CN113620024A - Data-driven multi-drive conveyor torque control method and device - Google Patents

Data-driven multi-drive conveyor torque control method and device Download PDF

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Publication number
CN113620024A
CN113620024A CN202110688958.4A CN202110688958A CN113620024A CN 113620024 A CN113620024 A CN 113620024A CN 202110688958 A CN202110688958 A CN 202110688958A CN 113620024 A CN113620024 A CN 113620024A
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torque
tail
drive unit
driving unit
conveyor
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CN113620024B (en
Inventor
尹小明
岑梁
何海国
王伟
季国良
范津津
林瑞学
汪剑荣
王佳峰
王晟
倪浅雨
毕祥宜
吕斌斌
邱泽晶
肖楚鹏
胡文博
余梦
朱亮亮
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Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Changxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Huzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Changxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
    • B65G43/08—Control devices operated by article or material being fed, conveyed or discharged
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G15/00—Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration
    • B65G15/60—Arrangements for supporting or guiding belts, e.g. by fluid jets
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G23/00—Driving gear for endless conveyors; Belt- or chain-tensioning arrangements
    • B65G23/22—Arrangements or mountings of driving motors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G23/00—Driving gear for endless conveyors; Belt- or chain-tensioning arrangements
    • B65G23/44—Belt or chain tensioning arrangements
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
    • B65G2203/02—Control or detection
    • B65G2203/0266—Control or detection relating to the load carrier(s)
    • B65G2203/0291—Speed of the load carrier

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Conveyors (AREA)

Abstract

The invention discloses a data-driven multi-drive conveyor torque control method and a device, wherein the control method comprises the following steps: the bearing section of the conveying belt is divided into n equal parts with fixed length, and the material quantity on each equal part of the conveying belt is obtained by combining an online image loading quantity measuring means. And establishing a data driving learning model of the deviation of the main driving torque and the middle driving torque and the deviation of the middle driving torque and the tail driving torque by taking the belt speed, the distribution vector, the main driving unit torque, the middle driving unit torque and the tail driving unit torque as input quantities. The control performance of the unloading type multi-point driving conveyor is improved, and the safety of the conveyor is improved. The LSSVM is adopted to complete modeling training, the requirements on storage space and on-line force calculation are low, the LSSVM is suitable for industrial controllers such as PLC and the like, and the problem of the tension of the rubber belt at the unloading point of the unloading type multi-point driving belt conveyor is solved.

Description

Data-driven multi-drive conveyor torque control method and device
Technical Field
The invention relates to the field of conveying devices, in particular to a torque control method and device for a data-driven multi-drive conveyor.
Background
The belt conveyor system is used as a continuous bulk material conveying device, is widely applied to various industries of national economy such as steel, coal, ports and docks, electric power, building materials and the like, and has large use amount and wide application range. The belt conveyor is continuously developed towards large-scale, long-distance, high-speed, large-capacity and intelligentized direction. When designing and developing a long-distance belt conveyor system, because a single drive motor can provide limited driving force and the maximum tension force that a conveying belt can bear is limited, a large belt conveyor usually adopts a mode of driving by a plurality of motors, so that the capacity of the single motor can be reduced, and the tension force of the belt can be reduced. The multipoint driving not only reduces the requirement on the strength of the adhesive tape, but also facilitates the type selection of equipment and realizes the miniaturization of the equipment. Essentially multi-point driving is one way to improve cost performance. The multi-point driving mode is many, including linear friction type, wire rope traction type and middle roller unloading type. The roller unloading type multi-point driving is to add one or more groups of driving devices in the middle of a common belt conveyor to distribute the driving force normally arranged at the head part to several parts. The unloading type intermediate driving mode has a simple structure, is convenient to arrange, can effectively reduce the cost of manufacturing, processing, conveying, mounting, maintenance, management and the like, and is more adopted in a multi-drive conveyor system. However, the middle transfer point causes the tension distribution of the whole adhesive tape to be completely different from the centralized driving arrangement form, the tension of the adhesive tape at the winding-out side of the middle driving wheel is obviously reduced, and the adhesive tape can slip if the control is not proper; the middle transfer point also causes the materials to fall for multiple times, the materials are not uniformly distributed on the rubber belt, and the driving system of the middle roller unloading type multi-drive conveyor is difficult to control.
For example, a novel belt conveyor disclosed in chinese patent literature, No. CN103662715, includes a body, an unloading unit, a nose, a belt storage and tensioning unit, a belt retracting and releasing device, a driving device, a transition deviation-preventing front-mounted carrier roller set, a belt, a self-moving tail, a belt protection and belt conveyor control system, and a video monitoring system. But above-mentioned scheme passes through tail cylinder and drives the belt operation, through converter control drive drum work, only afterbody cylinder drive, and the goods on the sticky tape distributes when uneven in long distance transportation and can lead to the distribution of whole sticky tape tension unbalanced, even adjust the afterbody cylinder also can't make whole sticky tape tension balanced, if control improper existence can cause the sticky tape to skid, the problem that middle reprint point still can make the material whereabouts.
Disclosure of Invention
The invention aims to solve the problem that the unloading type multi-point driving conveyor in the prior art slips due to the fact that the tension of a rubber belt wound out of a driving roller is reduced under the condition of working condition change, and provides a torque deviation prediction model between a main driving unit and a driven driving unit by adopting a data driving method, so that the coordination control between the main driving unit and the driven driving unit is realized, and the control performance of the unloading type multi-point driving conveyor and the safety of the conveyor are improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data-driven multi-drive conveyor torque control method comprises a main drive unit, a middle drive unit and a tail drive unit, and comprises the following steps:
step S1: the head driving unit is set to be in a speed driving mode, and the middle driving unit and the tail driving unit are both set to be in a torque control mode;
step S2: setting a main sampling period TSCollecting material distribution vector xkMain drive unit torque TETMiddle drive unit torque TEZTail drive unit torque TEW；
Step S3: will TET、TEWV addition of xkIn forming the input vector of the middle drive unit
Figure BDA0003124412010000021
Will TEZ、TETV distribution vector x of added materialkIn forming the input vector of the tail drive unit
Figure BDA0003124412010000022
Step S4: calling the off-line built and trained prediction model, reading the pre-stored model parameters, and inputting the vector of the middle driving unit
Figure BDA0003124412010000023
And input vector of tail drive unit
Figure BDA0003124412010000024
Inputting the data into a prediction model for calculation to obtain a model output value, namely a middle torque deviation delta TE(TZ)And tail torque deviation Δ TE(ZW)；
Step S6: according to the mid-section torque deviation Delta TE(TZ)And tail torque deviation Δ TE(ZW)And setting a torque adjustment value of the middle driving unit and a torque adjustment value of the tail driving unit.
Preferably, the obtaining of the material distribution vector in step S1 includes the following steps:
step S11: dividing the conveyor belt bearing section into n equal conveyor belt sections with fixed length to obtain the instantaneous loading q of the conveyor belt;
step S12: the material quantity of the first section of the conveying belt is obtained as follows:
Figure BDA0003124412010000025
wherein, N ═ t1/tsL is the length of the conveyor belt, v is the speed of the conveyor belt, tl＝l/v，tsIs a sampling period;
step S13: through
Figure BDA0003124412010000026
Obtain the output after the timeMaterial distribution vector on belt:
x＝(x1，x2...xm，xm+1...xm)，
wherein s is the belt length between the middle driving winding-out point and the blanking point, and v is the belt speed of the conveying belt.
Along with the time, the material will pass backward in proper order on the conveyer belt, forms the linked list: x is the number of1→x2→……→xm→xm+1→……→xn. When the conveyor run time is greater than
Figure BDA0003124412010000027
Thereafter, the loading of each segment will become a known amount. Vector x ═ x1，x2…xm，xm+1…xn) The material distribution on the conveyor belt is described, in this specification referred to as material distribution vector. And periodically acquiring images of the tail rubber belt, and calculating the instantaneous loading capacity of the belt conveyor. The trend of the loading capacity is obtained by data-driven modeling, and the absolute value of the trend is not needed, so that a measurement system does not need to be calibrated, and the method is more convenient to realize.
Preferably, the building and training of the prediction model in step S4 includes the following steps:
s41: setting the head driving unit, the middle driving unit and the tail driving unit to be in a speed control mode;
s42: setting on-line load measurement period tsStarting on-line measurement of the loading capacity; setting variable sampling period TSObtaining v, x, TET、TEZ、TEW，Ts＞ts；
S43: will TET、TEWV is added to X to form the input vector of the central drive unit, XZ＝(x1，x2...xm，xm+1…xn，v，TEW，TET) (ii) a Will TEZ，TETV addition of
Figure BDA0003124412010000031
In (1), a tail part is formedInput vector, X, of the drive unitW＝(x1，x2...xm，xm+ 1...xn，v，TEZ，TET)；
S44: the method comprises the steps of collecting data of multiple working conditions of a conveyor, and processing the data to obtain a middle driving unit learning sample
Figure BDA0003124412010000032
And tail drive unit learning samples
Figure BDA0003124412010000033
Deposit into a sample set
Figure BDA0003124412010000034
Where k is the kth TSPeriod, NZRepresenting the number of samples of the middle drive unit, NWRepresenting the number of tail drive unit samples;
s45: modeling and training a sample set through an LSSVM algorithm;
s46: and after the training is finished, storing the LSSVM model parameters into the PLC for online use.
Wherein N isεFor sample capacity, since the learning samples need to be removed by adopting a sparsification algorithm in the LSSVM training process, N isεLess than the number N of original samplesZ。
Conveyor drive unit motor torque TEDriving circumferential force F with driving wheeluIn a proportional relationship, i.e. TE＝k·FuAnd the driving circumferential force is equal to the difference between the tension at the winding point and the tension at the separation point. The conveyer belt is annular, the tension of the winding point of the main driving wheel, the middle driving wheel and the tail driving wheel is coupled with the tension of the separation point, and the resistance of the adhesive tape is also influenced by the running state, so that the accurate analytic relation of the torques of the multiple driving units cannot be obtained. However, the torque deviation model can be obtained by adopting a learning modeling method because a definite relation exists among the torque of the main driving unit, the torque of the auxiliary driving unit and the real-time running state of the conveyor.
If the main driving unit adopts speed control, the middle slave driving unit and the tail slave driving unit also adopt speed control, and the speed set value tracks the main driving frequency converter. In this case, there is no coupling relationship between the torques of the drive units, and the difference depends mainly on the operating parameters of the conveyor, such as the operating speed and the material distribution. The torque prediction model can be established by utilizing the operation data obtained in the working mode.
Preferably, the step S44 further includes the following steps
Step S441: establishing a head drive unit to mid drive unit torque differential Δ TE(TZ)Prediction model f1Torque difference Δ T between the middle drive unit and the tail drive unitE(ZW)Prediction model f2：
ΔTE(TZ)＝f1(Xz)，
ΔTE(ZW)＝f2(XW)；
Step S442: by TSTo periodically collect TET, TEZ, TEW, and v, samples are formed through steps S21 and S22
Figure BDA0003124412010000041
Adding model f1Collecting the sample to form a sample
Figure BDA0003124412010000042
Adding f2Collecting samples, wherein K is the Kth collection period;
step S443: obtaining a sample set
Figure BDA0003124412010000043
And
Figure BDA0003124412010000044
setting the main drive unit as speed control, the middle slave drive unit and the tail slave drive unit as speed control, setting the speed set value to track the main drive controller, and setting the data sampling period as TsAnd T isS＞tsAccording to tsThe on-line measurement of the load capacity is periodically executed, and the material distribution phasor X is periodically updated.
Preferably, the step S45 of performing modeling training on the sample set through the LSSVM algorithm includes: establishing a torque deviation decision function of the middle driving unit:
Figure BDA0003124412010000045
tail drive unit torque deviation decision function:
Figure BDA0003124412010000046
wherein alpha is a vector of support values, b is an offset, sigma is a kernel parameter, c is a normalization parameter,
Figure BDA0003124412010000047
and
Figure BDA0003124412010000048
is the kernel function of the LSSVM algorithm. After the modeling training is completed, the support vectors in the learning sample set are required to be collected
Figure BDA0003124412010000049
And a support value vector alpha, an offset b, a kernel parameter sigma, a normalized parameter c and the like are stored in the PLC. Approximating a non-linear model f by training using a learning sample set1、f2The LSSVM model has low requirement on storage space and low requirement on calculated amount, and is suitable for industrial controllers such as PLC.
Preferably, the LSSVM model parameters in step S46 include: the vector of support values α, offset b, kernel parameter σ, is the normalization parameter c. And in the online application stage, the extracted parameters are subjected to prediction calculation. In actual use, a kernel function K can be called for calculation, and accumulated summation is carried out to obtain a model prediction value.
Preferably, the kernel function is a radial basis kernel function RBF.
Figure BDA00031244120100000410
Wherein alpha iskB is the offset, which is the support value for the corresponding sample. Model parameter alphakAnd b can be solved by the following system of equations:
Figure BDA00031244120100000411
Figure BDA00031244120100000412
wherein the content of the first and second substances,
Figure BDA0003124412010000051
U＝(K+c-1I)-1；
in the U expression, I is a unit matrix, sigma is a nuclear parameter, and c is a normalized parameter, namely a penalty coefficient;
preferably, the step S6 further includes a torque deviation Δ T at the middle partE(TZ)Upper superimposed artificial correction value delta TZThen forming the torque adjustment value of the middle driving unit and the torque deviation delta T at the tail partE(ZW)Upper superimposed artificial correction value delta TWAnd then forming a tail drive unit torque adjustment value. Ideally,. DELTA.TZ＝0，ΔTWWhen the model is not matched, the output value of the model is predicted, the output value of the model is adjusted, and the output value of the model is adjusted.
Preferably, step S6 further includes a limiting module, which determines whether the torque adjustment value of the middle driving unit and the torque adjustment value of the tail driving unit exceed the limiting limits, respectively, if so, the torque adjustment value is limited to an upper limit value, and the torque change rate is appropriately reduced, otherwise, the torque adjustment value of the middle driving unit and the torque adjustment value of the tail driving unit are output. The upper limit of the amplitude limit is delta increase on the basis of the current torque, and the lower limit of the amplitude limit is delta decrease on the basis of the current actual torque. If the difference between the torque set value and the current torque exceeds delta, the change rate of the adjustment value is properly reduced through torque amplitude limiting, and the torque adjustment of the middle driving unit and the tail driving unit is gradually completed.
A data-driven multi-drive conveyor torque control device adopts the data-driven multi-drive conveyor torque control method. The head of the conveyor is provided with a main driving unit, the middle of the conveyor is provided with a middle driving unit, the tail of the conveyor is provided with a tail driving unit, each driving unit comprises two driving motors which are coaxially arranged, a motor control system controller adopts a PLC and adopts a frequency converter for driving, and the PLC and the frequency converter communicate by adopting a field bus protocol so as to realize the coordination control among the driving units. The bus is made of optical fiber materials, so that electromagnetic interference is prevented, and lightning stroke is prevented.
Therefore, the invention has the following beneficial effects: (1) the invention combines the on-line measuring means of the loading capacity and adopts a data driving method to obtain the torque deviation prediction models of the main driving unit, the middle driving unit and the tail driving unit so as to realize the coordination control between the main driving unit and the slave driving unit. (2) The data driving modeling means obtains the set torque value of the slave driving set, improves the control performance of the unloading type multipoint driving conveyor and improves the safety of the conveyor. (3) The LSSVM is adopted to complete modeling training, the LSSVM has low requirements on storage space and on-line force calculation, is suitable for industrial controllers such as PLC and the like, and solves the problem of the tension of the rubber belt at the unloading point of the unloading type multi-point drive belt conveyor.
Drawings
Fig. 1 is a schematic view of a multi-drive conveyor model according to an embodiment of the invention.
Fig. 2 is a schematic tension diagram of a multi-drive conveyor according to an embodiment of the invention.
FIG. 3 is a block diagram of a multi-drive conveyor control according to an embodiment of the invention.
Fig. 4 is a schematic diagram of material distribution phasors for a multi-drive conveyor according to an embodiment of the invention.
Fig. 5 is a schematic view of an input vector of a multi-drive conveyor according to an embodiment of the invention.
FIG. 6 is a schematic diagram of the control system mechanism of the multi-drive conveyor according to one embodiment of the invention.
Fig. 7 is a flow chart for modeling and using the multi-drive conveyor torque control method according to an embodiment of the invention.
In the figure: 1. main drive unit 2, middle part drive unit 3, afterbody drive unit 4, camera and light source 5, material.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
Example (b):
as shown in fig. 1 to 7, 3 driving units are configured at the head, the middle and the tail of the conveyer, and each driving unit comprises two coaxially mounted driving motors. The motor control system controller adopts a PLC and adopts a frequency converter for driving, and the PLC and the frequency converter communicate by adopting a field bus protocol so as to realize the coordination control among the driving units. The bus is made of optical fiber materials, so that electromagnetic interference is prevented, and lightning stroke is prevented.
Two motors in each group of driving units are coaxially arranged, belong to rigid body connection and are controlled by a master motor and a slave motor, and a slave motor frequency converter works in a direct torque control mode and receives a set torque value sent by a master motor. In the following description, the drive unit consisting of two coaxially mounted electric motors is described as a whole.
The torque of each drive unit is related to the distribution of material on the belt, the speed of the belt, etc. while the conveyor is running. The embodiment adopts an image processing means to realize the real-time measurement of the instantaneous conveying capacity, as shown in figure 1, an industrial camera uses tsPeriodically acquiring the adhesive tape image at the head driving unit, and calculating the instantaneous loading capacity of the belt conveyor, wherein the method comprises the following steps: as shown in fig. 4, the tape carrier segment is divided into n equal parts of fixed length, each equal part has a length of 1, in the inclined carrier segment, the length is calculated by the actual tape length, not by the horizontal projection, and the rear end of the middle unloading station takes the blanking point as the starting point of calculation. The method for calculating the instantaneous loading of the belt conveyor comprises the following steps: the industrial camera periodically acquires the tail rubber belt image and calculates the instantaneous loading capacity of the belt conveyor. The trend of the loading capacity is obtained by data-driven modeling, and the absolute value of the trend is not needed, so that the trend of the loading capacity does not need to be correctedThe measurement system is calibrated, and the realization is more convenient.
Step S11: dividing the conveyor belt bearing section into n equal conveyor belt sections with fixed length to obtain the instantaneous loading q of the conveyor belt; step S12: the material quantity of the first section of the conveying belt is obtained as follows:
Figure BDA0003124412010000061
wherein, N ═ t1/tsL is the length of the conveyor belt, v is the speed of the conveyor belt, tl＝l/v，tsIs a sampling period;
step S13: through
Figure BDA0003124412010000062
Obtaining a material distribution vector on the conveying belt after time:
x＝(x1，x2...xm，xm+1...xn)，
wherein s is the belt length between the middle driving winding-out point and the blanking point, and v is the belt speed of the conveying belt.
Along with the time, the material will pass backward in proper order on the conveyer belt, forms the linked list: x is the number of1→x2→……→xm→xm+1→……→xn. When the conveyor run time is greater than
Figure BDA0003124412010000071
Thereafter, the loading of each segment will become a known amount. Vector x ═ x1，x2…xm，xm+1...xn) The material distribution on the conveyor belt is described, in this specification referred to as material distribution vector.
The off-line establishment of the trained torque deviation prediction model comprises the following steps:
s41: setting the head driving unit, the middle driving unit and the tail driving unit to be in a speed control mode;
s42: setting on-line load measurement period tsStart of on-line measurement of load(ii) a Setting variable sampling period TSObtaining v, x, TET、TEZ、TEW，Ts＞ts；
S43: will TET、TEWV is added to X to form the input vector of the central drive unit, Xz＝(x1，x2...xm，xm+ 1...xn，v，TEW，TET) (ii) a Will TEZ，TETV addition of
Figure BDA0003124412010000072
In, form the tail drive unit input vector, XW＝(x1，x2...xm，xm+1...xn，v，TEZ，TET)；
S44: the method comprises the steps of collecting data of multiple working conditions of a conveyor, and processing the data to obtain a middle driving unit learning sample
Figure BDA0003124412010000073
And tail drive unit learning samples
Figure BDA0003124412010000074
Deposit into a sample set
Figure BDA0003124412010000075
And
Figure BDA0003124412010000076
where k is the kth TSPeriod, NZRepresenting the number of samples of the middle drive unit, NWRepresenting the number of tail drive unit samples;
step S441: establishing a head drive unit to mid drive unit torque differential Δ TE(TZ)Prediction model f1Torque difference Δ T between the middle drive unit and the tail drive unitE(ZW)Prediction model f2：
ΔTE(TZ)＝f1(Xz)，
ΔTE(ZW)＝f2(XW)；
Step S442: by TSFor periodic acquisition of TET、TEZ、TEWAnd v, forming a sample through steps S21 and S22
Figure BDA0003124412010000077
Adding model f1Collecting the sample to form a sample
Figure BDA0003124412010000078
Adding f2Collecting samples, wherein K is the Kth collection period;
step S443: obtaining a sample set
Figure BDA0003124412010000079
And
Figure BDA00031244120100000710
setting the main drive unit as speed control, the middle slave drive unit and the tail slave drive unit as speed control, setting the speed set value to track the main drive controller, and setting the data sampling period as TsAnd T isS＞tsAccording to tsThe on-line measurement of the load capacity is periodically executed, and the material distribution phasor X is periodically updated.
S45: modeling and training a sample set through an LSSVM algorithm;
establishing a torque deviation decision function of the middle driving unit:
Figure BDA00031244120100000711
tail drive unit torque deviation decision function:
Figure BDA0003124412010000081
wherein alpha is a vector of support values, b is an offset, sigma is a kernel parameter, c is a normalization parameter,
Figure BDA0003124412010000082
and
Figure BDA0003124412010000083
is the kernel function of the LSSVM algorithm. After the modeling training is completed, the support vectors in the learning sample set are required to be collected
Figure BDA0003124412010000084
And a support value vector alpha, an offset b, a kernel parameter sigma, a normalized parameter c and the like are stored in the PLC. Approximating a non-linear model f by training using a learning sample set1、f2The LSSVM model has low requirement on storage space and low requirement on calculated amount, and is suitable for industrial controllers such as PLC.
The kernel function is a radial basis kernel function RBF.
Figure BDA0003124412010000085
Wherein alpha iskB is the offset, which is the support value for the corresponding sample. Model parameter alphakAnd b can be solved by the following system of equations:
Figure BDA0003124412010000086
Figure BDA0003124412010000087
wherein the content of the first and second substances,
Figure BDA0003124412010000088
U＝(K+c-1I)-1；
in the U expression, I is a unit matrix, sigma is a nuclear parameter, and c is a normalized parameter, namely a penalty coefficient;
s46: and after the training is finished, storing the support value model parameters into the PLC for online use.
Wherein N isεFor the sample capacity, the LSSVM training process needs to adopt a sparsification algorithmEliminate the learning samples, so NεLess than the number N of original samplesZ。
Conveyor drive unit motor torque TEDriving circumferential force F with driving wheeluIn a proportional relationship, i.e. TE＝k·FuAnd the driving circumferential force is equal to the difference between the tension at the winding point and the tension at the separation point. The conveyer belt is annular, the tension of the winding point of the main driving wheel, the middle driving wheel and the tail driving wheel is coupled with the tension of the separation point, and the resistance of the adhesive tape is also influenced by the running state, so that the accurate analytic relation of the torques of the multiple driving units cannot be obtained. However, the torque deviation model can be obtained by adopting a learning modeling method because a definite relation exists among the torque of the main driving unit, the torque of the auxiliary driving unit and the real-time running state of the conveyor.
If the main driving unit adopts speed control, the middle slave driving unit and the tail slave driving unit also adopt speed control, and the speed set value tracks the main driving frequency converter. In this case, there is no coupling relationship between the torques of the drive units, and the difference depends mainly on the operating parameters of the conveyor, such as the operating speed and the material distribution. The torque prediction model can be established by utilizing the operation data obtained in the working mode.
The online torque control of the multi-drive conveyor comprises the following steps:
step S1: the head driving unit is set to be in a speed driving mode, and the middle driving unit and the tail driving unit are both set to be in a torque control mode;
step S2: setting a main sampling period TSCollecting material distribution vector xkMain drive unit torque TETMiddle drive unit torque TEZTail drive unit torque TEW；
Step S3: will TET、TEWV addition of xkIn forming the input vector of the middle drive unit
Figure BDA0003124412010000091
Will TEZ、TETV distribution vector x of added materialkIn (1), a tail part is formedInput vector of drive unit
Figure BDA0003124412010000092
Step S4: calling the off-line built and trained prediction model, reading the pre-stored model parameters, and inputting the vector of the middle driving unit
Figure BDA0003124412010000093
And input vector of tail drive unit
Figure BDA0003124412010000094
Inputting the data into a prediction model for calculation to obtain a model output value, namely a middle torque deviation delta TE(TZ)And tail torque deviation deltaTE(ZW)；
Step S6: according to the mid-section torque deviation Delta TE(TZ)And tail torque deviation Δ TE(ZW)And setting a torque adjustment value of the middle driving unit and a torque adjustment value of the tail driving unit. At mid-section torque deviation Δ TE(TZ)Upper superimposed artificial correction value delta TZThen forming the torque adjustment value of the middle driving unit and the torque deviation delta T at the tail partE(ZW)Upper superimposed artificial correction value delta TWAnd then forming a tail drive unit torque adjustment value. And respectively judging whether the torque adjustment value of the middle driving unit and the torque adjustment value of the tail driving unit exceed the amplitude limit, if so, limiting the torque adjustment value to an upper limit value, properly reducing the torque change rate, and if not, outputting the torque adjustment value of the middle driving unit and the torque adjustment value of the tail driving unit.
The control performance of the unloading type multi-point driving conveyor is improved, and the safety of the conveyor is improved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although the terms torque adjustment, predictive model, material distribution phasor, on-time loading, torque bias, etc. are used more herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.

Claims (10)

1. A data-driven multi-drive conveyor torque control method is characterized by comprising a main drive unit, a middle drive unit and a tail drive unit, and the control method comprises the following steps:
step S1: the head driving unit is set to be in a speed driving mode, and the middle driving unit and the tail driving unit are both set to be in a torque control mode;
step S2: setting a main sampling period TSCollecting material distribution vector xkMain drive unit torque TETMiddle drive unit torque TEZTail drive unit torque TEW；
Step S3: will TET、TEWV addition of xkIn forming the input vector of the middle drive unit
Figure FDA0003124412000000011
Will TEZ、TETV distribution vector x of added materialkIn forming the input vector of the tail drive unit
Figure FDA0003124412000000012
Step S4: calling the off-line built and trained prediction model, reading the pre-stored model parameters, and inputting the vector of the middle driving unit
Figure FDA0003124412000000013
And input vector of tail drive unit
Figure FDA0003124412000000014
Input to predictionCalculating in the model to obtain the output value of the model, namely the middle torque deviation delta TE(TZ)And tail torque deviation Δ TE(ZW)；
Step S6: according to the mid-section torque deviation Delta TE(TZ)And tail torque deviation Δ TE(ZW)And setting a torque adjustment value of the middle driving unit and a torque adjustment value of the tail driving unit.
2. The method as claimed in claim 1, wherein said step of obtaining the material distribution vector in step S1 comprises the steps of:
step S11: dividing the conveyor belt bearing section into n equal conveyor belt sections with fixed length to obtain the instantaneous loading q of the conveyor belt;
step S12: the material quantity of the first section of the conveying belt is obtained as follows:
Figure FDA0003124412000000015
wherein, N ═ t1/ts1 is the length of the conveyor belt, v is the speed of the conveyor belt, tl＝l/v，tsIs a sampling period;
step S13: through
Figure FDA0003124412000000016
Obtaining a material distribution vector on the conveying belt after time:
x＝(x1，x2...xm，xm+1…xn)，
wherein s is the belt length between the middle driving winding-out point and the blanking point, and v is the belt speed of the conveying belt.
3. The method as claimed in claim 2, wherein the step of building and training the predictive model of step S4 comprises the steps of:
s41: setting the head driving unit, the middle driving unit and the tail driving unit to be in a speed control mode;
s42: setting on-line load measurement period tsStarting on-line measurement of the loading capacity; setting variable sampling period TSObtaining v,
Figure FDA0003124412000000017
TET、TEZ、TEW，Ts＞ts；
S43: will TET、TEWV is added to X to form the input vector of the central drive unit, XZ＝(x1，x2...xm，xm+1...xn，v，TEW，TET) (ii) a Will TEZ，TETV addition of
Figure FDA0003124412000000021
In, form the tail drive unit input vector, Xw＝(x1，x2...xm，xm+1…xn，v，TEZ，TET)；
S44: the method comprises the steps of collecting data of multiple working conditions of a conveyor, and processing the data to obtain a middle driving unit learning sample
Figure FDA0003124412000000022
And tail drive unit learning samples
Figure FDA0003124412000000023
Deposit into a sample set
Figure FDA0003124412000000024
And
Figure FDA0003124412000000025
where k is the kth TSPeriod, NZRepresenting the number of samples of the middle drive unit, NWRepresenting the number of tail drive unit samples;
s45: modeling and training a sample set through an LSSVM algorithm;
s46: and after the training is finished, storing the LSSVM model parameters into the PLC for online use.
4. The method as claimed in claim 3, wherein the step S44 further comprises the step of
Step S441: establishing a head drive unit to mid drive unit torque differential Δ TE(TZ)Prediction model f1Torque difference Δ T between the middle drive unit and the tail drive unitE(ZW)Prediction model f2：
ΔTE(TZ)＝f1(Xz)，
ΔTE(ZW)＝f2(XW)；
Step S442: by TSFor periodic acquisition of TET、TEZ、TEWAnd v, forming a sample through steps S21 and S22
Figure FDA0003124412000000026
Adding model f1Collecting the sample to form a sample
Figure FDA0003124412000000027
Adding f2Collecting samples, wherein K is the Kth collection period;
step S443: obtaining a sample set
Figure FDA0003124412000000028
And
Figure FDA0003124412000000029
5. the data-driven multi-drive conveyor torque control method as claimed in claim 4, wherein the step S45 of performing modeling training on the sample set through the LSSVM algorithm comprises: establishing a torque deviation decision function of the middle driving unit:
Figure FDA00031244120000000210
tail drive unit torque deviation decision function:
Figure FDA00031244120000000211
wherein alpha is a vector of support values, b is an offset, sigma is a kernel parameter, c is a normalization parameter,
Figure FDA00031244120000000212
and
Figure FDA00031244120000000213
is the kernel function of the LSSVM algorithm.
6. The method of claim 5, wherein the LSSVM model parameters of step S46 include: the vector of support values α, offset b, kernel parameter σ, is the normalization parameter c.
7. The method as claimed in claim 6, wherein the kernel function is a radial basis kernel function (RBF).
8. The method as claimed in claim 7, wherein the step S6 further includes a torque deviation Δ T at the center portionE(TZ)Upper superimposed artificial correction value delta TZThen forming the torque adjustment value of the middle driving unit and the torque deviation delta T at the tail partE(ZW)Upper superimposed artificial correction value delta TWAnd then forming a tail drive unit torque adjustment value.
9. The method as claimed in claim 8, wherein the step S6 further comprises a limiting module for determining whether the torque adjustment value of the middle driving unit and the torque adjustment value of the tail driving unit exceed the limiting limit, respectively, if so, limiting the torque adjustment value to an upper limit value, and reducing the torque change rate, otherwise, outputting the torque adjustment value of the middle driving unit and the torque adjustment value of the tail driving unit.
10. A data-driven multi-drive conveyor torque control apparatus, characterized by adopting the data-driven multi-drive conveyor torque control method according to any one of claims 1 to 9.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114634005A (en) * 2022-03-31 2022-06-17 国能神东煤炭集团有限责任公司 Drive control method and system for belt conveyor

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS59198215A (en) * 1983-04-18 1984-11-10 コンバツシヨン・エンヂニアリング・インコ−ポレ−テツド Automatic speed controller for diving type scraper conveyor
JPH08251972A (en) * 1995-03-14 1996-09-27 Toshiba Corp Synchronism controller for conveyor
CN202114368U (en) * 2010-08-20 2012-01-18 南京熊猫电子装备有限公司 Mesh belt type conveying device for reflux welding furnace
CN202729271U (en) * 2012-05-25 2013-02-13 杜明芳 Belt conveyer self-adaptive energy-saving control device
CN105217271A (en) * 2015-08-31 2016-01-06 西安华光信息技术有限责任公司 Based on the self-adaptation control method of the belt transportation system of video identification
EP3317619A1 (en) * 2015-07-01 2018-05-09 Voith Patent GmbH Method and apparatus for determining a specific energy consumption of belt conveyors
CN110950002A (en) * 2019-10-16 2020-04-03 焦作煤业（集团）新乡能源有限公司 Operation control method of high-voltage variable-frequency main coal flow intelligent conveying system
CN111689170A (en) * 2020-06-17 2020-09-22 青岛中加特电气股份有限公司 Scraper conveyor and self-adaptive speed regulation method thereof
CN111689171A (en) * 2020-06-17 2020-09-22 青岛中加特电气股份有限公司 Automatic speed regulation method of lower-level conveyor based on variable-frequency output torque of upper-level conveyor
CN112938408A (en) * 2021-04-15 2021-06-11 毕玉龙 Automatic speed-regulating balance protection method for coal quantity of belt

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS59198215A (en) * 1983-04-18 1984-11-10 コンバツシヨン・エンヂニアリング・インコ−ポレ−テツド Automatic speed controller for diving type scraper conveyor
JPH08251972A (en) * 1995-03-14 1996-09-27 Toshiba Corp Synchronism controller for conveyor
CN202114368U (en) * 2010-08-20 2012-01-18 南京熊猫电子装备有限公司 Mesh belt type conveying device for reflux welding furnace
CN202729271U (en) * 2012-05-25 2013-02-13 杜明芳 Belt conveyer self-adaptive energy-saving control device
EP3317619A1 (en) * 2015-07-01 2018-05-09 Voith Patent GmbH Method and apparatus for determining a specific energy consumption of belt conveyors
CN105217271A (en) * 2015-08-31 2016-01-06 西安华光信息技术有限责任公司 Based on the self-adaptation control method of the belt transportation system of video identification
CN110950002A (en) * 2019-10-16 2020-04-03 焦作煤业（集团）新乡能源有限公司 Operation control method of high-voltage variable-frequency main coal flow intelligent conveying system
CN111689170A (en) * 2020-06-17 2020-09-22 青岛中加特电气股份有限公司 Scraper conveyor and self-adaptive speed regulation method thereof
CN111689171A (en) * 2020-06-17 2020-09-22 青岛中加特电气股份有限公司 Automatic speed regulation method of lower-level conveyor based on variable-frequency output torque of upper-level conveyor
CN112938408A (en) * 2021-04-15 2021-06-11 毕玉龙 Automatic speed-regulating balance protection method for coal quantity of belt

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114634005A (en) * 2022-03-31 2022-06-17 国能神东煤炭集团有限责任公司 Drive control method and system for belt conveyor
CN114634005B (en) * 2022-03-31 2023-07-25 国能神东煤炭集团有限责任公司 Driving control method and system for belt conveyor

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