CN104294476B - A kind of Warp Knitted Fabrics start-stop car intelligence control system and control method thereof - Google Patents
A kind of Warp Knitted Fabrics start-stop car intelligence control system and control method thereof Download PDFInfo
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- 239000004744 fabric Substances 0.000 title claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 13
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- 230000006870 function Effects 0.000 claims description 15
- 238000003032 molecular docking Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 8
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims 1
- 239000004753 textile Substances 0.000 abstract description 2
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- 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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Abstract
The present invention relates to a kind of Warp Knitted Fabrics start-stop car intelligence control system and control method thereof, belong to field of textile machinery, comprise warp beam intelligent motor controller, servo-driver servomotor, decelerator, and be arranged on the speed of mainshaft encoder on main shaft and be arranged on the warp beam speed measuring coder in warp beam, 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. algorithm of the present invention is simple, can self adaptation adjust warp beam Electric Machine Control aim curve and pid parameter, effectively improves the quick-reaction capability of system, has alleviated the hysteresis quality problem of warp beam start-stop car, can effectively process stopping line problem.
Description
Technical field
The present invention relates to a kind of Warp Knitted Fabrics and open the motor synchronization intelligence control system of docking process, particularly employing officePortion's speed of mainshaft is predicted to instruct the following control system changing through spindle motor, to ensure can better to follow the tracks of main shaft through spindle motorMotor speed changes, and alleviates and solves Warp Knitted Fabrics stopping line problem, belongs to field of textile machinery.
Background technology
In compiling production process, the fault the most often occurring is through compiling horizontal stripe, the running horizontal stripe while comprising normal production at a high speedWith the stopping line of opening docking process generation. At present, along with the application of high-speed servo motor and the Improvement and perfection of frame for movement,Running horizontal stripe problem solves substantially, but stopping line phenomenon is still generally perplexing all multiple enterprises, is having a strong impact on product qualityAnd the performance of enterprises.
Correlative study shows, Warp Knitted Fabrics is opened in docking process, and the rotating speed having through the relative spindle motor of spindle motor is stagnantRear property, causes the warp thread of sending can not adapt to the requirement of lopping completely, is the immediate cause that produces stopping line. Generally speaking,Machine rotational speed is higher, lags behind more obvious through spindle motor, and the stopping line of generation is larger. Thoroughly solve stopping line problem if think,Must ensure to open in docking process whole, through actual startup/braking curve and the spindle motor startup/braking curve of spindle motorCoupling completely.
But under requiring in the different speed of a motor vehicle, the startup/braking curve of motor differs greatly, and its theoretical startup/systemBetween moving curve and actual curve, also there is some difference, therefore wants to realize startup/braking curve completely consistent, is very tiredDifficult.
Therefore, people have opened some Based Intelligent Control schemes. For example, at journal of Zhejiang university (engineering version) 47 volumes 10 in 2013In phase 1712-1721 page, relate to the warp let-off scheme of a kind of fuzzy immunization-single neuron PID (FI_SNAPID), this scheme knotMatched moulds is stuck with paste immune optimization algorithm and MN algorithm is adjusted pid parameter, to follow the tracks of preferably main shaft speed change process, sendThrough amount accurate stable.
Obviously, the above-mentioned technology of sampling can normally be used the less situation of speed of mainshaft amplitude of variation in this way, butIt using current time sampled value as through spindle motor desired value, has determined that it is in the very fast feelings of this rotation speed change of start-stop car all the timeThe stage of intelligence in following the tracks of under condition, its algorithm is comparatively complicated simultaneously, and temporal hysteresis quality is difficult to eliminate, and is difficult to really avoidThe generation of stopping line.
Therefore, development a kind of can adapt to the different speed of a motor vehicle, have necessarily the property estimated and prospective high-performance high speed through volume intelligenceCan control system, particularly open the advanced control system in docking process, become Warp Knitted Fabrics development and needed badly the pass of solutionOne of key problem.
Summary of the invention
For overcoming the deficiencies in the prior art, the invention provides a kind of high speed that can effectively solve stopping line problem through compilingMachine start-stop car intelligence control system and control method thereof.
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, comprises that warp beam intelligent motor controller, servo-driver watchTake motor, decelerator, and be arranged on the 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 that (speed of mainshaft is estimated for the speed of mainshaft prediction device of serial connection and self-adaptive PID controllerDevice and self-adaptive PID controller all adopt PLC to control), the output of speed of mainshaft encoder and speed of mainshaft prediction deviceInput connect, the output of warp beam speed measuring coder is connected with the input of self-adaptive PID controller, servo-driver is watchedTake motor, decelerator, warp beam and connect successively, the output of self-adaptive PID controller is connected with servo-driver servomotor.
In the present invention, the speed of mainshaft sequence that speed of mainshaft prediction device can obtain according to detection, utilizes local learning model buildingMethods analyst is estimated the variation tendency (being that the speed of mainshaft is estimated sequence) that obtains the speed of mainshaft, and offers Adaptive PID ControlDevice is as warp beam motor speed reference target sequence; Self-adaptive PID controller turns according to rotating speed reference target sequence and main shaftSpeed is estimated sequence, and adaptively selected pid control parameter is optimized 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 sequence that speed of mainshaft encoder obtains detection is input to speed of mainshaft prediction device, the speed of mainshaftThe local learning model building methods analyst of prediction device utilization is estimated and is obtained the speed of mainshaft and estimate sequence, and it is defeated that the speed of mainshaft is estimated to sequenceGo out to self-adaptive PID controller;
2) self-adaptive PID controller estimates according to the speed of mainshaft real-time rotate speed that sequence and warp beam speed measuring coder detectSequence, 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 adaptation to adjust pid parameter, and it is servo that the pid parameter control instruction after adjusting is sent to servo-driverMotor, then adjust the rotating speed of warp beam by decelerator, 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 open docking process speed of mainshaft sequence historical data base, wherein, current sampling instant andIts top n speed of mainshaft sequence is as input feature vector, and subsequent prediction sequence is as output characteristic;
(2) the current speed of mainshaft sequence of Real-time Collection, is used sequence similarity estimation function to calculate the most similarSome groups of historical 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, Vmesure，VestimateAnd ViBeing respectively main shaft detects sequence, estimates sequence with the most similar to detection sequenceHistorical data sequence.
In step (1), the parameter N that input feature vector adopts is chosen as 5;
In step (2), sequence similarity estimation function used meets the kernel function of Mercer condition.
Described kernel function is gaussian kernel function:
Sim(v1,v2)＝exp[-||v1-v2||2/2σ2]
Wherein, σ represents Gaussian kernel width, v1,v2For speed of mainshaft list entries.
Described step 2) in, the expression formula that self-adaptive PID controller is set warp beam motor speed aim curve is:
Vset＝ηVmesure+(1-η)Vestimate
Wherein, η is compromise coefficient, 0≤η≤1.
In the present invention, the selective rule of parameter η is:
In the time that speed of mainshaft sequence variation is larger (as relative speed variation be greater than setting speed 10%), η gets smaller value, oneAs 0≤η≤0.5; When speed of mainshaft sequence variation hour (as relative speed variation be less than setting speed 10%), η gets largerValue, general 0.5≤η≤1; If η=0, deteriorates to without estimating PID control program.
The invention has the beneficial effects as follows: owing to having added speed of mainshaft prediction device in warp beam intelligent motor controller,Good estimation has obtained the development trend of the speed of mainshaft, and has effectively adjusted the control target song of self-adaptive PID by parameter ηLine, has improved the quick-reaction capability of system, has alleviated the hysteresis quality problem of warp beam motor speed, can effectively process stopping lineProblem.
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Brief description of the drawings
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 is estimated 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 onlyA part of embodiment of the present invention, instead of whole embodiment. Based on the embodiment in the present invention, ordinary skill peopleMember, not making the every other embodiment obtaining under creative work prerequisite, belongs 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, comprises the control of warp beam intelligent motorDevice 3, servo-driver servomotor 6, decelerator 7, and be arranged on speed of mainshaft encoder on main shaft 12 and be arranged on throughWarp beam speed measuring coder 9 on axle 8, wherein, warp beam intelligent motor controller 3 comprise serial connection speed of mainshaft prediction device 4 and fromAdapt to PID controller 5, the output of speed of mainshaft encoder 2 is connected with the input of speed of mainshaft prediction device 4, and warp beam tests the speedThe output of encoder 9 is connected with the input of self-adaptive PID controller 5, servo-driver servomotor 6, decelerator 7, warpAxle 8 connects successively, and the output of self-adaptive PID controller 5 is connected with servo-driver servomotor 6.
The present invention, the speed of mainshaft and warp beam that speed of mainshaft encoder 2 and warp beam speed measuring coder 9 obtain detection respectivelyRotating speed is input to warp beam intelligent motor controller 3. Speed of mainshaft prediction device 4 is responsible for the speed of mainshaft to estimate, self-adaptive PIDController 5 is responsible for the control of 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 sequence that speed of mainshaft encoder 2 obtains detection is input to speed of mainshaft prediction device 4, and main shaft turnsSpeed 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 to orderRow export self-adaptive PID controller 5 to;
2) self-adaptive PID controller 5 estimates according to the speed of mainshaft turning in real time that sequence and warp beam speed measuring coder 9 detectSpeed sequence, 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 adaptation to adjust pid parameter, and it is servo that the pid parameter control instruction after adjusting is sent to servo-driverMotor 6, then adjust the rotating speed of warp beam 8 by decelerator 7, and then keep the uniformity of warp beam 8 rotating speeds 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 open docking process speed of mainshaft sequence historical data base, wherein, current sampling instant andIts top n speed of mainshaft sequence is as input feature vector, and subsequent prediction sequence is as output characteristic;
(2) the current speed of mainshaft sequence of Real-time Collection, is used sequence similarity estimation function to calculate the most similarSome groups of historical rotating speed sequences;
(3) build local learning model, estimate that obtaining the speed of mainshaft estimates sequence, and export self-adaptive PID controller to5; Local learning model structure and sequence are estimated scheme and are:
Wherein, Vmesure，VestimateAnd ViBeing respectively main shaft detects sequence, estimates sequence with the most similar to detection sequenceHistorical data sequence.
Speed of mainshaft prediction device 4 is opened docking process speed of mainshaft change sequence and current speed of mainshaft sequence according to history,Build the local learning model analysis optimization speed of mainshaft and estimate sequence.
In step (1), the parameter N that input feature vector adopts is chosen as 5;
In step (2), sequence similarity estimation function used meets the kernel function of Mercer condition.
Simplify, local learning model building method comprises: (1) builds with the current speed of mainshaft and top n moment tachometer valueSpindle speed measurement sequence; (2) carry out similarity measurement with speed of mainshaft sequence data corresponding in historical data base; (3) pre-Estimate and obtain the speed of mainshaft and estimate sequence.
Described kernel function is gaussian kernel function:
Sim(v1,v2)＝exp[-||v1-v2||2/2σ2]
Wherein, σ represents Gaussian kernel width, v1,v2For speed of mainshaft list entries.
Described step 2) in, it (is Adaptive PID Control that self-adaptive PID controller 5 is set warp beam motor speed aim curveDevice is according to VmesureAnd VestimateLookup protocol and adjust warp beam motor speed aim curve) expression formula be:
Vset＝ηVmesure+(1-η)Vestimate
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.
Self-adaptive PID controller 5, according to control curve and error change situation after optimizing, utilizes Fuzzy Thought trueDetermine pid parameter, Δ Kp,ΔTi,ΔTdRule is as follows:
ΔKp,ΔTi,ΔTdRepresent respectively the change amount of ratio in pid parameter, integration, differential coefficient, specifically according to warpThe error e of axle rotating speed and setting value and rate of change ec thereof, according to three form determined values below. The trapezoidal membership function adoptingCarry out obfuscation and defuzzification.
Shown in Fig. 3, a kind of Warp Knitted Fabrics start-stop of the present invention car intelligence control system overall flow comprises:
(1), in the time detecting that larger variation occurs for start-stop car pushbutton enable or the speed of mainshaft, record obtains speed of mainshaft orderRow;
(2) utilize speed of mainshaft prediction device 4 to estimate that obtaining the speed of mainshaft estimates sequence, and export Adaptive PID Control toDevice 5;
(3) self-adaptive PID controller 5 is set warp beam motor speed aim curve, and the warp beam rotating speed obtaining according to detectionSituation, adopts fuzzy control Optimize Multivariable PID Controller, realizes warp beam motor speed and effectively follows the tracks of the speed of mainshaft.
In a word, in the present invention, the speed of mainshaft sequence analysis that speed of mainshaft prediction device 4 can obtain according to detection is estimated and is obtainedThe speed of mainshaft is estimated sequence, and self-adaptive PID controller 5 is estimated sequence according to the speed of mainshaft, adaptively selected pid control parameter,Optimize warp beam motor speed curve. Algorithm of the present invention is simple, can self adaptation adjust warp beam Electric Machine Control aim curve and PID ginsengCount, effectively improved the quick-reaction capability of system, alleviated the hysteresis quality problem of warp beam start-stop car, can effectively process the horizontal stroke that stopsBar problem.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for the skill of this areaArt personnel, the present invention can have various modifications and variations. Within the spirit and principles in the present invention all, to do any repairingProtection scope of the present invention changes, be equal to replacement, improvement etc., within all should be included in.
Claims (6)
1. a Warp Knitted Fabrics start-stop car intelligence control system, is characterized in that: comprise warp beam intelligent motor controller, servoDriver servomotor, decelerator, and be arranged on the speed of mainshaft encoder on main shaft and be arranged on the warp beam survey in warp beamSpeed encoder, wherein, warp beam intelligent motor controller comprises speed of mainshaft prediction device and the self-adaptive PID controller of serial connection, mainThe output of axle rotating speed coder is connected with the input of speed of mainshaft prediction device, the output of warp beam speed measuring coder and adaptiveAnswer the input of PID controller to connect, servo-driver servomotor, decelerator, warp beam connect successively, Adaptive PID ControlThe output of device 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 thatComprise the following steps:
1) speed of mainshaft sequence that speed of mainshaft encoder obtains detection is input to speed of mainshaft prediction device, and the speed of mainshaft is estimatedThe local learning model building methods analyst of device utilization is estimated and is obtained the speed of mainshaft and estimate sequence, and the speed of mainshaft is estimated to sequence exports toSelf-adaptive PID controller;
2) self-adaptive PID controller estimates according to the speed of mainshaft real-time rotate speed order that sequence and warp beam speed measuring coder detectRow, the warp beam motor speed aim curve in real-time update self-adaptive PID controller;
3) self-adaptive PID controller, according to the error amount between warp beam motor speed aim curve and current warp beam rotating speed, utilizesFuzzy control self adaptation is adjusted pid parameter, and the pid parameter control instruction after adjusting is sent to the servo electricity of servo-driverMachine, then adjust the rotating speed of warp beam by decelerator, 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 historical data base, wherein, current sampling instant and frontN speed of mainshaft sequence is as input feature vector, and subsequent prediction sequence is as output characteristic;
(2) the current speed of mainshaft sequence of Real-time Collection, is used sequence similarity estimation function to calculate the most similar someOrganize historical rotating speed sequence;
(3) build local learning model, estimate that obtaining the speed of mainshaft estimates sequence, and export self-adaptive PID controller to; LocalLearning model structure and sequence are estimated scheme and are:
Wherein, Vmesure，VestimateAnd ViBe respectively main shaft detect sequence, estimate sequence with to detect sequence the most similar going throughHistory data 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 the step (1) of described local learning model building method, 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, is characterized in that:In the step (2) of described local learning model building method, sequence similarity estimation function used meets the core letter of Mercer conditionNumber, kernel function is gaussian kernel function:
Sim(v1,v2)＝exp[-||v1-v2||2/2σ2]
Wherein, σ represents Gaussian kernel width, v1,v2For 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 that self-adaptive PID controller is set warp beam motor speed aim curve is:
Vset＝ηVmesure+(1-η)Vestimate
Wherein, η is compromise coefficient, 0≤η≤1.
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| 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 |
| 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 |
| CN107747161B (en) * | 2017-11-15 | 2019-05-17 | 江南大学 | A kind of no sensor warp knit tension positive type regulator control system |
| 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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