Intelligent control method for PCB etching process

Abstract

The application relates to the technical field of intelligent control, and discloses an intelligent control method for a PCB (Printed Circuit Board) etching process, which comprises the following steps: obtaining an image of a to-be-processed object and generating a discrete virtual load sequence, establishing a virtual shift register synchronized with a clock triggered by a feedback pulse of a coder of a conveying device, and driving the virtual load sequence to execute a logic shift locked in phase with a physical displacement; comparing a theoretical torque with an actual torque to obtain a torque residual error; calculating a dynamic gain correction coefficient based on the torque residual error, weighting and modulating a basic feedforward gain in real time, and generating a control instruction; and constructing a rigid space-time synchronization architecture based on the virtual shift register to avoid time sequence errors caused by conveying speed fluctuations; using the torque residual error as a non-invasive observation variable, combining a gain scheduling mechanism, and realizing adaptive compensation for impedance drift of a controlled medium without a physical sensor.

Description

Technical Field

[0001] This invention relates to an intelligent control method for the etching process of PCB circuit boards, belonging to the field of intelligent control technology. Background Technology

[0002] In current continuous PCB etching production, maintaining consistency between the chemical reaction amount per unit area and the preset process standard is the core to ensure stable etching quality. The industry generally adopts a closed-loop control strategy based on PID algorithm, which uses sensors to monitor the temperature, specific gravity or pH value of the etching solution in real time, and adjusts the conveying speed or spray pressure according to the feedback signal to offset the impact of environmental parameter fluctuations on the etching rate. This solution performs well under steady-state conditions and is the mainstream configuration of current automated production lines. With the popularization of high-density interconnect (HDI) and fine circuit board manufacturing processes, the physical properties of the etching medium exhibit nonlinear time-varying characteristics. The concentration of dissolved copper ions accumulates during the recycling of the etching solution, causing continuous and nonlinear drift in the liquid viscosity and fluid damping coefficient. The change in physical impedance destroys the preset controlled object model of the control system, making it difficult for traditional PID controllers based on fixed parameter models to maintain the expected dynamic response accuracy.

[0003] Besides single-algorithm control, existing technologies also attempt to avoid the above-mentioned problems through hardware structure physical assistance, but it is difficult to balance continuous production efficiency and accuracy. For example, the Chinese invention patent with authorization announcement number CN117355043B discloses a PCB circuit board etching processing device. The solution introduces a copper-clad sheet with the same copper thickness as the circuit boards in the same batch. The physical signal of the etched sheet being melted triggers a mechanical lifting mechanism to lift the circuit board off the liquid surface to terminate the etching. Although this physical reference method based on standard samples avoids the error of simply relying on time estimation to a certain extent, it is still essentially a discontinuous control method that relies on discrete sampling points. In a high-speed continuous modern production line, this method of blocking the etching process by mechanical action not only increases the mechanical complexity of the equipment and the cost of consumables, but also cannot make a continuous dynamic response to the real-time fluid viscosity resistance changes faced by each board during the transmission process, and cannot solve the problem of mismatch of the drive load model caused by the aging of the medium.

[0004] Therefore, the technical problem to be solved by this invention is how to achieve online observation of time-varying parameters and adaptive decoupling of the model by utilizing only the internal electrical characteristics of the driver without relying on easily interfered external physical sensors, and to ensure rigid synchronization between control commands and physical positions under variable speed conditions. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: an intelligent control method for the PCB circuit board etching process, wherein the method executes the following adaptive control steps based on nominal model observation and gain scheduling through a controller:

[0006] The image data of the object to be processed is acquired and the mesh parsing is performed to generate a discretized virtual load sequence distributed along the conveying direction. The virtual load sequence represents the distribution of the processing demand of the object to be processed at different spatial coordinate positions.

[0007] The controller maintains a virtual shift register that uses the feedback pulse from the encoder of the transmission device as the trigger clock. The virtual load sequence is driven to perform a logical shift within the virtual shift register that is strictly phase-locked with the physical displacement of the object to be processed. The data shifted out from the head of the virtual shift register is used as the input reference for the current control moment.

[0008] The preset nominal torque model of the execution unit is invoked, the theoretical torque is queried based on the current speed, the theoretical torque is compared with the actual torque calculated by the real-time collected execution unit drive current, and the torque residual, which quantitatively characterizes the degree of drift of the physical impedance of the working medium relative to the standard operating condition, is calculated.

[0009] The dynamic gain correction coefficient is calculated using the torque residual. The dynamic gain correction coefficient is used as a multiplicative factor to real-time weighted modulate the basic feedforward gain. The control command for the drive execution unit is generated by combining the modulated feedforward gain with the load differential characteristics of the virtual load sequence. The system gain change caused by the drift of the physical impedance of the working medium is adaptively compensated by adjusting the control amplitude of the control command.

[0010] Preferably, the controller calculates the dynamic gain correction coefficient using the following gain scheduling rule based on disturbance observation: ,in, This is the dynamic gain correction coefficient. The preset impedance compensation response factor, For torque residual, The theoretical torque is given; the controller applies the dynamic gain correction coefficient as a multiplicative factor to the basic feedforward gain, constructing a control response slope that increases nonlinearly with the dielectric impedance.

[0011] Preferably, before calculating the torque residual, the method further includes vector decomposition and digital filtering of the drive current: reading three-phase current data from the inverter of the drive execution unit, extracting the active current component that generates effective electromagnetic torque through coordinate transformation; smoothing the active current component using a Kalman filter to filter out high-frequency noise caused by mechanical vibration, and retaining the low-frequency component that characterizes the steady-state viscosity of the medium as the basis for calculating the torque residual.

[0012] Preferably, the specific operation of driving the virtual load sequence to perform logical shift includes: constructing a first-in-first-out queue structure as a virtual shift register, the storage depth of which strictly corresponds to the ratio of the physical distance between the image acquisition point and the execution unit's action point to the encoder resolution; whenever the controller receives a preset number of encoder feedback pulses, it triggers the queue structure to perform a shift operation, shifting out the head data of the virtual load sequence and using it as the input reference at the current control moment, while pushing the newly generated load data into the tail of the queue.

[0013] Preferably, the specific operations for generating the discretized virtual load sequence include: performing binarization processing on the image data and extracting connected component features; dividing the image data into continuous logical grids according to the physical stepping accuracy of the transmission device; for each logical grid, calculating the proportion of its internal effective feature area to the total area of ​​the grid, and defining this proportion as the load quantization value at that grid position; arranging the load quantization values ​​of all grids in chronological order to form a virtual load sequence.

[0014] Preferably, the operation of generating control commands based on load differential characteristics adopts an asymmetric dynamic response control strategy: the difference between the load data output by the virtual shift register at the current moment and the load data at the previous moment is calculated as the load differential characteristic; the polarity of the load differential characteristic is determined: when the difference is positive, it indicates that the load is in a sudden increase phase, and the controller applies the first weighting strategy to increase the feedforward gain based on the dynamic gain correction coefficient, generating an overshoot driving torque to overcome the inertia and viscous resistance of the medium; when the difference is negative, it indicates that the load is in a sudden decrease phase, and the controller applies the second weighting strategy, which is different from the first weighting strategy, and introduces a drag attenuation factor based on the current impedance state of the medium to delay and smooth the decline slope of the feedforward gain.

[0015] Preferably, the method further includes an online learning and calibration step for verifying the effectiveness of the model: during the period when the execution unit is in steady-state operation and the virtual load sequence value is constant, the statistical mean of the torque residual is continuously monitored; if the statistical mean exceeds a preset model mismatch threshold, the model calibration procedure is triggered, and the reference parameters of the nominal torque model are corrected using the current actual torque data to eliminate permanent model deviations caused by mechanical wear of the equipment.

[0016] Preferably, the nominal torque model is pre-constructed through the following system identification method: fill the execution unit pipeline with standard pure water medium, control the execution unit to operate at a step-changing speed covering the entire operating frequency band; after each speed step stabilizes, record the steady-state torque current of the execution unit; use the least squares method to fit all recorded points to generate a function curve describing the relationship between speed and torque under standard medium, and discretize and store the function curve as lookup data for the controller.

[0017] Preferably, the method further includes a circuit breaker protection step for abnormal operating conditions: real-time monitoring of the rate of change of torque residual; when the rate of change exceeds a preset safety threshold, it is determined that a physical abnormality has occurred in the control loop where the execution unit is located, the controller immediately cuts off the adaptive control loop based on gain scheduling, forces the control mode to switch to constant safe speed mode, and outputs a diagnostic signal for the abnormal state.

[0018] Preferably, the generation process of the control command also includes a multi-dimensional data fusion operation: multiplying the feedforward gain modulated by the dynamic gain correction coefficient with the current value of the virtual load sequence to obtain the basic feedforward command; weighting and summing the basic feedforward command with the feedback compensation command calculated based on the current speed deviation of the execution unit to generate the final torque current control command; wherein, the weight coefficient of the feedback compensation command decreases nonlinearly as the absolute value of the torque residual increases, prioritizing the stability of the system under severe model mismatch conditions.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. In the intelligent control of the PCB etching process, a rigid spatiotemporal phase-locked mechanism is constructed using a pulse-driven virtual shift register to eliminate phase lag caused by transmission speed fluctuations. This abandons the traditional time-axis-based delay buffer strategy and instead constructs a first-in-first-out queue structure within the controller, where the storage depth corresponds to the ratio of physical distance to encoder resolution. The encoder feedback pulse from the transmission device is used as the trigger clock to drive the virtual load sequence to perform logical shifts. The discrete event-driven data flow architecture establishes a rigid mapping between the control input data flow speed and the physical displacement speed of the controlled object. Even if the transmission device is in an unsteady state with acceleration, deceleration, or speed jitter, the timing of the control command output is automatically compressed or stretched under the constraint of the pulse mechanism, eliminating the need for additional complex speed compensation calculations. This ensures that the spatial dimension of the feedforward control command is always phase-aligned with the actual position of the physical load, solving the timing misalignment problem that inevitably occurs under the traditional time-based control in variable speed conditions.

[0021] 2. Based on torque residual ratio multiplicative gain scheduling, the system achieves low-computing-power adaptive decoupling of time-varying impedance of the controlled object. It compares the actual torque of the execution unit with the theoretical torque of the nominal model in real time, extracts the torque residual that represents the drift state of the physical impedance of the medium, and calculates the ratio of the residual to the current theoretical torque to generate a dynamic gain correction coefficient. This control logic transforms the identification problem of complex nonlinear time-varying systems into a linear scalar multiplication operation based on a normalized ratio. The controller uses the correction coefficient as a multiplicative factor to apply to the basic feedforward gain, so that the energy output density of the control command automatically and linearly expands and contracts across the entire range with the change of the viscous resistance of the working medium. The non-invasive soft measurement and compensation mechanism does not require the introduction of a physical viscosity sensor that is susceptible to environmental interference at the execution end, and does not require interruption of the operation model parameter reconstruction. It uses the existing current loop data inside the driver to offset the system model mismatch caused by medium aging or temperature changes in real time, ensuring the consistency of response characteristics of the control system throughout its entire life cycle.

[0022] 3. Combining vector decomposition filtering and asymmetric dynamic response strategies to improve the effectiveness of the control loop under complex disturbances: At the signal processing level, three-phase current data is collected and coordinate transformation and Kalman filtering are performed to remove high-frequency noise caused by mechanical vibration and retain the low-frequency active components that characterize the steady-state load as observation basis; at the control decision level, logic based on load sequence differential polarity judgment is introduced. When the load enters a sudden increase in inertia, the overshoot torque is generated by positive gain enhancement to avoid static friction and fluid resistance. When the load leaves the resistance and the resistance drops suddenly, a drag attenuation factor is introduced to smooth the slope of the gain drop. The synergistic effect of signal-level noise reduction and strategy-level asymmetric response prevents the gain adjustment from being falsely triggered by mechanical noise, suppresses common system oscillations and cavitation phenomena during high-dynamic load changes, and ensures the mechanical stability and output smoothness of the execution unit under high-speed intermittent load conditions. Attached Figure Description

[0023] Figure 1 This is a timing diagram of the signal interaction of the control system for torque residual observation in this invention;

[0024] Figure 2 This is a comparison curve of the speed response error as a function of medium concentration under different control strategies of the present invention;

[0025] Figure 3 This is a diagram of the overall logic architecture of the intelligent control system integrating spatiotemporal phase-locked loop and gain scheduling in this invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0027] This invention provides an intelligent control method for the PCB circuit board etching process. Based on a data flow-driven architecture with spatial displacement as the reference, it is implemented through an automated control system integrating a machine vision unit, a servo drive unit, and a central process controller. During system initialization, the controller executes a system identification procedure for the execution unit. A standard pure water medium is filled into the etching chamber, which is the controlled object. The execution unit is controlled to cover the operating frequency band with a preset stepped rotation speed sequence. This rotation speed sequence is set to start from zero speed with preset step sizes. The speed is gradually increased to the rated speed. After reaching steady state at each speed step, the driver records the current steady-state torque and current data. The controller uses the least squares method to fit all recorded points, generating a nominal torque model describing the correspondence between speed and theoretical torque under standard medium conditions. This model is discretized and stored as lookup table data, serving as the zero-point reference for subsequent online adaptive control. In the real-time control process, the system executes an input mapping step based on image analysis. An industrial camera set at the entrance of the conveyor continuously acquires image data of the object to be processed. The controller performs binarization processing on the acquired image data and extracts connected component features. Based on the physical stepping accuracy of the conveyor, such as... The controller divides the image data into continuous logical grids along the transmission direction. For each logical grid, the controller calculates the proportion of its internal effective feature area, i.e. the area of ​​the copper layer to be etched, to the total area of ​​the grid, and defines this proportion as the load quantization value of the grid position. The controller arranges the load quantization values ​​of all grids in physical spatial order to generate a discrete virtual load sequence distributed along the transmission direction. This virtual load sequence directly represents the distribution of the theoretical processing requirements of the object to be processed at different spatial coordinate positions.

[0028] To eliminate timing errors caused by transmission speed fluctuations, the controller maintains a virtual shift register to construct a rigid synchronization architecture in the spatiotemporal dimension. This virtual shift register is constructed as a first-in-first-out queue structure, and its storage depth corresponds to the ratio of the physical distance from the image acquisition point to the execution unit's action point to the encoder resolution of the transmission device. The encoder feedback pulse of the transmission device serves as the trigger clock for this virtual shift register. Whenever a preset number of pulses are received, such as those corresponding to... When the encoder feedback pulse of physical displacement is received, the controller triggers the queue structure to perform a logical shift operation. During the shift, the head data of the virtual load sequence is removed and used as the input reference for the current control moment, while the newly generated load data is pushed into the tail of the queue. This mechanism ensures that the input data stream of the control system remains phase-locked with the physical displacement of the object to be processed, so that the control command always acts on the exact position of the physical object. The controller executes a closed-loop state observation mechanism in parallel. By reading the three-phase current data of the inverter of the drive execution unit, the controller uses coordinate transformation to extract the active current component that generates effective electromagnetic torque. To eliminate the high-frequency noise introduced by mechanical vibration, this active current component is input to a Kalman filter for processing. This filter removes high-frequency random noise and retains the low-frequency component that characterizes the steady-state viscosity of the medium. The controller queries the preset nominal torque model based on the current real-time speed to obtain the theoretical torque. The actual torque after filtering is compared with the theoretical torque to calculate the torque residual. The torque residual quantitatively characterizes the degree of drift of the physical impedance of the current working medium relative to the standard pure water condition, reflecting the viscosity change of the etching solution due to the increase in ion concentration or temperature change.

[0029] Based on the torque residual, the controller performs gain-scheduled adaptive control based on disturbance observation, and calculates the dynamic gain correction coefficient using the gain scheduling rules. Its calculation logic follows the formula: ,in, This is the dynamic gain correction coefficient. The preset impedance compensation response factor, For torque residual, The theoretical torque is the torque that is measured when the actual torque is greater than the theoretical torque. When it is the correct time, Greater than The controller uses the dynamic gain correction coefficient as a multiplicative factor and performs real-time weighted modulation on the basic feedforward gain calculated based on the virtual load sequence. Through multiplicative modulation, the energy output density of the control command is adaptively compensated for the drift of the physical impedance of the working medium, constructing a control response slope that increases nonlinearly with the medium impedance, thus achieving online decoupling of the model of the nonlinear time-varying system. The system applies an asymmetric dynamic response control strategy to improve dynamic performance. The controller calculates the difference between the load data output by the virtual shift register at the current moment and the load data at the previous moment, i.e., the load differential characteristic. When this difference is positive, it indicates that the load is in a sudden increase phase. The controller applies the first weighting strategy to increase the feedforward gain based on the dynamic gain correction coefficient, generating overshoot drive. The driving torque is used to overcome the inertia and viscous resistance of the medium. When the difference is negative, it indicates that the load is in a sudden drop phase. The controller applies a second weighting strategy and introduces a drag attenuation factor based on the current impedance state of the medium to smooth the decline slope of the feedforward gain. In addition, the system has online learning and calibration functions. During the steady-state operation and the period when the virtual load sequence value is constant, the controller monitors the statistical mean of the torque residual. If the statistical mean exceeds the preset model mismatch threshold for a long time, the system triggers the model calibration program to correct the reference parameters of the nominal torque model using the current actual torque data. If the rate of change of the torque residual exceeds the preset safety threshold, the controller cuts off the adaptive control loop, forces a switch to the constant safe speed mode and outputs a diagnostic signal.

[0030] Example 1: In a high-density interconnect (HDI) circuit board production scenario where the intelligent control method of this invention is applied, the production line operates at full load continuously for 24 hours. Due to the accumulation of ion concentration and fluctuations in ambient temperature during the etching solution recycling process, the physical viscosity and fluid damping coefficient of the working medium exhibit nonlinear time-varying characteristics, causing continuous and unpredictable drift in the load impedance of the execution unit. Under this condition, traditional time-based control strategies often lead to a mismatch between etching rate and conveying speed because they cannot perceive changes in the physical properties of the medium in real time, resulting in quality defects such as local over-etching or under-etching. To address the above conditions, the intelligent control system constructed in this embodiment initiates virtual [processing] based on image analysis. The pseudo-load mapping mechanism involves an industrial camera at the conveyor entrance capturing real-time image data of the object to be processed. The controller parses this data into a discrete virtual load sequence distributed along the conveyor direction. This sequence serves as a feedforward input, quantifying not only the spatial processing requirements but also achieving rigid phase-locking with physical displacement through a virtual shift register. Simultaneously, the closed-loop state observation mechanism, the core of the system, operates in real-time. The controller reads the three-phase current from the inverter of the drive execution unit, extracts the active current component through coordinate transformation, and uses a Kalman filter to filter out mechanical vibration noise, accurately extracting the low-frequency component characterizing the steady-state viscosity of the medium. The controller then compares this actual torque with the theoretical torque obtained based on the nominal torque model. By comparing and calculating, the torque residual is obtained. .

[0031] During this process, torque residual As a key observation variable characterizing dielectric impedance drift, the direct-drive gain scheduling module, when the viscosity of the etching solution increases due to the increased copper ion concentration, causing the actual load torque of the execution unit to exceed the nominal value, the torque residual... When a positive value is displayed, the controller follows the formula. Real-time calculation of dynamic gain correction coefficient ,because For positive, Automatically adjust to greater than The value of this coefficient, acting as a multiplicative factor, directly affects the basic feedforward gain, causing the energy output density of the control command to increase linearly with the increase of the medium impedance. This mechanism, without the need for an external viscosity sensor, utilizes the internal electrical characteristics of the driver to achieve online observation and compensation of time-varying parameters, ensuring that the actuator can still output a driving torque that strictly matches the current load requirements even under conditions of medium impedance drift.

[0032] Example 2: This example aims to verify the effectiveness of the intelligent control method of the present invention in dealing with nonlinear time-varying conditions through comparative experiments. To this end, a hardware platform based on a standard horizontal conveyor etching machine was built. This platform integrates a high-precision servo drive system, a real-time current monitoring module, and an industrial-grade image acquisition device, which can realistically simulate various complex working conditions in actual production. To quantitatively evaluate the performance advantages of the present invention method compared with the prior art, three parallel control groups were designed: Control group A adopts the industry-standard fixed-parameter PID control strategy, which adjusts the conveying speed only according to a preset time base; Control group B adopts a single strategy based on feedforward control, which lacks a real-time feedback compensation mechanism for dielectric impedance drift; The present invention sample group fully deploys an adaptive control scheme based on virtual load mapping and torque residual observation.

[0033] The experiment was conducted under standard pure water conditions for benchmark calibration. Copper ion concentrate was added to the etching solution in a gradient to simulate the nonlinear drift of physical impedance caused by media aging during actual production. During this dynamic process, the system continuously monitored and recorded key performance indicators such as rotational speed response error, etching uniformity index, and system energy consumption for each group. The signal-to-noise ratio of the actively superimposed signal source in the experiment was [value missing]. Gaussian white noise, and simulated frequency of The experiment showed that as the copper ion concentration gradually increased, the viscosity of the etching solution exhibited a non-linear upward trend, leading to an increase in the load torque of the actuator. In control group A, due to the lack of predictive ability of the PID controller for load changes, the system frequently experienced speed overshoot and undershoot, resulting in a sharp decrease in etching uniformity as the medium aged. Although control group B introduced feedforward control, it could not sense the real-time drift of the medium impedance, and its control commands always lagged behind the actual load demand, failing to maintain a constant etching rate. In contrast, the sample group of this invention calculated the torque residual in real time. And dynamically adjust the gain correction factor It successfully achieves adaptive compensation for changes in dielectric impedance. Even under strong noise interference, the Kalman filter can still effectively extract the low-frequency components that characterize the viscosity of the dielectric, ensuring the accurate output of control commands.

[0034] Table 1: Performance Comparison Data under Different Control Strategies

[0035]

[0036] Referring to Table 1, the data reveals the superior performance of the method of the present invention in dealing with dielectric impedance drift. As the copper ion concentration increases, the rotational speed response error of both control group A and control group B shows an upward trend, while the etching uniformity index decreases significantly, especially when the concentration reaches a certain level. Under high impedance conditions, the rotational speed error of control group A is as high as Etching uniformity dropped to This method can no longer meet the process requirements of high-precision etching, while the sample of this invention maintains an extremely low rotational speed response error (not exceeding) across the entire concentration range. ) and extremely high etching uniformity (maintained) (The above) This is directly attributed to its unique torque residual observation and gain scheduling mechanism, which can convert changes in dielectric impedance into multiplicative corrections of control gain in real time, thereby achieving online decoupling of system model mismatch.

[0037] Example 3: This example combines Figures 1 to 3 This describes an intelligent control method for the etching process of a PCB circuit board, such as... Figure 1As shown, the system's data interaction process begins with the inverter reading three-phase current data. After receiving the data, the controller extracts the active current through coordinate transformation and inputs it into a Kalman filter. The Kalman filter performs high-frequency noise filtering on the input signal and returns the filtered actual torque to the controller. The controller then queries the nominal model for the theoretical torque corresponding to the current speed and receives the returned theoretical torque value. The controller calculates the torque residual using the comparison between the actual torque and the theoretical torque and passes the residual to the gain scheduling module. This module calculates the dynamic gain correction coefficient and returns the corrected feedforward gain to the controller. Finally, the controller generates control commands based on the above data.

[0038] like Figure 2 As shown in the chart, this graph illustrates the performance differences of the present invention's sample group, control group A (traditional PID), and control group B (single feedforward) under different operating conditions. The horizontal axis represents copper ion concentration in g / L, and the vertical axis represents speed response error in rpm. The chart contains three trend curves. The data shows that as the copper ion concentration gradually increases from 0 to 80 g / L, the error curve of the present invention's sample group remains stable and has the lowest value, while the error curve of control group A shows a sharp upward trend, and the error curve of control group B shows a linear growth trend between the two. Figure 3 As shown, the overall control logic performs binarization and connected component feature extraction on the image of the object to be processed, and then performs gridded analysis to generate a discrete virtual load sequence. This sequence is input to a virtual shift register triggered by encoder feedback pulses, realizing physical displacement phase-locked logic shift and outputting input reference data. At the same time, the system reads the drive current of the execution unit, i.e., the three-phase current of the frequency converter, and extracts the steady-state active component through vector decomposition and digital filtering, i.e., Kalman filter. Combined with the nominal torque model in the form of preset lookup table data, the torque residual characterizing the degree of drift of the physical impedance of the medium is calculated. Based on the input reference data, gain scheduling factor and torque residual, the system performs dynamic gain correction coefficient calculation, and enters the asymmetric dynamic response control strategy stage by real-time weighted modulation of the feedforward gain based on the residual. By judging the load differential characteristics of sudden overshoot or sudden drop drag, the final drive output after adaptive compensation, i.e., torque current control command, is finally output.

[0039] Example 4: To verify the effectiveness of the asymmetric dynamic response control strategy proposed in this invention in handling extreme working conditions in actual production, this example constructs a high-dynamic test scenario that includes sudden load disturbances. In this scenario, the conveyor system of the etching machine is intentionally introduced with interference signals simulating mechanical jamming and instantaneous workpiece detachment to test the steady-state recovery capability and mechanical safety of the control system under severe load fluctuations. The test selects control group B (single feedforward control) as the benchmark, focusing on comparing and examining the speed overshoot, settling time, and current surge amplitude of the two when facing sudden load changes. The test simulates a sudden load increase condition, that is, a load equivalent to the rated load is suddenly applied during the steady-state operation of the conveyor system. The monitoring data showed that, due to the lack of a mechanism to identify the polarity of load changes, the control group B experienced a lag in its feedforward gain adjustment, resulting in a sudden drop in speed exceeding [a certain value]. And the recovery time is long. In contrast, the prototype of this invention rapidly identifies the positive surge trend of the load by calculating the load differential characteristics in real time and immediately triggers the first weighting strategy. This strategy instantaneously increases the feedforward gain based on the dynamic gain correction coefficient, generating a controlled overshoot driving torque to avoid system inertia and sudden viscous drag. The results show that the speed drop of the prototype of this invention is controlled within seconds. Within, and in It recovers to a steady state within seconds, improving the system's ability to resist disturbances.

[0040] The test simulated a sudden load drop condition, where the load was suddenly removed during heavy-load operation. The load torque of the control group B was not reduced in time, resulting in a large overshoot of the speed and even a brief oscillation of the mechanical transmission chain. However, when the load differential characteristic of the present invention was detected to be negative and exceeded the dead zone threshold, it immediately switched to the second weighting strategy. This strategy introduces a drag attenuation factor related to the current medium impedance state to perform nonlinear delay smoothing on the decline slope of the feedforward gain. This mechanism uses the high viscous resistance of the medium itself to naturally consume the remaining kinetic energy of the system, thereby achieving precise matching between control commands and fluid inertial characteristics at the physical level.

[0041] Example 5: During the initial configuration and parameter tuning phase of the control system, to eliminate model uncertainties caused by the mechanical tolerances of the actuator and the nonlinear characteristics of the drive circuit, the system executes a standardized nominal torque model identification procedure. After filling the actuator pipeline with standard pure water medium and removing air bubbles, the controller drives the actuator into automatic scanning mode, and the rotational speed... From zero The step size is increased to the rated speed in a stepped manner. Maintain at each speed step To ensure fluid stability, the steady-state active current at that rotational speed is collected and converted into the actual torque value. The controller employs a third-order polynomial fitting algorithm to perform regression analysis on the collected discrete speed-torque data pairs, constructing an analytical nominal torque model. ,in, , , , The undetermined coefficients represent the turbulent drag term, laminar viscosity term, mechanical friction term, and static friction torque in fluid dynamics, respectively. These coefficients are solved using the least squares method to minimize the sum of squares of the fitting residuals, thereby obtaining a continuous reference model that covers the entire operating frequency band and establishing the torque residuals. Calculated zero-point reference; impedance compensation response factor for core control parameters. The system is configured to use a self-excited oscillation calibration method based on the critical stability boundary. Before the etching machine is put into normal processing, the controller introduces a torque with an amplitude of the rated torque into the feedback loop. The step disturbance signal, and the initial setting for The system is based on The step size is gradually increased. Simultaneously monitor torque residuals in real time. Time series variance ,when When the gain value first exceeds the preset oscillation threshold, record this value as the critical gain. To ensure sufficient phase margin while maintaining system response speed, the controller sets the final operating gain to [value missing]. This calibration process ensures that the gain scheduling mechanism can maintain the system in an overdamped or critically damped state when the dielectric impedance drifts over a wide range, thus avoiding system divergence or mechanical resonance caused by excessive gain.

[0042] In the implementation phase of the asymmetric dynamic response control strategy, the controller explicitly defines the algorithm for the second weighted strategy during the load drop phase, when load differential characteristics are detected. When the value is negative and its absolute value exceeds the preset dead zone threshold, the controller no longer directly uses the real-time gain based on the torque residual, but instead activates the drag attenuation logic. At this time, the feedforward gain... Follows the law of exponential decay Adjustments are made, among which, This represents the gain hold value at the moment before the load suddenly drops. The drag decay time constant is The value of is not fixed, but depends on the current torque residual. There is a positive correlation mapping relationship, that is ,in, Based on the decay time constant, As the viscous damping coefficient, this logic ensures that when the medium viscosity is high... When the value is large, the system uses a longer time constant. By delaying gain decline and utilizing the high viscous resistance of the medium to naturally dissipate the system's kinetic energy, precise matching between control commands and fluid inertial characteristics is achieved at the physical level. This eliminates the risk of liquid surface oscillation under conditions of severe load fluctuations. The system executes the basic feedforward gain calibration procedure to establish the mapping between image grayscale and drive current dimensions. In calibration mode, the conveyor delivers a standard sample with 100% copper layer coverage. When the sample is completely within the execution unit's operating area and the operating speed is stable, the controller records the increment of the inverter's output active current. The controller is based on the formula Calculate the basic feedforward gain , The normalized load quantization value corresponding to the standard template is calibrated and stored. This value is then used in subsequent production to convert the virtual load sequence into a basic feedforward instruction proportional constant, mapping the dimensionless image grayscale ratio to a physically meaningful current control quantity. For the determination of key parameters of the Kalman filter, a field tuning process based on variance analysis is adopted. Under the steady-state operation of the execution unit at rated speed under no-load conditions, the controller continuously acquires data for a certain length. like The active current time series is calculated, and the statistical variance of the series is directly assigned to the measurement noise covariance matrix. In the step load test, with The noise covariance matrix for a process with progressively increasing step size Diagonal element values ​​are calculated until the rise time of the torque residual step response matches the predicted hydrodynamic time constant of the system. A definite balance is established between filtering out high-frequency mechanical vibration noise and retaining the true dynamic characteristics of the load. The position verification window mechanism is used to eliminate timing phase errors accumulated by conveyor belt slippage or mechanical wear before the execution unit inlet. A through-beam photoelectric switch is installed. When the physical leading edge of the object to be processed blocks the light path and triggers the switch signal, the controller immediately reads the non-zero data index value at the head of the virtual shift register queue. If the deviation between the virtual position corresponding to the index value and the physical position of the photoelectric switch exceeds the allowable threshold... like The controller performs a pointer forced alignment operation, shifting the virtual shift register read / write pointer by the corresponding deviation steps, re-establishing the correspondence between the virtual data stream and the physical space, so that the control commands always act on the actual physical coordinates of the object to be processed.

[0043] Example 6: To eliminate systematic deviations caused by differences in production environment or equipment aging, this invention further integrates a set of offline calibration and data filling procedures. This procedure aims to provide high-precision reference data for the nominal torque model through standardized engineering experiments. After the etching machine is initially deployed or undergoes major maintenance, the system enters a dedicated calibration mode. In this mode, the execution unit and transmission mechanism are disconnected, and only the motor body is kept running. The controller drives the motor to accelerate from rest to maximum speed with constant acceleration, and then decelerates to stop with the same acceleration. The current and speed data of the motor are recorded throughout the process. By analyzing the current difference during acceleration and deceleration, the controller calculates the rotational inertia and viscous friction coefficient of the motor, and corrects the dynamic compensation term in the nominal torque model accordingly.

[0044] To address potential fluctuations in the physical properties of different batches of etching solutions, the system also includes a pre-deployment calibration procedure. Before formal processing, operators must inject the standard etching solution of the current batch into the etching chamber and control the system to perform a full-speed-range no-load run. During this process, the controller records the steady-state torque current at each speed and compares it with the factory-preset standard model. If the overall deviation exceeds the preset threshold, the controller will automatically calculate the global correction factor and perform an overall translation or scaling of the nominal torque model. This calibration process not only compensates for batch-to-batch differences in the basic viscosity of the etching solution but also provides an accurate initial operating point for subsequent online adaptive control.

[0045] Example 7: To verify the adaptability and control accuracy of the intelligent control method of the present invention under different batches of etching solution and complex production environments, this example establishes a standardized pre-deployment calibration procedure, which is executed before the etching machine is first put into operation or before changing the batch of etching solution. The aim is to obtain the optimal control parameters under the current operating conditions through the system's self-learning function. During the calibration process, the execution unit is placed in an unloaded state, and the controller drives the motor to accelerate from rest to the rated speed at a preset acceleration. The current and speed response curves of the entire process are recorded. By least-squares fitting of the acceleration segment data, the system automatically identifies the moment of inertia of the execution unit. With viscous friction coefficient This updates the dynamic compensation terms in the nominal torque model accordingly.

[0046] After completing the no-load calibration, the operator injects the current batch of working medium into the etching chamber and starts the full-speed domain load scan program. The controller drives the motor in... to It operates in a stepped manner within the rated speed range, with each speed step maintaining... To ensure fluid stability, the system continuously collects steady-state torque and current data at various speed points and compares it with the factory-preset standard model. If the root mean square (RMS) value of the torque deviation exceeds a preset threshold, the controller will automatically calculate a global correction factor. The nominal torque model is calibrated as a whole, and the corrected model serves as the benchmark for subsequent online control to ensure torque residuals. The calculation accuracy is unaffected by fluctuations in the basic viscosity of the medium; furthermore, for key parameters in the asymmetric dynamic response strategy, the system also integrates an adaptive tuning process based on step response. In closed-loop control mode, the controller applies a load with an amplitude equal to the rated load to the system. The system detects step disturbance signals and monitors the overshoot and settling time of the rotational speed in real time. It employs a gradient-based optimization algorithm to automatically adjust the impedance compensation response factor. With drag decay time constant The tuning process continues until the system's dynamic response indicators meet the preset critical damping conditions. This tuning process not only optimizes the system's control accuracy in steady state, but also ensures that the system can recover balance as quickly as possible when faced with sudden load disturbances, without generating harmful mechanical oscillations.

[0047] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method of intelligent control of a PCB circuit board etching process, characterized by, The method performs the following nominal model-based observation and gain scheduling adaptive control steps through a controller: Obtain image data of the object to be processed and perform meshing analysis to generate a discrete virtual load sequence distributed along the conveying direction, which represents the processing demand distribution of the object to be processed at different spatial coordinate positions; Maintain a virtual shift register in the controller, which takes the encoder feedback pulses of the conveying device as a trigger clock, and drive the virtual load sequence to perform a logic shift strictly locked in phase with the physical displacement of the object to be processed in the virtual shift register, and take the data moved out of the head of the virtual shift register as the input reference at the current control time; Call a preset nominal torque model of the execution unit, query the theoretical torque according to the current speed, compare the theoretical torque with the actual torque converted from the real-time collected driving current of the execution unit, and calculate a torque residual error quantitatively representing the drift degree of the physical impedance of the working medium relative to the standard working condition; Use the torque residual error to calculate a dynamic gain correction coefficient, use the dynamic gain correction coefficient as a multiplicative factor to real-time weighted modulate the basic feedforward gain, combine the modulated feedforward gain with the load difference feature of the virtual load sequence to generate a control instruction for driving the execution unit, and adjust the control amount amplitude of the control instruction to adaptively compensate for the system gain change caused by the drift of the physical impedance of the working medium; The specific operation of driving the virtual load sequence to perform a logic shift includes: constructing a first-in-first-out queue structure as the virtual shift register, the storage depth of the queue structure strictly corresponds to the ratio of the physical distance between the image acquisition point and the execution unit action point to the encoder resolution; every time the controller receives a preset number of encoder feedback pulses, trigger the queue structure to perform a shift operation, move the head data of the virtual load sequence out and take it as the input reference at the current control time, and push the newly generated load data to the tail of the queue at the same time; The operation of generating the control instruction in combination with the load difference feature adopts an asymmetric dynamic response control strategy: calculate the difference between the load data output by the virtual shift register at the current time and the load data at the last time as the load difference feature; judge the positive and negative polarity of the load difference feature: when the difference is positive, it represents that the load is in the stage of sudden increase, the controller applies a first weighting strategy, increases the feedforward gain based on the dynamic gain correction coefficient, and generates an overshoot driving torque to overcome the inertia and viscous resistance of the medium; when the difference is negative, it represents that the load is in the stage of sudden decrease, the controller applies a second weighting strategy different from the first weighting strategy, introduces a drag attenuation factor based on the current impedance state of the medium, and delays and smooths the descending slope of the feedforward gain.

2. The intelligent control method of PCB etching process according to claim 1, wherein, The controller calculates the dynamic gain correction coefficient based on the following gain scheduling rule of disturbance observation: wherein, is the dynamic gain correction coefficient, is a preset impedance compensation response factor, is a torque residual, is a theoretical torque; the controller uses the dynamic gain correction coefficient as a multiplicative factor to act on the basic feedforward gain, and constructs a control response slope that increases nonlinearly with the medium impedance.

3. The intelligent control method of PCB etching process as claimed in claim 1 wherein, Before calculating the torque residual error, the operation of vector decomposition and digital filtering processing of the driving current is further included: read the three-phase current data from the frequency converter driving the execution unit, extract the active current component generating the effective electromagnetic torque through coordinate transformation; use a Kalman filter to smooth the active current component, filter out the high-frequency noise caused by mechanical vibration, and retain the low-frequency component representing the steady-state viscous characteristic of the medium as the basis for calculating the torque residual error.

4. The intelligent control method of a PCB etching process of claim 1, wherein, The specific operation of generating the discretized virtual load sequence comprises: binarizing the image data and extracting connected domain features, and dividing the image data into continuous logical grids according to the physical stepping accuracy of the conveying device; for each logical grid, calculating the proportion of the effective feature area in the total area of the grid, and defining the proportion as the load quantization value of the position of the logical grid; and arranging the load quantization values of all the grids in chronological order to form the virtual load sequence.

5. The intelligent control method of a PCB etching process of claim 1, wherein, The method further comprises an online learning and calibration step for verifying the effectiveness of the model: continuously monitoring the statistical mean of the torque residual during a period when the actuator is in steady-state operation and the virtual load sequence value is constant; if the statistical mean exceeds a preset model mismatch threshold, triggering a model calibration program to correct the reference parameters of the nominal torque model using the current actual torque data.

6. The intelligent control method of a PCB etching process of claim 1, wherein, The nominal torque model is pre-constructed through the following system identification method: filling the pipeline of the actuator with standard pure water medium, and controlling the actuator to operate at a stepwise varying speed covering the entire working frequency band; after each speed step stabilizes, recording the steady-state torque current of the actuator; using the least squares method to fit all the recorded points to generate a function curve describing the relationship between speed and torque under standard medium, and discretizing and storing the function curve as lookup table data of the controller.

7. The intelligent control method of a PCB etching process of claim 1, wherein, The method further comprises a fuse protection step for abnormal conditions: real-time monitoring of the change rate of the torque residual; when the change rate exceeds a preset safety threshold, it is determined that a physical abnormality has occurred in the control loop of the actuator, the controller immediately cuts off the adaptive control loop based on gain scheduling, and the control mode is forcibly switched to a constant safe speed mode, while outputting a diagnostic signal of the abnormal state.

8. The intelligent control method of a PCB etching process of claim 1, wherein, The generation process of the control command further comprises a multi-dimensional data fusion operation: multiplying the feedforward gain after dynamic gain correction coefficient modulation by the current value of the virtual load sequence to obtain a basic feedforward command; performing weighted summation of the basic feedforward command and a feedback compensation command calculated based on the current speed deviation of the actuator to generate the final torque current control command; wherein the weight coefficient of the feedback compensation command is nonlinearly reduced with the increase of the absolute value of the torque residual, and the stability of the system is preferentially guaranteed in the model mismatch condition.

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