CN104294476A - Intelligent control system for starting and stopping of high-speed warp knitting machine and control method of intelligent control system - Google Patents
Intelligent control system for starting and stopping of high-speed warp knitting machine and control method of intelligent control system Download PDFInfo
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- CN104294476A CN104294476A CN201410563698.8A CN201410563698A CN104294476A CN 104294476 A CN104294476 A CN 104294476A CN 201410563698 A CN201410563698 A CN 201410563698A CN 104294476 A CN104294476 A CN 104294476A
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000009940 knitting Methods 0.000 title abstract 2
- 239000004744 fabric Substances 0.000 claims description 22
- 230000006870 function Effects 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 12
- 238000003032 molecular docking Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 abstract description 4
- 239000004753 textile Substances 0.000 abstract description 2
- 230000003044 adaptive effect Effects 0.000 abstract 4
- 239000003638 chemical reducing agent Substances 0.000 abstract 2
- 230000008859 change Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000003053 immunization Effects 0.000 description 1
- 238000002649 immunization Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Classifications
-
- D—TEXTILES; PAPER
- D04—BRAIDING; LACE-MAKING; KNITTING; TRIMMINGS; NON-WOVEN FABRICS
- D04B—KNITTING
- D04B27/00—Details of, or auxiliary devices incorporated in, warp knitting machines, restricted to machines of this kind
- D04B27/10—Devices for supplying, feeding, or guiding threads to needles
- D04B27/16—Warp beams; Bearings therefor
- D04B27/20—Warp beam driving devices
- D04B27/22—Warp beam driving devices electrically controlled
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- Engineering & Computer Science (AREA)
- Textile Engineering (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to an intelligent control system for starting and stopping of a high-speed warp knitting machine and a control method of the intelligent control system and belongs to the field of textile machinery. The intelligent control system comprises a beam motor intelligent controller, a servo driver servo motor, a reducer, a spindle speed encoder which is arranged on a spindle and a beam speed measurement encoder arranged on a beam. The beam motor intelligent controller comprises a spindle speed estimator and an adaptive PID (proportional integral derivative) controller which are sequentially connected. An output end of the spindle speed encoder is connected with an input end of the spindle speed estimator. An output end of the beam speed measurement encoder is connected with an input end of the adaptive PID controller. The servo driver servo motor, the reducer and the beam are sequentially connected. An output end of the adaptive PID controller is connected with the servo driver servo motor. The intelligent control system and the control method thereof have the advantages that algorithm is simple, the beam motor is adjusted in the adaptive manner to control the target curve and PID parameters, quick reaction capability of the system is effectively improved, and the problem of lagging in the starting and stopping of the beam is relieved, and stopping lines are handled.
Description
Technical field
The present invention relates to the motor in synchrony intelligence control system that a kind of Warp Knitted Fabrics opens docking process, local speed of mainshaft prediction is particularly adopted to instruct the following control system changed through spindle motor, to ensure can better follow the tracks of spindle motor rotation speed change through spindle motor, alleviate and solve Warp Knitted Fabrics stopping line problem, belonging to field of textile machinery.
Background technology
High speed is in volume production process, and the fault the most often occurred is through compiling horizontal stripe, comprises running horizontal stripe when normally producing and the stopping line opening docking process generation.At present, along with the application of high-speed servo motor and the Improvement and perfection of mechanical structure, running horizontal stripe problem solves substantially, but stopping line phenomenon still generally annoyings all multiple enterprises, drastically influence product quality and the performance of enterprises.
Correlative study shows, Warp Knitted Fabrics is opened in docking process, through the rotating speed hysteresis quality that spindle motor relative main motor has, causes the warp thread sent can not adapt to the requirement of lopping completely, is the immediate cause producing stopping line.Generally speaking, machine rotational speed is higher, delayed more obvious through spindle motor, and the stopping line of generation is larger.Thoroughly solving stopping line problem if think, must ensure to open in docking process whole, the actual startup/braking curve through spindle motor starts with spindle motor/and braking curve mates completely.
But under requiring in the different speed of a motor vehicle, the startup/braking curve of motor differs greatly, and between its theoretical startup/braking curve and actual curve, also there is some difference, and therefore wanting to realize startup/braking curve completely consistent, is very difficult.
Therefore, people have opened some intelligent control scheme.Such as in journal of Zhejiang university (engineering version) 47 volume 10 phase 1712-1721 pages in 2013, relate to a kind of warp let-off scheme of fuzzy immunization-single neuron PID (FI_SNAPID), the program adjusts pid parameter in conjunction with fuzzy immunization optimized algorithm and MN algorithm, to follow the tracks of main shaft speed change process preferably, warp run-in accurate stable.
Obviously, the situation that above-mentioned technology of sampling is less to speed of mainshaft amplitude of variation in this way can normally use, but it is all the time using current time sampled value as through spindle motor desired value, determine its intelligence when this rotation speed change of start-stop car is very fast and be in the stage of tracking, its algorithm is comparatively complicated simultaneously, temporal hysteresis quality is difficult to eliminate, and is difficult to the generation really avoiding stopping line.
Therefore, development is a kind of can adapt to the different speed of a motor vehicle, have the necessarily property estimated and prospective high-performance, at a high speed through volume intelligence control system, particularly opens the advanced control system in docking process, has become Warp Knitted Fabrics and has developed one of key issue needing solution badly.
Summary of the invention
For overcoming the deficiencies in the prior art, the invention provides a kind of Warp Knitted Fabrics start-stop car intelligence control system and the control method thereof that effectively can solve stopping line problem.
For achieving the above object, the technical solution adopted in the present invention is:
A kind of Warp Knitted Fabrics start-stop car intelligence control system, comprise warp beam intelligent motor controller, servo-driver servomotor, decelerator, and be arranged on speed of mainshaft encoder on main shaft and be arranged on the warp beam speed measuring coder in warp beam, wherein, warp beam intelligent motor controller comprises speed of mainshaft prediction device and the self-adaptive PID controller (speed of mainshaft prediction device and self-adaptive PID controller all adopt PLC to control) of serial connection, the output of speed of mainshaft encoder is connected with the input of speed of mainshaft prediction device, the output of warp beam speed measuring coder is connected with the input of self-adaptive PID controller, servo-driver servomotor, decelerator, warp beam connects successively, the output of self-adaptive PID controller is connected with servo-driver servomotor.
In the present invention, speed of mainshaft prediction device can according to detecting the speed of mainshaft sequence obtained, utilize local learning model building methods analyst to estimate the variation tendency (namely the speed of mainshaft estimates sequence) obtaining the speed of mainshaft, and be supplied to self-adaptive PID controller as warp beam motor speed reference target sequence; Self-adaptive PID controller then estimates sequence according to rotating speed reference target sequence and the speed of mainshaft, adaptively selected pid control parameter, optimizes warp beam motor speed curve.
A control method for Warp Knitted Fabrics start-stop car intelligence control system, comprises the following steps:
1) speed of mainshaft encoder will detect the speed of mainshaft sequence inputting that obtains to speed of mainshaft prediction device, speed of mainshaft prediction device utilizes local learning model building methods analyst to estimate to obtain the speed of mainshaft estimates sequence, and the speed of mainshaft is estimated sequence exports self-adaptive PID controller to;
2) self-adaptive PID controller estimates according to the speed of mainshaft real-time rotate speed sequence that sequence and warp beam speed measuring coder detect, the warp beam motor speed aim curve in real-time update self-adaptive PID controller;
3) self-adaptive PID controller is according to the error amount between warp beam motor speed aim curve and current warp beam rotating speed, utilize fuzzy control self-adaptative adjustment pid parameter, and the pid parameter control instruction after adjustment is sent to servo-driver servomotor, again by the rotating speed of decelerator adjustment warp beam, and then keep the uniformity of warp beam rotating speed and the speed of mainshaft.
As the further setting of such scheme, described local learning model building method comprises the following steps:
(1) build Warp Knitted Fabrics and open docking process speed of mainshaft sequence history database, wherein, current sample time and top n speed of mainshaft sequence thereof are as input feature vector, and subsequent prediction sequence is as output characteristic;
(2) the current speed of mainshaft sequence of Real-time Collection, uses sequence similarity estimation function to calculate some groups of the most similar history rotating speed sequences;
(3) build local learning model, estimate that obtaining the speed of mainshaft estimates sequence, and export self-adaptive PID controller to; Local learning model structure and sequence are estimated scheme and are:
Wherein, V
mesure, V
estimateand V
ibe respectively main shaft to detect sequence, estimating sequence and the historical data sequence the most similar to detecting sequence.
In step (1), the Parameter N that input feature vector adopts is chosen as 5;
Sequence similarity estimation function used in step (2) meets the kernel function of Mercer condition.
Described kernel function is gaussian kernel function:
Sim(v
1,v
2)＝exp[-||v
1-v
2||
2/2σ
2]
Wherein, σ represents Gaussian kernel width, v
1, v
2for speed of mainshaft list entries.
Described step 2) in, the expression formula of self-adaptive PID controller setting warp beam motor speed aim curve is:
V
set＝ηV
mesure+(1-η)V
estimate
Wherein, η is compromise coefficient, 0≤η≤1.
In the present invention, the selective rule of parameter η is:
When speed of mainshaft sequence variation is larger (as relative speed variation is greater than 10% of setting speed), η gets smaller value, general 0≤η≤0.5; When speed of mainshaft sequence variation is less (as relative speed variation is less than 10% of setting speed), η gets higher value, general 0.5≤η≤1; If η=0, deteriorate to without estimating PID control program.
The invention has the beneficial effects as follows: owing to adding speed of mainshaft prediction device in warp beam intelligent motor controller, good estimation obtains the development trend of the speed of mainshaft, and the control objectives curve of self-adaptive PID is effectively have adjusted by parameter η, improve the quick-reaction capability of system, alleviate the hysteresis quality problem of warp beam motor speed, can effectively process stopping line problem.
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Accompanying drawing explanation
Fig. 1 is the structural representation of a kind of Warp Knitted Fabrics start-stop of the present invention car intelligence control system;
Fig. 2 is the schematic diagram that in the present invention, the speed of mainshaft estimates local learning model building method;
Fig. 3 is the overall flow schematic diagram of a kind of Warp Knitted Fabrics start-stop of the present invention car intelligence control system.
Detailed description of the invention
Below in conjunction with accompanying drawing, technical scheme of the present invention is described further.Obviously, described embodiment is only a part of embodiment of the present invention, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
As shown in Figure 1, a kind of Warp Knitted Fabrics start-stop of the present invention car intelligence control system, comprise warp beam intelligent motor controller 3, servo-driver servomotor 6, decelerator 7, and the warp beam speed measuring coder 9 being arranged on speed of mainshaft encoder on main shaft 12 and being arranged in warp beam 8, wherein, warp beam intelligent motor controller 3 comprises speed of mainshaft prediction device 4 and the self-adaptive PID controller 5 of serial connection, the output of speed of mainshaft encoder 2 is connected with the input of speed of mainshaft prediction device 4, the output of warp beam speed measuring coder 9 is connected with the input of self-adaptive PID controller 5, servo-driver servomotor 6, decelerator 7, warp beam 8 connects successively, the output of self-adaptive PID controller 5 is connected with servo-driver servomotor 6.
The present invention, the speed of mainshaft that detection obtains by speed of mainshaft encoder 2 and warp beam speed measuring coder 9 respectively and warp beam rotating speed are input to warp beam intelligent motor controller 3.Speed of mainshaft prediction device 4 is responsible for estimating the speed of mainshaft, and self-adaptive PID controller 5 is responsible for controlling warp beam motor speed.
A control method for Warp Knitted Fabrics start-stop car intelligence control system, comprises the following steps:
1) speed of mainshaft encoder 2 will detect the speed of mainshaft sequence inputting that obtains to speed of mainshaft prediction device 4, speed of mainshaft prediction device 4 utilizes local learning model building methods analyst to estimate to obtain the speed of mainshaft estimates sequence, and the speed of mainshaft is estimated sequence and export self-adaptive PID controller 5 to;
2) self-adaptive PID controller 5 estimates according to the speed of mainshaft real-time rotate speed sequence that sequence and warp beam speed measuring coder 9 detect, the warp beam motor speed aim curve in real-time update self-adaptive PID controller 5;
3) self-adaptive PID controller 5 is according to the error amount between warp beam motor speed aim curve and current warp beam rotating speed, utilize fuzzy control self-adaptative adjustment pid parameter, and the pid parameter control instruction after adjustment is sent to servo-driver servomotor 6, adjusted the rotating speed of warp beam 8 again by decelerator 7, and then keep the uniformity of warp beam 8 rotating speed and main shaft 1 rotating speed.
As preferably, described local learning model building method as shown in Figure 2, comprises the following steps:
(1) build Warp Knitted Fabrics and open docking process speed of mainshaft sequence history database, wherein, current sample time and top n speed of mainshaft sequence thereof are as input feature vector, and subsequent prediction sequence is as output characteristic;
(2) the current speed of mainshaft sequence of Real-time Collection, uses sequence similarity estimation function to calculate some groups of the most similar history rotating speed sequences;
(3) build local learning model, estimate that obtaining the speed of mainshaft estimates sequence, and export self-adaptive PID controller 5 to; Local learning model structure and sequence are estimated scheme and are:
Wherein, V
mesure, V
estimateand V
ibe respectively main shaft to detect sequence, estimating sequence and the historical data sequence the most similar to detecting sequence.
Speed of mainshaft prediction device 4 opens docking process speed of mainshaft change sequence and current speed of mainshaft sequence according to history, builds the local learning model analysis optimization speed of mainshaft and estimates sequence.
In step (1), the Parameter N that input feature vector adopts is chosen as 5;
Sequence similarity estimation function used in step (2) meets the kernel function of Mercer condition.
Simplify, local learning model building method comprises: (1) builds spindle speed measurement sequence with the current speed of mainshaft and top n moment tachometer value; (2) corresponding with historical data base speed of mainshaft sequence data carries out similarity measurement; (3) estimate and obtain the speed of mainshaft and estimate sequence.
Described kernel function is gaussian kernel function:
Sim(v
1,v
2)＝exp[-||v
1-v
2||
2/2σ
2]
Wherein, σ represents Gaussian kernel width, v
1, v
2for speed of mainshaft list entries.
Described step 2) in, self-adaptive PID controller 5 sets warp beam motor speed aim curve, and (namely self-adaptive PID controller is according to V
mesureand V
estimatelookup protocol and adjustment warp beam motor speed aim curve) expression formula be:
V
set＝ηV
mesure+(1-η)V
estimate
Wherein, η is compromise coefficient, 0≤η≤1.
As preferentially, the parameter η plan of establishment can be thought of as:
Wherein, T is setting threshold value.
Self-adaptive PID controller 5, according to the controlling curve after optimization and error change situation, utilizes Fuzzy Thought determination pid parameter, Δ K
p, Δ T
i, Δ T
drule is as follows:
Δ K
p, Δ T
i, Δ T
drepresent the knots modification of ratio in pid parameter, integration, differential coefficient respectively, specifically according to error e and the rate of change ec thereof of warp beam rotating speed and setting value, according to three form determined values below.The trapezoidal membership function adopted carries out obfuscation and defuzzification.
Shown in composition graphs 3, a kind of Warp Knitted Fabrics start-stop of the present invention car intelligence control system overall flow comprises:
(1) when start-stop car pushbutton enable being detected or larger change occurs the speed of mainshaft, record obtains speed of mainshaft sequence;
(2) obtaining the speed of mainshaft estimates sequence to utilize speed of mainshaft prediction device 4 to estimate, and exports self-adaptive PID controller 5 to;
(3) self-adaptive PID controller 5 sets warp beam motor speed aim curve, and according to detecting the warp beam speed conditions obtained, adopting fuzzy control Optimize Multivariable PID Controller, realizing warp beam motor speed and effectively follow the tracks of the speed of mainshaft.
In a word, in the present invention, speed of mainshaft prediction device 4 can be estimated and obtains the speed of mainshaft and estimate sequence according to detecting the speed of mainshaft sequence analysis that obtain, and self-adaptive PID controller 5 estimates sequence according to the speed of mainshaft, adaptively selected pid control parameter, optimizes warp beam motor speed curve.Algorithm of the present invention is simple, can self-adaptative adjustment warp beam motor control objective curve and pid parameter, effectively improves the quick-reaction capability of system, alleviates the hysteresis quality problem of warp beam start-stop car, can effectively process stopping line problem.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (6)
1. a Warp Knitted Fabrics start-stop car intelligence control system, it is characterized in that: comprise warp beam intelligent motor controller, servo-driver servomotor, decelerator, and be arranged on speed of mainshaft encoder on main shaft and be arranged on the warp beam speed measuring coder in warp beam, wherein, warp beam intelligent motor controller comprises speed of mainshaft prediction device and the self-adaptive PID controller of serial connection, the output of speed of mainshaft encoder is connected with the input of speed of mainshaft prediction device, the output of warp beam speed measuring coder is connected with the input of self-adaptive PID controller, servo-driver servomotor, decelerator, warp beam connects successively, the output of self-adaptive PID controller is connected with servo-driver servomotor.
2. the control method of a kind of Warp Knitted Fabrics start-stop car intelligence control system as claimed in claim 1, is characterized in that comprising the following steps:
1) speed of mainshaft encoder will detect the speed of mainshaft sequence inputting that obtains to speed of mainshaft prediction device, speed of mainshaft prediction device utilizes local learning model building methods analyst to estimate to obtain the speed of mainshaft estimates sequence, and the speed of mainshaft is estimated sequence exports self-adaptive PID controller to;
2) self-adaptive PID controller estimates according to the speed of mainshaft real-time rotate speed sequence that sequence and warp beam speed measuring coder detect, the warp beam motor speed aim curve in real-time update self-adaptive PID controller;
3) self-adaptive PID controller is according to the error amount between warp beam motor speed aim curve and current warp beam rotating speed, utilize fuzzy control self-adaptative adjustment pid parameter, and the pid parameter control instruction after adjustment is sent to servo-driver servomotor, again by the rotating speed of decelerator adjustment warp beam, and then keep the uniformity of warp beam rotating speed and the speed of mainshaft.
3. the control method of a kind of Warp Knitted Fabrics start-stop car intelligence control system as claimed in claim 2, is characterized in that: described local learning model building method comprises the following steps:
(1) build Warp Knitted Fabrics and open docking process speed of mainshaft sequence history database, wherein, current sample time and top n speed of mainshaft sequence thereof are as input feature vector, and subsequent prediction sequence is as output characteristic;
(2) the current speed of mainshaft sequence of Real-time Collection, uses sequence similarity estimation function to calculate some groups of the most similar history rotating speed sequences;
(3) build local learning model, estimate that obtaining the speed of mainshaft estimates sequence, and export self-adaptive PID controller to; Local learning model structure and sequence are estimated scheme and are:
Wherein, V
mesure, V
estimateand V
ibe respectively main shaft to detect sequence, estimating sequence and the historical data sequence the most similar to detecting sequence.
4. the control method of a kind of Warp Knitted Fabrics start-stop car intelligence control system as claimed in claim 3, is characterized in that: in described step (1), and the Parameter N that input feature vector adopts is chosen as 5.
5. the control method of a kind of Warp Knitted Fabrics start-stop car intelligence control system as claimed in claim 3, it is characterized in that: sequence similarity estimation function used in described step (2) meets the kernel function of Mercer condition, and kernel function is gaussian kernel function:
Sim(v
1,v
2)＝exp[-||v
1-v
2||
2/2σ
2]
Wherein, σ represents Gaussian kernel width, v
1, v
2for speed of mainshaft list entries.
6. the control method of a kind of Warp Knitted Fabrics start-stop car intelligence control system as claimed in claim 3, is characterized in that: described step 2) in, the expression formula of self-adaptive PID controller setting warp beam motor speed aim curve is:
V
set＝ηV
mesure+(1-η)V
estimate
Wherein, η is compromise coefficient, 0≤η≤1.
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ID=52314416
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107394968A (en) * | 2017-08-25 | 2017-11-24 | 嘉兴荣星针纺自动化设备有限公司 | Encoder and single motor multistation motion control device comprising encoder |
| CN107700063A (en) * | 2017-11-15 | 2018-02-16 | 江南大学 | A kind of self-adapting type warp knit tension force intelligent equalization regulates and controls method |
| CN107747161A (en) * | 2017-11-15 | 2018-03-02 | 江南大学 | One kind is without sensor warp knit tension force positive type regulator control system |
| CN117434828A (en) * | 2023-12-18 | 2024-01-23 | 南京德克威尔自动化有限公司 | Numerical control system spindle closed-loop control method based on encoder feedback |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1367164A2 (en) * | 2002-06-01 | 2003-12-03 | KARL MAYER TEXTILMASCHINENFABRIK GmbH | Knitting machine, paricularly warp knitting machine |
| EP1498529A1 (en) * | 2003-07-18 | 2005-01-19 | KARL MAYER TEXTILMASCHINENFABRIK GmbH | method for operation of a fast running warp knitting machine |
| CN101231524A (en) * | 2008-01-15 | 2008-07-30 | 常州市第八纺织机械有限公司 | Real time dual bus control method for warp knitting machine |
| CN101638827A (en) * | 2008-06-04 | 2010-02-03 | 圣东尼公司 | Method to produce textile articles with warp-knitting machines and machine to carry out such a method |
| CN101858014A (en) * | 2010-05-20 | 2010-10-13 | 常州市第八纺织机械有限公司 | Ten-axial synchronous control method of biaxial warp knitting machine |
| CN101956293A (en) * | 2010-09-29 | 2011-01-26 | 常州市第八纺织机械有限公司 | Constant linear speed electronic let-off control method and device of warp knitting machine |
| CN103592876A (en) * | 2013-11-15 | 2014-02-19 | 福建宏宇电子科技有限公司 | Electronic shogging control system and method used in warp knitting industry |
-
2014
- 2014-10-22 CN CN201410563698.8A patent/CN104294476B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1367164A2 (en) * | 2002-06-01 | 2003-12-03 | KARL MAYER TEXTILMASCHINENFABRIK GmbH | Knitting machine, paricularly warp knitting machine |
| EP1498529A1 (en) * | 2003-07-18 | 2005-01-19 | KARL MAYER TEXTILMASCHINENFABRIK GmbH | method for operation of a fast running warp knitting machine |
| CN101231524A (en) * | 2008-01-15 | 2008-07-30 | 常州市第八纺织机械有限公司 | Real time dual bus control method for warp knitting machine |
| CN101638827A (en) * | 2008-06-04 | 2010-02-03 | 圣东尼公司 | Method to produce textile articles with warp-knitting machines and machine to carry out such a method |
| CN101858014A (en) * | 2010-05-20 | 2010-10-13 | 常州市第八纺织机械有限公司 | Ten-axial synchronous control method of biaxial warp knitting machine |
| CN101956293A (en) * | 2010-09-29 | 2011-01-26 | 常州市第八纺织机械有限公司 | Constant linear speed electronic let-off control method and device of warp knitting machine |
| CN103592876A (en) * | 2013-11-15 | 2014-02-19 | 福建宏宇电子科技有限公司 | Electronic shogging control system and method used in warp knitting industry |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107394968A (en) * | 2017-08-25 | 2017-11-24 | 嘉兴荣星针纺自动化设备有限公司 | Encoder and single motor multistation motion control device comprising encoder |
| CN107700063A (en) * | 2017-11-15 | 2018-02-16 | 江南大学 | A kind of self-adapting type warp knit tension force intelligent equalization regulates and controls method |
| CN107747161A (en) * | 2017-11-15 | 2018-03-02 | 江南大学 | One kind is without sensor warp knit tension force positive type regulator control system |
| CN107700063B (en) * | 2017-11-15 | 2019-07-23 | 江南大学 | A kind of self-adapting type warp knit tension intelligent equalization regulation method |
| CN117434828A (en) * | 2023-12-18 | 2024-01-23 | 南京德克威尔自动化有限公司 | Numerical control system spindle closed-loop control method based on encoder feedback |
| CN117434828B (en) * | 2023-12-18 | 2024-03-15 | 南京德克威尔自动化有限公司 | Numerical control system spindle closed-loop control method based on encoder feedback |
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|---|---|
| CN104294476B (en) | 2016-05-11 |
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