CN110153461A - Intelligent posture regulation device and its regulation method towards drilling machine in parallel - Google Patents
Intelligent posture regulation device and its regulation method towards drilling machine in parallel Download PDFInfo
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- CN110153461A CN110153461A CN201910561900.6A CN201910561900A CN110153461A CN 110153461 A CN110153461 A CN 110153461A CN 201910561900 A CN201910561900 A CN 201910561900A CN 110153461 A CN110153461 A CN 110153461A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23B—TURNING; BORING
- B23B39/00—General-purpose boring or drilling machines or devices; Sets of boring and/or drilling machines
- B23B39/04—Co-ordinate boring or drilling machines; Machines for making holes without previous marking
- B23B39/08—Devices for programme control
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2609—Process control
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Abstract
The invention discloses a kind of intelligent posture regulation device towards drilling machine in parallel, including computer, sensor unit, communication unit, A/D converting unit, centralized control unit, neural network unit and PID control unit, the moving platform of the centralized control unit receiving sensor unit detection boring point coordinate value incoming to piece surface distance and computer, it is transmitted in the trained neural network unit configured on FPGA, the length and XY worktable for obtaining each driving leg after mapping output are respectively along the parameters such as X-axis and Y-axis amount of exercise, parameter is sent into PID control unit, the PID control of position is carried out to each driving leg and XY worktable, realize drilling machine automatic-posture-adjustment in parallel, complete the operation for being automatically aligned to boring point surface normal direction；The regulation method of the invention also discloses a kind of intelligent posture regulation device towards drilling machine in parallel, the present invention solve the problems, such as existing drilling machine posture regulation low efficiency in parallel.
Description
Technical field
The present invention relates to drilling processing technical field, especially a kind of intelligent posture regulation device towards drilling machine in parallel and
It regulates and controls method.
Background technique
At present tradition drilling machine can only vertical direction drilling, cannot achieve any attitude angle bore operation, in order to complete multiple
The bore operation of miscellaneous curved surface proposes a kind of base as shown in Figure 1 in the patent document application No. is 201610885614.1
In the drilling machine in parallel of 1TP+3TPS parallel robot.The body supports of the parallel connection drilling machine are a gantry support 1, in gantry support
An XY worktable 4 is fixed in 1 lower section, which can be such that workpiece to be machined fast moves in the horizontal plane, to make work
Part, which quickly reaches, is processed position.A 1TP+3TPS type parallel robot has been inversely installed in 1 upper cross-beam of gantry support
2.1TP+3TPS type parallel robot 2 is rigidly connected by silent flatform and gantry support 1,1TP+3TPS type parallel robot 2
Moving platform and drill bit feed mechanism thereon constitute cutter platform 3.By adjusting three drives of 1TP+3TPS parallel robot 2
Dynamic leg controls the position of workpiece to be machined by adjusting XY worktable 4 to adjust the posture of cutter platform 3, so as to
Meet along workpiece to be machined surface normal direction the needs of being drilled.Application No. is 201710730949.0 patent documents
In propose a kind of automatic drilling method towards drilling machine in parallel, in drilling in order to realize the automatic drilling of drilling machine in parallel, according to
According to the range data of the three laser sensor platforms collected uniformly installed on cutter platform to curved surface part surface, into
The real-time motion control of row solves the amount of exercise of the length and XY worktable that calculate three driving legs.However, this patent document
It is not related to the hardware realization of corresponding control system.
It is highly coupled since the driving of drilling machine in parallel exists with drilling posture, gesture stability algorithm often has height
Nonlinear characteristic, it is often and sufficiently complex so as to cause its solution.Therefore, control variable is solved by traditional controller
When the problem of being faced with solution procedure time-consuming, in a degree of real-time for reducing automatic-posture-adjustment and reduce plus
Work efficiency rate.
Summary of the invention
To solve problems of the prior art, the object of the present invention is to provide a kind of intelligent appearances towards drilling machine in parallel
State regulation device and its regulation method solve existing drilling machine in parallel when being drilled since complicated control variable solves
Caused by process the problem of low efficiency.
To achieve the above object, the technical solution adopted by the present invention is that: it is a kind of towards drilling machine in parallel intelligent posture regulation
Device, comprising:
Communication unit；
Computer, for by the coordinate value (X of Workpiece boring pointP,YP) by the communication unit to be transferred to center control single
Member；
Sensor unit, it is several including surrounding 1TP+3TPS type parallel robot moving platform on the cutter platform of drilling machine in parallel
Three laser range sensors what center is uniformly installed, the cutter platform for being surveyed three laser range sensors
Sensor data transmission to curved surface part surface distance gives A/D converting unit；
A/D converting unit, for converting received analog data, and by the range data (d after conversion1, d2,
d3) it is transferred to centralized control unit；
Centralized control unit is used for the range data (d1, d2, d3) and from computer be passed to boring point coordinate
It is worth (XP,YP) it is transferred to neural network unit；
Neural network unit, the range data (d for being passed to according to the centralized control unit1, d2, d3) and boring point
Coordinate value (XP,YP) mapping obtain three driving leg length l of 1TP+3TPS type parallel robot1, l2, l3With XY worktable edge
The displacement X of X-axis and Y-axis, Δ Y, and the parameter acquired is sent into PID control unit；
PID control unit, the driving according to the parameter that the neural network unit solves, to 1TP+3TPS parallel connection drilling machine
Leg and XY worktable carry out the PID control of position, to realize that the control to drilling machine pose in parallel, completion are automatically aligned to boring point song
Face normal direction.
As a preferred embodiment, the neural network model of the neural network unit include BP neural network,
RBF neural and fuzzy neural network.
As another preferred embodiment, neural network hardware used by the neural network unit is FPGA.
As another preferred embodiment, the training of the neural network unit is specifically included:
By the repetition learning training to sample data, using the learning outcome after convergence as trained neural network mould
The weight and threshold parameter of type use Hardware Description Language VHDL with FPGA, establish neural network model, weight and threshold value are deposited
Enter register for neural network cell call, completes the realization of neural network unit hardware, neuron concurrent working can be quick
Obtain the output of neural network unit.
The present invention also provides a kind of regulation method of intelligent posture regulation device towards drilling machine in parallel as described above, packets
It includes:
Computer is by the coordinate value (X of Workpiece boring pointP,YP) centralized control unit is transferred to by communication unit；
Sensing of the cutter platform that sensor unit is surveyed three laser range sensors to curved surface part surface distance
Device data are transferred to A/D converting unit；
A/D converting unit converts received analog data, and by the range data (d after conversion1, d2, d3) pass
It is defeated by centralized control unit；
Centralized control unit is by range data (d1, d2, d3) and the boring point coordinate value (X incoming from computerP,YP) transmission
Give neural network unit；
Range data (the d that neural network unit is passed to according to the centralized control unit1, d2, d3) and boring point coordinate
It is worth (XP,YP) mapping obtain three driving leg length (l of 1TP+3TPS type parallel robot1, l2, l3) and XY worktable along X-axis
With the displacement (Δ X, Δ Y) of Y-axis, and by the parameter acquired be sent into PID control unit；
The parameter that PID control unit is solved according to the neural network unit, the driving to 1TP+3TPS parallel connection drilling machine
Leg and XY worktable carry out the PID control of position, to realize that the control to drilling machine pose in parallel, completion are automatically aligned to boring point song
Face normal direction.
The beneficial effects of the present invention are:
Intelligent posture regulation device and its regulation method towards drilling machine in parallel of the invention, centralized control unit, which receives, to swash
Ligh-ranging sensor detection cutter platform arrive workpiece surface range data and boring point coordinate value, by trained completion mind
The length and XY worktable that each driving leg is quickly exported through the network hardware are along the parameters such as X-axis and Y-axis displacement, the ginseng that will be acquired
Number is sent into PID control unit, and PID control unit carries out the PID control of position to each driving leg and XY worktable, realizes
The behaviour that drilling machine in parallel is automatically aligned to boring point surface normal direction is completed in effective control to cutter of drill machine platform's position and pose in parallel
Make；Since the neural fusion in the present invention based on FPGA has the advantages that concurrency is high, fireballing, input parameter can be fast
Speed is exported, and the requirement of real time of drilling machine work in parallel is met.When centralized control unit receives sensing data, pass through
The controllable drilling machine in parallel of the present invention completes automatic-posture-adjustment to be directed at boring point surface normal direction.
Detailed description of the invention
Fig. 1 is the overall structure diagram of the drilling machine in parallel based on 1TP+3TPS parallel robot；
Fig. 2 is the motion control functional block diagram of parallel connection drilling machine in the embodiment of the present invention；
Fig. 3 is the structural schematic diagram of BP neural network model in the embodiment of the present invention；
Fig. 4 is the structural block diagram that module is multiplied accumulating in the embodiment of the present invention；
Fig. 5 is the design flow diagram of excitation function module in the embodiment of the present invention；
Fig. 6 is that the FPGA of BP neural network in the embodiment of the present invention realizes overall structure figure.
Specific embodiment
The embodiment of the present invention is described in detail with reference to the accompanying drawing.
Embodiment:
Present embodiments provide a kind of intelligent posture regulation device towards drilling machine in parallel, comprising: sensor unit, calculating
Machine, FPGA device and ARM controller include neural network unit in FPGA device, include in ARM controller A/D converting unit,
Communication unit, centralized control unit and PID control unit.Specifically, the ARM controller in the present embodiment uses model
The ARM controller of STM32F407ZG realizes that the FPGA device of model SPARTAN-7 can be used in the FPGA of neural network unit,
Electric cylinder uses the servo electric jar of model DDG60 model；Servo-driver uses the servo-drive of MCDD T3520 model
Device；Laser range sensor uses the laser range sensor of model SK60；Communication unit can be USB communication interface,
It can be RS232/RS485 serial communication interface.The present embodiment is a preferred embodiment of the invention, in addition to this can also be with
For the equipment of other models.
The present embodiment is used application No. is 201610885614.1, the teaching playback system of entitled parallel connection type drilling machine
Disclosed in 1TP+3TPS parallel robot drilling machine in parallel, structural schematic diagram is as shown in Figure 1.
As shown in Fig. 2, in the present embodiment, intelligent posture regulation device includes computer 5, sensor unit 6, communication unit
The compositions such as member 7, centralized control unit 8, A/D converting unit 9, neural network unit 10, PID control unit 11 and drilling machine in parallel；
The parallel connection drilling machine is uniformly installed three around 1TP+3TPS type parallel robot moving platform geometric center on cutter platform and is swashed
Ligh-ranging sensor, the measurement direction of laser range sensor are and consistent with the direction of drill bit perpendicular to moving platform.The Laser Measuring
Range data is passed to A/D conversion list by the distance for being used to measure dynamic plane to curved surface part surface away from sensor, sensor unit 6
Member 9, which converts the analog data received, and it is single that the data after conversion are transferred to center control
Boring point coordinate value is passed to centralized control unit 8 by communication unit 7 by member 8, simultaneous computer 5, and centralized control unit 8 will connect
The range data and boring point coordinate value received is transmitted to neural network unit 10, and neural network unit 10 receives 3 sensings
Each item of drilling machine 4 in parallel driving leg length and XY worktable distinguish institute when the surveyed distance value of device calculates drilling with boring point coordinate value
Amount of exercise is needed, and kinematic parameter is passed to PID control unit 11, PID control unit 11 is solved according to the neural network unit 3
Parameter out, driving leg and XY worktable to drilling machine in parallel carry out the PID control of position, to realize the pose to drilling machine in parallel
Control.
The range data that centralized control unit 8 is sent by A/D converting unit 9 is three laser range sensor institutes
Cutter platform is surveyed to curved surface part surface distance (d1,d2,d3)。
What the computer 5 was passed to centralized control unit 8 by communication unit 7 is required boring point coordinate value (XP, YP).
The data for being sent into neural network unit 10 by centralized control unit 8 are surveyed by three laser range sensors
Cutter platform is to curved surface part surface distance (d1,d2,d3) and boring point coordinate value (XP, YP).
Range data (the d1,d2,d3) and boring point coordinate value (XP, YP) mapped by neural network unit 10
To three driving leg length l of drilling machine in parallel1、l2、l3Amount of exercise Δ X, Δ Y are distinguished with XY worktable.
The neural network unit 10 is the neural network hardware realization based on FPGA.Use Hardware Description Language VHDL
It realizes neural network, completes off-line learning on computers, carry out repetition learning training using sample data, obtain training mind
Weight and threshold parameter through network model establish its network model with FPGA, by weight and threshold value deposit register for nerve
Network call completes the realization of neural network hardware, can quickly obtain the output of neural network.
A variety of neural network models can realize the Nonlinear Mapping of input and output, such as BP neural network, RBF nerve net
Network, fuzzy neural network etc..The present embodiment is by taking BP neural network as an example, as shown in figure 3, Fig. 3 is the knot of BP neural network model
Structure schematic diagram.BP neural network is a kind of multilayer feedforward network, and BP neural network structure is at least divided into three layers, is respectively
Input layer, output layer and at least one hidden layer.Its learning process is mainly by forward-propagating, backpropagation and right value update three
A stage composition.When forward-propagating, learning sample enters neural network from input layer, using the calculating of hidden layer, output layer
It is compared afterwards with desired output, if the back-propagation phase of error can be entered not within desired error range.Accidentally
Poor back-propagation phase and right value update stage, error can constantly reduce with the amendment of weight, be finally completed the instruction of network
Practice.
It is assumed that BP neural network one shares m layers, the net input of j-th of neuron of kth layer is denoted asOutput is denoted as
Threshold value is denoted asThe output of -1 layer of i-th of neuron of kth is denoted asCouple power between j-th of neuron of kth layer
Value is denoted asThe following are the expression of the formula of BP neural network algorithm different phase, the forward-propagating stage:
Back-propagation phase:
The quadratic sum of each neuron error of output layer is denoted as error function, it may be assumed that
Wherein, yjFor the target output value of j-th of neuron,For its real output value.It can be obtained by formula:
Wherein α is learning rate, is a constant.
For output layer k=m:
For other layers of k < m, can be obtained according to total derivative formula:
The right value update stage:
It enablesWeighed value adjusting formula is as follows:
For the output layer of k=mAre as follows:
So output layer weighed value adjusting is as follows:
For other layers of k < mAre as follows:
So the weighed value adjusting of hidden layer is as follows:
In carrying out BP neural network training learning process, according to actual needs, can set error can for BP neural network
Receive range, maximum frequency of training.The present embodiment selects 3 layers of BP neural network to realize, wherein input layer is 5 neurons,
Output layer is 5 neurons, and excitation function selects sigmoid function.
Above-mentioned BP neural network algorithm forward-propagating process is realized in the present embodiment with Hardware Description Language VHDL.BP mind
Forward-propagating process through network includes multiplying accumulating the operation of calculating and excitation function.To BP neural network forward-propagating process into
Row module divides, and can be divided into and multiply accumulating the basic modules such as (MAC) module, excitation function (Sigmoid) module, basic by these
The propagated forward process of neural network may be implemented by reasonable distribution for module.
As shown in figure 4, Fig. 4 is the structural block diagram for multiplying accumulating module, multiplies accumulating module and mainly complete multiplying for input and weight
Accumulating operation, fpga chip have been integrated with numerous adder and multiplier, can by the structure of multiplier and an adder
Module is multiplied accumulating to realize；
As shown in figure 5, Fig. 5 is excitation function module design flow chart, excitation function can be by searching for table and piecewise function
The method that approach combines is realized, can be approached by constant, linear function or quadratic function on different sections
Then sigmoid function goes to search different approximating functions according to section by searching for table.Be directed to constant approach section can be with
Value will be approached, and there are in RAM；It is directed to linear approximation section, can be realized by a multiplier and an adder；Needle
Approach section for conic section then calls multiplier and adder to realize repeatedly.
As shown in fig. 6, Fig. 6 is that BP neural network FPGA realizes overall structure figure, it is reasonable by the way that basic module is carried out
Distribution, combination, realize the calculating process of neural network.Weight is stored in weight storage RAM, and input data is stored in input layer RAM,
Weight and input value, which are sent to, to be multiplied accumulating module and carries out multiplying accumulating calculating, and the result after multiplying accumulating operation enters activation primitive mould
Calculated result is stored in hidden layer output RAM after the calculating of excitation function by block, is read from hidden layer output RAM hidden
Containing the weight in layer output and corresponding output layer weight RAM, it is sent to and multiplies accumulating module and carry out multiplying accumulating calculatings, it is available
Output layer output.
Offline learning training is carried out to the BP neural network, learning sample data are by application No. is 201710730949.0
Patent document in algorithm generate, obtain weight and threshold parameter after convergence, VHDL realize neural network download to
In FPGA, learning outcome writes direct weight storage RAM, in FPGA epineural member concurrent working, can quickly obtain nerve net
The output of network.
The present embodiment also provides a kind of regulation method of intelligent posture regulation device towards drilling machine in parallel as described above,
Include:
Computer is by the coordinate value (X of Workpiece boring pointP,YP) centralized control unit is transferred to by communication unit；
Sensing of the cutter platform that sensor unit is surveyed three laser range sensors to curved surface part surface distance
Device data are transferred to A/D converting unit；
A/D converting unit converts received analog data, and by the range data (d after conversion1, d2, d3) pass
It is defeated by centralized control unit；
Centralized control unit is by range data (d1, d2, d3) and the boring point coordinate value (X incoming from computerP,YP) transmission
Give neural network unit；
Range data (the d that neural network unit is passed to according to the centralized control unit1, d2, d3) and boring point coordinate
It is worth (XP,YP) mapping obtain three driving leg length (l of 1TP+3TPS type parallel robot1, l2, l3) and XY worktable along X-axis
With the displacement (Δ X, Δ Y) of Y-axis, and by the parameter acquired be sent into PID control unit；
The parameter that PID control unit is solved according to the neural network unit, the driving to 1TP+3TPS parallel connection drilling machine
Leg and XY worktable carry out the PID control of position, to realize that the control to drilling machine pose in parallel, completion are automatically aligned to boring point song
Face normal direction.
The present embodiment is fitted complicated control variable solution procedure using neural network algorithm, and the algorithm is led to
FPGA (field programmable gate array) device for crossing design is realized, so as to shorten the time for calculating drilling machine control variable in parallel.
Neural network can be very good to solve nonlinear problem, its function is obtained by learning training, the nerve net after study
Network has good input → output mapping ability.Neural fusion has hardware realization and software realization two ways, hardware
Realize that neural network has the concurrency of height, processing speed is fast, so that neural network has the ability for carrying out real-time operation.
FPGA (field programmable gate array) embeds stone multiplier and memory resource abundant, while the parallel computation of FPGA itself
The characteristics of ability and repeatable configuration, so that FPGA becomes the ideal component of neural network hardware realization.Using this based on mind
The intelligent posture adjustment regulation device and method of drilling machine in parallel through the network hardware meet and upper carry out different postures complex-curved
Automatic drilling job requirements, and improve the efficiency of drilling machine drilling in parallel.
A specific embodiment of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.
Claims (5)
1. a kind of intelligent posture regulation device towards drilling machine in parallel characterized by comprising
Communication unit；
Computer, for by the coordinate value (X of Workpiece boring pointP,YP) centralized control unit is transferred to by the communication unit；
Sensor unit, including being surrounded in 1TP+3TPS type parallel robot moving platform geometry on the cutter platform of drilling machine in parallel
Three laser range sensors that the heart is uniformly installed, cutter platform for being surveyed three laser range sensors to song
The sensor data transmission of surface parts surface distance gives A/D converting unit；
A/D converting unit, for converting received analog data, and by the range data (d after conversion1, d2, d3)
It is transferred to centralized control unit；
Centralized control unit is used for the range data (d1, d2, d3) and from computer be passed to boring point coordinate value (XP,
YP) it is transferred to neural network unit；
Neural network unit, the range data (d for being passed to according to the centralized control unit1, d2, d3) and boring point seat
Scale value (XP,YP) mapping obtain three driving leg length l of 1TP+3TPS type parallel robot1, l2, l3With XY worktable along X-axis
With displacement Δ X, the Δ Y of Y-axis, and by the parameter acquired be sent into PID control unit；
PID control unit, according to the parameter that the neural network unit solves, driving leg to 1TP+3TPS parallel connection drilling machine and
XY worktable carries out the PID control of position, to realize that the control to drilling machine pose in parallel, completion are automatically aligned to boring point Surface Method
Line direction.
2. the intelligent posture regulation device according to claim 1 towards drilling machine in parallel, which is characterized in that the nerve net
The neural network model of network unit includes BP neural network, RBF neural and fuzzy neural network.
3. the intelligent posture regulation device according to claim 1 or 2 towards drilling machine in parallel, which is characterized in that the mind
It is FPGA through neural network hardware used by network unit.
4. the intelligent posture regulation device according to claim 3 towards drilling machine in parallel, which is characterized in that the nerve net
The training of network unit specifically includes:
By the repetition learning training to sample data, using the learning outcome after convergence as trained neural network model
Weight and threshold parameter use Hardware Description Language VHDL with FPGA, establish neural network model, and weight and threshold value deposit are posted
Storage completes the realization of neural network unit hardware for neural network cell call, and neuron concurrent working can be obtained quickly
The output of neural network unit.
5. a kind of regulation method of the intelligent posture regulation device according to any one of claims 1-4 towards drilling machine in parallel,
It is characterised by comprising:
Computer is by the coordinate value (X of Workpiece boring pointP,YP) centralized control unit is transferred to by communication unit；
Sensor number of the cutter platform that sensor unit is surveyed three laser range sensors to curved surface part surface distance
According to being transferred to A/D converting unit；
A/D converting unit converts received analog data, and by the range data (d after conversion1, d2, d3) be transferred to
Centralized control unit；
Centralized control unit is by range data (d1, d2, d3) and the boring point coordinate value (X incoming from computerP,YP) it is transferred to mind
Through network unit；
Range data (the d that neural network unit is passed to according to the centralized control unit1, d2, d3) and boring point coordinate value
(XP,YP) mapping obtain three driving leg length (l of 1TP+3TPS type parallel robot1, l2, l3) and XY worktable along X-axis and Y
The displacement (Δ X, Δ Y) of axis, and the parameter acquired is sent into PID control unit；
The parameter that PID control unit is solved according to the neural network unit, to the driving leg of 1TP+3TPS parallel connection drilling machine and
XY worktable carries out the PID control of position, to realize that the control to drilling machine pose in parallel, completion are automatically aligned to boring point Surface Method
Line direction.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112507462A (en) * | 2020-12-16 | 2021-03-16 | 广西双英集团股份有限公司 | Verifiable design mode for automobile seat framework and automatic design method thereof |
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Application publication date: 20190823 |