CN104615092B - work machine control system and method thereof - Google Patents
work machine control system and method thereof Download PDFInfo
- Publication number
- CN104615092B CN104615092B CN201310695337.4A CN201310695337A CN104615092B CN 104615092 B CN104615092 B CN 104615092B CN 201310695337 A CN201310695337 A CN 201310695337A CN 104615092 B CN104615092 B CN 104615092B
- Authority
- CN
- China
- Prior art keywords
- work mechanism
- study
- module
- kinematics
- learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012937 correction Methods 0.000 claims abstract description 35
- 230000007246 mechanism Effects 0.000 claims description 78
- 238000012545 processing Methods 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000003801 milling Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 239000002131 composite material Substances 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 238000010862 gear shaping Methods 0.000 claims 2
- 238000010801 machine learning Methods 0.000 abstract 1
- 239000011159 matrix material Substances 0.000 description 4
- 230000002452 interceptive effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000003754 machining Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 241001061225 Arcos Species 0.000 description 1
- 206010044565 Tremor Diseases 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 238000007514 turning Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a working machine control system and a method thereof, and the working machine learning control method comprises the following steps: the kinematics solution module generates coordinate information; the learning mode module generates information of a learning object; the learning control module receives the coordinate information and the learning target information to generate a correction control input command; the path attitude correction module receives the correction control command and provides the correction control command to the working machine so as to enable the working machine to generate working motion; and the working movement generates an actual error, and if the actual error exceeds a preset allowable range, the learning target information is corrected to generate another correction control input command.
Description
Technical field
The present invention relates to a kind of work mechanism control system and its method, and particularly, the present invention relates to one kind to apply
In an at least axle work mechanism, and linear axle carries out study and automatic amendment with rotation axis error, and produces new processing
Path.
Background technology
Intelligent board, including host controller, lower level controller, servo gain study control parameter adjustment unit and work
Tool machine.
Host controller includes numerical controller.Numerical controller has numerical control motion control loop.
Lower level controller is electrically connected with host controller, to receive from instruction caused by numerical control motion control loop.
Lower level controller includes dedicated servo driver, and patent servo-driver includes position control loop, speed control loop, electric current
Control loop.Dedicated servo driver provides the control of position, speed and current loop.
Position control loop is received with providing position feedback signal.Speed control loop receives with providing velocity feedback letter
Number.Current control loop is provided with receiving electric current feedback signal.
Servo gain study control parameter adjustment unit comes from lower level controller to be electrically connected with lower level controller to receive
Control signal, and produce adjustment signal, and be supplied to lower level controller, and lower level controller is produced new control signal.
Toolroom machine receives the control signal from lower level controller to be electrically connected with lower level controller, so that toolroom machine
Acted corresponding to producing.
Though above-mentioned intelligent board, according to the numerical value of toolroom machine institute feedback, can produce tune during actual operation
School signal, the adjustment signal are learning signal, and then produce new control signal.The generation of the right adjustment signal is not as described above
Discussion it is so simple, operating personnel need to also according to the numerical value of the feedback, adjust dedicated servo driver control gain value, side
Servo gain study control parameter adjustment unit can be made to produce above-mentioned adjustment signal, if without adjusting the control gain value, on
The adjustment signal stated can not produce.
Can described in presenting, existing intelligent board be limited to adjust the control gain value, to determine produce adjustment letter
Number.In addition, the stability of intelligent board can be influenceed adjusting the control gain value, and need level controller under adjustment again.Separately
Outside, above-mentioned toolroom machine is restricted within three axles, can not be implemented if beyond three axles.
The content of the invention
To solve the above problems, the present invention provides a kind of work mechanism learning control system, it includes：
Kinematics model solves module, and it produces coordinate information；
Mode of learning module, it is electrically connected with the kinematics model and solves module, learns target information to produce；
Study control module, it is electrically connected with the mode of learning module, and receives the coordinate information and believe with the study target
Breath, to produce Correction and Control input order；And
Path poses correcting module, it is electrically connected with the study control module, and receives Correction and Control input order, and
Work mechanism is supplied to, the work mechanism inputs life life, to perform working motion, the path poses amendment mould according to the Correction and Control
Block updates path poses order according to the working motion, to produce, and the renewal path poses order provides the work mechanism, so that
The work mechanism performs another working motion.
The present invention also provides a kind of work mechanism learning control method, and it includes：
Kinematics solution module produces coordinate information；
Mode of learning module produces study target information；
Study control module receives the coordinate information and the study target information, to produce Correction and Control input order；
Path poses correcting module receives the Correction and Control order, and is supplied to work mechanism, so that the work mechanism produces
Raw working motion；And
The working motion produces actual error, if the actual error exceeds predetermined allowed band, corrects the study target
Information, to produce another Correction and Control input order.
Brief description of the drawings
Fig. 1 is a kind of schematic diagram of work mechanism learning control system of the present invention.
Fig. 2 is a kind of schematic flow sheet of work mechanism learning control method of the present invention.
Fig. 3 is the schematic perspective view of TTTRR types.
Fig. 4 is the cutter of TTTRR types and the schematic perspective view of workpiece.
Fig. 5 is the schematic perspective view of RRTTT types.
Fig. 6 is the cutter of RRTTT types and the schematic perspective view of workpiece.
Fig. 7 is the schematic perspective view of RTTTR types.
Fig. 8 is the cutter of RTTTR types and the schematic perspective view of workpiece.
Fig. 9 is the schematic diagram of the three-dimensional axial direction of RTTTR types.
Figure 10 is the formula of coordinates matrix.
Figure 11 is the formula of straight line error.
Figure 12 is the formula of circular contour error.
Figure 13 is the formula of three-dimensional figure error.
Description of reference numerals
10 kinematics modules
11 kinematics models solve module
12 mode of learning modules
13 study control modules
14 path poses learn correcting module
20 work mechanisms
30 TTTRR types
31 cutters
32 workpiece
40 RRTTT types
41 cutters
42 workpiece
50 RTTTR types
51 cutters
52 workpiece
S1~S7 steps
Embodiment
It is that embodiments of the present invention are illustrated by particular specific embodiment below, persons skilled in the art can be by
Content disclosed by this specification, other advantages and effect of the present invention are understood easily.
It please coordinate with reference to shown in figure 1, the present invention is a kind of work mechanism learning control system, and it is electrically connected with work mechanism
20, the work mechanism learning control system includes kinematics module 10, kinematics model solves module 11, mode of learning module
12nd, study control module 13 and path poses study correcting module 14.
Kinematics module 10 selects with work mechanism, and work mechanism selection can produce kinematics geological information, should
Work mechanism selection is single shaft board, twin shaft board, three axle boards, four axle boards, five axle boards, composite processor, gear add
The selection of work machine, lathe process machine, complex milling machine or parallel rod processing machine.
The five axles board can be RTTTR, RRTTT or TTTRR type.
RTTTR types are that turntable adds yaw type（Table/Spindle-Tilting Type）.Workpiece, which is arranged at, to be turned
Platform.RTTTR types are used to process small workpiece.
RRTTT types are Double swing head type（Spindle-Tilting Type）.RRTTT types are used to cut higher work
Part.
TTTRR types are double turn table types（Table-Tilting Type）, TTTRR types are for cutting rectangular piece.
Kinematics model solve module 11 have the corresponding at least direct kinematics equation of an axle toolroom machine with it is corresponding extremely
The inverse kinematics equation of a few axle toolroom machine.Kinematics model solves module 11 to be electrically connected with kinematics module 10, with
The kinematics geological information is received, these foregoing equations are according to the kinematics geological information, to calculate coordinate information, are somebody's turn to do
Coordinate information is joint coordinates, axial coordinate, Cartesian coordinate or posture point coordinates.
Mode of learning module 12 is electrically connected with kinematics model and solves module 11.Mode of learning module 12 is to be marked with study
Selection with it is default study target information, the study target selection can produce study target information.The study target selects
The selection pointed to for the axial coordinate of work mechanism, geometry, positioning, tracking, end points, Cartesian coordinate, position, profile or posture.
Study control module 13 solves module 11 and mode of learning module 12 for kinematics model is electrically connected, to connect
Receive the coordinate information and the study target information, study control module 13 be according to the coordinate information and the study target information,
To produce error, the error amount is axial coordinate error amount, Cartesian coordinate error amount, attitude error value, tracking error value or more
Axle profile errors value.Study control module 13 be according to the error amount, with the adjustment coordinate information and the study target information, and
Produce Correction and Control input order.Correction and Control input order is to learn control calculation with kinematics model, injunctive iterate
Method, Fuzzy learning control, Adaptive-learning control or the study control of class neural network are calculated.Or the study control module
13 be with learning gains, study number, filtering frequency range or path poses study amendment, to produce Correction and Control input order.
Kinematics module 10, study control module 13 and working machine is electrically connected in path poses study correcting module 14
Tool 20, path poses study correcting module 14 receives Correction and Control input order, and Correction and Control input order is passed respectively
Give kinematics module 10 and work mechanism 20.
Work mechanism 20 is inputted according to the Correction and Control and ordered, to perform working motion, path poses study amendment mould
Block 14 is supplied to work mechanism 20 according to the working motion, generation renewal path poses order, the renewal path poses order, with
Work mechanism 20 is set to perform another working motion.Foregoing renewal path poses order can be considered as new control input life
Order.
The kinematics module 10 is inputted according to the Correction and Control and ordered, to produce another Correction and Control input order.
It please coordinate with reference to shown in figure 2, the present invention is a kind of work mechanism learning control method, and it applies above-mentioned working machine
Tool learning control system.The work mechanism learning control method includes：
S1, chosen in above-mentioned work mechanism, so that work mechanism selection produces kinematics geological information.
S2, above-mentioned kinematics model solves module 11 and receives the kinematics geological information, and produces coordinate information.
Described in presenting, the mode of trying to achieve of the coordinate information is discussed below：
It please coordinate with reference to shown in figure 3 and Fig. 4, if with above-mentioned TTTRR types 30, and by taking AC direction of rotation as an example.TTTRR
The cutter 31 of type 30 is to be processed technique to workpiece 32.Compensation vector is further labeled with Fig. 4（Offset Vector）
Formula, LxI+LyJ+Lzk.R in figure is the point of rotation.
The coordinate information is：
A=ΦA=arccos(Kz)(0≦ΦA≦π)
C=ΦC=arccos(Kx,Ky)(0≦ΦC≦π)
X=Lx+ Px=(Qx- Lx)cos(ΦC)-(Qy- Ly)sin(ΦC)+Lx
Y=Ly+ Py=(Qx- Lx)cos(ΦA)sin(ΦC)+(Qy- Ly)cos(ΦA)cos(ΦC)-(Qz- Lz)sin
(ΦA)+Ly
Z=Lz+ Pz=(Qx- Lx)sin(ΦA)sin(ΦC)+(Qy- Ly)sin(ΦA)cos(ΦC)+(Qz- Lz)cos
(ΦA)+Lz
It please coordinate with reference to shown in figure 5 and Fig. 6, if with above-mentioned RRTTT types 40, and by taking AB direction of rotation as an example.RRTTT
The cutter 41 of type 40 is processed technique to workpiece 42.
The coordinate information is：
A=ΦA=arcsin (- Ky) (- π/2≤ΦA≦π/2)
B=ΦB=arctan2(Kx,Kz) (- π≤ΦB≦π)
X=Px=Qx+ Ltcos(ΦA)S(ΦB)
Y=Py=Qy+ Ltsin(ΦA)
Z=Pz- Lt=Qz+ Ltcos(ΦA)cosΦB- Lt
Please refer to shown in Fig. 7 and Fig. 8, if with above-mentioned RTTTR types 50, and by taking AB direction of rotation as an example.RTTTR
The cutter 51 of type 50 is processed technique to workpiece 52.Compensation vector is further labeled with Fig. 8（Offset Vector）'s
Formula, LxI+LyJ+Lzk.R in figureA、RBFor turning point.
The coordinate information is：
B=ΦB=arcsin(Kx) (- π/2≤ΦB≦π/2)
A=ΦA=arctan2(Ky,Kz) (- π≤ΦA≦π)
X=Lx+ Px=Qx+ Lxsin(ΦB)
Y=Ly+ Py=(Qy- Ly)cos(ΦA)-(Qt- Lz)sin(ΦA)+Ly
Z=Lz+ Pz- Lt=(Qy- Ly)sin(ΦA)+(Qz- Lz)cos(ΦA)+Lz
Wherein, above-mentioned A, B, C are the rotating shaft of work mechanism, also can be considered toolroom machine in this work mechanism.
Above-mentioned Kx、Ky、KzFor the composition of cutter shaft orientation.
Above-mentioned Lx、Ly、Lz、LtFor by the O that originates fromwTo the composition of the compensation vector of the effective tool length of centring point.
Above-mentioned X, Y, Z is the linear axes of work mechanism, and as shown in Fig. 4,6 or 8, foregoing X, Y, Z can also be considered as figure
In Xw、Yw、ZwOr Xt、Yt、Zt；Foregoing Qt、Xt、Yt、ZtFor the coordinate system of cutter.Foregoing Qw、Xw、Yw、ZwFor workpiece
Coordinate system.
Above-mentioned Px、Py、PzTo be relevant to the translation distance of X, Y, Z platform.
Above-mentioned P is surface parameter equation.
Above-mentioned Qx、Qy、QzFor the coordinate at tool tip center.
Above-mentioned ΦA、ΦB、ΦCFor the corner of X, Y, Z axis.
Above-mentioned source is to refer to RS Lee, CH She. " Developing a postprocessor for three
types of five-axis machine tools”The international Journal of Advanced
Manufacturing,Vol.16,pp.658-665,1997.
It please coordinate with reference to shown in figure 9, if further by taking above-mentioned RTTTR types as an example.In Fig. 9, it is representated by X-axis
C axles, and fixture, cutter, workpiece, turntable central shaft are sequentially represented as by top to bottom.
Wherein, workpiece coordinaterTWIn turntable.Turntable coordinateWTCIn C axles.C axial coordinatescTXIn X-axis.Other coordinates turn
Move matrix：xTy、yTz、zTb、bTh、hTt。
Direct kinematics equation and inverse kinematics equation are illustrated with above-mentioned matrix again.Refer to shown in Figure 10
Matrix equation, its discussion are forward and inverse to kinematic equations, tool vector K and tool position Q.
Wherein, Figure 10 Cb、Sb、Cc、ScRespectively cos (B), sin (B), cos (C), sin (C) symbol are write a Chinese character in simplified form.
As described above, it obtains coordinate information with forward and inverse to kinematic equations.The coordinate information also can be by kinematics mould
Type, injunctive iterate learn control algorithm（Iterative Learning Control）, Fuzzy learning control（Fuzzy
Learning Control）, Adaptive-learning control（Adaptive Learning Control）Or class neural network study control
System（Neural networks for self-learning control）.
From the above, direct kinematics equation can be also reduced to：
qx=cos(C)xm+ sin (C)cym- sin (B) cos (C) Zbt+ Xrw
qy=-sin (C) xm+ cos (C) ym+ sin (B) sin (C) Zbt+ Yrw
qz=zm- cos (B) Zbt+ Zrb
kz=sin(B)cos(C)
ky=-sin (B) sin (C)
kz=sin(B)
Inverse kinematics equation can be also reduced to：
xm=cos(C)qx- sin (C) qy- cos (C) Xrw+ sin (C) Yrw+ sin (B) Zbt
ym=sin(C)qx+ cos (C) qy- sin (C) Xrw- cos (C) Yrw
zm=qz+ cos (C) Zbt- Zrb
θb=arcos(kz)
θc=arctan (- ky/kx)
Study controls the equation of algorithm to be：
Wherein, k is constant.Q (z) is 0 stage multicarrier.Z was 0 stage.Φ (z) is interactive learning controller.rjTo be defeated
It is interactive in j to enter order.ejIt is interactive in j for mistake.yjTo be output in j interactions.ydIt is expected to order.
S3, chosen in above-mentioned study target, select to produce study target information with the study target.If no
Choose, then above-mentioned mode of learning module 12 provides default study target information.
S4, above-mentioned study control module 13 receive the coordinate information, and the study target information or default study
Target information, to produce error, the study control module 13 is according to the error amount, with the adjustment coordinate information and the study mark
Information, or the default study target information, and produce Correction and Control input order.The study control module 13 is to learn to increase
Benefit, study number, filtering frequency range or path poses study amendment, to produce Correction and Control input order.
As described above, the algorithm of the error amount can be outline of straight line error, circle contour error, free curve error.
Referring to shown in Figure 11, it is outline of straight line error formula and relevant indicators, wherein, ε is error；X is X-axis；Y is
Y-axis；θ is angle；P is coordinate position.
Referring to shown in Figure 12, it is circle contour error work formula and relevant indicators, wherein, R is radius；ε is error；X is X
Axle；Y is Y-axis；P is coordinate position.
Please refer to shown in Figure 13, it is free curve error formula and relevant indicators.The free curve error also may be used
It is considered as three-dimensional figure error.Wherein, E is error；X is X-axis；Y is Y-axis；S, C, D, P, Q represent two dimension and three-dimensional respectively
Coordinate position, to represent true path and expected path.If illustrating further, S (Sx,Sy) ordered for position coordinates in path
Order；P(Px,Py) it is true location coordinate；C is profile errors present coordinate；S(Sx,Sy) ordered for another location coordinate in path
Order.
S5, above-mentioned path poses correcting module 14 receives Correction and Control input life life, and the Correction and Control is inputted
Order is supplied to work mechanism 20, so that work mechanism 20 performs working motion.The working motion is the cutter of work mechanism 20
Be processed technique in workpiece, such as cut, milling, the processing technology cut, bore or dug.
S6, judge whether actual error is stable or restrains, and measures the above-mentioned workpiece by processing technology, is actually missed to obtain
Difference, if the actual error in stable and restrain, and is located at predetermined allowed band, then to terminating S7.If the actual error is in shakiness
Determine and do not restrain, and be more than the predetermined allowed band, be then back to S3, adjust the study target information, or return to S4, adjustment is learned
The error amount of control module 13 is practised, and carries out subsequent step.If or be not intended to return to S4, amplify the predetermined allowed band,
If the actual error is located at amplified reservation allowed band, to S7.
Summary, the present invention is applied to work mechanism, and directly provides instructions to work mechanism, and receives work
The feedback of machinery, to decide whether to produce a new instruction.Foregoing instruction is above-mentioned Correction and Control input order；It is foregoing
Feedback be above-mentioned actual error；Foregoing new instruction is above-mentioned renewal path poses order.
As described above, the present invention is directly and work mechanism, a such as at least axle toolroom machine, it is electrically connected with, therefore when present invention production
During raw above-mentioned renewal path poses order, the present invention need adjustment control gain value without such as existing technology, therefore working machine
The stability of tool is maintained, and the present invention also needs adjustment again without such as existing technology.In addition, the present invention can be applied to
The work mechanism of multiaxis, such as three axles, five axles.
Furthermore the present invention for the processing of multiaxis repeatability carry out simultaneously linear axes and rotation axis error carry out study with it is automatic
Amendment, and new machining path is produced, to lift Multi-axis Machining precision.The present invention does not also sell the limitation of dedicated servo driver,
And the processing equipment of all kinds of patterns is can be applied to, and simplify with design and reduce cost.
Particular embodiments described above, it is only used for example and releases the features of the present invention and effect, it is of the invention not for limiting
Implement category, under the scope of without departing from the spirit of present disclosure and technology, it is any with disclosed content and
The equivalent change and modification completed, the claim that still should be the present invention are covered.
Claims (24)
1. a kind of work mechanism learning control system, it includes：
Kinematics model solves module, and it produces coordinate information；
Mode of learning module, it is electrically connected with the kinematics model and solves module, learns target information to produce；
Study control module, it receives the coordinate information and the study target information to be electrically connected with the mode of learning module,
To produce Correction and Control input order, the study control module is according to the coordinate information and the study target information, to produce
Error amount；And
Path poses correcting module, it is electrically connected with the study control module, and receives Correction and Control input order, and provides
To work mechanism, the work mechanism inputs life life according to the Correction and Control, to perform working motion, the path poses correcting module according to
According to the working motion, path poses order is updated to produce, the renewal path poses order provides the work mechanism, so that the work
Make machinery and perform another working motion.
2. work mechanism learning control system as claimed in claim 1, further has kinematics module, the kinematics mould
Block solves module to be electrically connected with the kinematics model, and the kinematics module produces kinematics geometric data, the kinematics geometry
Data are supplied to the kinematics model to solve module.
3. work mechanism learning control system as claimed in claim 2, wherein the kinematics module have work mechanism selection,
Work mechanism selection produces the kinematics geological information.
4. the selection of work mechanism learning control system as claimed in claim 3, the wherein work mechanism is single shaft board, twin shaft
Board, three axle boards, four axle boards, five axle boards, composite processor, gear-shaping machine, lathe process machine, complex milling machine or
The selection of parallel rod processing machine.
5. work mechanism learning control system as claimed in claim 1, the wherein kinematics model, which solve module, has forward direction
Kinematic equations, inverse kinematics equation, kinematics model, injunctive iterate learn control algorithm, fuzzy learning control
System, Adaptive-learning control or the study control of class neural network, to produce the coordinate information.
6. work mechanism learning control system as claimed in claim 1, the wherein coordinate information be joint coordinates, axial coordinate,
Cartesian coordinate or posture point coordinates.
7. work mechanism learning control system as claimed in claim 1, wherein the mode of learning module also have study target
Selection, study target selection produce the study target information, and the mode of learning module further has default study mark
Information.
8. work mechanism learning control system as claimed in claim 1, wherein the study target information are the axle of work mechanism
The selection that coordinate, geometry, positioning, tracking, end points, Cartesian coordinate, position, profile or posture are pointed to.
9. work mechanism learning control system as claimed in claim 1, the wherein error amount are axial coordinate error amount, Cattell seat
Mark error amount, attitude error value, tracking error value or multiaxis profile errors value.
10. work mechanism learning control system as claimed in claim 1, the wherein study control module have kinematics mould
Type, injunctive study control algorithm, Fuzzy learning control, Adaptive-learning control or the class neural network of iterating learn control,
So that the study control module is according to the error amount, and produce Correction and Control input order, or the study control module with
Learning gains, study number, filtering frequency range or path poses study amendment, to produce Correction and Control input order.
11. a kind of work mechanism learning control method, it includes：
Kinematics solution module produces a coordinate information；
Mode of learning module produces a study target information；
Study control module receives the coordinate information and the study target information, to produce Correction and Control input order；
Path poses correcting module receives the Correction and Control order, and is supplied to work mechanism, so that the work mechanism produces work
Move, the path poses correcting module updates path poses order, the renewal path poses according to the working motion to produce
Order provides the work mechanism, so that the work mechanism performs another working motion；And
The working motion produces actual error, if the actual error exceeds predetermined allowed band, corrects the study target information,
To produce another Correction and Control input order.
12. work mechanism learning control method as claimed in claim 11, it also has kinematics module, the kinematics module
Kinematics geometric data is produced, the kinematics geological information is supplied to the kinematics solution module, to produce the coordinate information.
13. work mechanism learning control method as claimed in claim 12, wherein the kinematics geometric data is by work mechanism
Produced by selection.
It is single shaft board that 14. work mechanism learning control method as claimed in claim 13, the wherein work mechanism, which select, double
Axle board, three axle boards, four axle boards, five axle boards, composite processor, gear-shaping machine, lathe process machine, complex milling machine
Or the selection of parallel rod processing machine.
15. work mechanism learning control method as claimed in claim 11, wherein the coordinate information is with direct kinematics equation
Formula, inverse kinematics equation, kinematics model, injunctive iterate learn control algorithm, Fuzzy learning control, adaptive
Practise control or class neural network learns produced by controlling.
16. work mechanism learning control method as claimed in claim 11, the wherein coordinate information are joint coordinates, axle seat
Mark, Cartesian coordinate or posture point coordinates.
17. work mechanism learning control method as claimed in claim 11, wherein the study target information are default study
Target information or as study target select produced by.
18. the selection of work mechanism learning control method as claimed in claim 11, wherein the study target is work mechanism
The selection that axial coordinate, geometry, positioning, tracking, end points, Cartesian coordinate, position, profile or posture are pointed to.
19. work mechanism learning control method as claimed in claim 11, the wherein study control module are according to the coordinate
Information and the study target information, to produce error, the study control module is also according to the error amount, to produce the amendment control
System input order.
20. work mechanism learning control method as claimed in claim 19, the wherein error amount are axial coordinate error amount, Cattell
Error of coordinate value, attitude error value, tracking error value or multiaxis profile errors value.
21. work mechanism learning control method as claimed in claim 20, the wherein error amount are taken turns with outline of straight line error, circle
Produced by wide error or free curve error.
22. the input order of work mechanism learning control method as claimed in claim 11, the wherein Correction and Control is to learn
Produced by gain, study number, filtering frequency range or path poses study amendment.
23. work mechanism learning control method as claimed in claim 11, the wherein working motion are the knife of the work mechanism
Have and be processed technique in workpiece, the processing technology is cuts, milling, cuts, bores or dig.
24. work mechanism learning control method as claimed in claim 19, if wherein the actual error is in stable and restrain,
And be located at the predetermined allowed band, then terminate；If the actual error is in unstable and do not restrain, and is more than this and predetermined allows model
Enclose, be then back to the step of producing Correction and Control input order, and adjust the error amount, or return to generation study target letter
The step of breath, adjust the study target information；If or the step of being not intended to return to generation Correction and Control input order, put
The big predetermined allowed band, if the actual error is located at amplified reservation allowed band, terminates.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW102139761 | 2013-11-01 | ||
| TW102139761A TWI492008B (en) | 2013-11-01 | 2013-11-01 | Working machine controlling system and method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104615092A CN104615092A (en) | 2015-05-13 |
| CN104615092B true CN104615092B (en) | 2018-03-13 |
Family
ID=53149593
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310695337.4A Active CN104615092B (en) | 2013-11-01 | 2013-12-13 | work machine control system and method thereof |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN104615092B (en) |
| TW (1) | TWI492008B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SG10201804397UA (en) * | 2015-06-22 | 2018-06-28 | Electro Scientific Industries Inc | Multi-axis machine tool and methods of controlling the same |
| TWI604290B (en) * | 2016-10-31 | 2017-11-01 | 智泰科技股份有限公司 | Numerically controlled machine tool with spatial positional error compensation |
| TWI600987B (en) * | 2016-11-15 | 2017-10-01 | Method of constructing processing expert system and electronic device using the method | |
| JP6412086B2 (en) | 2016-11-21 | 2018-10-24 | ファナック株式会社 | Numerical controller |
| DE112017007025T5 (en) * | 2017-02-09 | 2019-10-24 | Mitsubishi Electric Corporation | POSITION CONTROL DEVICE AND POSITION CONTROL PROCESS |
| US11173613B2 (en) * | 2017-02-09 | 2021-11-16 | Mitsubishi Electric Corporation | Position control device and position control method |
| CN109634218B (en) * | 2018-08-01 | 2022-02-01 | 广州市机电高级技工学校（广州市机电技师学院、广州市机电高级职业技术培训学院） | Multi-spindle intelligent drilling machine control system with self-learning function |
| TWI679507B (en) * | 2018-10-23 | 2019-12-11 | 國立中正大學 | Learning system and method for controlling dual-axis machine tool with equivalent contour error |
| TWI722344B (en) | 2018-12-05 | 2021-03-21 | 財團法人工業技術研究院 | Automatic generation system for machining parameter |
| TWI809705B (en) | 2022-02-09 | 2023-07-21 | 財團法人工業技術研究院 | Processing path generating method and device |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4043996B2 (en) * | 2003-01-20 | 2008-02-06 | ファナック株式会社 | Servo motor drive control device |
| JP3898686B2 (en) * | 2003-10-20 | 2007-03-28 | ファナック株式会社 | Numerical controller |
| CN101571712A (en) * | 2008-11-26 | 2009-11-04 | 天津大学 | Monitoring method with processing self-adapting function and on-machine quality detection |
| CN101733678B (en) * | 2009-12-04 | 2013-04-24 | 北京数码大方科技股份有限公司 | Graphical cutter position tracking and controlling system and method |
| TW201202875A (en) * | 2010-07-14 | 2012-01-16 | shi-le Chen | Command learning type multi axis synchronous control system used for rigid tapping machine center and method thereof |
| CN101943896B (en) * | 2010-07-16 | 2012-02-29 | 浙江大学 | Trajectory regeneration compensation method of numerical control machine error |
| JP4980453B2 (en) * | 2010-09-06 | 2012-07-18 | ファナック株式会社 | Servo control system for high-precision machining |
| TWI454335B (en) * | 2011-07-27 | 2014-10-01 | Nat Univ Chung Cheng | Learning control system for simultaneously reduction in process time and machining errors |
| TW201314472A (en) * | 2011-09-29 | 2013-04-01 | Nat Univ Chung Cheng | Repetitive learning control method adopting empirical mode decomposition technique and repetitive learning controller |
| CN102501136B (en) * | 2011-10-10 | 2013-09-18 | 华中科技大学 | On-machine detection measuring head and detection system for numerical control machine tool |
| CN102929207B (en) * | 2012-11-09 | 2015-01-21 | 西安交通大学 | Optimization method of control parameters of servo system of numerical controlled machine tool |
-
2013
- 2013-11-01 TW TW102139761A patent/TWI492008B/en active
- 2013-12-13 CN CN201310695337.4A patent/CN104615092B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN104615092A (en) | 2015-05-13 |
| TW201518887A (en) | 2015-05-16 |
| TWI492008B (en) | 2015-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104615092B (en) | work machine control system and method thereof | |
| CN101362511B (en) | Synergetic control method of aircraft part pose alignment based on four locater | |
| KR101744962B1 (en) | System for processing wood member using multi-articulated robot | |
| US20160132039A1 (en) | Cutting tool controller and method of controlling the same | |
| US10146204B2 (en) | Machining method and a control device for a machine tool | |
| CN100565407C (en) | Synergetic control method of aircraft part pose alignment based on three steady arms | |
| WO2018179401A1 (en) | Tool path generation method and device | |
| JP2010046753A (en) | Method of preparing offline teaching data, and robot system | |
| CN111452033A (en) | Double NURBS curve milling trajectory planning method for industrial robot | |
| US10203684B2 (en) | System and method for virtually calibrating computer numeric controlled machine axes | |
| CN105824289B (en) | The complex-curved universal method of aspherical cutter multi-axis NC machining | |
| CN115113582A (en) | Five-axis position loop gain offline correction method for part contour error | |
| CN102107361B (en) | Closed three-element impeller slot welding machining and robot automatic welding control method | |
| CN113634871A (en) | Robot friction stir welding track planning method based on offline programming | |
| CN103135497A (en) | Conic cartridge receiver appearance five-axis vector processing technology | |
| CN106054814A (en) | Image grayscale-based computer aided machining method | |
| CN106020123A (en) | Control device, machine tool and control method | |
| CN106020125B (en) | One kind being based on the continuous industrial robot kinematics algorithm of speed | |
| CN206140014U (en) | Welding system suitable for right angle welding seam laser tracking | |
| CN107942942A (en) | A kind of machine tool is applied to the tilt coordinates system method for building up of intersecting inclined plane | |
| WO2018011990A1 (en) | Machining program generation device and machining method | |
| CN107861467B (en) | Four axis dead axle of endless knife processing knife position determines method, apparatus and electronic equipment | |
| CN102354153A (en) | Machine inching control system and method and numerical control machine | |
| JP2014238782A (en) | Control method of machine tool | |
| JP2003005811A (en) | Method and program for generating 6-axis control nc program, generating device and computer readable recording medium with the program stored therein |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |