtain it is fuzzy defeated Output U；
4) maximum value process defuzzification is used, fuzzy output amount U is converted into exact value u；
5) controller scale factor k is introducedi(i=1,2 ..., n) and controller quantizing factor ku, the fuzzy theories of input and output is set Domain, fuzzy rule function；
6) real-time update is carried out to fuzzy rule function according to the change of input/output amount, calculates and export the inclined of controlled device Put magnitude of voltage.
2. the adaptive fuzzy control method of weld joint tracking application according to claim 1, it is characterised in that described step It is rapid 1) to specifically include：
According to obtained deviation e and deviation variation rateA two-dimensional fuzzy controller is selected, by deviation e and deviation Rate of change ecFuzzy inputing method module, select gauss of distribution function be used as membership function, blurring after output fuzzy variable E with EC。
3. the adaptive fuzzy control method of weld joint tracking application according to claim 1, it is characterised in that described step It is rapid 2) to specifically include：
21) according to deviation e and deviation variation rate ec, with reference to expertise and operating experience, establish 42 following fuzzy rules：
If E=NB and EC=NB then U=NB
Or if E=NB and EC=NM then U=NB
or…
Or if E=PB and EC=PM then U=PB
Or if E=PB and EC=PB then U=PB
22) implication relation of whole fuzzy rule base is：
<mrow> <mi>R</mi> <mo>=</mo> <msub> <mi>R</mi> <mn>1</mn> </msub> <mo>&amp;cup;</mo> <msub> <mi>R</mi> <mn>2</mn> </msub> <mo>&amp;cup;</mo> <mo>...</mo> <mo>&amp;cup;</mo> <msub> <mi>R</mi> <mn>42</mn> </msub> <mo>=</mo> <munderover> <mrow> <mi></mi> <mo>&amp;cup;</mo> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>42</mn> </munderover> <msub> <mi>R</mi> <mi>i</mi> </msub> <mo>.</mo> </mrow>
4. the adaptive fuzzy control method of weld joint tracking application according to claim 1, it is characterised in that described step It is rapid 3) to specifically include：
31) by the fuzzy variable deviation E and deviation variation rate E of inputCIt is input in indistinct logic computer, fuzzy reasoning expression formula is：
32) deviation e and deviation variation rate ec4 fuzzy rules can be triggered, for each fuzzy rule, the every rules and regulations being calculated Output quantity U theni(i=1,2,3,4) it is：
U1=μNB(e)∧μNB(ec)∧μNB(u)
U1=μNB(e)∧μNM(ec)∧μNB(u)
U1=μNS(e)∧μNB(ec)∧μNB(u)
U1=μNS(e)∧μNM(ec)∧μNS(u)；
33) calculating total fuzzy output amount is：
5. the adaptive fuzzy control method of weld joint tracking application according to claim 1, it is characterised in that described step It is rapid that fuzzy output amount U 4) is converted to by exact value u using maximum membership degree method, because fuzzy output amount under normal circumstances include it is more Individual domain, to obtain accurate output valve, exact value is obtained using maximum value process, and its expression formula is：
U=sign (xi)max(|xi|)
Wherein, xiRepresent the centrifugal pump on input domain, sign (xi) it is sign function, expression takes xiSymbol.
6. the adaptive fuzzy control method of weld joint tracking application according to claim 1, it is characterised in that described step It is rapid 5) in, the controller quantizing factor ki(i=1,2 ..., n) and controller scale factor kuExpression formula be：
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <msub> <mi>n</mi> <mi>i</mi> </msub> <msub> <mi>x</mi> <mi>i</mi> </msub> </mfrac> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>n</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>k</mi> <mi>u</mi> </msub> <mo>=</mo> <mfrac> <mi>u</mi> <mi>l</mi> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced>
Wherein：ki(i=1,2 ..., n) represents the quantizing factor of controller, niRepresent the fuzzy domain subset of input, kuRepresent control The scale factor of device, u represent fuzzy output, and l represents the fuzzy domain subset of output.
7. the adaptive fuzzy control method of weld joint tracking application according to claim 6, it is characterised in that described step It is rapid 5) in, the size of the fuzzy domain is
Xi(xi)=[- α (xi)xi,α(xi)xi],
α(xi) be domain updating factor, its value is；
<mrow> <mi>&amp;alpha;</mi> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>&amp;lambda;</mi> <mi>i</mi> </msub> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>k</mi> <mrow> <mi>c</mi> <mi>i</mi> </mrow> </msub> <msubsup> <mi>x</mi> <mi>i</mi> <mn>2</mn> </msubsup> </mrow> </msup> </mrow>
Wherein：λi∈ (0,1) represents weight coefficient, kci>0 representsCoefficient correlation.
8. the adaptive fuzzy control method of weld joint tracking application according to claim 7, it is characterised in that described step It is rapid 5) in, the fuzzy rule function by fuzzy controller fuzzy variable deviation E, deviation variation rate ECWith the mould of control output Paste domain design gained：
In formula, N be fuzzy system quantification gradation, i.e. E={ 0, ± 1 ..., ± N }, weight coefficient α and absolute value of the bias | E | into Linear functional relation, take 0.1≤α0≤ 0.5,0.5≤αs≤1。
9. the adaptive fuzzy control method of weld joint tracking application according to claim 8, it is characterised in that described step It is rapid 6) to specifically include：
61) time-varying characteristics of controlled device in automatic Arc Welding are directed to, the undated parameter of adaptive fuzzy controller is set：
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>k</mi> <mi>e</mi> </msub> <mo>=</mo> <mfrac> <mi>K</mi> <mrow> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> <mi>e</mi> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msub> <mi>e</mi> <mi>c</mi> </msub> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>k</mi> <mi>u</mi> </msub> <mo>=</mo> <mi>A</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> <mi>e</mi> <mo>+</mo> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> </mrow> <mo>)</mo> <msub> <mi>e</mi> <mi>c</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> <mo>=</mo> <mi>N</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>s</mi> </msub> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>|</mo> <mi>e</mi> <mo>|</mo> <mo>+</mo> <msub> <mi>&amp;alpha;</mi> <mn>0</mn> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced>
In formula, 0<α1<1 is deviation weight coefficient, and K, A are initial subset parameters, chooses K=0.8004, A=0.4998, error Quantification gradation N=3, weight parameter αs=0.8, α0=0.5；
62) following real-time update is carried out to fuzzy rule function：
63) deviation of weld bead feature points is transformed into bias voltage, is sent to robot controller in real time and carries out automatic welding Operation.
CN201710731424.9A 2017-08-23 2017-08-23 A kind of adaptive fuzzy control method of weld joint tracking application Pending CN107390528A (en)

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CN111650831A (en) * 2019-05-29 2020-09-11 北京航空航天大学 Design of interval 2 type fuzzy logic controller of virtual flexible needle in medical robot controller
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