Foil edge surface density control method and device and coating machine

Abstract

The invention discloses a foil edge surface density control method and device and a coater, and the method comprises the steps: setting the displacement adjustment amount of a plurality of execution parts as an input variable, setting the surface density value of a plurality of edge partitions corresponding to the execution parts as an output variable, obtaining a coupling influence matrix between the input variable and the output variable, and setting the surface density value of a plurality of edge partitions corresponding to the execution parts as the output variable; a predictive control model is constructed; in each control period, based on a preset surface density target value and a predictive control model, calculating a global control sequence of a specified time domain in the future, and respectively applying a displacement adjustment amount corresponding to the current control period in the global control sequence to each execution component; and updating the predictive control model based on the feedback value of the surface density value of each edge partition in the current control period, and applying the updated predictive control model to global control sequence calculation of the next control period. According to the technical scheme provided by the invention, the control precision of the foil edge surface density can be improved.

Description

Technical Field

[0001] This application relates to the field of electrode coating control, and in particular to a method, apparatus and coating machine for controlling the surface density of foil edges. Background Technology

[0002] In the manufacturing process of lithium batteries, electrode coating is a crucial step determining battery performance. Its core involves using a coating die to uniformly coat a slurry containing active materials onto a metal foil. During coating, the areal density of the active material in different areas of the foil is an important control indicator, directly affecting the battery's energy density and thermal stability. Because the slurry is typically a viscous fluid with poor flowability, defects such as edge thickening or thinning are prone to occur at the foil edges. This unevenness in edge areal density reduces lithium battery performance and, in severe cases, can lead to internal short circuits, threatening battery safety.

[0003] To address the issue of uneven coating that commonly occurs at the edges of foil materials, modern coating machines are generally equipped with multiple adjustable T-blocks. These T-blocks are distributed along the width of the coating die and their extension can be independently adjusted by actuators such as servo motors. This alters the local coating gap, regulates the slurry flow rate in the corresponding area, and ultimately achieves precise areal density control. However, significant coupling effects exist between the T-blocks at the edge of the coating die. Independent adjustment of each T-block can easily lead to mutual interference, causing system oscillation, overshoot, or steady-state deviation, making it impossible to achieve high-precision areal density control at the foil edges.

[0004] Therefore, it is necessary to provide a new method, apparatus, and coating machine for controlling the surface density of foil edges to address the aforementioned shortcomings. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus and coating machine for controlling the surface density of foil edges, which can improve the control accuracy of foil edge surface density.

[0006] To achieve the above objectives, this application provides a method for controlling the areal density of foil edges. The method is applied to a coating machine having multiple actuators for adjusting the coating thickness at the foil edges. The method includes: setting the displacement adjustment amount of the multiple actuators as input variables, setting the areal density values ​​of multiple edge partitions corresponding to the multiple actuators as output variables, obtaining a coupling influence matrix between the input variables and the output variables to construct a predictive control model; within each control cycle, calculating a global control sequence for a future specified time domain based on a preset areal density target value and the predictive control model, and applying the displacement adjustment amount corresponding to the current control cycle in the global control sequence to each actuator, wherein the global control sequence includes the displacement adjustment amount of each actuator in the future specified time domain; updating the predictive control model based on the feedback value of the areal density values ​​of each edge partition in the current control cycle, and using the updated predictive control model for calculating the global control sequence in the next control cycle.

[0007] To achieve the above objectives, this application also provides a foil edge areal density control device, comprising: a model building module, configured to set the displacement adjustment amounts of multiple actuators as input variables, set the areal density values ​​of multiple edge partitions corresponding to the multiple actuators as output variables, and obtain the coupling influence matrix between the input variables and the output variables to construct a predictive control model; a displacement adjustment module, configured to calculate a global control sequence for a future specified time domain based on a preset areal density target value and the predictive control model in each control cycle, and apply the displacement adjustment amount corresponding to the current control cycle in the global control sequence to each actuator, wherein the global control sequence includes the displacement adjustment amount of each actuator in the future specified time domain; and a model updating module, configured to update the predictive control model based on the feedback values ​​of the areal density values ​​of each edge partition in the current control cycle, and use the updated predictive control model for calculating the global control sequence in the next control cycle.

[0008] To achieve the above objectives, this application also provides a foil edge surface density control device, comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, so that the device performs the foil edge surface density control method as described above.

[0009] To achieve the above objectives, this application also provides a coating machine, including the foil edge surface density control device as described above.

[0010] To achieve the above objectives, this application also provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, cause the processor to implement the foil edge surface density control method as described above.

[0011] To achieve the above objectives, this application also provides a computer program product comprising computer program code, which, when run on a computer, enables the computer to implement the foil edge surface density control method as described above.

[0012] To achieve the above objectives, this application also provides a chip that includes a circuit for performing the foil edge surface density control method as described above.

[0013] Therefore, the technical solution provided in this application defines the areal density values ​​of multiple zones at the edge of the coating machine as the system's state variables, enabling the controller to comprehensively perceive the areal density changes of each edge zone. Simultaneously, the displacement adjustment of multiple T-blocks in the corresponding edge region is set as input variables, constructing a multi-input multi-output predictive control model. This model introduces a coupling influence matrix that quantifies the coupling effects between various actuators. Through this matrix, the dynamic influence relationship of a single T-block displacement adjustment on multiple areal density zones can be quantified, thus solving the control conflict problem caused by neglecting coupling effects in traditional models. Furthermore, this solution obtains the optimal control sequence for multiple future control cycles by constructing a rolling time-domain optimization problem, and only issues the current cycle control quantity in the sequence to each actuator for execution. This ensures the forward-looking nature of control decisions and avoids command inaccuracies due to uncertain future operating conditions. Simultaneously, when the deviation between the areal density feedback value and the model prediction value exceeds a preset threshold, the system can correct the coupling influence matrix based on the current feedback data, ensuring that the dynamic influence relationship in the predictive control model remains consistent with the current coating condition, ultimately significantly improving the system's control accuracy for the areal density of the foil edge. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0015] Figure 1 This is a flowchart of a foil edge surface density control method according to an embodiment of this application; Figure 2 This is a schematic diagram of the functional modules of the foil edge surface density control device in the embodiments of this application; Figure 3 This is a schematic diagram of the foil edge surface density control device in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, the terms "first," "second," "third," etc., are only used to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, when an element is described as "connected" to another element, it can be directly connected to the other element, or there can be one or more intermediate elements between them. "Multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0017] In the manufacturing process of lithium batteries, electrode coating is a crucial step that determines battery performance. Its core involves using a coating die to uniformly coat a slurry containing active materials onto a metal foil. During coating, the areal density of the active material in different areas of the foil is an important control indicator, directly affecting the battery's energy density and thermal stability. Because the slurry is typically a viscous fluid with poor flowability, defects such as edge thickening or thinning are prone to occur at the edges of the foil. This unevenness in edge areal density not only leads to edge cracking or wrinkling in subsequent rolling processes but also causes alignment deviations during cell winding or stacking, potentially resulting in internal short circuits and threatening battery safety.

[0018] To address the issue of uneven coating at the edges of foil materials, modern coating machines are generally equipped with multiple adjustable T-blocks. These T-blocks are distributed along the width of the coating die and their extension can be independently adjusted by actuators such as servo motors, thereby changing the local coating gap, controlling the slurry flow rate in the corresponding area, and ultimately achieving fine adjustment of the areal density. However, there is a significant hydrodynamic coupling effect between the T-blocks at the edge of the coating die. For example, adjusting a certain T-block not only changes the areal density of the area it faces, but also affects the areal density of adjacent and even distant zones through slurry pressure transmission and flow disturbance. This strong coupling characteristic makes the independent adjustment of each T-block highly susceptible to mutual interference, leading to system oscillation, overshoot, or steady-state deviation, making it impossible to achieve high-precision areal density control at the foil edges.

[0019] Therefore, how to accurately characterize the coupling relationship between each T-block and then synchronously control the displacement of each T-block to improve the system's control accuracy of the foil edge surface density has become an urgent issue to be addressed in this field.

[0020] The present application will now be described in more detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and do not constitute a limitation on the embodiments of the present application. The embodiments described herein are only a part of the embodiments of the present application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0021] The foil edge areal density control device of this application is used in a coating machine. This device can adjust the areal density of the active material coated on the edge area of ​​the foil. The device mainly includes the following parts: The actuators are specifically m (m≥2) independently operating servo motors located at the edge of the coating die. Each actuator is connected to a T-block, and the actuator can be displaced to change the extension of the T-block, ultimately adjusting the coating thickness at the edge of the foil.

[0022] Measuring device: Online areal density measuring device, such as a beta-ray areal density meter or an X-ray sensor. The measuring device scans along the width of the foil and divides the edge area into m monitoring zones for real-time output of the areal density value of each zone.

[0023] Controller: A computer or PLC (Programmable Logic Controller) that runs the foil edge surface density control method described in this application. The controller receives surface density data from the measuring device, performs steps such as model prediction, optimization solution, and online update, and finally generates displacement control commands for the T-block and sends them to the execution component.

[0024] Please see Figure 1 , Figure 1 This is a flowchart of a foil edge surface density control method according to an embodiment of this application.

[0025] S101: Set the displacement adjustment amount of the multiple actuators as input variables, set the surface density value of the multiple edge partitions corresponding to the multiple actuators as output variables, and obtain the coupling influence matrix between the input variables and the output variables to construct a predictive control model.

[0026] In this embodiment, the foil edge is divided into multiple zones according to the coating area of ​​the T-block, with each foil zone corresponding to a T-block. Since each actuator is connected to a T-block, each actuator corresponds to a foil zone, and the displacement adjustment of the actuator can be considered as the displacement adjustment of the T-block. The solution of this application mainly targets the coating surface density control of the foil edge region. For ease of description and to distinguish it from the central region of the foil, the zones involved in the foil edge are uniformly defined as edge zones below. In particular, unless otherwise explicitly stated, the actuators mentioned below specifically refer to the actuators corresponding to the aforementioned edge zones, and do not involve the actuators in the central region.

[0027] This application defines the displacement adjustment of multiple actuators in the corresponding edge region as input variables, and the areal density values ​​of multiple edge partitions corresponding to the aforementioned actuators as output variables, thereby establishing a dynamic mapping relationship between input and output to construct a predictive control model. Simultaneously, to quantify the coupling effect of the displacement of a single actuator on the areal density of multiple edge partitions, the coupling effect matrix between input and output variables can be obtained through controlled variable experiments, machine learning, subspace identification, etc., and then incorporated into the construction process of the predictive control model. This predictive control model is a multi-input multi-output model, which can explicitly characterize the coupling relationship between each actuator through the built-in coupling effect matrix, thereby enabling the controller to formulate a globally coordinated control strategy, rather than implementing multiple isolated and potentially conflicting single-point adjustment strategies.

[0028] In one feasible implementation, model predictive control (MPC) algorithms can be used to achieve areal density coupling control in the edge region of the coating machine. The predictive control model can be constructed as follows: ,

[0029] in, Let be the state variable at time t, i.e., the areal density value of the edge partition. Let t be the input variable at time t, i.e., the displacement adjustment amount of the actuator. t represents the output variable at time t, i.e., the observed surface density value of the edge partition. This is the state matrix, used to characterize the self-correlation properties of the areal density values ​​of each edge partition. This is the coupling effect matrix, whose off-diagonal elements are used to characterize the cross-coupling interference strength between various execution components.

[0030] The above , , As column vectors, we'll use an example where all three types have the same degree of freedom, i.e., degrees of freedom. When m ≥ 2, it means that m displacement control commands are issued to m actuators, resulting in m areal densities. For ease of understanding, this application will... , , Since it is treated as a whole, it is not considered in the explanation of matrix dimensions. , , Whether it is a vector or a scalar, the actual matrix dimension can be expanded according to the size of m.

[0031] The core of this application is to construct a coupling influence matrix B to explicitly characterize the coupling relationship between various execution components, ultimately improving the control accuracy of the foil edge surface density. In one feasible implementation, the coupling influence matrix between input and output variables can be obtained as follows: First, for any target execution component among the execution components, obtain the target coupling curve between the displacement adjustment amount of the target execution component and the surface density value of each edge partition. Then, generate target coupling influence parameters based on the target coupling curve, and construct a coupling influence matrix based on the target coupling influence parameters. In the coupling influence matrix, the matrix element B(i,j) represents the change in surface density of the i-th edge partition when the j-th execution component generates a displacement adjustment amount of 1 step.

[0032] In practical applications, the coupling effect matrix can be obtained through controlled variable experiments. Specifically, assuming there are m actuators, one actuator (denoted as the target actuator) can be randomly selected from the m actuators. The displacement of this target actuator is then adjusted individually, while simultaneously collecting the areal density response values ​​of all edge partitions using a measuring device. This forms a scatter plot of data: "Target actuator displacement adjustment amount -- multi-edge partition areal density values". Then, linear fitting can be performed on this scatter plot data to obtain the target coupling curve, which is bound to the target actuator. Further processing of the target coupling curve can be performed, for example, taking its slope as the target coupling effect parameter. Since the number of edge partitions is the same as the number of actuators, we can create an m×m zero matrix B and fill the corresponding positions of the target coupling effect parameter into matrix B. Then, the same operation is performed on the remaining m-1 execution components, and their corresponding coupling influence parameters are filled into the above matrix B to construct the complete coupling influence matrix. The matrix element B(i,j) represents the change in the surface density of the i-th edge partition when the j-th execution component generates a displacement adjustment of 1 step.

[0033] It should be noted that we can also use recursive least squares or sliding window regression methods based on historical operating data to identify the displacement sequence and areal density observation sequence of the actuators, thereby estimating the coupling influence parameters corresponding to each actuator. Alternatively, we can construct a neural network model, using the displacement adjustment of the actuators as input and the areal density value changes of each edge partition as output for training, and extract the coupling influence parameters through the Jacobian matrix of the model at the operating point.

[0034] In one feasible implementation, the target coupling curve between the displacement adjustment of the target actuation component and the surface density values ​​of each edge partition can be obtained in the following manner: First, fix all execution components except the target execution component, and sequentially adjust the displacement of the target execution component within a preset range and in steps of a set size. Then, corresponding to each displacement adjustment of the target execution component, collect the areal density value of each edge partition, and fit the areal density value with the corresponding displacement adjustment to obtain the target coupling curve.

[0035] For example, first define the displacement of m actuators as... ,in, All meet , [ , [ ] represents the effective travel range (i.e., the preset range) of the execution component. Similarly, the average surface density of the edge partitions controlled by each execution component is defined as [ ]. After the coating stabilizes, the calibration zero point of each actuator is recorded. Then, the i-th actuator is marked as the target actuator, and the displacement of the i-th actuator is gradually changed from... Change to Then by Gradually change to (That is, adjust the displacement of the i-th actuator sequentially with a set step size). While moving the i-th actuator, keep the remaining actuators stationary and record the changes. The measured values ​​are obtained. This process is repeated several times to finally obtain the coordinate graph. Numerous dense scatter points on the surface. Then, each of these scatter plots is fitted using smooth curve regression, resulting in m curves. These curves characterize the relationship between the displacement adjustment of the i-th actuator and the j-th areal density partition. After completing the displacement adjustment of the i-th actuator, Reset to the calibration zero point, By repeating the above experiments, we can obtain... A curve, for and The relationship curve is named Since the curves described above are all smooth curves within the interval, the nth-order gradient representation at each point can be obtained using Taylor expansion. and The relationship curve, when At that time, the curve can be in Expand the points to:

[0036] Ignoring higher-order terms, we can obtain an approximation:

[0037] Right now:

[0038] in The amount of displacement change of the actuator. Let u(t) be the change in surface density. In MPC modeling, each element u(t) in U(t) is defined as the change in the T block, i.e. Therefore, here The meaning is: if the surface density at time t is If the displacement of block i in region T changes by u, then the expected change in the areal density of region j is... Therefore, the values ​​of each element of matrix B can be obtained:

[0039] In practice, the steps to obtain a certain element in matrix B are as follows: For example: First, the areal density of the current partition 1 is measured to be... (This can be obtained through a measuring device), and then... The theoretical position of block T can be obtained by inverse calculation from the curve. Finally, the curve is obtained at... The elements of matrix B can be obtained by taking the first derivative of the position. .

[0040] S102: In each control cycle, based on the preset surface density target value and the predictive control model, calculate the global control sequence for a future specified time domain, and apply the displacement adjustment amount corresponding to the current control cycle in the global control sequence to each of the execution components, wherein the global control sequence includes the displacement adjustment amount of each of the execution components in the future specified time domain.

[0041] In this embodiment, after obtaining the predictive control model, the controller can perform rolling optimization once per control cycle. Specifically, within each control cycle, the controller can calculate a global control sequence for a future specified time domain based on a preset areal density target value and the predictive control model. This global control sequence includes the displacement adjustment amount of each actuator within the future specified time domain. After obtaining the displacement adjustment amount of each actuator within the future specified time domain, the controller can send it to the corresponding actuator, enabling each actuator to adjust its displacement according to the received instructions.

[0042] In the rolling optimization process of MPC, each time the controller calculates the control quantity, it first "predicts the future" and then "plans the control action." These two actions correspond to the prediction time domain Np and the control time domain Nc, respectively. Based on the prediction of the next Np steps, MPC solves for the input of the next Nc steps, predicts the result of the next Np steps through the model, and solves for the output variables of the next Np steps. The input variable that is closest to the control target y_ref overall. Typically, Nc ≤ Np, the aim being to avoid short-sighted decision-making and ensure the model can predict the "natural changes in the system after control stops," avoiding blind control. Ultimately, the loss function achieves overall optimality at Np steps, allowing the coating surface density to remain stable near the target value in the long term. Taking Np = Nc as an example, the future control input sequence would be:

[0043] in, This represents the input for the i-th step in the future, predicted at time t.

[0044] Predict the state in the next Np steps by recursively applying the state equation:

[0045]

[0046]

[0047]

[0048] Combining the above formulas, we can obtain and , The relationship is noteworthy; it is important to note that in the following formulas... It is a known quantity, and it is a single sub-block (an element or an m×1 column vector); while and It is an unknown quantity, and a column vector composed of multiple sub-blocks:

[0049] in:

[0050]

[0051] Furthermore, the controller can construct an objective function that includes the surface density tracking error and the cost of control actions based on the above predictive control model and the target value sequence of surface density in the future specified time domain, thereby transforming the control problem into an optimization problem. Then, based on the displacement constraints of each actuator, the range constraints of the surface density values ​​of each edge partition, and the objective function, the global control sequence can be obtained by solving the optimal solution.

[0052] Here, the objective function is chosen to be a quadratic form:

[0053] Where Q is the output weight matrix, R is the input weight matrix, and P is the terminal constraint weight matrix. This serves as a reference trajectory for the terminal's status.

[0054] Will Substitution We can obtain:

[0055] in, for diag(Q1, Q2, …, Q_np); for diag(R1, R2, …,R_nc); P is... , where m is the number of variables in the state vector.

[0056] The weight matrix is ​​typically a diagonal matrix, containing not only the relative weights of different sub-items but also the importance ranking of elements within each sub-item. Therefore, by adjusting the elements in the Q, R, and P matrices, the degree of emphasis placed on each stage by the control objective and the control output action can be adjusted, such as prioritizing near-end error over far-end error, or favoring a specific approach. This includes specifying which sub-element in Q corresponds to which action output. In general, a larger element in Q indicates that the control action tends to quickly eliminate errors in the corresponding output; a larger element in R indicates that the input action tends to produce a smaller output.

[0057] The above The three main terms in the formula respectively encompass the control objective (i.e., reducing output error), the allocation of control variables, and the requirement for the terminal state to follow the input. Since the terminal state can be derived from x(k), therefore... The unknowns in This means that by solving the above formula and minimizing it, the predicted control output sequence can be obtained. Therefore, the above objective function, after being rearranged and with added constraints (e.g., ...), This yields a quadratic programming (QP) form:

[0058]

[0059] in, Let f be the Hessian matrix, and let f be the gradient term. This is the constraint matrix. To constrain boundary quantities.

[0060] Solving the above optimization problem will output the global control sequence for the current control cycle. The first item in the sequence This is the output quantity we need. Applying it to the real system will complete the output of the MPC control quantity. At this point, the subsequent actions in the sequence will be discarded. In the next control cycle, a similar output sequence will be obtained again. The first item of the sequence will be taken as the control input in the same way. By continuously repeating this process, the complete MPC control process can be achieved.

[0061] S103: Based on the feedback values ​​of the surface density values ​​of each edge partition in the current control cycle, update the predictive control model and use the updated predictive control model for the calculation of the global control sequence in the next control cycle.

[0062] In practical applications, coating process conditions are not constant. Key process parameters such as coating speed, slurry viscosity, die temperature, and foil tension can drift with batch changes, environmental variations, or equipment aging. These changes directly alter the flow characteristics of the slurry at the die lip, leading to a shift in the dynamic response relationship between the displacement of the actuator and the areal density. In other words, the originally identified coupling effect matrix no longer accurately reflects the actual behavior of the current system. If the original coupling effect matrix is ​​continued to be used for model predictive control, a systematic deviation will occur between the model prediction and the actual areal density.

[0063] To address the aforementioned issues, in one feasible implementation, the controller can receive areal density values ​​(i.e., areal density feedback values) from the measuring device in real time. Then, based on the areal density feedback values ​​of each edge partition within the current control cycle, the predictive control model is updated to ensure it continuously approximates the actual operating conditions. The updated predictive control model is then used for calculating the global control sequence in the next control cycle, thereby improving the model's predictive accuracy.

[0064] When updating the predictive control model based on the feedback values ​​of the areal density of each edge partition within the current control cycle, the controller first determines whether the areal density feedback value (denoted as the target feedback value) of any edge partition (referred to as the target edge partition for ease of description) deviates from the reference range. If the target feedback value deviates from the reference range, the coupling effect matrix is ​​corrected. In other words, if any edge partition has an areal density feedback value that deviates from the reference range, it indicates that the prediction error of the predictive control model is too large. At this point, the predictive control model can no longer reflect the actual coating conditions, and the controller needs to correct the coupling effect matrix based on the actual conditions. When correcting the coupling effect matrix, the controller can recalculate the elements in the coupling effect matrix using first-order linearization based on the real-time areal density values ​​fed back by the measuring device, and then reconstruct the predictive control model using the corrected coupling effect matrix.

[0065] It should be noted that the reference range can be set based on empirical values, for example, the reference range can be set to [[ , This application does not impose any restrictions on the specific numerical values ​​of the reference range.

[0066] Please see Figure 2 This application also provides a foil edge surface density control device, which includes: The model building module is used to set the displacement adjustment amount of multiple actuators as input variables, set the areal density value of multiple edge partitions corresponding to multiple actuators as output variables, and obtain the coupling influence matrix between the input variables and the output variables to build a predictive control model. The displacement adjustment module is used to calculate the global control sequence for a future specified time domain based on a preset surface density target value and the predictive control model in each control cycle, and to apply the displacement adjustment amount corresponding to the current control cycle in the global control sequence to each of the execution components respectively, wherein the global control sequence includes the displacement adjustment amount of each of the execution components in the future specified time domain; The model update module is used to update the predictive control model based on the feedback values ​​of the surface density values ​​of each edge partition in the current control cycle, and to use the updated predictive control model for the calculation of the global control sequence in the next control cycle.

[0067] In one feasible implementation, the foil edge surface density control device further includes: The coupling influence matrix generation module is used to obtain the target coupling curve between the displacement adjustment amount of any target execution component and the surface density value of each edge partition for any target execution component among the execution components, and to generate target coupling influence parameters based on the target coupling curve, and to construct the coupling influence matrix according to the target coupling influence parameters, wherein the matrix element B(i,j) in the coupling influence matrix represents the change in surface density of the i-th edge partition when the j-th execution component generates a displacement adjustment amount of 1 step.

[0068] Please see Figure 3 This application also provides a foil edge surface density control device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the foil edge surface density control method described above can be implemented. Specifically, at the hardware level, the foil edge surface density control device may include a processor, an internal bus, and a memory. The memory may include main memory and non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory into main memory and then runs it. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned foil edge surface density control device. For example, the aforementioned foil edge surface density control device may also include a... Figure 3 The components shown may include more or fewer components, such as other processing hardware like a GPU (Graphics Processing Unit) or external communication ports. Of course, this application does not exclude other implementation methods besides software implementations, such as logic devices or a combination of hardware and software.

[0069] In this embodiment, the processor may include a central processing unit (CPU) or a graphics processing unit (GPU), and may also include other microcontrollers, logic gates, integrated circuits, or appropriate combinations thereof with logic processing capabilities. The memory described in this embodiment can be a storage device for storing information. In digital systems, a device capable of storing binary data can be a memory; in integrated circuits, a circuit without physical form but with storage function can also be a memory, such as RAM or FIFO; in a system, a storage device with physical form can also be called a memory. In implementation, this memory can also be implemented using a cloud storage method; the specific implementation method is not limited in this specification.

[0070] It should be noted that the specific implementation method of the foil edge surface density control device in this specification can be referred to the description of the method implementation method, and will not be repeated here.

[0071] This application also provides a coating machine, which may include the above-mentioned foil edge surface density control device.

[0072] This application also provides a computer-readable medium storing instructions that, when executed by a processor, enable the processor to implement the foil edge surface density control method described in the above embodiments.

[0073] This application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, the computer can implement the foil edge surface density control method in the above embodiments.

[0074] This application also provides a chip, the chip including a circuit for performing the foil edge surface density control method in the above embodiments.

[0075] Therefore, the technical solution provided in this application defines the areal density values ​​of multiple zones at the edge of the coating machine as the system's state variables, enabling the controller to comprehensively perceive the areal density changes of each edge zone. Simultaneously, the displacement adjustment of multiple T-blocks in the corresponding edge region is set as input variables, constructing a multi-input multi-output predictive control model. This model introduces a coupling influence matrix that quantifies the coupling effects between various actuators. Through this matrix, the dynamic influence relationship of a single T-block displacement adjustment on multiple areal density zones can be quantified, thus solving the control conflict problem caused by neglecting coupling effects in traditional models. Furthermore, this solution obtains the optimal control sequence for multiple future control cycles by constructing a rolling time-domain optimization problem, and only issues the current cycle control quantity in the sequence to each actuator for execution. This ensures the forward-looking nature of control decisions and avoids command inaccuracies due to uncertain future operating conditions. Simultaneously, when the deviation between the areal density feedback value and the model prediction value exceeds a preset threshold, the system can correct the coupling influence matrix based on the current feedback data, ensuring that the dynamic influence relationship in the predictive control model remains consistent with the current coating condition, ultimately significantly improving the system's control accuracy for the areal density of the foil edge.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling the surface density of foil edges, the method being applied to a coating machine, the coating machine having multiple actuating components for adjusting the coating thickness at the edges of the foil, characterized in that... The method includes: The displacement adjustment of multiple actuators is set as input variables, and the areal density values ​​of multiple edge partitions corresponding to multiple actuators are set as output variables. The coupling influence matrix between the input variables and the output variables is obtained to construct a predictive control model. Within each control cycle, based on a preset surface density target value and the predictive control model, a global control sequence for a future specified time domain is calculated, and the displacement adjustment amount corresponding to the current control cycle in the global control sequence is applied to each of the execution components, wherein the global control sequence includes the displacement adjustment amount of each of the execution components in the future specified time domain; Based on the feedback values ​​of the surface density values ​​of each edge partition within the current control cycle, the predictive control model is updated, and the updated predictive control model is used for the calculation of the global control sequence in the next control cycle.

2. The method for controlling the surface density of foil edges according to claim 1, characterized in that, The predictive control model takes the following form: ， ， in, Let be the state variable at time t, i.e., the areal density value of the edge partition. Let t be the input variable at time t, i.e., the displacement adjustment amount of the actuator. Let be the output variable at time t, i.e., the observed areal density value of the edge partition. The state matrix, Let be the coupling influence matrix.

3. The foil edge surface density control method according to claim 2, characterized in that, The state matrix A is an identity matrix used to characterize the self-correlation characteristics of the areal density values ​​of each edge partition; The off-diagonal elements of the coupling influence matrix B are used to characterize the cross-coupling interference intensity between the execution components.

4. The foil edge surface density control method according to claim 3, characterized in that, Obtaining the coupling influence matrix between the input variable and the output variable includes: For any target execution component among the execution components, obtain the target coupling curve between the displacement adjustment amount of the target execution component and the surface density value of each edge partition; Based on the target coupling curve, target coupling influence parameters are generated, and the coupling influence matrix is ​​constructed according to the target coupling influence parameters. The matrix element B(i,j) in the coupling influence matrix represents the change in surface density of the i-th edge partition when the j-th execution component generates a displacement adjustment of 1 step.

5. The foil edge surface density control method according to claim 4, characterized in that, Obtaining the target coupling curve between the displacement adjustment amount of the target execution component and the surface density value of each edge partition includes: Fix the other execution components except the target execution component, and adjust the displacement adjustment amount of the target execution component sequentially within a preset range according to a set step size; Corresponding to each displacement adjustment amount of the target execution component, the surface density value of each edge partition is collected, and the surface density value is fitted with the corresponding displacement adjustment amount to obtain the target coupling curve.

6. The method for controlling the surface density of foil edges according to claim 5, characterized in that, Based on the preset surface density target value and the predictive control model, the global control sequence for a specified future time domain is calculated as follows: Based on the predictive control model and the target value sequence of surface density in the future specified time domain, an objective function is constructed that includes surface density tracking error and control action cost. The global control sequence is solved based on the displacement constraints of each of the execution components, the range constraints of the surface density values ​​of each of the edge partitions, and the objective function.

7. The foil edge surface density control method according to claim 6, characterized in that, Updating the predictive control model based on the feedback values ​​of the areal density values ​​of each edge partition within the current control cycle includes: Determine whether the surface density target feedback value of any target edge partition in each of the edge partitions deviates from the reference range within the current control cycle. If the target feedback value deviates from the reference range, then the coupling influence matrix is ​​corrected. The predictive control model is updated based on the revised coupling effect matrix.

8. A foil edge surface density control device, characterized in that, The device includes: The model building module is used to set the displacement adjustment amount of multiple actuators as input variables, set the areal density value of multiple edge partitions corresponding to multiple actuators as output variables, and obtain the coupling influence matrix between the input variables and the output variables to build a predictive control model. The displacement adjustment module is used to calculate the global control sequence for a future specified time domain based on a preset surface density target value and the predictive control model in each control cycle, and to apply the displacement adjustment amount corresponding to the current control cycle in the global control sequence to each of the execution components respectively, wherein the global control sequence includes the displacement adjustment amount of each of the execution components in the future specified time domain; The model update module is used to update the predictive control model based on the feedback values ​​of the surface density values ​​of each edge partition in the current control cycle, and to use the updated predictive control model for the calculation of the global control sequence in the next control cycle.

9. The foil edge surface density control device according to claim 8, characterized in that, The device further includes: The coupling influence matrix generation module is used to obtain the target coupling curve between the displacement adjustment amount of any target execution component and the surface density value of each edge partition for any target execution component among the execution components, and to generate target coupling influence parameters based on the target coupling curve, and to construct the coupling influence matrix according to the target coupling influence parameters, wherein the matrix element B(i,j) in the coupling influence matrix represents the change in surface density of the i-th edge partition when the j-th execution component generates a displacement adjustment amount of 1 step.

10. A foil edge surface density control device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program stored in the memory to cause the apparatus to perform the method as described in any one of claims 1 to 7.

11. A coating machine, characterized in that, Includes the apparatus as described in any one of claims 8 to 10.

12. A computer-readable storage medium, characterized in that, It stores instructions that, when executed by a processor, cause the processor to implement the method as described in any one of claims 1 to 7.

13. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 7.

14. A chip, characterized in that, The chip includes circuitry for performing the method as described in any one of claims 1 to 7.

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