Control method for tensioning device of belt conveyor

Abstract

The invention discloses a control method for a tensioning device of a belt conveyor, and relates to the field of tensioning device control, and the tensioning device of the belt conveyor comprises a control system and a tension sensor, the belt conveyor tensioning device control method comprises the steps that the control system obtains a target tension interval, a target tension value and a real-time tension value measured by a tension sensor, and the real-time tension value is compared with the target tension interval; when the real-time tension value exceeds a target tension interval, the control system carries out fuzzy PID control on the tension of the conveying belt of the belt conveyor according to the target tension value and the real-time tension value, and the target tension interval is configured by the control system according to the working state, the transportation volume state or the speed state of the belt conveyor. According to the control method, the working loss of the belt conveyor can be reduced, the transportation efficiency is improved, and the working stability is enhanced.

Description

Technical Field

[0001] The invention relates to the field of tensioning device control, in particular to a belt conveyor tensioning device control method. Background Art

[0002] Coal is one of the main fossil energy sources and an important cornerstone and guarantee of energy supply. As a key large-scale operating equipment in the coal production process, belt conveyors play an important role in the long-distance, efficient and safe transportation of coal due to their good stability, continuity and low cost. However, with the emergence of high-yield and high-efficiency mines in my country, traditional belt conveyors cannot meet the requirements of high production and high efficiency in terms of main parameters or operating performance, and there is a lot of energy waste in the working process.

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

[0004] In view of the above problems and technical requirements, the inventors have proposed a belt conveyor tensioning device control method. The technical solution of the present invention is as follows:

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

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

[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 of the belt conveyor according to the target tension value and the real-time tension value, wherein:

[0008] The target tension interval is configured by the control system according to the working state, transportation state or speed state 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 tension deviation change rate ec are calculated according to the target tension value and the real-time tension value;

[0011] The fuzzy controller outputs an adjustment coefficient according to the tension deviation e and the tension deviation change rate ec;

[0012] The PID controller determines the control coefficient according to the adjustment coefficient, and outputs a PID control signal according to the control coefficient and the tension deviation e, wherein:

[0013] The control coefficient includes a proportional control coefficient K p , integral control coefficient K i And the differential control coefficient K d , the adjustment coefficient includes a 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 is the initial proportional control coefficient, K i0 is the initial integral control coefficient, K d0 is the initial differential control coefficient.

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

[0017] Determine the input domain and the output domain, map the tension deviation e and the tension deviation change rate ec to the input domain respectively, and obtain the fuzzy input vector according to the input domain;

[0018] Establish fuzzy control rules, perform fuzzy reasoning 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 is that when obtaining the fuzzy input vector according to the input domain, it includes:

[0021] Determine an input fuzzy language set and an input membership function, determine the membership of each element in the input domain to each fuzzy language value in the input fuzzy language set according to the input membership function, so as to construct an input membership assignment table, and determine the fuzzy input vector according to the input membership assignment table;

[0022] Before establishing fuzzy control rules, it also includes determining the output fuzzy language set and the output membership function, and determining the membership of each element in the output domain to each fuzzy language value in the output fuzzy language set according to the output membership function to construct an output membership assignment table.

[0023] A further technical solution is that the fuzzy control rule includes a plurality of fuzzy conditional statements, and when fuzzy reasoning is performed according to the fuzzy control rule and the fuzzy input vector, it includes:

[0024] Determine the sub-fuzzy relation matrix corresponding to each fuzzy conditional statement in the fuzzy control rule according to the input membership value assignment table and the output membership value assignment table, and determine the total fuzzy relation matrix R according to all the sub-fuzzy relation matrices;

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

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

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

[0028] The input domain is: {-4,-3,-2,-1,0,1,2,3,-4}; the output domain 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 tension deviation change rate ec is NB, then the proportional adjustment coefficient ΔK p is PB, integral adjustment coefficient ΔK i is NB and the differential adjustment coefficient ΔK d For PS;

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

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

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

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

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

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

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

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

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

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

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

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

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

[0044] If the tension deviation e is NS and the tension deviation change rate ec is NB, then the proportional adjustment coefficient ΔK p is PM, integral adjustment coefficient ΔK i is NB and the differential adjustment coefficient ΔK d For 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 a target tension value, and the target tension value is within a target tension range.

[0046] A further technical solution is that the working state includes a start state, a running state and an emergency stop state, and the start state, the running state and the emergency stop state correspond to an interval value respectively;

[0047] The transport capacity state includes an empty state, a half-loaded state and a full-loaded state, and the empty state, the half-loaded state and the full-loaded state correspond to an interval value respectively;

[0048] The speed state includes a low speed state, a medium speed state and a high speed state, and the low speed state, the medium speed state and the high speed state correspond to an interval value respectively;

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

[0050] When configuring the target tension interval according to the transport state of the belt conveyor, the system determines the target tension interval according to the target tension value and the interval value corresponding to the current transport state;

[0051] When configuring the target tension interval according to the speed state of the belt conveyor, the system determines the target tension interval according to 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 further 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 the steel wire rope is wound around the pulley block at the same time;

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

[0054] The beneficial technical effects of the present invention are:

[0055] (1) In order to avoid a large amount of work loss caused by the tensioning device frequently adjusting the tension of the conveyor belt of the belt conveyor, 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 fuzzy controller is used to adjust the control coefficient of the PID controller, thereby improving the response speed of the tension adjustment, enhancing the dynamic adjustment effect of the tension, and improving the transportation efficiency of the belt conveyor.

[0057] (3) The target tension range can be adjusted according to the working state / volume state / speed state of the belt conveyor, and smooth tension adjustment can be achieved when the working state / volume state / speed state changes, thereby reducing the impact of emergency stop and other working conditions on the belt conveyor and improving its operating stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0060] Figure 3 It is a structural schematic diagram of an embodiment of the tensioning device provided by the present invention.

[0061] Figure 4 It is a structural frame diagram of an embodiment of the tensioning device provided by the present invention.

[0062] Figure 5 It is a function diagram of an embodiment of the input membership function provided by the present invention.

[0063] Figure 6 It is a function diagram of an embodiment of the output membership function provided by the present invention.

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

[0065] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0066] The present invention provides a belt conveyor tensioning device control method, the belt conveyor tensioning device comprises a control system and a tension sensor 7, the belt conveyor tensioning device control method comprises:

[0067] The control system obtains the target tension interval, 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 interval;

[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 according to the target tension value and the real-time tension value, wherein:

[0069] The target tension interval is configured by the control system according to the working state, transportation state or speed state of the belt conveyor.

[0070] Specifically, during the operation of the belt conveyor, the tension on the conveyor belt is always in a slightly fluctuating state. For the tensioning device, if a certain set value is used as the tensioning target, the tensioning device will frequently operate, which will cause a lot of unnecessary losses. Therefore, 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, so as to reduce 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, so that 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 the 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 the normal operation of the belt conveyor under the current working conditions. The minimum operating tension can usually be calculated according to the belt transmission theory. The calculation method of the minimum operating tension adopted by the present invention is consistent with the prior art and will not be repeated here.

[0072] The target tension interval described in the present invention is configured according to the working state, transportation state or speed state of the belt conveyor, that is, the target tension interval described in the present invention changes dynamically according to the working condition of the belt conveyor, and the dynamically changing target tension interval can better adapt to the working condition change of the belt conveyor to achieve a better tension adjustment effect. The specific method of the present invention for fuzzy PID control of the tension of the conveyor belt of the belt conveyor and the specific method of configuring the target tension interval can refer to the following description.

[0073] Further, the working state includes a start state, a running state and an emergency stop state, and the start state, the running state and the emergency stop state correspond to an interval value respectively;

[0074] The transport capacity state includes an empty state, a half-loaded state and a full-loaded state, and the empty state, the half-loaded state and the full-loaded state correspond to an interval value respectively;

[0075] The speed state includes a low speed state, a medium speed state and a high speed state, and the low speed state, the medium speed state and the high speed state correspond to an interval value respectively;

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

[0077] When configuring the target tension interval according to the transport state of the belt conveyor, the system determines the target tension interval according to the target tension value and the interval value corresponding to the current transport state;

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

[0079] Specifically, the control system includes a PLC, and the control system is connected to the main control system of the belt conveyor to collect the belt speed, transportation volume, power-on state, working voltage and other working parameters of the belt conveyor. When the control system configures the target tension interval according to the working state of the belt conveyor, the control system can automatically determine whether it is in the starting state, running state or emergency stop state according to the collected working voltage; similarly, when the control system configures the target tension interval according to the transportation volume state of the belt conveyor, the control system can automatically determine whether it is in the no-load state, half-load state or full-load state according to the collected transportation volume; when the control system configures the target tension interval according to the speed state of the belt conveyor, the control system can automatically determine whether it is in the low-speed state, medium-speed state or high-speed state according to the collected belt speed. The working voltage range corresponding to the starting state, running state and emergency stop state, the transportation volume range corresponding to the no-load state, half-load state and full-load state, and the belt speed range corresponding to the low-speed state, medium-speed state and high-speed state 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 a possible embodiment, the control system can use the current target tension value as the minimum value of the target tension interval, and use 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, and determine the range between the minimum value and the maximum value 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 tension deviation change rate ec are calculated according to the target tension value and the real-time tension value;

[0083] The fuzzy controller outputs an adjustment coefficient according to the tension deviation e and the tension deviation change rate ec;

[0084] The PID controller determines the control coefficient according to the adjustment coefficient, and outputs a PID control signal according to the control coefficient and the tension deviation e, wherein:

[0085] The control coefficient includes a proportional control coefficient K p , integral control coefficient K i And the differential control coefficient K d , the adjustment coefficient includes a 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 is the initial proportional control coefficient, K i0 is the initial integral control coefficient, K d0 is the initial differential control coefficient.

[0088] Specifically, Figure 2 The control principle diagram of fuzzy PID is shown. The input variables of the fuzzy controller are tension deviation e and tension deviation change rate ec, and the output variable of the fuzzy controller is the adjustment coefficient used to adjust the PID control coefficient. Tension deviation e = target tension value - real-time tension value, and tension deviation change rate ec is the rate of change of tension deviation e over time. Fuzzy PID control combines fuzzy control and PID control through fuzzy controller and PID controller, so that both control methods can play their advantages and achieve a control effect with good dynamics and stability.

[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 taken according to actual needs. The PID control signal u(t) can be expressed as:

[0090]

[0091] Furthermore, the belt conveyor tensioning device further includes a frequency converter 14, a motor 13, a tensioning roller 11 and a pulley block 5, wherein the frequency converter 14 is connected to the motor 13 and a control system, and the motor 13 is connected to the tensioning roller 11 through a second coupling, and a steel wire rope 6 is wound around the tensioning roller 11, and the steel wire rope 6 is wound around the pulley block 5 at the same time;

[0092] The PID control signal output by the PID controller is input to the frequency converter 14 , and the frequency converter 14 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 The structural block diagram of the tensioning device is shown in FIG. Figure 3-Figure 4As shown, the tensioning device also includes a brake hydraulic station 8, a brake 9 and a control box 15, the control system is arranged in the control box 15, the brake 9 is connected to the tensioning roller 11 through the 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 action of the brake 9 through the brake hydraulic station 8. In this embodiment, the motor 13 is a permanent magnet synchronous motor, the tension sensor 7 is connected to the wire rope 6 in the pulley block 5, and 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. The conveyor roller 4 is installed on the traveling trolley 2. The conveyor roller 4 is used to drive the conveyor belt 3 to run. The specific working process of the PID control signal to control the tension of the conveyor belt 3 is as follows: the PID control signal is output to the control end of the frequency converter 14, and the rotation direction of the motor 13 is controlled by controlling the output frequency of the frequency converter 14. The motor 13 drives the tensioning roller 11 to rotate. The steel wire rope 6 on the tensioning roller 11 bypasses the pulley block 5 to pull the traveling trolley 2, thereby driving the conveyor roller 4 on the traveling trolley 2 to slide along the track 1 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 reversely, the conveyor belt 3 is relaxed, and the tension of the conveyor belt 3 decreases. The brake 9 is a necessary device for achieving tension maintenance and tension release. When the motor 13 rotates, the control system controls the brake 9 to release. The motor 13 can change the tension of the conveyor belt 3 through the tensioning roller 11, the pulley block 5 and the traveling trolley 2. After the motor 13 stops rotating, the control system controls the brake 9 to engage, and the tension of the conveyor belt 3 is maintained at the current value.

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

[0096] Determine an input domain and an output domain, and map the tension deviation e and the tension deviation change rate ec to the input domain respectively;

[0097] Determine an input fuzzy language set and an input membership function, determine the membership of each element in the input domain to each fuzzy language value in the input fuzzy language set according to the input membership function, so as to construct an input membership assignment table, and determine the fuzzy input vector according to the input membership assignment table;

[0098] Determine the output fuzzy language set and the output membership function, determine the membership of each element in the output domain to each fuzzy language value in the output fuzzy language set according to the output membership function, so as to construct an output membership assignment table;

[0099] A fuzzy control rule is established, and fuzzy reasoning is performed according to the fuzzy control rule and the fuzzy input vector to obtain a fuzzy adjustment coefficient mapped to an output domain; and the fuzzy adjustment coefficient is clarified to obtain an adjustment coefficient.

[0100] Specifically, it can be seen from the above description that the input variables and output variables of the fuzzy controller are both precise numerical values, but the fuzzy controller needs to perform fuzzy reasoning based on the fuzzy values. Therefore, the input / output variables must be fuzzified first, and the fuzzy output values, namely the fuzzy adjustment coefficients, must be output based on the fuzzified input variables. Then the fuzzy adjustment coefficients must be clarified to obtain the adjustment coefficients.

[0101] The input variable is fuzzified according to the input domain and the input fuzzy language set, and the output variable is fuzzified according to the output domain and the output fuzzy language set. The input domain and the output domain are both discrete finite domains. The input domain is determined according to the variation range of the input variable, that is, the variation range of the tension deviation e and the tension deviation variation rate ec, and the output domain is determined according to the variation range of the output variable, that is, the variation range of the fuzzy adjustment coefficient. The variation range of the input variable / output variable can be set according to actual needs.

[0102] In one embodiment of the present invention, the input variables are divided into 9 levels within the range of variation of the input variables, and the output variables are divided into 7 levels within the range of variation of the output variables, that is, the input domain is defined as: {-4, -3, -2, -1, 0, 1, 2, 3, -4}, and the output domain is defined as: {-3, -2, -1, 0, 1, 2, 3}.

[0103] The input / output exact values ​​within the input / output variable variation range can be mapped to the input / output domain, that is, each element in the input / output domain corresponds to a certain range of values ​​within the input / output variable variation range from small to large. The method of mapping the input / output exact values ​​to the input / output domain can be the same as the prior art, and the range of the input / output variables corresponding to each element in the input / output domain can be set according to the actual application situation.

[0104] In order to fuzzify the input / output variables, it is also necessary to establish a relationship between the elements in the input / output domain and the input / output fuzzy language set, that is, to determine the membership of each element in the input / output domain to each fuzzy language value in the input / output fuzzy language set according to the input / output membership function, so as to establish the 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 fuzzy language values ​​commonly used 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 express that the tension deviation e / tension deviation change rate ec is negative and large, negative medium is used to express that the tension deviation e / tension deviation change rate ec is negative and medium, and negative small is used to express that the tension deviation e / tension deviation change rate ec is negative and small. Close to zero is used to express that the tension deviation e / tension deviation change rate ec is close to zero. Similarly, positive small is used to express that the tension deviation e / tension deviation change rate ec is positive and small, positive medium is used to express that the tension deviation e / tension deviation change rate ec is positive and medium, and positive large is used to express that the tension deviation e / tension deviation change rate ec is positive and large.

[0106] The value of the input / output membership function indicates the degree to which a certain element in the input / output domain belongs to a certain fuzzy language value, i.e., the membership degree. The membership degree is represented by a numerical value between 0 and 1. The closer the membership degree is to 1, the higher the degree to which the element belongs to the fuzzy language value. The input / output membership function generally adopts a trapezoidal function, a trigonometric function, a Gaussian function, etc. In one embodiment of the present invention, the output membership function adopts a trigonometric function. Considering the large fluctuation of the input variable, the input membership function adopts a combination of a trapezoidal function and a trigonometric function to better fuzzify the input variable.

[0107] Figure 5-Figure 6 The function diagrams of the input membership function and the output membership function are shown respectively. Figure 5 The horizontal axis is the element of the input domain corresponding to the input variable, and the vertical axis is the degree of membership. Figure 6 The horizontal axis is the element of the output domain corresponding to the output variable, and the vertical axis is the membership. By calculating the membership function value of each element in the input domain for each fuzzy language value in the input fuzzy language set, the input membership assignment table can be obtained. The input membership assignment table is shown in Table 1.

[0108] Table 1 Input membership assignment table

[0109]

[0110] The fuzzy input vector includes a fuzzy input vector of tension deviation e and a fuzzy input vector of tension deviation change rate ec. Consistent with the prior art, the input fuzzy vector is composed of the membership of the input variable to the corresponding fuzzy language value. The fuzzy input vector is determined according to the input membership assignment table, specifically referring to a row vector formed according to all memberships of the fuzzy language values ​​of the input variable (tension deviation e / tension deviation change rate ec) in the input domain. If the fuzzy language value of tension deviation e is NB, the fuzzy input vector of tension deviation e is (1.0, 1.0, 0, 0, 0, 0, 0, 0).

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

[0112] Table 2 Output membership assignment table

[0113]

[0114] In order for the PID controller to reasonably adjust the tension, it is expected that the control coefficient of the PID controller follows the following variation law:

[0115] When the tension deviation e is too large, in order to quickly remove the deviation, increase the speed of reaching stability, and avoid large overshoot, the proportional control coefficient K p Should be set to a larger value, integral control coefficient K i should be set to zero; when the tension deviation e is small, in order to continue to reduce the deviation and suppress the occurrence of overshoot and oscillation, the proportional control coefficient K p Should be reduced and the integral control coefficient K should be set i Take a smaller value; when the tension deviation e is close to zero, in order to eliminate the static error and ensure the stable operation of the tensioning device, the proportional control coefficient K p Should continue to decrease and ensure the integral control coefficient K i No change or increase.

[0116] Those skilled in the art will know that when the tension deviation e and the tension deviation change rate ec are both positive or negative, the direction of change of the controlled quantity, 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 and the resulting oscillation caused by the integral link, the proportional control coefficient K should be guaranteed. p A negative value is used to suppress the deviation of the controlled quantity and reduce the integral control coefficient K. i ; When the tension deviation e is large, and the tension deviation change rate ec has a different sign from 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 size of the tension value deviation change rate ec indicates how fast the tension deviation e changes. The larger the tension deviation change rate ec, the larger the proportional control coefficient K. p The smaller the value should be, the greater the integral control coefficient K i The larger the value should be; on the contrary, the smaller the tension deviation change rate ec is, the greater the proportional control coefficient K p The larger the value of the integral control coefficient K i It should be noted that when establishing the control rule according to the above-mentioned change law, it is necessary to consider both the tension deviation e and the tension deviation change rate ec for specific analysis.

[0117] Based on the above-mentioned changing rules, fuzzy control rules are established. The fuzzy control rules established in this embodiment include 15 fuzzy conditional statements:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0133] Fuzzy conditional statements are used to describe the fuzzy language value of the output variable after fuzzy inference under the given fuzzy language value of the input variable. Taking the first fuzzy conditional statement as an example, the fuzzy conditional statement indicates that under the conditions that the tension deviation e is NB (large negative) and the tension deviation change rate ec is NB (large negative), the proportional adjustment coefficient ΔK should be p is PB (positive), integral adjustment coefficient ΔK i NB (negative large) and the differential adjustment coefficient ΔK d PS (positive small). Where the input / output variable is a fuzzy language value, specifically, after the input / output variable is mapped to the input / output domain, the corresponding element in the input / output domain has the largest membership to the fuzzy language value. For example, the tension deviation e is NB, specifically, after the tension deviation e is mapped to the input domain, the corresponding element in the input domain has the largest membership to NB.

[0134] In practical applications, the above fuzzy rules are mainly expressed in the form of fuzzy control rule tables. Tables 3-5 are the proportional adjustment coefficients ΔK p Fuzzy control rule table, integral adjustment coefficient ΔK i Fuzzy control rule table, differential adjustment coefficient ΔK d Fuzzy control rules 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 fuzzy reasoning is performed according to the fuzzy control rule and the fuzzy input vector, it includes:

[0142] The sub-fuzzy relation matrices corresponding to each fuzzy conditional statement in the fuzzy control rule are determined according to the input membership assignment table and the output membership assignment table, and the total fuzzy relation matrix R is determined according to 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 reasoning adopts the Mamdani method, that is, a sub-fuzzy relationship matrix is ​​obtained according to each fuzzy conditional statement, and the total fuzzy relationship matrix is ​​the union of all sub-fuzzy relationship matrices. Consistent with the prior art, the sub-fuzzy relationship matrix corresponding to each fuzzy conditional statement is the Cartesian product of the input fuzzy vector and the output fuzzy vector corresponding to the fuzzy conditional statement. The output fuzzy vector is composed of the membership of the output variable in the fuzzy conditional statement to the corresponding fuzzy language value. The fuzzy output vector can be determined according to the output membership assignment table, that is, a row vector is formed according to all memberships corresponding to the fuzzy language values ​​of the output variables. The fuzzy adjustment coefficient is usually expressed in matrix form. 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 Cartesian operation, Represents a fuzzy composite operation.

[0144] The adjustment coefficient U can be obtained by performing a clearing process on the fuzzy adjustment coefficient U. The clearing process can be performed by a common method 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 only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0146] The above is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiments. It is understood that other improvements and changes directly derived or associated by those skilled in the art without departing from the spirit and concept of the present invention should be considered to be included in the protection scope of the present invention.

Claims

1. A belt conveyor tensioning device control method, characterized in that: The belt conveyor tensioning device includes a control system and a tension sensor, and the belt conveyor tensioning device control method includes: The control system acquires a target tension interval, a target tension value, and a real-time tension value measured by a tension sensor, and compares the real-time tension value with the target tension interval; 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 of the belt conveyor according to the target tension value and the real-time tension value, wherein: The target tension interval is configured by the control system according to the working state, transportation state or speed state of the belt conveyor.

2. The belt conveyor tensioning device control method 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 tension deviation change rate ec are calculated according to the target tension value and the real-time tension value; The fuzzy controller outputs an adjustment coefficient according to the tension deviation e and the tension deviation change rate ec; The PID controller determines the control coefficient according to the adjustment coefficient, and outputs a PID control signal according to the control coefficient and the tension deviation e, wherein: The control coefficient includes a proportional control coefficient K p , integral control coefficient K i And the differential control coefficient K d , the adjustment coefficient includes a 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 is the initial proportional control coefficient, K i0 is the initial integral control coefficient, K d0 is the initial differential control coefficient.

3. The belt conveyor tensioning device control method according to claim 2, characterized in that: When the fuzzy controller outputs the adjustment coefficient according to the tension deviation e and the tension deviation change rate ec, it includes: Determine the input domain and the output domain, map the tension deviation e and the tension deviation change rate ec to the input domain respectively, and obtain the fuzzy input vector according to the input domain; Establish fuzzy control rules, perform fuzzy reasoning 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 belt conveyor tensioning device control method according to claim 3, characterized in that: When obtaining the fuzzy input vector according to the input domain, it includes: Determine an input fuzzy language set and an input membership function, determine the membership of each element in the input domain to each fuzzy language value in the input fuzzy language set according to the input membership function, so as to construct an input membership assignment table, and determine the fuzzy input vector according to the input membership assignment table; Before establishing fuzzy control rules, it also includes determining the output fuzzy language set and the output membership function, and determining the membership of each element in the output domain to each fuzzy language value in the output fuzzy language set according to the output membership function to construct an output membership assignment table.

5. The belt conveyor tensioning device control method according to claim 3, characterized in that: The fuzzy control rule includes a plurality of fuzzy conditional statements. When fuzzy reasoning is performed according to the fuzzy control rule and the fuzzy input vector, it includes: The sub-fuzzy relation matrices corresponding to each fuzzy conditional statement in the fuzzy control rule are determined according to the input membership assignment table and the output membership assignment table, and the total fuzzy relation matrix R is determined according to 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 belt conveyor tensioning device control method according to claim 4, characterized in that: The input fuzzy language set and the output fuzzy language set are: {NB, NM, NS, ZO, PS, PM, PB}; The input domain is: {-4,-3,-2,-1,0,1,2,3,-4}; the output domain is: {-3,-2,-1,0,1,2,3}.

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

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

9. The belt conveyor tensioning device control method according to claim 8, characterized in that: The working state includes a start state, a running state and an emergency stop state, and the start state, the running state and the emergency stop state correspond to an interval value respectively; The transport capacity state includes an empty state, a half-loaded state and a full-loaded state, and the empty state, the half-loaded state and the full-loaded state correspond to an interval value respectively; The speed state includes a low speed state, a medium speed state and a high speed state, and the low speed state, the medium speed state and the high speed state correspond to an interval value respectively; When configuring the target tension interval according to the working state of the belt conveyor, the system determines the target tension interval according to the target tension value and the interval value corresponding to the current working state; When configuring the target tension interval according to the transport state of the belt conveyor, the system determines the target tension interval according to the target tension value and the interval value corresponding to the current transport state; When configuring the target tension interval according to the speed state of the belt conveyor, the system determines the target tension interval according to the target tension value and the interval value corresponding to the current speed state.

10. The belt conveyor tensioning device control method 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, wherein 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 the steel wire rope is wound around the pulley block at the same time; The PID control signal output by the PID controller is input to the frequency converter, and the working state of the motor is controlled by the frequency converter. The motor controls the tension of the conveyor belt of the belt conveyor through the tensioning roller and the pulley block.

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