Weightlessness type feeding control system

Abstract

The invention provides a weightlessness type feeder control system which comprises a model pre-control module. The model pre-control module controls a motor to drive a screw to rotate at a fixed speed within a preset time and collects the weight of materials conveyed by the screw. And performing formula operation according to the feedback weight information and a preset screw speed to obtain flow information or selecting according to parameters to obtain a flow unit of kilogram / hour. The method has the advantages that clutters are eliminated by filtering signal data fed back by the gravity sensor, data correction is performed on the feedback weight, and the control precision is effectively improved. Meanwhile, the MCP model is used for processing data, the motor is accurately controlled based on the given control quantity of the model, and the precision requirement of solid preparation continuous manufacturing equipment can be met. According to the scheme, the anti-disturbance capability is high, the robustness is good, the influence caused by mechanical or external slight disturbance can be resisted, the control precision is not influenced, meanwhile, the self-learning function is achieved, the control quantity can be predicted according to the model, and the overall precision is further improved.

Description

Technical Field

[0001] The present invention relates to the field of loss-in-weight feeding control systems, and more particularly to a loss-in-weight feeder control method and system. Background Art

[0002] Currently, most loss-in-weight feeding equipment on the market uses PID algorithm based on feedback control technology. PID control is based on the data of the previous data collection cycle and the set value is adjusted in sequence. It has a slow response and low accuracy, and cannot meet the accuracy requirements of continuous manufacturing of solid dosage equipment. Summary of the invention

[0003] The invention overcomes the deficiencies in the prior art and provides a loss-in-weight feeding control system.

[0004] The purpose of the present invention is achieved through the following technical solutions.

[0005] Loss-in-weight feeder control system, including: Model pre-control module, The model pre-control module controls the vacuum feeder to refill the storage barrel to the full weight value preset by the parameters. When the barrel is full, the motor is controlled to rotate the screw at the preset speed value to collect and record the flow rate and weight information of the material conveyed by the screw at fixed intervals until the barrel weight reaches the preset value of automatic refilling, and then the screw stops running. The acquired data is restored to flow information by collecting weight data / preset time, and a complete digital model of the flow curve is obtained with time data as the horizontal coordinate and flow information as the vertical coordinate, realizing the self-learning function; The model pre-control module controls the motor to drive the screw to rotate at a fixed speed within the preset time and collects the weight of the material conveyed by the screw and the current time in real time. The actual operating speed value is calculated based on the collected feedback data. The speed of the feeder servo motor is controlled according to the actual operating speed value to make it close to the preset fixed speed value, thereby realizing the model pre-control function.

[0006] The obtained flow curve digital model is scaled by using a digital scaling algorithm to obtain the flow curve digital model corresponding to different flow setting values.

[0007] Actual operating speed value = [current speed value + (reference value - (current flow value / model flow value * reference value))] * pre-control value coefficient Among them, the reference value is the theoretical calculated value of the speed at the current flow rate. The current speed value is the real-time speed value of the current operation. The model flow value is the flow information value stored in the digital model of the flow curve at that time node. The current flow value is (the collection weight of the previous time node - the collection weight of the current time node) * the time difference between the two time nodes, The pre-control value coefficient is the speed control coefficient value set according to different material densities.

[0008] The weight data measurement module estimates the weight at that moment calculated by the preset flow data of the system through the flow curve digital model obtained by the model pre-control module under the minimum external disturbance environment during self-learning to obtain the weight estimation value, performs bubble sorting on the multiple data collected by the time axis data at that moment, and selects 12 values ​​that meet the set coefficients according to the estimated value for the sorted weight value to perform de-jittering filtering and median value filtering, and uses the filtered value as the flow calculation value; The external disturbance control module uses the flow value obtained after filtering in the weight data measurement module as the actual flow value, compares it with the set value corresponding to that moment in the flow curve digital model of the model pre-control module, and adjusts the servo motor speed in real time through spatial approximation to make the actual flow value always close to the error range of the set value.

[0009] Automatic feeding control module, The filtered weight value in the weight data measurement module is compared with the feeding weight value set by the system. When the filtered weight value is less than the feeding weight value set by the system, the speed is calculated according to the numerical value of the flow curve digital model of the model pre-control module combined with the feeding speed prediction coefficient to obtain the estimated speed value, the feeder servo motor is controlled to adjust the speed according to the estimated speed value, the vacuum feeder is turned on to feed the storage barrel, and the vacuum feeder is closed when the storage barrel is full of material, and the filtered weight value in the weight data measurement module is detected. When the weight value decreases linearly, the predicted speed setting mode is exited, and the external disturbance control module adjusts the speed of the feeder servo motor.

[0010] Manual feeding module, used to run the feeder motor at a set fixed speed; Automatic control module, used to automatically open the stirring motor contactor to control the start and stop of the stirring motor; The manual control module is used to start and stop the stirring motor contactor through a manual external control device.

[0011] The beneficial effects of the present invention are: The present invention provides a loss-in-weight feeder control method and system, which effectively improves the control accuracy by filtering the signal data fed back by the gravity sensor to eliminate clutter and correcting the feedback weight. At the same time, the MCP model is used to process the data, and the motor is accurately controlled based on the given control amount of the model, which can meet the accuracy requirements of the continuous manufacturing solid preparation equipment.

[0012] This solution has strong anti-disturbance capability and good robustness. It can resist the influence caused by mechanical or external slight disturbances without affecting the control accuracy. At the same time, it has a self-learning function and can predict the control quantity according to the model to further improve the overall accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a work flow chart of the present invention. DETAILED DESCRIPTION

[0014] The technical solution of the present invention is further described below through specific embodiments.

[0015] Example In this embodiment, the module construction hardware adopts the AC702 intelligent controller of Inovance in the weightless feeding equipment and is equipped with the GL20 series IO expansion module of Inovance.

[0016] The loss-in-weight feeding equipment uses a 380V AC power supply to provide the main power supply, a three-phase five-wire standard power supply circuit, and a built-in 220V to DC24V switching power supply for the central controller, human-machine interface, and sensors.

[0017] like Figure 1 As shown in the figure, the working process of the loss-in-weight feeding equipment is as follows: S1. Initialize the system.

[0018] S2, detect the sensor signal, if the signal is abnormal, the system will be locked and the system alarm will be issued as a prompt. If the signal is normal, the system will enter the working system.

[0019] S3. Enter the working system and judge the system control according to the manual / automatic selection mode.

[0020] S4. Select the automatic working mode and check the linkage signal of the production line. If the linkage signal is on, it will enter the automatic operation program. If the linkage signal is off, it will enter the standby preparation program.

[0021] S5. Enter the automatic operation program and check whether the material level is within the set range. If it is within the set range, start the screw operation program. If it is not within the set material level range, perform the material filling action.

[0022] S6. After the screw program is running, the model pre-control module, namely the MCP model pre-control module, is executed to adjust the screw speed.

[0023] S7: When the material level is lower than the set value, the material replenishment operation starts, and the screw feeding does not stop.

[0024] S8. After executing the manual program, control the operation of each operating mechanism according to the manual operation instructions.

[0025] Model pre-control module, The model pre-control module controls the vacuum feeder to refill the storage barrel to the full weight value preset by the parameters. When the barrel is full, the motor is controlled to rotate the screw at the preset speed value to collect and record the flow rate and weight information of the material conveyed by the screw at fixed intervals until the barrel weight reaches the preset value of automatic refilling, and then the screw stops running. The acquired data is restored to flow information by collecting weight data / preset time, and a complete digital model of the flow curve is obtained with time data as the horizontal coordinate and flow information as the vertical coordinate, realizing the self-learning function; The model pre-control module controls the motor to drive the screw to rotate at a fixed speed within the preset time and collects the weight of the material conveyed by the screw and the current time in real time. The actual operating speed value is calculated based on the collected feedback data. The speed of the feeder servo motor is controlled according to the actual operating speed value to make it close to the preset fixed speed value, thereby realizing the model pre-control function.

[0026] Compared with the prior art, the present invention adds a model pre-control module, which obtains the digital model of the flow curve through the self-learning function, and calculates the actual operating speed value according to the digital model of the flow curve and the feedback flow information during system operation. The speed of the feeder servo motor is controlled according to the value to meet the precision requirements of the continuous manufacturing solid preparation equipment.

[0027] The obtained flow curve digital model is scaled by using a digital scaling algorithm to obtain the flow curve digital model corresponding to different flow setting values.

[0028] Actual operating speed value = [current speed value + (reference value - (current flow value / model flow value * reference value))] * pre-control value coefficient Among them, the reference value is the theoretical calculated value of the speed at the current flow rate. The current speed value is the real-time speed value of the current operation. The model flow value is the flow information value stored in the digital model of the flow curve at that time node. The current flow value is (the collection weight of the previous time node - the collection weight of the current time node) * the time difference between the two time nodes, The pre-control value coefficient is the speed control coefficient value set according to different material densities.

[0029] The weight data measurement module estimates the weight at that moment calculated by the preset flow data of the system through the flow curve digital model obtained by the model pre-control module under the minimum external disturbance environment during self-learning to obtain the weight estimation value, performs bubble sorting on the multiple data collected by the time axis data at that moment, and selects 12 values ​​that meet the set coefficients according to the estimated value for the sorted weight value to perform de-jittering filtering and median value filtering, and uses the filtered value as the flow calculation value; The external disturbance control module uses the flow value obtained after filtering in the weight data measurement module as the actual flow value, compares it with the set value corresponding to that moment in the flow curve digital model of the model pre-control module, and adjusts the servo motor speed in real time through spatial approximation to make the actual flow value always close to the error range of the set value.

[0030] Furthermore, in order to overcome the technical problem that the feedback value has noise due to external disturbance, thus affecting the overall accuracy, a weight data measurement module and an external disturbance control module are set up, and the data is de-jittered and filtered by the median value, and the value obtained after filtering is used as the flow calculation value, so as to achieve the purpose of predicting and compensating for external disturbances, and ensure the batching accuracy and stable output.

[0031] Automatic feeding control module, The filtered weight value in the weight data measurement module is compared with the feeding weight value set by the system. When the filtered weight value is less than the feeding weight value set by the system, the speed is calculated according to the numerical value of the flow curve digital model of the model pre-control module combined with the feeding speed prediction coefficient to obtain the estimated speed value, the feeder servo motor is controlled to adjust the speed according to the estimated speed value, the vacuum feeder is turned on to feed the storage barrel, and the vacuum feeder is closed when the storage barrel is full of material, and the filtered weight value in the weight data measurement module is detected. When the weight value decreases linearly, the predicted speed setting mode is exited, and the external disturbance control module adjusts the speed of the feeder servo motor.

[0032] Among them, the feeding speed prediction coefficient is manually set according to the filling material.

[0033] The automatic feeding control module monitors the material remaining in the feeder in real time and automatically replenishes the material when the remaining amount is lower than the preset value. The feeding program performs feeding operations according to the preset feeding rate, and at the same time, the feeding rate is precisely controlled by adjusting the parameters of the servo control system. The material mixing program fully mixes the material at the preset mixing rate and mixing time to ensure the uniformity and fluidity of the material. The alarm program automatically triggers an alarm when an abnormal situation occurs, such as insufficient material, equipment failure, etc., to remind the operator to deal with it in time.

[0034] Manual feeding module, used to run the feeder motor at a set fixed speed; Automatic control module, used to automatically open the stirring motor contactor to control the start and stop of the stirring motor; The manual control module is used to start and stop the stirring motor contactor through a manual external control device.

[0035] The manual feeding module, automatic control module and manual control module correspond to the operations of manual feeding, automatic control and manual control in the system respectively, realizing the mode of freely switching between manual and automatic control devices.

[0036] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. Loss-in-weight feeder control system, characterized in that: include: Model pre-control module, The model pre-control module controls the vacuum feeder to refill the storage barrel to the full weight value preset by the parameters. When the barrel is full, the motor is controlled to rotate the screw at the preset speed value to collect and record the flow rate and weight information of the material conveyed by the screw at fixed intervals until the barrel weight reaches the preset value of automatic refilling, and then the screw stops running. The acquired data is restored to flow information by collecting weight data / preset time, and a complete digital model of the flow curve is obtained with time data as the horizontal coordinate and flow information as the vertical coordinate, realizing the self-learning function; The model pre-control module controls the motor to drive the screw to rotate at a fixed speed within the preset time and collects the weight of the material conveyed by the screw and the current time in real time. The actual operating speed value is calculated based on the collected feedback data. The speed of the feeder servo motor is controlled according to the actual operating speed value to make it close to the preset fixed speed value, thereby realizing the model pre-control function.

2. The loss-in-weight feeder control system according to claim 1, characterized in that: The obtained flow curve digital model is scaled by using a digital scaling algorithm to obtain the flow curve digital model corresponding to different flow setting values.

3. The loss-in-weight feeder control system according to claim 1, characterized in that: Actual operating speed value = [current speed value + (reference value - (current flow value / model flow value * reference value))] * pre-control value coefficient Among them, the reference value is the theoretical calculated value of the speed at the current flow rate. The current speed value is the real-time speed value of the current operation. The model flow value is the flow information value stored in the digital model of the flow curve at that time node. The current flow value is (the collection weight of the previous time node - the collection weight of the current time node) * the time difference between the two time nodes, The pre-control value coefficient is the speed control coefficient value set according to different material densities.

4. The loss-in-weight feeder control system according to claim 1, characterized in that: include: The weight data measurement module estimates the weight at that moment calculated by the preset flow data of the system through the flow curve digital model obtained by the model pre-control module under the minimum external disturbance environment during self-learning to obtain the weight estimation value, performs bubble sorting on the multiple data collected by the time axis data at that moment, and selects 12 values ​​that meet the set coefficients according to the estimated value for the sorted weight value to perform de-jittering filtering and median value filtering, and uses the filtered value as the flow calculation value; The external disturbance control module uses the flow value obtained after filtering in the weight data measurement module as the actual flow value, compares it with the set value corresponding to that moment in the flow curve digital model of the model pre-control module, and adjusts the servo motor speed in real time through spatial approximation to make the actual flow value always close to the error range of the set value.

5. The loss-in-weight feeder control system according to claim 4, characterized in that: include: Automatic feeding control module, The filtered weight value in the weight data measurement module is compared with the feeding weight value set by the system. When the filtered weight value is less than the feeding weight value set by the system, the speed is calculated according to the numerical value of the flow curve digital model of the model pre-control module combined with the feeding speed prediction coefficient to obtain the estimated speed value, the feeder servo motor is controlled to adjust the speed according to the estimated speed value, the vacuum feeder is turned on to feed the storage barrel, and the vacuum feeder is closed when the storage barrel is full of material, and the filtered weight value in the weight data measurement module is detected. When the weight value decreases linearly, the predicted speed setting mode is exited, and the external disturbance control module adjusts the speed of the feeder servo motor.

6. The loss-in-weight feeder control system according to claim 1, characterized in that: include: Manual feeding module, used to run the feeder motor at a set fixed speed; Automatic control module, used to automatically open the stirring motor contactor to control the start and stop of the stirring motor; The manual control module is used to start and stop the stirring motor contactor through a manual external control device.

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