Coating machine die head motor control method and system based on PID type iterative learning

Abstract

The invention discloses a coating machine die head motor control method and system based on PID type iterative learning, and belongs to the technical field of automatic control. The method comprises the following steps: discretizing a surface density error and an error change rate to form a plurality of intervals, and setting an initial coefficient according to different intervals; operating parameters of a coating machine system and data of a surface density weighing system and a die head motor are obtained in real time, and the current surface density error and the error change rate are calculated; querying a proportion parameter P corresponding to the interval, and inputting the proportion parameter P into a proportion controller to obtain a first adjusting quantity corresponding to each partition; querying a historical iteration record of a corresponding interval through a PID type iteration controller, and calculating a second adjusting quantity corresponding to each partition output by an iteration control part; and superposing the first regulating variable and the second regulating variable of each partition to generate a final regulating variable. According to the method, simplicity and easiness in implementation of traditional proportional control and adaptive characteristics of iterative control are considered, so that the control is more flexible and stable, and the cost is low.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic control, and in particular relates to a coating machine die head motor control method and system based on PID type iterative learning. Background Art

[0002] The coating machine is used to coat key components such as positive electrodes, negative electrodes and separators to ensure their performance and quality. Among them, for the extrusion coating machine, the lateral adjustment can be controlled by the motor to move the slider to accurately control the position of the coating head or nozzle to ensure that the coating material evenly covers the electrode material.

[0003] In the coating machine system, the classic PID and the deformed PID algorithms are widely used because they have the advantages of good stability and simple implementation, but these algorithms have difficulties such as difficult parameter adjustment. Although there are many nonlinear complex control systems at present, in the lateral closed-loop control of the coating machine, it is difficult to establish an accurate mathematical model because the parameter adjustment of the PID system in the motor control of the coating machine die head is too time-consuming, resulting in these algorithms being unable to achieve good results. At the same time, since the coefficient of PID is fixed, it cannot adapt to changes in working conditions. In order to overcome the difficulty of manual parameter debugging, PID type iterative control is directly adopted, which has the problems of large storage requirements and slow iterative updates. Summary of the invention

[0004] The purpose of the present invention is to provide a coating machine die head motor control method and system based on PID type iterative learning, which can save storage space and speed up the iteration speed.

[0005] The technical solution adopted by the present invention is as follows: A coating machine die head motor control method based on PID type iterative learning, comprising: S1. Discretize the surface density error and error change rate to form multiple intervals, set the initial coefficients of the proportional controller and PID iterative control according to different intervals, and load the configuration parameters saved at the last shutdown; S2, real-time acquisition of the operating parameters of the coating machine system, the surface density weight measurement system and the die head motor data, and after filtering the data, the current surface density error and error change rate are calculated according to the filtered data; S3, according to the current surface density error and error change rate, query the proportional parameter P corresponding to the interval in which it is located, and input it into the proportional controller to obtain the first adjustment amount corresponding to each partition; S4, according to the data processed by S2, query the historical iteration record of the corresponding interval through the PID type iteration controller, combine the current surface density error, error change rate and historical iteration record, and calculate the second adjustment amount corresponding to each partition output by the iterative control part; S5, superimposing the first adjustment amount and the second adjustment amount of each partition to generate a final adjustment amount and drive the die head motor of the corresponding partition to perform adjustment; S6. Execute steps S2-S5 in a loop.

[0006] In a preferred embodiment, the step of setting the coefficients of the proportional controller and the PID type iterative control in step S1 comprises: According to the production process parameters, the surface density error and error change rate are fuzzy processed and divided into multiple discrete intervals; Each discrete interval is independently associated with a proportional parameter P and a parameter of a PID-type iterative controller to achieve differentiated control between discrete intervals; The initial coefficients of the proportional controller and PID-type iterative control are set according to the corresponding discrete intervals.

[0007] In a preferred embodiment, the filtering of data in step S2 includes: abnormal data filtering and mean filtering; Among them, the abnormal data filtering adopts upper and lower limit filtering. When the data exceeds the upper or lower limit, the abnormal data is filtered out to exclude invalid data. Invalid data includes: coating machine die head advance and retreat, other coating machine abnormal time data and abnormal millimeter surface density data; The mean filter processing formula is as follows: Among them, m is the sliding window size, g is the input value, x is the input parameter, t is the time series number, and z is the data after mean filtering.

[0008] In a preferred embodiment, the step S2 calculates the surface density error and the error change rate based on the filtered data, including: The data after mean filtering is calculated with the target value of this production to obtain all current surface density errors; Calculate the surface density error and error change rate; The surface density error is calculated as follows: in, is the surface density error of partition r in the tth pass, is the surface density value of partition r in the tth pass, The target value of the area density specified for the production process; The error rate of change calculation is as follows: in, is the surface density error of partition r in the t-1th pass.

[0009] In a preferred embodiment, the historical iteration record includes the adjustment amount of the historical output of the iterative control part of each motor and the corresponding surface density error and error change rate.

[0010] In a preferred solution, obtaining the first adjustment amount corresponding to each partition in step S3 includes: Using the intervals corresponding to the surface density error and the error change rate, the corresponding proportional parameter P is queried, and then the adjustment amount of the proportional controller of each partition is calculated; in, is the regulation amount output by the proportional controller, is the proportional parameter of the proportional controller, which is obtained by querying the interval of the surface density error and the error change rate.

[0011] In a preferred solution, the PID-type iterative formula for calculating the second adjustment amount corresponding to each partition output by the iterative control part in step S4 is: in, , , are the three PID coefficient values ​​of PID iterative control, t is the tth adjustment, is the surface density error of the t-th adjustment of the r-th partition, is the error change rate of the tth adjustment of the rth partition, which is obtained by subtracting the last adjustment error and the current adjustment error in the interval corresponding to the surface density error and the error change rate. is the cumulative adjustment error of the rth partition, which is calculated by querying the historical adjustment error of the corresponding interval of the surface density error and the error change rate. is the adjustment value output by the iterative controller of the t-1th iteration of the rth partition, It is the adjustment value output by the iterative controller of the tth pass in the rth partition.

[0012] In a preferred embodiment, the adjustment amount of the proportional controller part in step S5 plus the adjustment amount of the iterative control part is the final adjustment amount. According to the final adjustment amount, the motor corresponding to each partition executes its corresponding adjustment amount. After execution, the result of this iteration is stored in the data storage for the next iteration.

[0013] The present invention also provides a coating machine die head motor control system based on PID type iterative learning, including a data acquisition module, a data storage device, a control algorithm unit and an execution module; Among them, the data acquisition module is used to obtain the data of the coating machine system operating parameters, the surface density weight measurement system and the die head motor in real time, and store the data in the data storage device. After filtering the data, it is used as the input of the control algorithm unit, and the execution module performs adjustment according to the output of the control algorithm unit.

[0014] The technical effects achieved by the present invention are: By discretizing the surface density error and error change rate, the continuous value is divided into multiple intervals, which reduces the data storage requirements and accelerates the iterative learning speed. This design effectively solves the problems of large memory usage and slow query speed caused by continuous state recording in traditional PID iterative control. It is especially suitable for the poor fluidity of high-viscosity materials in coating machines, making the impact of changes in the adjustment amount on the system more controllable.

[0015] The present invention adopts a segmented proportional control strategy to dynamically adjust the proportional parameters for different surface density errors and error change rate intervals, thereby enhancing the nonlinear fitting ability of the system. Combined with the historical error accumulation adjustment mechanism of PID type iterative control, dynamic compensation is achieved through the PID type iterative formula, so that the surface density error gradually converges in multiple iterations, thereby significantly improving the control accuracy.

[0016] The present invention combines the simplicity of traditional proportional control and the adaptability of iterative control, retaining the advantage of rapid response of proportional regulation and realizing parameter self-tuning through iterative learning. This method avoids the need for complex mathematical modeling, while optimizing the adjustment amount using historical data, ultimately achieving a comprehensive effect of flexible control, high stability and low hardware cost, meeting the dual requirements of reliability and economy in industrial scenarios. At the same time, the present invention indirectly reflects the error between the adjustment amount and the ideal adjustment amount through the error between the surface density and the target value of the surface density, and uses it to replace the error between the adjustment amount and the ideal adjustment amount, so that normal iteration can be performed under the premise that the ideal adjustment amount is unknown. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In the description of the present invention, it should be noted that the technical features involved in the different implementation methods of the present invention described below can be combined with each other as long as there is no conflict between them.

[0020] See also Figure 1 , a coating machine die motor control method based on PID type iterative learning, comprising the following steps: S1. Discretize the surface density error and error change rate to form multiple intervals, and set the initial coefficients of the proportional controller and PID iterative control according to different intervals, and load the configuration parameters saved at the last shutdown (parameters such as the last adjustment error); Among them, during coating production, the manufacturer's process personnel will design the production process parameters of this product. Therefore, the surface density error and the error change rate are fuzzy processed according to the process parameters, and divided into multiple discrete intervals (corresponding to the above discretization to form multiple intervals). Each discrete interval is independently associated with the proportional parameter P and the parameters of the PID type iterative controller, which can realize differentiated control between the intervals and store and query iterative data for iterative control. That is, different surface density error and error change rate intervals have independent proportional parameters P and iterative controller parameters, so that the initial coefficients of the proportional controller and PID type iterative control can be set according to the corresponding intervals, including the proportional coefficient, the three PID coefficient values ​​of the PID type iterative control, etc. S2, real-time acquisition of the operating parameters of the coating machine system, the surface density weight measurement system and the die head motor data, and after filtering the data, the current surface density error and error change rate are calculated according to the filtered data; Among them, filtering the data includes: abnormal data filtering and mean filtering processing; Abnormal data filtering uses upper and lower limit filtering. When the data exceeds the upper or lower limit, the abnormal data is filtered out to exclude invalid data, including: coating machine die head advance and retreat, other coating machine abnormal time data and abnormal millimeter surface density data; Mean filtering is mainly used to smooth data and reduce fluctuations. The processing formula is as follows: Among them, m is the sliding window size, g is the input value, x is the input parameter, t is the time series number, and z is the data after mean filtering.

[0021] The processed data is used to calculate the surface density error; in, is the surface density error of partition r in the tth pass, is the surface density value of partition r in the tth pass, The target value for the area density specified for the production process.

[0022] The error change rate is calculated as follows: is the surface density error of partition r in the t-1th pass.

[0023] After obtaining the surface density error and error change rate, query the interval of the proportional parameter table to obtain coefficient.

[0024] S3. According to the current surface density error and error change rate, query the proportional parameter P corresponding to the interval in which it is located, and record it as , and input into the proportional controller to obtain the first adjustment amount corresponding to each partition; Here, the first adjustment amount is the output corresponding to the proportional controller obtained through the proportional control formula; in, is the first adjustment value output by the proportional controller, is the coefficient value of the proportional controller, which is obtained by querying the interval of the surface error and the error change rate.

[0025] S4, according to the data processed by S2, query the historical iteration record of the corresponding interval through the PID type iteration controller, combine the current surface density error, error change rate and historical iteration record, and calculate the second adjustment amount corresponding to each partition output by the iterative control part; In this step, the iterative history records under the same state are queried according to the surface density error and the error change rate, such as the adjustment amount output by the iterative control part of each motor and the corresponding surface density error and error change rate, and then the second adjustment amount corresponding to each partition output by the iterative control part is calculated; At the same time, since the surface density error and the error change rate are continuous values, if the state is recorded in a continuous manner, there will be a problem, that is, the state space is too large, the iteration speed is slow and the memory requirement is large. Therefore, in this application, the surface density error and the error change rate are discretized into multiple intervals, which can save storage space and speed up the iteration speed. At the same time, since the production material of the coater is a high-viscosity liquid with poor fluidity, the flow state changes very little when one adjustment amount is added, which is also the main reason for discretizing the iteration space. However, the nonlinear ability of iterative control is low. In order to increase the nonlinear ability of control, the basic proportional control is segmented, that is, different intervals of surface density error and error change rate correspond to different basic proportional controls. This can increase the fitting ability of the control system and make the control more precise.

[0026] Specifically, the corresponding iteration parameters are queried in the data storage according to the interval of the current surface density error and the error rate, including the previous iteration item output and the historical adjustment error.

[0027] And because the state of the coating machine die head is complex, it is difficult to establish an effective system mathematical model. Therefore, the ideal adjustment amount of the motor control under different states is unknown.

[0028] In theory, iterative control uses the difference between the adjustment amount output by the controller and the ideal adjustment amount as the error of iterative learning. Since the ideal adjustment amount cannot be obtained, the error between the adjusted surface density and the target value of the surface density can indirectly reflect the error between the adjustment amount and the ideal adjustment amount. Therefore, it is used to replace the error between the adjustment amount and the ideal adjustment amount as the error of iterative learning. The PID type iteration formula is: in, , , are the three PID coefficient values ​​of PID iterative control, t is the tth adjustment, is the surface density error of the t-th adjustment of the r-th partition, is the error change rate of the tth adjustment of the rth partition, which is obtained by subtracting the last adjustment error and the current adjustment error in the interval corresponding to the surface density error and the error change rate. is the cumulative adjustment error of the rth partition, which is calculated by querying the historical adjustment error of the corresponding interval of the surface density error and the error change rate. is the adjustment value output by the iterative controller of the t-1th adjustment of the rth partition, The adjustment value output by the iterative controller of the tth adjustment of the rth partition.

[0029] S5, superimposing the first adjustment amount and the second adjustment amount of each partition to generate a final adjustment amount and drive the die head motor of the corresponding partition to perform adjustment, that is, obtained by the following formula; in, is the total adjustment amount of the tth adjustment in the rth partition.

[0030] Since the role of the proportional controller is an initial adjustment amount, and the adjustment amount output by the iterative controller is the iterative change amount, the adjustment amount executed by the final execution module is the sum of the adjustment amounts of the proportional controller and the iterative controller.

[0031] S6. Execute steps S2-S5 in a loop.

[0032] The actual calculation process of the algorithm of the present invention is explained below with reference to a calculation example. In this example, there are 25 partitions on the production line, and the data of one of the partitions is selected for display and analysis. These three adjustments are not continuous in time, and there will be adjustments in other intervals in between.

[0033] Table 1: Cumulative number of adjustments within the same range of surface density error and surface density error change rate.

[0034]

[0035] According to the process parameters, the fuzzification is performed, and the surface density error and the error change rate are discretized into 7 sets, corresponding to 36 intervals. Different intervals correspond to different proportional control parameters and iterative control storage spaces.

[0036] Parameters of the initial PID-type iterative controller , , Set them to 1.2, 0.01, and 0.2 respectively.

[0037] Table 2: Proportional parameters of proportional controller.

[0038]

[0039] When the algorithm is executed for the first time, the surface density error is -1.1, and the surface density error change rate is 1, as shown in Table 2. The proportional parameter table of the proportional controller is obtained. =6.

[0040] The adjustment amount of the proportional controller output = 6*(-1.1)=-6.6.

[0041] Since this is the first adjustment, t=0, i.e. the zeroth adjustment, it has no information about the previous adjustment, so The surface density error of the tth adjustment of the rth partition is , The error change rate of the t-th adjustment of the r-th partition is , The cumulative adjustment error of the rth partition is , so the iteration controller , , and are all 0. Therefore, the adjustment amount of this iterative controller is = 0.

[0042] The total adjustment amount of this output = + =-6.6+0=-6.6. After adjustment, the data collector detects that the error between the adjusted surface density and the target surface density is -0.5, which is the adjustment error. =-0.5.

[0043] When the algorithm is executed for the second time, the surface density error is -1.1, and the surface density error change rate is 1, as shown in Table 2. The proportional parameter table of the proportional controller is obtained. =6. Query the data memory and get the control error of the previous trip: =-0.5 and = 0.

[0044] The adjustment amount of the proportional controller output = 6*(-1.1)=-6.6.

[0045] The adjustment amount of the iterative controller = + * + * + * = 0 + 1.2 * (-0.5) + 0.01 * (-0.5) + 0.2 * (-0.5) = -0.705.

[0046] The total adjustment amount of this output = + =-0.705-6.6=-7.305. After adjustment, the data collector detects that the error between the adjusted surface density and the target surface density is 0.1, that is, =0.1.

[0047] When the algorithm is executed for the third time, the surface density error is -1.1, and the surface density error change rate is 1, as shown in Table 2. The proportional parameter table of the proportional controller is obtained. =6.

[0048] The adjustment amount of the proportional controller output = 6*(-1.1)=-6.6.

[0049] The adjustment amount of the iterative controller = + * + * + * =-0.705+ 1.2 * 0.1 + 0.01 * (-0.5 + 0.1) + 0.2 * (0.1- (-0.5)) =-0.469.

[0050] The total adjustment amount of this output = + =-0.469-6.6=-7.069. After adjustment, the data collector detects that the error between the adjusted surface density and the target surface density is 0, that is, =0.

[0051] In the present invention, since the PID system is too time-consuming to adjust the parameters in the motor control of the coating machine die head, and since the coefficient of PID is fixed, it cannot adapt to the changes in the working conditions. In order to overcome the difficulty of manual debugging of parameters, PID type iterative control is directly adopted, which has the problems of large storage requirements and slow iteration updates. Therefore, the present invention discretizes the surface density error and the error change rate, which can save storage space and speed up the iteration speed. At the same time, since the production material of the coating machine is a high-viscosity liquid with poor fluidity, the increase of 1 adjustment amount will cause its flow state to change very little, which is also the main reason for discretizing the iteration space. And in order to avoid the nonlinear ability of iterative control caused by the above operation and increase the nonlinear ability of control, the present application segments the basic proportional control, that is, different intervals of surface density error and error change rate correspond to different basic proportional controls. In this way, the fitting ability of the control system can be increased, making the control more precise.

[0052] The present invention also applies for protection of a coating machine die motor control system based on PID type iterative learning, comprising: a data acquisition module, a data storage device, a control algorithm unit and an execution module; Among them, the data acquisition module is used to obtain the data of the coating machine system operating parameters, the surface density weight measurement system and the die head motor in real time, and store the data in the data storage device. After filtering the data, it is used as the input of the control algorithm unit, and the execution module performs adjustment according to the output of the control algorithm unit.

[0053] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A coating machine die motor control method based on PID iterative learning, characterized in that: include: S1. Discretize the surface density error and error change rate to form multiple intervals, set the initial coefficients of the proportional controller and PID iterative control according to different intervals, and load the configuration parameters saved at the last shutdown; S2, real-time acquisition of the operating parameters of the coating machine system, the surface density weight measurement system and the die head motor data, and after filtering the data, the current surface density error and error change rate are calculated according to the filtered data; S3, according to the current surface density error and error change rate, query the proportional parameter P corresponding to the interval in which it is located, and input it into the proportional controller to obtain the first adjustment amount corresponding to each partition; S4, according to the data processed by S2, query the historical iteration record of the corresponding interval through the PID type iteration controller, combine the current surface density error, error change rate and historical iteration record, and calculate the second adjustment amount corresponding to each partition output by the iterative control part; S5, superimposing the first adjustment amount and the second adjustment amount of each partition to generate a final adjustment amount and drive the die head motor of the corresponding partition to perform adjustment; S6. Execute steps S2-S5 in a loop.

2. A coating machine die motor control method based on PID iterative learning according to claim 1, characterized in that: The step of setting the initial coefficients of the proportional controller and the PID type iterative control in step S1 includes: According to the production process parameters, the surface density error and error change rate are fuzzy processed and divided into multiple discrete intervals; Each discrete interval is independently associated with a proportional parameter P and a parameter of a PID-type iterative controller to achieve differentiated control between discrete intervals; The initial coefficients of the proportional controller and PID-type iterative control are set according to the corresponding discrete intervals.

3. A coating machine die motor control method based on PID iterative learning according to claim 1, characterized in that: The filtering of data in step S2 includes: abnormal data filtering and mean filtering; Among them, the abnormal data filtering adopts upper and lower limit filtering. When the data exceeds the upper or lower limit, the abnormal data is filtered out to exclude invalid data. Invalid data includes: coating machine die head advance and retreat, other coating machine abnormal time data and abnormal millimeter surface density data; The mean filter processing formula is as follows: Among them, m is the sliding window size, g is the input value, x is the input parameter, t is the time series number, and z is the data after mean filtering.

4. A coating machine die motor control method based on PID iterative learning according to claim 3, characterized in that: The step S2 calculates the surface density error and the error change rate according to the filtered data, including: The data after mean filtering is calculated with the target value of this production to obtain all current surface density errors; Calculate the surface density error and error change rate; The surface density error is calculated as follows: in, is the surface density error of partition r in the tth pass, is the surface density value of partition r in the tth pass, The target value of the area density specified for the production process; The error rate of change calculation is as follows: in, is the surface density error of partition r in the t-1th pass.

5. The coating machine die motor control method based on PID iterative learning according to claim 1, characterized in that: The historical iteration record includes the adjustment amount of the historical output of the iterative control part of each motor and the corresponding surface density error and error change rate.

6. A coating machine die motor control method based on PID iterative learning according to claim 1, characterized in that: The step S3 of obtaining the first adjustment amount corresponding to each partition includes: Using the intervals corresponding to the surface density error and the error change rate, the corresponding proportional parameter P is queried, and then the adjustment amount of the proportional controller of each partition is calculated; The calculation of the adjustment amount is as follows: in, is the regulation amount output by the proportional controller, is the proportional parameter of the proportional controller, is the surface density error of partition r in the tth pass, which is obtained by interval query of surface density error and error change rate.

7. A coating machine die motor control method based on PID iterative learning according to claim 6, characterized in that: The PID-type iterative formula for calculating the second adjustment amount corresponding to each partition output by the iterative control part in step S4 is: in, , , are the three PID coefficient values ​​of PID iterative control, t is the tth adjustment, is the surface density error of the t-th adjustment of the r-th partition, is the error change rate of the tth adjustment of the rth partition, which is obtained by subtracting the last adjustment error and the current adjustment error in the interval corresponding to the surface density error and the error change rate. is the cumulative adjustment error of the rth partition, which is calculated by querying the historical adjustment error of the corresponding interval of the surface density error and the error change rate. is the adjustment value output by the iterative controller of the t-1th iteration of the rth partition, It is the adjustment value output by the iterative controller of the tth pass in the rth partition.

8. The coating machine die motor control method based on PID iterative learning according to claim 1, characterized in that: The first adjustment amount and the second adjustment amount in step S5 are superimposed to form a final adjustment amount. According to the final adjustment amount, the motor corresponding to each partition executes its corresponding adjustment amount. After execution, the result of this iteration is stored in the data storage for the next iteration.

9. A coating machine die motor control system based on PID iterative learning, characterized in that: Used to implement the coating machine die motor control method based on PID iterative learning as described in any one of claims 1 to 8, the system includes a data acquisition module, a data storage device, a control algorithm unit and an execution module; Among them, the data acquisition module is used to obtain the data of the coating machine system operating parameters, the surface density weight measurement system and the die head motor in real time, and store the data in the data storage device. After filtering the data, it is used as the input of the control algorithm unit, and the execution module performs adjustment according to the output of the control algorithm unit.

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