A control method for a belt conveyor tensioning device

Abstract

This invention discloses a control method for a belt conveyor tensioning device, relating to the field of tensioning device control. The belt conveyor tensioning device includes a control system and a tension sensor. The control method includes: the control system acquiring a target tension range, a target tension value, and a real-time tension value measured by the tension sensor, and comparing the real-time tension value with the target tension range; when the real-time tension value exceeds the target tension range, the control system performs fuzzy PID control on the tension of the conveyor belt based on the target tension value and the real-time tension value. The target tension range is configured by the control system according to the working state, transport volume, or speed state of the belt conveyor. This control method can reduce the working losses of the belt conveyor, improve transport efficiency, and enhance operational stability.

Description

Technical Field

[0001] This invention relates to the field of tensioning device control, and in particular to a control method for a belt conveyor tensioning device. Background Technology

[0002] Coal is one of the main fossil fuels and a crucial cornerstone and guarantee of energy supply. Belt conveyors, as key large-scale equipment in coal production, play a vital role in the long-distance, efficient, and safe transportation of coal due to their excellent stability, continuity, and low cost. However, with the emergence of high-yield and high-efficiency mines in my country, traditional belt conveyors, in terms of both main parameters and operating performance, can no longer meet the requirements of high yield and efficiency, resulting in significant energy waste during operation.

[0003] As an important component of belt conveyors, the quality of the tensioning device directly affects the conveying efficiency of the belt conveyor. Especially for long-distance belt conveyors, there are often problems such as untimely or unreasonable adjustment of the belt tension due to unsatisfactory control of the tensioning device, which in turn leads to a decrease in coal mine output. Summary of the Invention

[0004] To address the aforementioned problems and technical requirements, the inventors have proposed a control method for a belt conveyor tensioning device. The technical solution of this invention is as follows:

[0005] A control method for a belt conveyor tensioning device, the belt conveyor tensioning device including a control system and a tension sensor, the control method comprising:

[0006] The control system acquires the target tension range, the target tension value, and the real-time tension value measured by the tension sensor, and compares the real-time tension value with the target tension range;

[0007] When the real-time tension value exceeds the target tension range, the control system performs fuzzy PID control on the tension of the conveyor belt based on the target tension value and the real-time tension value, wherein...

[0008] The target tension range is configured by the control system according to the working status, conveying capacity, or speed status of the belt conveyor.

[0009] A further technical solution is that the control system includes a fuzzy controller and a PID controller. When the control system performs fuzzy PID control on the tension of the belt conveyor tensioning device, it includes:

[0010] The tension deviation e and the rate of change of tension deviation ec are calculated based on the target tension value and the real-time tension value.

[0011] The fuzzy controller outputs an adjustment coefficient based on the tension deviation e and the rate of change of tension deviation ec.

[0012] The PID controller determines the control coefficient based on the adjustment coefficient, and outputs a PID control signal based on the control coefficient and the tension deviation e.

[0013] The control coefficient includes the proportional control coefficient K. p Integral control coefficient K i and differential control coefficient K d The adjustment coefficient includes the proportional adjustment coefficient ΔK. p Integral adjustment coefficient ΔK i and the differential adjustment coefficient ΔK d The control coefficient can be expressed as:

[0014]

[0015] Among them, K p0 K is the initial proportional control coefficient. i0 K represents the initial integral control coefficient. d0 These are the initial differential control coefficients.

[0016] A further technical solution is that when the fuzzy controller outputs the adjustment coefficient based on the tension deviation e and the rate of change of tension deviation ec, it includes:

[0017] Determine the input and output universes of discourse, map the tension deviation e and the rate of change of tension deviation ec to the input universe of discourse respectively, and obtain the fuzzy input vector based on the input universe of discourse;

[0018] Establish fuzzy control rules, and perform fuzzy inference based on the fuzzy control rules and fuzzy input vectors to obtain fuzzy adjustment coefficients mapped to the output domain;

[0019] The fuzzy adjustment coefficient is clarified to obtain the adjustment coefficient.

[0020] A further technical solution involves obtaining the fuzzy input vector based on the input universe of discourse, including:

[0021] Determine the input fuzzy language set and input membership function. Based on the input membership function, determine the membership degree of each element in the input universe of discourse with respect to each fuzzy language value in the input fuzzy language set, so as to construct an input membership degree assignment table. Then, determine the fuzzy input vector based on the input membership degree assignment table.

[0022] Before establishing fuzzy control rules, it is also necessary to determine the output fuzzy language set and the output membership function. Based on the output membership function, the membership degree of each element in the output universe of discourse with respect to each fuzzy language value in the output fuzzy language set is determined in order to construct the output membership degree assignment table.

[0023] A further technical solution is that the fuzzy control rule includes multiple fuzzy conditional statements, and when performing fuzzy inference based on the fuzzy control rule and the fuzzy input vector, it includes:

[0024] The sub-fuzzy relation matrix corresponding to each fuzzy condition statement in the fuzzy control rule is determined based on the input membership assignment table and the output membership assignment table, and the total fuzzy relation matrix R is determined based on all the sub-fuzzy relation matrices.

[0025] The fuzzy input vector and the fuzzy relation matrix R are fuzzy synthesized to obtain the fuzzy adjustment coefficient.

[0026] A further technical solution is that both the input fuzzy language set and the output fuzzy language set are:

[0027] {NB,NM,NS,ZO,PS,PM,PB};

[0028] The input universe of discourse is {-4,-3,-2,-1,0,1,2,3,4}; the output universe of discourse is {-3,-2,-1,0,1,2,3}.

[0029] A further technical solution is that the multiple fuzzy conditional statements in the fuzzy control rule include:

[0030] If the tension deviation e is NB and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For PS;

[0031] If the tension deviation e is NB and the rate of change of tension deviation ec is NM, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For NS;

[0032] If the tension deviation e is NB and the rate of change of tension deviation ec is NS, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK for NM d For NB;

[0033] If the tension deviation e is NB and the rate of change of tension deviation ec is ZO, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The derivative adjustment coefficient is NM and ΔK d For NB;

[0034] If the tension deviation e is NB and the rate of change of tension deviation ec is PS, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NB;

[0035] If the tension deviation e is NB and the rate of change of tension deviation ec is PM, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For NM;

[0036] If the tension deviation e is NB and the rate of change of tension deviation ec is PB, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For PS;

[0037] If the tension deviation e is NM and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For PS;

[0038] If the tension deviation e is NM and the rate of change of tension deviation ec is NM, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For NS;

[0039] If the tension deviation e is NM and the rate of change of tension deviation ec is NS, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The derivative adjustment coefficient is NM and ΔK d For NB;

[0040] If the tension deviation e is NM and the rate of change of tension deviation ec is ZO, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NM;

[0041] If the tension deviation e is NM and the rate of change of tension deviation ec is PS, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NM;

[0042] If the tension deviation e is NM and the rate of change of tension deviation ec is PM, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For NS;

[0043] If the tension deviation e is NM and the rate of change of tension deviation ec is PB, then the proportional adjustment coefficient ΔK p NS, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d ZO;

[0044] If the tension deviation e is NS and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d It is ZO.

[0045] A further technical solution is that the control system calculates the minimum operating tension of the conveyor belt of the belt conveyor in real time as the target tension value, and the target tension value is within the target tension range.

[0046] A further technical solution is that the working state includes a start-up state, a running state, and an emergency stop state, and the start-up state, running state, and emergency stop state each correspond to a range of values.

[0047] The transport volume status includes empty, half-loaded, and fully loaded status, and each of the empty, half-loaded, and fully loaded statuses corresponds to a range of values.

[0048] The speed states include low speed, medium speed, and high speed, and each of the low speed, medium speed, and high speed states corresponds to a range of values.

[0049] When configuring the target tension range according to the working state of the belt conveyor, the system determines the target tension range based on the target tension value and the interval value corresponding to the current working state;

[0050] When configuring the target tension range according to the conveyor's capacity status, the system determines the target tension range based on the target tension value and the interval value corresponding to the current capacity status.

[0051] When configuring the target tension range according to the speed state of the belt conveyor, the system determines the target tension range based on the target tension value and the interval value corresponding to the current speed state.

[0052] A further technical solution is that the belt conveyor tensioning device also includes a frequency converter, a motor, a tensioning drum, and a pulley block. The frequency converter is connected to the motor and the control system. The motor is connected to the tensioning drum through a second coupling. A steel wire rope is wound around the tensioning drum and passes over the pulley block.

[0053] The PID control signal output by the PID controller is input to the frequency converter, which controls the working state of the motor. The motor controls the tension of the conveyor belt of the belt conveyor through the tensioning drum and pulley block.

[0054] The beneficial technical effects of this invention are:

[0055] (1) In order to avoid the tensioning device frequently adjusting the tension of the belt conveyor belt, which would cause a lot of work loss, the tensioning device control method provided by the present invention enables the tensioning device to adjust the tension only when the tension exceeds the target tension range, thereby reducing work loss.

[0056] (2) The control coefficient of the PID controller is adjusted by using a fuzzy controller, thereby improving the response speed of tension adjustment, enhancing the dynamic adjustment effect of tension, and improving the transportation efficiency of the belt conveyor.

[0057] (3) It can adjust the target tension range according to the working state / carrying capacity / speed state of the belt conveyor, and achieve smooth tension adjustment when the working state / carrying capacity / speed state changes, reducing the impact of sudden stop and other working conditions on the belt conveyor, and improving its operational stability and reliability. Attached Figure Description

[0058] Figure 1 This is a flowchart of an embodiment of the belt conveyor tensioning device control method provided by the present invention.

[0059] Figure 2 This is a control principle diagram of fuzzy PID control provided by the present invention.

[0060] Figure 3 This is a schematic diagram of one embodiment of the tensioning device provided by the present invention.

[0061] Figure 4 This is a structural block diagram of one embodiment of the tensioning device provided by the present invention.

[0062] Figure 5 This is a function graph of one embodiment of the input membership function provided by the present invention.

[0063] Figure 6 This is a function graph of one embodiment of the output membership function provided by the present invention.

[0064] Reference numerals: 1-track, 2-moving trolley, 3-conveyor belt, 4-conveyor drum, 5-pulley block, 6-wire rope, 7-tension sensor, 8-brake hydraulic station, 9-brake, 10-first coupling, 11-tensioning drum, 12-second coupling, 13-motor, 14-frequency converter, 15-control box. Detailed Implementation

[0065] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0066] This invention provides a control method for a belt conveyor tensioning device, the belt conveyor tensioning device including a control system and a tension sensor 7, and the control method for the belt conveyor tensioning device includes:

[0067] The control system acquires the target tension range, the target tension value, and the real-time tension value measured by the tension sensor 7, and compares the real-time tension value with the target tension range;

[0068] When the real-time tension value exceeds the target tension range, the control system performs fuzzy PID control on the tension of the conveyor belt 3 of the belt conveyor based on the target tension value and the real-time tension value, wherein...

[0069] The target tension range is configured by the control system according to the working status, conveying capacity, or speed status of the belt conveyor.

[0070] Specifically, during the operation of a belt conveyor, the tension on the conveyor belt fluctuates slightly at all times. If a certain set value is used as the tension target for the tensioning device, frequent operation of the tensioning device will occur, resulting in a large amount of unnecessary wear and tear. Therefore, the tensioning device control method provided by this invention allows the tensioning device to adjust the tension only when the tension exceeds the target tension range, thereby reducing working losses.

[0071] In one embodiment of the present invention, the control system calculates the minimum operating tension of the conveyor belt of the belt conveyor in real time as the target tension value, and the target tension value is within the target tension range. When the real-time tension value exceeds the target tension range, the tension of the conveyor belt is adjusted according to the target tension value, and fuzzy PID control is used to stabilize it within the target tension range. The minimum operating tension is the minimum tension required for the conveyor belt 3 to ensure normal operation of the belt conveyor under the current working conditions. The minimum operating tension can usually be calculated based on belt drive theory. The calculation method of the minimum operating tension used in this invention is consistent with the prior art and will not be elaborated here.

[0072] The target tension range described in this invention is configured according to the working state, conveying capacity, or speed state of the belt conveyor. In other words, the target tension range in this invention dynamically changes according to the operating conditions of the belt conveyor. This dynamically changing target tension range can better adapt to the changing operating conditions of the belt conveyor, thereby achieving a better tension adjustment effect. The specific method for fuzzy PID control of the belt tension of the belt conveyor, and the specific way to configure the target tension range, can be found in the following description.

[0073] Furthermore, the working states include a start-up state, a running state, and an emergency stop state, and each of the start-up state, running state, and emergency stop state corresponds to a range of values.

[0074] The transport volume status includes empty, half-loaded, and fully loaded status, and each of the empty, half-loaded, and fully loaded statuses corresponds to a range of values.

[0075] The speed states include low speed, medium speed, and high speed, and each of the low speed, medium speed, and high speed states corresponds to a range of values.

[0076] When configuring the target tension range according to the working state of the belt conveyor, the system determines the target tension range based on the target tension value and the interval value corresponding to the current working state;

[0077] When configuring the target tension range according to the conveyor's capacity status, the system determines the target tension range based on the target tension value and the interval value corresponding to the current capacity status.

[0078] When configuring the target tension range according to the speed state of the belt conveyor, the system determines the target tension range based on the target tension value and the interval value corresponding to the current speed state.

[0079] Specifically, the control system includes a PLC, which is connected to the main control system of the belt conveyor to collect operating parameters such as belt speed, conveying capacity, power-on status, and operating voltage. When the control system configures the target tension range based on the operating status of the belt conveyor, it can automatically determine whether the conveyor is in a start-up, running, or emergency stop state based on the collected operating voltage. Similarly, when the control system configures the target tension range based on the conveying capacity, it can automatically determine whether the conveyor is in an unloaded, half-loaded, or full-loaded state based on the collected conveying capacity. When the control system configures the target tension range based on the speed, it can automatically determine whether the conveyor is in a low-speed, medium-speed, or high-speed state based on the collected belt speed. The operating voltage range corresponding to the start-up, running, and emergency stop states, the conveying capacity range corresponding to the unloaded, half-loaded, and full-loaded states, and the belt speed range corresponding to the low-speed, medium-speed, and high-speed states can all be set according to actual application needs.

[0080] The interval value is used to describe the size of the target tension interval under the corresponding state. In one possible embodiment, the control system can take the current target tension value as the minimum value of the target tension interval, and take the sum of the current target tension value and the interval value corresponding to the current working state / transportation state / speed state as the maximum value of the target tension interval. The range between the minimum value and the maximum value is determined as the target tension interval. The interval values ​​corresponding to different states in the working state / transportation state / speed state can be the same or different.

[0081] Furthermore, the control system includes a fuzzy controller and a PID controller. When the control system performs fuzzy PID control on the tension of the belt conveyor tensioning device, it includes:

[0082] The tension deviation e and the rate of change of tension deviation ec are calculated based on the target tension value and the real-time tension value.

[0083] The fuzzy controller outputs an adjustment coefficient based on the tension deviation e and the rate of change of tension deviation ec.

[0084] The PID controller determines the control coefficient based on the adjustment coefficient and outputs a PID control signal based on the control coefficient and the tension deviation e.

[0085] The control coefficient includes the proportional control coefficient K. p Integral control coefficient K i and differential control coefficient K d The adjustment coefficient includes the proportional adjustment coefficient ΔK. p Integral adjustment coefficient ΔK i and the differential adjustment coefficient ΔK d The control coefficient can be expressed as:

[0086]

[0087] Among them, K p0 K is the initial proportional control coefficient. i0 K represents the initial integral control coefficient. d0 These are the initial differential control coefficients.

[0088] Specifically, Figure 2 The diagram illustrates the control principle of a fuzzy PID controller. The input variables of the fuzzy controller are the tension deviation *e* and the rate of change of the tension deviation *ec*. The output variable of the fuzzy controller is the adjustment coefficient used to adjust the PID control coefficients. Tension deviation *e* = target tension value - real-time tension value, and the rate of change of the tension deviation *ec* is the rate of change of the tension deviation *e* over time. Fuzzy PID control combines fuzzy control and PID control through a fuzzy controller and a PID controller, allowing both control methods to leverage their advantages and achieving a control effect with good dynamics and steady-state performance.

[0089] The initial proportional control coefficient K p0 Initial integral control coefficient K i0 and the initial differential control coefficient K d0 The value can be selected according to actual needs. The PID control signal u(t) can be expressed as:

[0090]

[0091] Furthermore, the belt conveyor tensioning device also includes a frequency converter 14, a motor 13, a tensioning drum 11, and a pulley block 5. The frequency converter 14 is connected to the motor 13 and the control system. The motor 13 is connected to the tensioning drum 11 through a second coupling. A steel wire rope 6 is wound around the tensioning drum 11, and the steel wire rope 6 also passes around the pulley block 5.

[0092] The PID control signal output by the PID controller is input to the frequency converter 14, which controls the working state of the motor 13. The motor 13 controls the tension of the conveyor belt 3 of the belt conveyor through the tensioning roller 11 and the pulley block 5.

[0093] Figure 3 The specific structure of the tensioning device is shown. Figure 4 A structural block diagram of the tensioning device is shown, such as... Figures 3-4As shown, the tensioning device also includes a brake hydraulic station 8, a brake 9, and a control box 15. The control system is located inside the control box 15. The brake 9 is connected to the tensioning roller 11 via a first coupling 10. The brake hydraulic station 8 is connected to the brake 9 and the control system, and the control system can control the operation of the brake 9 through the brake hydraulic station 8. In this embodiment, the motor 13 is a permanent magnet synchronous motor, and the tension sensor 7 is connected to the wire rope 6 in the pulley block 5. The tension value of the conveyor belt 3 is determined by detecting the tension value of the wire rope 6.

[0094] The belt conveyor includes a track 1, a traveling trolley 2, a conveyor belt 3, and a conveyor roller 4. The traveling trolley 2 can slide along the track 1, and the conveyor roller 4 is mounted on the traveling trolley 2 and drives the conveyor belt 3. The specific working process of the PID control signal controlling the tension of the conveyor belt 3 is as follows: The PID control signal is output to the control terminal of the frequency converter 14. By controlling the output frequency of the frequency converter 14, the rotation direction of the motor 13 is controlled. The motor 13 drives the tensioning roller 11 to rotate. The steel wire rope 6 on the tensioning roller 11 passes around the pulley block 5 and pulls the traveling trolley 2, thereby driving the conveyor roller 4 on the traveling trolley 2 to slide along the track 1, so as to control the tension or relaxation of the conveyor belt 3, thereby controlling the tension of the conveyor belt 3. When the motor 13 rotates forward, the conveyor belt 3 is tensioned and the tension of the conveyor belt 3 increases; when the motor 13 rotates in reverse, the conveyor belt 3 relaxes and the tension of the conveyor belt 3 decreases. The brake 9 is a necessary device for maintaining and releasing tension. When the motor 13 rotates, the control system controls the brake 9 to release the brake. The motor 13 can change the tension of the conveyor belt 3 through the tensioning roller 11, pulley block 5 and traveling trolley 2. After the motor 13 stops rotating, the control system controls the brake 9 to engage the brake, and the tension of the conveyor belt 3 is maintained at the current value.

[0095] Furthermore, when the fuzzy controller outputs the adjustment coefficient based on the tension deviation e and the rate of change of tension deviation ec, it includes:

[0096] Determine the input and output universes of discourse, and map the tension deviation e and the rate of change of tension deviation ec onto the input universe of discourse, respectively.

[0097] Determine the input fuzzy language set and input membership function. Based on the input membership function, determine the membership degree of each element in the input universe of discourse with respect to each fuzzy language value in the input fuzzy language set, so as to construct an input membership degree assignment table. Then, determine the fuzzy input vector based on the input membership degree assignment table.

[0098] Determine the output fuzzy language set and the output membership function. Based on the output membership function, determine the membership degree of each element in the output universe of discourse with respect to each fuzzy language value in the output fuzzy language set, so as to construct the output membership degree assignment table.

[0099] Establish fuzzy control rules, perform fuzzy inference based on the fuzzy control rules and fuzzy input vectors to obtain fuzzy adjustment coefficients mapped to the output domain; clarify the fuzzy adjustment coefficients to obtain the adjustment coefficients.

[0100] Specifically, as explained above, the input and output variables of the fuzzy controller are precise values. However, the fuzzy controller needs to perform fuzzy inference based on the fuzzy values. Therefore, the input / output variables need to be fuzzified first, and the fuzzy output value, i.e. the fuzzy adjustment coefficient, is output based on the fuzzified input variables. Then, the fuzzy adjustment coefficient is clarified to obtain the adjustment coefficient.

[0101] The input variables are fuzzified based on the input universe of discourse and the input fuzzy language set, and the output variables are fuzzified based on the output universe of discourse and the output fuzzy language set. Both the input and output universes of discourse are discrete finite universes of discourse. The input universe of discourse is determined based on the range of variation of the input variables, i.e., the range of variation of the tension deviation e and the rate of change of tension deviation ec, and the output universe of discourse is determined based on the range of variation of the output variables, i.e., the range of variation of the fuzzy adjustment coefficient. The range of variation of the input / output variables can be set according to actual needs.

[0102] In one embodiment of the present invention, the input variable is divided into 9 levels within its range of variation, and the output variable is divided into 7 levels within its range of variation. That is, the input universe of discourse is defined as {-4,-3,-2,-1,0,1,2,3,-4}, and the output universe of discourse is defined as {-3,-2,-1,0,1,2,3}.

[0103] The precise values ​​of input / output within the range of input / output variable variation can be mapped to the input / output universe of discourse. That is, each element in the input / output universe of discourse corresponds to a certain range of values ​​within the range of input / output variable variation, from small to large. The way the precise values ​​of input / output are mapped to the input / output universe of discourse can be the same as the existing technology. The range of input / output variables corresponding to each element in the input / output universe of discourse can be set according to the actual application.

[0104] To fuzzify the input / output variables, it is also necessary to establish a relationship between the elements in the input / output universe of discourse and the input / output fuzzy language set. That is, to determine the membership degree of each element in the input / output universe of discourse with respect to each fuzzy language value in the input / output fuzzy language set according to the input / output membership function, so as to establish an input / output membership assignment table.

[0105] In one embodiment of the present invention, the input / output fuzzy language sets are defined as: {NB, NM, NS, ZO, PS, PM, PB}, where NB, NM, NS, ZO, PS, PM, and PB are commonly used fuzzy language values ​​in the fuzzy language set, representing negative large, negative medium, negative small, close to zero, positive small, positive medium, and positive large, respectively. Negative large is used to describe tension deviation e / tension deviation change rate ec as negative and large; negative medium is used to describe tension deviation e / tension deviation change rate ec as negative and medium; and negative small is used to describe tension deviation e / tension deviation change rate ec as negative and small. Close to zero is used to describe tension deviation e / tension deviation change rate ec as close to zero. Similarly, positive small is used to describe tension deviation e / tension deviation change rate ec as positive and small; positive medium is used to describe tension deviation e / tension deviation change rate ec as positive and medium; and positive large is used to describe tension deviation e / tension deviation change rate ec as positive and large.

[0106] The value of the input / output membership function represents the degree to which an element in the input / output universe of discourse belongs to a certain fuzzy linguistic value, i.e., the membership degree. The membership degree is represented by a value between 0 and 1, with the closer the membership degree is to 1, the higher the degree to which the element belongs to the fuzzy linguistic value. The input / output membership function generally uses trapezoidal functions, trigonometric functions, Gaussian functions, etc. In one embodiment of the present invention, the output membership function uses a trigonometric function. Considering the large fluctuations in the input variables, the input membership function uses a combination of trapezoidal and trigonometric functions to better fuzzify the input variables.

[0107] Figures 5-6 The function graphs of the input membership function and the output membership function are shown respectively. Figure 5 The horizontal axis represents the elements of the input universe corresponding to the input variables, and the vertical axis represents the membership degree. Figure 6 The horizontal axis represents the elements of the output universe of discourse corresponding to the output variables, and the vertical axis represents the membership degree. The input membership degree assignment table is obtained by calculating the membership function value of each element in the input universe of discourse for each fuzzy linguistic value in the input fuzzy linguistic set, as shown in Table 1.

[0108] Table 1 Input Membership Degree Assignment Table

[0109]

[0110] The fuzzy input vector includes the fuzzy input vector of tension deviation e and the fuzzy input vector of tension deviation change rate ec. Consistent with existing technology, the input fuzzy vector consists of the membership degrees of the input variable to the corresponding fuzzy linguistic value. The fuzzy input vector is determined according to the input membership assignment table, specifically, it refers to the row vector formed by all membership degrees in the input universe corresponding to the fuzzy linguistic value of the input variable (tension deviation e / tension deviation change rate ec). For example, if the fuzzy linguistic value of tension deviation e is NB, then the fuzzy input vector of tension deviation e is (1.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0).

[0111] The output membership assignment table can be obtained by calculating the membership function value of each element in the output universe of discourse for each fuzzy language value in the output fuzzy language set. The output membership assignment table is shown in Table 2.

[0112] Table 2 Output Membership Assignment Table

[0113]

[0114] To ensure that the PID controller adjusts the tension effectively, the control coefficients of the PID controller should ideally follow the following variation pattern:

[0115] When the tension deviation e is too large, in order to quickly remove the deviation, increase the speed at which stability is reached, and avoid large overshoot, the proportional control coefficient K... p The integral control coefficient K should be set to a larger value. i Then it should be set to zero; while when the tension deviation e is small, in order to further reduce the deviation and suppress the occurrence of excessive overshoot and oscillation, the proportional control coefficient K is increased. p It should be reduced, and the integral control coefficient K should be set. i Take the smaller value; while when the tension deviation e is close to zero, in order to eliminate static error and ensure the stable operation of the tensioning device, the proportional control coefficient K is [value missing]. p It should be further reduced while ensuring the integral control coefficient K is maintained. i It remains unchanged or increases.

[0116] Those skilled in the art will know that when the tension deviation e and the rate of change of tension deviation ec are both positive or both negative, the direction of change of the controlled variable, i.e., the real-time tension value, is away from the target tension value. Therefore, when the real-time tension value is close to the target tension value, in order to avoid overshoot caused by the integral element and the resulting oscillation, the proportional control coefficient K should be kept within acceptable limits. p The value is negative to suppress deviations of the controlled variable and reduce the integral control coefficient K. i When the tension deviation e is large, and the rate of change of tension deviation ec has the opposite sign to the tension value deviation e, the proportional control coefficient K... pIt should be zero or negative, which can speed up the dynamic response of the control. The magnitude of the rate of change of tension deviation (ec) indicates how quickly the tension deviation (e) changes; the larger the rate of change of tension deviation (ec), the stronger the proportional control coefficient (K). p The smaller the value of the integral control coefficient K, the better. i The larger the value of K, the better; conversely, the smaller the rate of change of tension deviation (ec), the better. p The larger the value of the integral control coefficient K, the better. i The smaller the value, the better. It should be noted that when establishing control rules based on the above variation patterns, both the tension deviation e and the rate of change of tension deviation ec must be considered for specific analysis.

[0117] Based on the above-mentioned changing patterns, fuzzy control rules are established. In this embodiment, the established fuzzy control rules contain a total of 15 fuzzy conditional statements:

[0118] If the tension deviation e is NB and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For PS;

[0119] If the tension deviation e is NB and the rate of change of tension deviation ec is NM, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For NS;

[0120] If the tension deviation e is NB and the rate of change of tension deviation ec is NS, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK for NM d For NB;

[0121] If the tension deviation e is NB and the rate of change of tension deviation ec is ZO, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The derivative adjustment coefficient is NM and ΔK d For NB;

[0122] If the tension deviation e is NB and the rate of change of tension deviation ec is PS, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NB;

[0123] If the tension deviation e is NB and the rate of change of tension deviation ec is PM, then the proportional adjustment coefficient ΔK pZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For NM;

[0124] If the tension deviation e is NB and the rate of change of tension deviation ec is PB, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For PS;

[0125] If the tension deviation e is NM and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For PS;

[0126] If the tension deviation e is NM and the rate of change of tension deviation ec is NM, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i It is NB and the differential adjustment coefficient ΔK d For NS;

[0127] If the tension deviation e is NM and the rate of change of tension deviation ec is NS, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The derivative adjustment coefficient is NM and ΔK d For NB;

[0128] If the tension deviation e is NM and the rate of change of tension deviation ec is ZO, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NM;

[0129] If the tension deviation e is NM and the rate of change of tension deviation ec is PS, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NM;

[0130] If the tension deviation e is NM and the rate of change of tension deviation ec is PM, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For NS;

[0131] If the tension deviation e is NM and the rate of change of tension deviation ec is PB, then the proportional adjustment coefficient ΔK pNS, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d ZO;

[0132] If the tension deviation e is NS and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NB. d It is ZO.

[0133] Fuzzy conditional statements are used to describe the fuzzy linguistic value of the output variable after fuzzy inference, given a fuzzy linguistic value for the input variable. Taking the first fuzzy conditional statement above as an example, this fuzzy conditional statement indicates that, given a tension deviation e of NB (large negative) and a tension deviation change rate ec of NB (large negative), the proportional adjustment coefficient ΔK should be adjusted accordingly. p PB (positive), integral adjustment coefficient ΔK i It is NB (negative and large) and the differential adjustment coefficient ΔK d Let PS be positive small. Here, the input / output variable is a fuzzy linguistic value, specifically meaning that after mapping the input / output variable to the input / output universe of discourse, the element in the corresponding input / output universe of discourse has the highest membership degree to the fuzzy linguistic value. For example, tension deviation e is NB, specifically meaning that after mapping tension deviation e to the input universe of discourse, the element in the corresponding input universe of discourse has the highest membership degree to NB.

[0134] In practical applications, the aforementioned fuzzy rules are mainly expressed in the form of fuzzy control rule tables. Table 3-5 shows the proportional adjustment coefficient ΔK. p Fuzzy control rule table, integral adjustment coefficient ΔK i Fuzzy control rule table, differential adjustment coefficient ΔK d Fuzzy control rule table.

[0135] Table 3 Proportional Adjustment Coefficient ΔK p Fuzzy control rule table

[0136]

[0137] Table 4 Integral Adjustment Coefficient ΔK i Fuzzy control rule table

[0138]

[0139] Table 5 Differential Adjustment Coefficient ΔK d Fuzzy control rule table

[0140]

[0141] Furthermore, when performing fuzzy inference based on fuzzy control rules and fuzzy input vectors, it includes:

[0142] The sub-fuzzy relation matrix corresponding to each fuzzy condition statement in the fuzzy control rule is determined based on the input membership assignment table and the output membership assignment table, and the total fuzzy relation matrix R is determined based on all the sub-fuzzy relation matrices; the fuzzy input vector and the fuzzy relation matrix R are fuzzily synthesized to obtain the fuzzy adjustment coefficient.

[0143] Specifically, the fuzzy inference employs the Mamdani method, which obtains a sub-fuzzy relation matrix corresponding to each fuzzy condition statement. The total fuzzy relation matrix is ​​the union of all sub-fuzzy relation matrices. Consistent with existing technologies, the sub-fuzzy relation matrix corresponding to each fuzzy condition statement is the Cartesian product of the input fuzzy vector and the output fuzzy vector corresponding to the fuzzy condition statement. The output fuzzy vector consists of the membership degrees of the output variable in the fuzzy condition statement to the corresponding fuzzy linguistic value. The fuzzy output vector can be determined according to the output membership assignment table, i.e., a row vector is formed based on all membership degrees corresponding to the fuzzy linguistic value of the output variable. The fuzzy adjustment coefficient is usually represented in matrix form. If the input fuzzy vector of the tension deviation e is A, and the input fuzzy vector of the tension deviation change rate ec is B, then the fuzzy adjustment coefficient... Where × represents the Cartesian operation. This indicates a fuzzy synthesis operation.

[0144] The adjustment coefficient can be obtained by clarifying the fuzzy adjustment coefficient U. This clarification can employ methods commonly used by those skilled in the art, such as the area centroid method and the average maximum value method.

[0145] It should be noted that the terms "first" and "second" used in the above description are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0146] The above descriptions are merely preferred embodiments of the present invention, and the present invention is not limited to the above embodiments. It is understood that other improvements and variations that can be directly derived or conceived by those skilled in the art without departing from the spirit and concept of the present invention should be considered to be included within the protection scope of the present invention.

Claims

1. A control method for a belt conveyor tensioning device, characterized in that, The belt conveyor tensioning device includes a control system and a tension sensor, and the control method for the belt conveyor tensioning device includes: The control system acquires the target tension range, the target tension value, and the real-time tension value measured by the tension sensor, and compares the real-time tension value with the target tension range; When the real-time tension value exceeds the target tension range, the control system performs fuzzy PID control on the tension of the conveyor belt based on the target tension value and the real-time tension value, wherein... The target tension range is configured by the control system according to the working status, conveying capacity, or speed status of the belt conveyor.

2. The control method for the tensioning device of a belt conveyor according to claim 1, characterized in that, The control system includes a fuzzy controller and a PID controller. When the control system performs fuzzy PID control on the tension of the belt conveyor tensioning device, it includes: The tension deviation e and the rate of change of tension deviation ec are calculated based on the target tension value and the real-time tension value. The fuzzy controller outputs an adjustment coefficient based on the tension deviation e and the rate of change of tension deviation ec. The PID controller determines the control coefficient based on the adjustment coefficient, and outputs a PID control signal based on the control coefficient and the tension deviation e. The control coefficient includes the proportional control coefficient K. p Integral control coefficient K i and differential control coefficient K d The adjustment coefficient includes the proportional adjustment coefficient ΔK. p Integral adjustment coefficient ΔK i and the differential adjustment coefficient ΔK d The control coefficient can be expressed as: Among them, K p0 K is the initial proportional control coefficient. i0 K represents the initial integral control coefficient. d0 These are the initial differential control coefficients.

3. The control method for the tensioning device of a belt conveyor according to claim 2, characterized in that, When the fuzzy controller outputs the adjustment coefficient based on the tension deviation e and the rate of change of tension deviation ec, it includes: Determine the input and output universes of discourse, map the tension deviation e and the rate of change of tension deviation ec to the input universe of discourse respectively, and obtain the fuzzy input vector based on the input universe of discourse; Establish fuzzy control rules, and perform fuzzy inference based on the fuzzy control rules and fuzzy input vectors to obtain fuzzy adjustment coefficients mapped to the output domain; The fuzzy adjustment coefficient is clarified to obtain the adjustment coefficient.

4. The control method for the tensioning device of a belt conveyor according to claim 3, characterized in that, When obtaining the fuzzy input vector based on the input universe of discourse, the following are included: Determine the input fuzzy language set and input membership function. Based on the input membership function, determine the membership degree of each element in the input universe of discourse with respect to each fuzzy language value in the input fuzzy language set, so as to construct an input membership degree assignment table. Then, determine the fuzzy input vector based on the input membership degree assignment table. Before establishing fuzzy control rules, it is also necessary to determine the output fuzzy language set and the output membership function. Based on the output membership function, the membership degree of each element in the output universe of discourse with respect to each fuzzy language value in the output fuzzy language set is determined in order to construct the output membership degree assignment table.

5. The control method for the tensioning device of a belt conveyor according to claim 3, characterized in that, The fuzzy control rules include multiple fuzzy conditional statements, and when performing fuzzy inference based on the fuzzy control rules and fuzzy input vectors, they include: The sub-fuzzy relation matrix corresponding to each fuzzy condition statement in the fuzzy control rule is determined based on the input membership assignment table and the output membership assignment table, and the total fuzzy relation matrix R is determined based on all the sub-fuzzy relation matrices; the fuzzy input vector and the fuzzy relation matrix R are fuzzily synthesized to obtain the fuzzy adjustment coefficient.

6. The control method for the tensioning device of a belt conveyor according to claim 4, characterized in that, The input fuzzy language set and the output fuzzy language set are both: {NB,NM,NS,ZO,PS,PM,PB}; The input universe of discourse is {-4,-3,-2,-1,0,1,2,3,4}; the output universe of discourse is {-3,-2,-1,0,1,2,3}.

7. The control method for the tensioning device of a belt conveyor according to claim 5, characterized in that, The fuzzy conditional statements in the fuzzy control rules include: If the tension deviation e is NB and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NB. d For PS; If the tension deviation e is NB and the rate of change of tension deviation ec is NM, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NB. d For NS; If the tension deviation e is NB and the rate of change of tension deviation ec is NS, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK for NM d For NB; If the tension deviation e is NB and the rate of change of tension deviation ec is ZO, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The derivative adjustment coefficient is NM and ΔK d For NB; If the tension deviation e is NB and the rate of change of tension deviation ec is PS, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NB; If the tension deviation e is NB and the rate of change of tension deviation ec is PM, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For NM; If the tension deviation e is NB and the rate of change of tension deviation ec is PB, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For PS; If the tension deviation e is NM and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NB. d For PS; If the tension deviation e is NM and the rate of change of tension deviation ec is NM, then the proportional adjustment coefficient ΔK p PB, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NB. d For NS; If the tension deviation e is NM and the rate of change of tension deviation ec is NS, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The derivative adjustment coefficient is NM and ΔK d For NB; If the tension deviation e is NM and the rate of change of tension deviation ec is ZO, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NM; If the tension deviation e is NM and the rate of change of tension deviation ec is PS, then the proportional adjustment coefficient ΔK p PS, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NS. d For NM; If the tension deviation e is NM and the rate of change of tension deviation ec is PM, then the proportional adjustment coefficient ΔK p ZO, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d For NS; If the tension deviation e is NM and the rate of change of tension deviation ec is PB, then the proportional adjustment coefficient ΔK p NS, integral adjustment coefficient ΔK i ZO and the differential adjustment coefficient ΔK d ZO; If the tension deviation e is NS and the rate of change of tension deviation ec is NB, then the proportional adjustment coefficient ΔK p PM, integral adjustment coefficient ΔK i The differential adjustment coefficient ΔK is NB. d It is ZO.

8. The control method for the tensioning device of a belt conveyor according to claim 1, characterized in that, The control system calculates the minimum operating tension of the conveyor belt in real time as the target tension value, which is within the target tension range.

9. The control method for the tensioning device of a belt conveyor according to claim 8, characterized in that, The working states include start-up state, running state, and emergency stop state, and each of the start-up state, running state, and emergency stop state corresponds to a range of values. The transport volume status includes empty, half-loaded, and fully loaded status, and each of the empty, half-loaded, and fully loaded statuses corresponds to a range of values. The speed states include low speed, medium speed, and high speed, and each of the low speed, medium speed, and high speed states corresponds to a range of values. When configuring the target tension range according to the working state of the belt conveyor, the system determines the target tension range based on the target tension value and the interval value corresponding to the current working state; When configuring the target tension range according to the conveyor's capacity status, the system determines the target tension range based on the target tension value and the interval value corresponding to the current capacity status. When configuring the target tension range according to the speed state of the belt conveyor, the system determines the target tension range based on the target tension value and the interval value corresponding to the current speed state.

10. The control method for the tensioning device of a belt conveyor according to claim 6, characterized in that, The belt conveyor tensioning device also includes a frequency converter, a motor, a tensioning drum, and a pulley block. The frequency converter is connected to the motor and the control system. The motor is connected to the tensioning drum through a second coupling. A steel wire rope is wound around the tensioning drum and passes over the pulley block. The PID control signal output by the PID controller is input to the frequency converter, which controls the working state of the motor. The motor controls the tension of the conveyor belt of the belt conveyor through the tensioning drum and pulley block.

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