Motion control method and system for laser galvanometer

Abstract

The invention relates to the technical field of modern precision machining and optical scanning, and discloses a motion control method and system of a laser galvanometer. The method comprises the steps that a real-time position signal is collected and preprocessed, and a filtered position signal is obtained; the deviation between the trajectory model and a preset trajectory model is calculated, when the deviation exceeds a threshold value, the trajectory model is identified as a complex trajectory segment, offset vector data is generated, and an offset compensation demand signal is generated accordingly; driving an instruction device in combination with historical data to obtain compensation parameters; when the deviation is lower than an updating threshold value, updating the trajectory model; a stable control signal is generated based on the optimization model and filtering signal prediction, and the environmental interference is calibrated and the signal smoothness is enhanced through the stable control signal; and finally, comparing the enhanced signal with the optimization model, and repeating the compensation process when the deviation exceeds the standard to obtain a final stable control signal. According to the method, through multi-level deviation processing and iterative optimization, the control precision and the anti-interference performance of the laser galvanometer in complex track movement are improved.

Description

Technical Field

[0001] This invention relates to the fields of modern precision machining and optical scanning technology, and in particular to a motion control method and system for a laser galvanometer. Background Technology

[0002] Currently, laser galvanometer systems are widely used in laser marking, precision machining, and 3D imaging. As a general control or adjustment system, it achieves high-speed scanning of the laser beam by controlling the deflection of the mirrors, featuring fast response and high precision. However, as application scenarios increasingly demand more complex motion trajectories and dynamic performance, the accuracy and stability issues of existing control methods in complex trajectory tracking are becoming increasingly prominent.

[0003] In a current technology, laser galvanometer systems typically employ a trajectory tracking strategy based on PID (Proportional-Integral-Derivative) control, combined with sensor feedback for real-time correction of the galvanometer position. This system acquires the actual position signal of the galvanometer and compares it with a preset trajectory model. When a deviation is detected, the PID controller generates an adjustment command to drive the galvanometer motor, thereby reducing trajectory errors. Furthermore, some systems incorporate filtering algorithms, such as low-pass filtering, to preprocess the position signal and suppress noise interference.

[0004] However, laser galvanometers are susceptible to mechanical vibration, external interference, and system delays during high-speed motion, especially at trajectory turning points or high-curvature sections. The response lag of the PID controller and the phase delay of the filtering algorithm lead to inaccurate key point identification and untimely offset compensation, resulting in trajectory distortion and decreased processing quality. This limits the application of laser galvanometers in high-precision, complex path scanning. In summary, existing technologies suffer from insufficient real-time offset detection and compensation capabilities at key points in complex trajectories. Summary of the Invention

[0005] This invention provides a motion control method and system for a laser galvanometer to solve the problem of insufficient real-time offset detection and compensation capabilities at key points of complex trajectories.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a motion control method for a laser galvanometer, comprising: Real-time position signals are acquired and preprocessed to obtain filtered position signals; The deviation between the filtered position signal and the preset trajectory model is calculated. If the deviation is greater than the preset trajectory deviation detection threshold, complex trajectory segments are identified and offset vector data is calculated. Generate an offset compensation requirement signal based on the offset vector data; The offset compensation demand signal is processed by combining historical motion data to generate an adjustment command sequence to drive the motion control device and obtain the compensated motion parameter data. Compare the compensated motion parameter data with the actual position feedback results. If the feedback deviation is less than the preset model update condition threshold, then update the parameters of the preset trajectory model based on the historical trajectory features to obtain the optimized trajectory model. Based on the target trajectory provided by the optimized trajectory model, the current motion state reflected by the filtered position signal is predicted and simulated. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated. The environmental interference characteristics are calibrated using the stabilized control signal, and the real-time position signal is enhanced and smoothed to obtain an enhanced and filtered position signal. The enhanced and filtered position signal is compared with the expected position of the optimized trajectory model. If the deviation value is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated to obtain the final stable control signal.

[0007] In a second aspect, the present invention provides a motion control system for a laser galvanometer, comprising: The signal acquisition and preprocessing module acquires real-time position signals and performs preprocessing to obtain filtered position signals. The trajectory analysis and offset detection module calculates the deviation between the filtered position signal and the preset trajectory model. If the deviation is greater than the preset trajectory deviation detection threshold, it identifies complex trajectory segments and calculates offset vector data. The constraint judgment and compensation decision module generates an offset compensation demand signal based on the offset vector data; The data fusion and instruction generation module processes the offset compensation requirement signal by combining historical motion data, generates an adjustment instruction sequence to drive the motion control device, and obtains the compensated motion parameter data. The model verification and adaptive update module compares the compensated motion parameter data with the collected actual position feedback results. If the feedback deviation is less than the preset model update condition threshold, the parameters of the preset trajectory model are updated based on the historical trajectory features to obtain the optimized trajectory model. The prediction simulation and steady-state control module, based on the target trajectory provided by the optimized trajectory model, performs prediction simulation on the current motion state reflected by the filtered position signal. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated. The environmental calibration and signal enhancement module calibrates the environmental interference characteristics through the stable control signal and enhances and smooths the real-time position signal to obtain the enhanced and filtered position signal. The iterative control and feedback closed-loop module compares the enhanced filtered position signal with the expected position of the optimized trajectory model. If the deviation value is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated to obtain the final stable control signal.

[0008] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a motion control method for a laser galvanometer as described in any one of the above.

[0009] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a motion control method for a laser galvanometer as described above.

[0010] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention constructs a complete control chain that includes signal acquisition, trajectory deviation detection, offset vector calculation, compensation generation, and prediction simulation, forming a highly efficient general control or regulation system. The system uses multi-level smoothing to filter out noise interference, identifies complex trajectory segments through trajectory deviation detection thresholds, and quantifies trajectory deviation based on offset vectors, achieving real-time and accurate detection of key point offsets. This solves the problem of inaccurate key point identification caused by response lag and noise in existing technologies.

[0011] (2) This invention constructs the decision-making basis for the functional units of this system by synergistically using multiple thresholds such as trajectory deviation detection, model update conditions, and simulation verification. The system generates a compensation signal by judging whether the displacement deviation exceeds the limit, and dynamically adjusts the instructions in conjunction with historical data, thus overcoming the problem of untimely compensation in complex trajectory segments of traditional PID, and realizing fast and accurate compensation under high-speed motion.

[0012] (3) This invention significantly improves the intelligence and stability of the system by integrating predictive simulation and iterative feedback mechanisms and configuring a monitoring or testing device for such a system or unit to monitor its status. The system predicts the deviation trend based on the optimization model and real-time signals, adjusts the control strategy in advance, and forms a continuously optimized feedback loop through cyclic comparison and calibration to ensure that the laser galvanometer maintains high-precision tracking in high-speed and complex environments, thereby improving processing quality and efficiency. Attached Figure Description

[0013] Figure 1 This is a schematic flowchart of a motion control method for a laser galvanometer provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the motion control system structure of a laser galvanometer provided in the second embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Reference Figure 1 The first embodiment of the present invention provides a motion control method for a laser galvanometer, comprising the following steps: S11: Acquire real-time position signals and perform preprocessing to obtain filtered position signals; S12, calculate the deviation between the filtered position signal and the preset trajectory model. If the deviation is greater than the preset trajectory deviation detection threshold, identify the complex trajectory segment and calculate the offset vector data. S13, Generate an offset compensation requirement signal based on the offset vector data; S14, Combine historical motion data to process the offset compensation demand signal, generate an adjustment command sequence to drive the motion control device, and obtain the compensated motion parameter data; S15, compare the compensated motion parameter data with the collected actual position feedback results. If the feedback deviation is less than the preset model update condition threshold, then update the parameters of the preset trajectory model based on the historical trajectory features to obtain the optimized trajectory model. S16. Based on the target trajectory provided by the optimized trajectory model, predict and simulate the current motion state reflected by the filtered position signal. If the simulation output meets the preset accuracy requirement verification standard, then generate a stable control signal. S17, the environmental interference characteristics are calibrated using the stable control signal, and the real-time position signal is enhanced and smoothed to obtain the enhanced and filtered position signal; S18, compare the enhanced filtered position signal with the expected position of the optimized trajectory model. If the deviation value is greater than the preset trajectory deviation detection threshold, repeat the process of extracting the offset vector data and generating the offset compensation requirement signal to obtain the final stable control signal.

[0016] In step S11, real-time position signals are acquired and preprocessed to obtain filtered position signals, including: S1101, the position detection device acquires the real-time position signal from the motion control device at a preset signal acquisition frequency and performs preliminary cleaning to obtain the cleaned position signal; S1102, perform noise smoothing filtering on the cleaned position signal to obtain the filtered position signal.

[0017] In step S1101, the position detection device acquires the real-time position signal from the motion control device at a preset signal acquisition frequency and performs preliminary cleaning to obtain the cleaned position signal.

[0018] It should be noted that the preset signal acquisition frequency is set based on a comprehensive consideration of the dynamic response characteristics of the motion control device and the performance indicators of the position detection device. Specifically, the determination of this frequency must satisfy the Nyquist sampling theorem and allow for a certain margin to accurately capture all key frequency components of the galvanometer during complex trajectory movements, such as high-curvature corners. The preliminary cleaning process mainly targets obvious outliers in the signal introduced by electromagnetic noise, power fluctuations, or mechanical vibrations to ensure the validity and rationality of the data. The galvanometer system uses conversion coefficients... The deflection angle is converted into displacement on the workpiece plane; the specific relationship is as follows: Where s is the displacement, For angle.

[0019] In the laser galvanometer control system, a position detection device, such as a photoelectric encoder, acquires the real-time angular position signal of the galvanometer shaft at a fixed frequency of 100,000 times per second (100 kHz). Initial cleaning is performed, setting a reasonable deflection angle range for the galvanometer to ±0.5 radians. If the acquired real-time angle value exceeds this range, it is considered an anomaly and discarded. For example, the original acquired angle signal sequence is... The values ​​in radians, with 0.200 radians far exceeding the normal fluctuation range, were removed after cleaning, and the sequence was corrected. The radians provide a reliable data foundation for subsequent processing.

[0020] In step S1102, the cleaned position signal is subjected to noise smoothing filtering to obtain a filtered position signal.

[0021] It should be noted that this step aims to further suppress any high-frequency noise and random fluctuations that may still remain in the signal after the initial cleaning. This processing employs a moving average filter, which smooths the signal trajectory and improves signal quality by calculating the arithmetic mean of multiple sampling points within a local time window. This filtering process does not rely on comparisons with a preset trajectory model; it is executed unconditionally based on the signal's inherent noise characteristics and the system's preset fixed filtering parameters. The size of the moving window is preset based on the system sampling frequency, control response speed, and the required level of noise suppression, requiring a balance between smoothing effect and signal phase lag.

[0022] In one embodiment, the cleaned position signal is smoothed for noise reduction using a moving average filter. Furthermore, a moving window averaging method is employed to smooth the signal, with the window size set to 5 sampling points to reduce the impact of high-frequency noise. The cleaned position signal more closely approximates the true trajectory. For example, the original angle signal sequence might be... The radius, obtained after cleaning Radius was removed, eliminating obvious outliers of 0.200. Cleaning improved signal reliability and provided accurate data for subsequent deviation calculations. k = 0.1 m / rad, meaning 1 milliradian deflection corresponds to 100 micrometer displacement.

[0023] In step S12, the deviation between the filtered position signal and the preset trajectory model is calculated. If the deviation is greater than the preset trajectory deviation detection threshold, complex trajectory segments are identified and offset vector data is calculated, including: S1201, Calculate the deviation between the filtered position signal and the preset trajectory model to obtain the deviation data; S1202, if the deviation value data is greater than the preset trajectory deviation detection threshold, then analyze the deviation value data, determine the complex trajectory segment, and obtain the complex trajectory segment data; S1203, calculate the offset vector of the complex trajectory segment data to obtain the offset vector data.

[0024] In step S1201, the deviation value between the filtered position signal and the preset trajectory model is calculated to obtain the deviation value data.

[0025] It should be noted that the preset trajectory model is a mathematical representation of the ideal motion path of the laser beam, based on a predefined target processing graphic such as a precision marking pattern or cutting path. It is an explicit model uniquely defined by a specific set of parameters. The filtered position signal is usually smoothed coordinate data obtained by acquiring data from sensors and preprocessing it, such as by sliding window averaging or Kalman filtering. The deviation value is defined as the Euclidean distance between the actual filtered position and the ideal position point corresponding to the same time or path parameters on the preset trajectory model.

[0026] In one embodiment, the filtered position coordinates of the beam controlled by the galvanometer on the workpiece plane are (x = 520 μm, y = 980 μm). The preset trajectory model is a circular pattern to be scanned, and its model form is a parametric equation. The ideal position under the current path parameters is (x = 520 μm, y = 1000 μm). Then the deviation value in the y direction is calculated as |980 - 1000| = 20 μm.

[0027] In step S1202, if the deviation value data is greater than the preset trajectory deviation detection threshold, the deviation value data is analyzed to determine the complex trajectory segment and obtain the complex trajectory segment data.

[0028] It should be noted that the preset trajectory deviation detection threshold is a critical value used to determine in real time whether the laser galvanometer has experienced significant trajectory deviation and to trigger the compensation mechanism. Its setting is not an arbitrary empirical value, but rather a comprehensive quantitative determination based on the overall performance indicators of the laser galvanometer system and the precision requirements of the specific manufacturing process. Complex trajectory segments of the laser galvanometer, identified by this threshold as path segments with continuously exceeding deviation limits, typically refer to regions with high acceleration and high curvature, such as sharp corners, small-radius arcs, or high-speed jump segments, for example, trajectory segments with curvature greater than 0.05 rad / m or acceleration greater than 5000 rad / s². These areas are prone to large tracking errors due to system dynamic lag. The formula for calculating the trajectory deviation threshold is as follows: ; in, Indicates the trajectory deviation detection threshold. This represents the standard deviation of the positioning accuracy of the motion control device. This represents the standard deviation of the measurement error of the position detection device. This represents the system's safety margin, and D represents the operational fault tolerance distance in a specific application scenario. This represents the coverage factor, used to extend the standard deviation of random errors to a larger confidence interval. For example, =2 corresponds to a confidence level of approximately 95.4% (approximately 2σ principle). =3 corresponds to a confidence level of approximately 99.7% (approximately the 3σ principle). The value is chosen based on the system's tolerance for risk.

[0029] In one embodiment, when scanning a graphic containing a 90-degree sharp turn, it may be detected that the deviation values ​​of multiple consecutive sampling points significantly exceed the trajectory deviation detection threshold. For example, the deviation values ​​of five consecutive sampling points are respectively... Micrometers. This indicates that the galvanometer's dynamic tracking performance is insufficient at this sharp turn. The system analyzes the spatiotemporal distribution of the deviation value and marks the complex trajectory segment, for example, "the trajectory segment from time t1 to t2 is a complex trajectory segment".

[0030] In step S1203, the offset vector of the complex trajectory segment data is calculated to obtain offset vector data.

[0031] It should be noted that the offset vector is described by calculating the vector difference between the actual location point and the nearest point on the preset trajectory model. Its direction points in the direction of deviation, and its magnitude represents the magnitude of the deviation. This vector can be used for subsequent feedforward compensation.

[0032] In one embodiment, for this galvanometer system, micrometer, , micrometers, D=2 micrometers Choosing 2 for higher confidence levels, the preset trajectory deviation detection threshold is 7.97 micrometers. When scanning a graphic containing a 90-degree sharp turn, the system detected that the deviation values ​​at multiple consecutive sampling points significantly exceeded the 7.97-micrometer threshold. For example, the deviation values ​​at five consecutive sampling points were... Micrometers. This indicates that the galvanometer's dynamic tracking performance is insufficient at this sharp turn. By analyzing the spatiotemporal distribution of these out-of-limit deviations, the system can accurately mark the complex trajectory segment, such as "the trajectory segment from time t1 to t2 is a complex trajectory segment," providing a target for subsequent precise vector compensation.

[0033] In step S13, an offset compensation requirement signal is generated based on the offset vector data, including: S1301, Extract the displacement deviation from the offset vector data and analyze the displacement components to obtain displacement deviation data; S1302, if the displacement deviation data exceeds the preset drive system performance limit, a constraint over-limit flag is generated to obtain constraint over-limit data; S1303, map the constraint over-limit flag in the constraint over-limit data with the preset control parameters to generate the initial offset compensation requirement signal and obtain the initial compensation signal data; S1304, The initial compensation signal data is vector-adjusted and optimized to obtain the offset compensation requirement signal.

[0034] In step S1301, displacement deviation is extracted from the offset vector data and displacement components are analyzed to obtain displacement deviation data.

[0035] It should be noted that the offset vector data typically contains direction and distance information between the actual position of the laser beam and the preset trajectory. By analyzing the displacement components of the offset vector, the displacement deviations in the x and y directions can be separated.

[0036] In one embodiment, in the laser galvanometer control system, the offset vector is (8, 12), representing a beam offset of 8 micrometers along the x-axis and 12 micrometers along the y-axis. By analyzing the components of the offset vector, displacement deviation data is generated, including an 8-micrometer deviation in the x-direction and a 12-micrometer deviation in the y-direction. This data reflects the tracking error of the galvanometer at a specific sampling point, providing a basis for subsequent compensation decisions.

[0037] In step S1302, if the displacement deviation data exceeds the performance limit of the drive system, a constraint over-limit flag is generated, and constraint over-limit data is obtained.

[0038] It should be noted that the displacement deviation data is compared with the performance limits of the galvanometer drive system to evaluate whether the system can complete the deviation correction within a single control cycle. A flag of "1" indicates exceeding the limit, generating constraint over-limit data. The drive system performance limit is a quantitative representation of the maximum trajectory correction capability that the galvanometer motor can achieve under specific operating conditions. This limit value is set based on the physical characteristics and dynamic parameters of the galvanometer motor and the response capability of the control system; its specific calculation formula is as follows: ; ; ; in, This represents the maximum correctable displacement within a single control cycle. This represents the maximum correctable angular displacement within a single control cycle. This is the maximum angular acceleration of the motor. This is the maximum output torque of the motor. The transmission ratio is... For transmission efficiency, For the system friction torque, Let the system's rotational inertia be... To control the cycle, This is the conversion coefficient from angle to displacement. This formula is applicable to small time steps. For smaller approximate calculations, numerical simulation verification is recommended for highly nonlinear systems.

[0039] In one embodiment, the maximum torque of the drive motor , , , , , , The maximum correctable displacement within a single control cycle was calculated. Considering a safety factor of 1.2, the system is set to have a maximum correction capability of 1.2 in both the x and y directions. If the displacement deviation is (55, 60) micrometers, then both directions exceed the limit, and the system generates a constraint over-limit flag.

[0040] In step S1303, the constraint over-limit flag in the constraint over-limit data is mapped with the preset control parameters to generate an initial offset compensation requirement signal, thereby obtaining initial compensation signal data.

[0041] It should be noted that the preset control parameters are a set of control rules predefined based on the galvanometer dynamics and manufacturing requirements, including current gain, feedforward compensation parameters, and safety limiting parameters. Based on the constraint over-limit data, the constraint over-limit flags are mapped to the preset control parameters to generate the initial offset compensation requirement signal.

[0042] In one embodiment, preset control parameters specify that the motor drive current gain should be increased by 5% for every 1 micrometer exceeding the limit of displacement deviation. For an exceedance of 4.4 micrometers in the x-direction and 8.4 micrometers in the y-direction, the system generates initial compensation signal data, such as increasing the x-axis current gain by 22% and the y-axis current gain by 42%. This signal ensures that the system can respond quickly to large deviations.

[0043] In step S1304, the initial compensation signal data is vector-adjusted and optimized to obtain the offset compensation requirement signal.

[0044] It should be noted that vector adjustment optimization smooths the initial compensation signal by considering trajectory continuity and mirror dynamic characteristics, thus avoiding mirror oscillation caused by sudden current changes. The vector adjustment optimization employs a first-order low-pass filtering algorithm, whose transfer function is expressed in the continuous domain as follows: The difference equation discretized in the discrete domain using the forward Euler method is: .

[0045] in, For smoothing coefficients, The filter time constant is The control period is 0.001 seconds. The filter cutoff frequency is... Based on the dynamic response characteristics of the galvanometer system, the cutoff frequency is typically set within the range of 50-200Hz, corresponding to the time constant. millisecond.

[0046] In one embodiment, in a high-speed scanning corner region, the initial signal may cause a current step. The vector adjustment method sets the filter time constant by analyzing the offset trend of multiple consecutive sampling points. The smoothing coefficient is calculated in milliseconds (corresponding to a cutoff frequency of approximately 80Hz). A first-order low-pass filter is used to smooth the compensation signal. For example, a 22% current gain adjustment on the x-axis is filtered to generate a smoother compensation signal, which is achieved through a recursive formula. The offset gradually increased from 0% to approximately 19.8% over five control cycles. The final offset compensation signal data better matched the dynamic response characteristics of the galvanometer.

[0047] In step S14, the offset compensation demand signal is processed in conjunction with historical motion data to generate an adjustment command sequence to drive the motion control device, thereby obtaining compensated motion parameter data, including: S1401: Acquire historical motion data and extract trajectory features from it, analyze path patterns, and obtain trajectory feature data; S1402, Process the deviation component between the offset compensation demand signal and the trajectory feature data to obtain deviation analysis data; S1403, The deviation analysis data is mapped with preset control parameters to obtain adjustment instruction sequence data; S1404, the motion control device is driven by the adjustment instruction sequence data to adjust the operating parameters and obtain the compensated motion parameters.

[0048] In step S1401, historical motion data is acquired and trajectory features are extracted from it. Path patterns are analyzed to obtain trajectory feature data.

[0049] It should be noted that historical motion data typically includes information on the position, velocity, and acceleration of the galvanometer during similar machining tasks. Path pattern analysis can extract trajectory features, such as mean curvature, maximum acceleration, or corner frequency, from consecutive location points based on trajectory geometry.

[0050] In one embodiment, historical motion data of the galvanometer during the processing of similar patterns shows that straight segments account for 60%, the average curvature of circular segments is 0.02 rad / m, and the maximum acceleration is 8000 rad / s². These characteristic data reflect the typical motion pattern of the galvanometer and provide a reference for subsequent compensation optimization.

[0051] In step S1402, the deviation components between the offset compensation demand signal and the trajectory feature data are processed to obtain deviation analysis data.

[0052] It should be noted that by jointly analyzing the correlation between the offset compensation demand signal and the trajectory feature data, the systematic characteristics of the deviation can be identified.

[0053] In one embodiment, the offset compensation demand signal indicates a significant increase in the y-axis current gain required in the high curvature segment. Through joint analysis, the system, combining the high acceleration characteristics in the trajectory feature data, identifies that the deviation is mainly caused by the system's dynamic hysteresis. The system generates deviation analysis data, records the main deviation component as dynamic hysteresis error, and quantifies the hysteresis time as 0.2 ms. This data provides a precise basis for feedforward compensation.

[0054] In step S1403, the deviation analysis data is mapped with preset control parameters to obtain adjustment command sequence data.

[0055] It should be noted that, based on the deviation analysis data, preset control parameters are mapped to generate an adjustment instruction sequence that includes timing relationships.

[0056] In one embodiment, preset control parameters specify that the identified dynamic hysteresis error can be compensated by applying a drive current 0.2 ms in advance. For a detected 12-micron deviation, the system generates a sequence of instructions to increase the y-axis current gain from the reference value to 142% in three steps, starting 0.2 ms before the trajectory point. These instruction sequences define the timing of the galvanometer's correction actions.

[0057] In step S1404, the motion control device is driven by the adjustment instruction sequence data to adjust the operating parameters and obtain the compensated motion parameters.

[0058] In one embodiment, after receiving a sequence of current gain adjustment commands, the galvanometer controller fine-tunes the current output curve based on real-time position feedback. After optimization, compensated motion parameter data is generated, with the final y-axis current gain adjusted to 138% and the peak current limit set at 85% of the maximum value. These parameters ensure effective compensation while preventing motor overload, thus improving the stability of the galvanometer during high-speed motion.

[0059] In step S15, the compensated motion parameter data is compared with the collected actual position feedback results. If the feedback deviation is less than a preset model update condition threshold, the parameters of the preset trajectory model are updated based on historical trajectory features to obtain an optimized trajectory model, including: S1501, collects the actual position feedback results of the galvanometer; S1502, calculate the difference between the actual position feedback result and the expected position corresponding to the compensated motion parameter data to obtain the first fused deviation data; S1503, compare the first fused deviation data with a preset model update condition threshold. If the first fused deviation data is less than the preset model update condition threshold, then obtain the deviation judgment data. S1504, Based on the deviation judgment data, extract trajectory features and velocity distribution data from historical trajectory data, update the parameters in the preset trajectory model, and obtain updated parameter data; S1505, Based on the updated parameter data, adjust the preset trajectory model to generate an optimized trajectory model.

[0060] In step S1501, the actual position feedback result of the galvanometer is acquired.

[0061] It should be noted that the actual position feedback result of the galvanometer is collected from the position detection device. The position detection device usually refers to angle or position sensors such as photoelectric encoders and rotary transformers, which are used to obtain the actual physical position of the galvanometer after executing the compensation command, so as to form the feedback link of closed-loop control.

[0062] In one embodiment, the photoelectric encoder directly acquires the actual deflection angle of the galvanometer shaft on the X-axis as 0.102 radians.

[0063] In step S1502, the difference between the actual position feedback result and the expected position corresponding to the compensated motion parameter data is calculated to obtain the first fused deviation data.

[0064] It should be noted that the compensated motion parameter data includes the motion state that the controller expects to achieve, such as the expected angle or position. By calculating the difference between the actual position and the expected position, the residual tracking error of the system is obtained, resulting in more accurate first fused deviation data. To improve data reliability, Kalman filtering can be used to smooth this residual error, yielding even more accurate first fused deviation data.

[0065] In one embodiment, the expected X-axis position corresponding to the compensated motion parameters is 10200 micrometers (calculated based on the expected angle and conversion factor). The actual position detected by the encoder is also 10200 micrometers (corresponding to 0.102 radians, k=0.1m / rad). The initial difference is 0. The system combines the deviation data from multiple current and historical sampling points and fuses them using Kalman filtering to finally generate the first fused deviation data, for example, a residual deviation of 1.2 micrometers in the X direction and a residual deviation of 0.8 micrometers in the Y direction. This data reflects the systematic error that still exists after compensation.

[0066] In step S1503, the fused deviation data is compared with a preset model update condition threshold. If the fused deviation data is less than the preset model update condition threshold, deviation judgment data is obtained.

[0067] It should be noted that the preset model update condition threshold is a critical condition used to determine whether the system is in a stable state and allows trajectory model updates. Its setting is based on system stability requirements, model update effectiveness conditions, and control performance optimization objectives. It can be quantified using specific mathematical formulas. ; in, This indicates the threshold condition for model updates; The reference threshold, measured in a stable laboratory environment without external interference, represents the deviation boundary. The laser galvanometer system is repeatedly executed along a simple trajectory, such as a low-speed straight line, collecting a large amount of trajectory tracking deviation data, for example, 10,000 times. The 95th percentile of this deviation data distribution is calculated, and this value is set as [value missing]. For example, measured by this method =2 micrometers; This represents the noise standard deviation of the excitation position detection device; This represents the weighting coefficient, used to adjust the contribution of each error to the total threshold. It is determined through system identification and experimental calibration, such as optimization using gradient descent. The calibration is a system optimization process, conducted through orthogonal experimental design, testing multiple sets of data under typical machining tasks of varying trajectory complexity, such as sharp turns and small arcs. The combination with the smallest tracking error is selected as the calibration result. The system is run using the threshold T generated by each set of coefficients, and the overall performance score of the system under each set of coefficients is recorded. This score is a weighted function of the model update frequency (which should be kept moderate) and the trajectory tracking accuracy (which should be as high as possible). The set of coefficients that results in the optimal overall performance score is selected. This serves as the final calibration result. For example, through fitting and optimization of a large amount of experimental data, a set of effective values ​​is determined. .

[0068] In one embodiment, The calculation results are obtained (All values ​​are in micrometers.) The system sets the model update threshold to 2.71 micrometers. During operation, comparison revealed that the Euclidean distance between the first fused deviation data (1.2, 0.8 micrometers) was approximately 1.44 micrometers, less than the threshold of 2.71 micrometers. Based on this, the system generates deviation judgment data, such as a flag or trigger signal, indicating that the system is currently in a stable state, meets the model update conditions, and allows the subsequent model parameter update process to proceed.

[0069] In step S1504, based on the deviation judgment data, trajectory features and velocity distribution data are extracted from historical trajectory data, and the parameters in the preset trajectory model are updated to obtain updated parameter data.

[0070] It should be noted that the trajectory model parameter update process is initiated based on the deviation judgment data, which indicates that the system is in a stable state. The update mechanism is based on a preset adjustment rule, which defines the mapping relationship from "trajectory features and velocity distribution data" to "trajectory model parameter adjustment amount". This adjustment rule can be implemented using a lookup table method or a proportional coefficient method. The system calculates trajectory features and velocity distribution data in real time from historical trajectory data. Calculating trajectory features includes calculating the approximate curvature of discrete trajectory points, based on continuous position points of the most recent historical trajectory. , , Calculate each intermediate point instantaneous curvature at First, calculate the vector. , ; Its approximate calculation formula is: ; The arithmetic mean of all instantaneous curvatures is then taken to obtain the average curvature C of the trajectory segment. The velocity distribution data includes the standard deviation of acceleration, which is calculated by first calculating the angular acceleration sequence based on the time series of historical position data. Then calculate the standard deviation of the sequence. The system will calculate trajectory features such as mean curvature C, rate of change of curvature, and velocity distribution data such as mean velocity and standard deviation of acceleration. The system establishes a correlation with preset trajectory model parameters such as path smoothness, look-ahead distance, and acceleration curve time constant adjustment amounts. Using a lookup table method, the system retrieves the corresponding parameter adjustment amount from a preset lookup table based on the currently extracted feature value; using a proportional coefficient method, the system multiplies the feature value by a preset proportional coefficient to obtain the parameter adjustment amount.

[0071] In one embodiment, key parameters of the preset trajectory model include the path smoothness factor ( Dimensionless (range 0-1, larger values ​​result in smoother trajectories) and acceleration feedforward gain ( (Dimensionless) The system extracts the average curvature C (rad / m) and acceleration standard deviation of the current trajectory segment from historical data of the most recent 100 sampling points. The preset adjustment rule table is as follows, when... and Parameter adjustment action , ;when and Parameter adjustment action , When C 0.05 and >5. Parameter adjustment action , ;when and Parameter adjustment action , If the system calculates the current... , Then, based on the adjustment rule table, the first rule is matched, and the updated parameter data is generated. , At the same time, the system records the timestamp of this update, for example, " This feature-rule-based adaptive update mechanism enables the trajectory model to be dynamically optimized, better matching the current running state and trajectory complexity.

[0072] In step S1505, the preset trajectory model is adjusted based on the updated parameter data to generate an optimized trajectory model.

[0073] In one embodiment, the updated parameter data is used to adjust the smoothing constraints and acceleration limiting of the trajectory model. The smoothing factor of 0.651 is mapped to the trajectory filter cutoff frequency, which is adjusted from 500Hz to 480Hz, and the acceleration weight of 0.4184 is mapped to the maximum acceleration, which is adjusted from 15000rad / s² to 14800rad / s². This ultimately generates optimized trajectory model data, which further reduces tracking error while maintaining processing efficiency.

[0074] In step S16, based on the target trajectory provided by the optimized trajectory model, the current motion state reflected by the filtered position signal is predicted and simulated. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated, including: S1601, Based on the optimized trajectory model, acquire historical motion data and perform data cleaning to obtain cleaned historical data; S1602, Based on the filtered position signal and the cleaned historical data, calculate the deviation between the current motion state and the expected state of the optimized trajectory model to obtain the second fused deviation data; S1603, predict and simulate the development trend of the second fused deviation data, and generate simulation output data; S1604, if the deviation between the simulated output data and the preset accuracy requirement verification standard is less than the preset simulation verification threshold, then the control strategy is confirmed to be effective based on the simulated output data, and a stable control signal is generated.

[0075] In step S1601, based on the optimized trajectory model, historical motion data is acquired and cleaned to obtain cleaned historical data.

[0076] It should be noted that historical motion data similar to the motion characteristics described by the current optimized trajectory model are acquired for subsequent prediction simulations. Specifically, the system uses the trajectory characteristics of the optimized trajectory model, such as the curvature and velocity of the current and adjacent path points, as query conditions to retrieve historical motion data recorded on similar trajectory segments, such as those with similar curvature and velocity distributions, from the time series database. This data includes information such as the angular position, angular velocity, and drive current of the galvanometer. The acquired raw historical data needs to be cleaned to remove outliers and noise to ensure the reliability of the prediction. The cleaning method includes smoothing the data using a median filter with a window size of 5; defining data points whose deviation from the median within the sliding window exceeds three times the standard deviation within the window as outliers, and replacing them with the mean of their adjacent valid data.

[0077] In one embodiment, the current optimized trajectory model indicates that the system is on a circular arc segment with a curvature of approximately 0.1 rad / m. Based on this, the system retrieves historical data from the database showing past operations on similar curvature trajectory segments. One retrieved original historical record shows an angle position of x = 0.105 mRA and y = 0.042 mRA at a certain moment, but the records before and after it show x ≈ 0.100 mRA and y ≈ 0.040 mRA. After median filtering, this point is identified as an outlier and replaced with x = 0.100 mRA and y = 0.040 mRA, generating cleaned historical data. This model-feature-based targeted data acquisition and cleaning method ensures that the historical data used for prediction is highly relevant to the current task and of reliable quality.

[0078] In step S1602, based on the filtered position signal and the cleaned historical data, the deviation between the current motion state and the expected state of the optimized trajectory model is calculated to obtain the second fused deviation data.

[0079] It should be noted that this step aims to quantify the current tracking error trend of the system. The current motion state is characterized by the filtered position signal (reflecting real-time position) and its derived velocity / acceleration information. The expected state of the optimized trajectory model is defined by the ideal position, velocity, and acceleration sequence given by the model. By calculating the differences between the actual state and the expected state provided by the current cleaned historical data in multiple dimensions such as position and velocity, and performing weighted fusion, a second fused deviation data is obtained. This second fused deviation data reflects the amplitude and trend of the system tracking error.

[0080] In one embodiment, the filtered position signal shows the current galvanometer position as x = 0.101 mRA and y = 0.041 mRA. The optimized trajectory model's expected position at the current moment is x = 0.100 mRA and y = 0.040 mRA. Simultaneously, the recent velocity deviation trend is derived by analyzing the cleaned historical data. The system performs a weighted average of the position and velocity deviations, for example, assigning a weight of 0.6 to the real-time position deviation and a weight of 0.4 to the historical average deviation, to calculate the second fused deviation data, such as a combined deviation of 0.02 mRA in the X direction and 0.03 mRA in the Y direction. This data characterizes the system's likely persistent error state in the near future.

[0081] In step S1603, the development trend of the second fused deviation data is predicted and simulated to generate simulation output data.

[0082] It should be noted that the core of the predictive simulation is the system dynamics model. This model describes the electromechanical dynamic characteristics of the laser galvanometer, and its discrete state-space equations are as follows: ; in, Let be the state vector of the system at time t. Indicates the deflection angle of the galvanometer. Indicates the angular velocity of the galvanometer; To control inputs such as drive current; This is the system output (i.e., the predicted position). The state matrix A, input matrix B, and output matrix C are determined by the physical parameters of the galvanometer, such as the moment of inertia J and damping coefficient. Torque constant and control cycle The moment of inertia J and damping coefficient of the galvanometer were determined through step response testing. The torque constant is obtained through torque testing. .

[0083] The prediction simulation process is as follows: taking the current state (estimated from the filtered position signal and cleaned historical data) as the initial state x(0), and the future control sequence u(0), u(1),... given by the optimized trajectory model as input, the above-mentioned dynamic model and numerical integration methods such as the Euler method are used. Forward simulation is performed to predict the state sequence over several future control cycles, thereby generating simulated output data, i.e., the predicted position / deviation at future times.

[0084] In one embodiment, the moment of inertia is based on the galvanometer parameters. Damping coefficient Torque constant Control cycle Calculated .

[0085] Using the current error state reflected by the second fused deviation data as the initial perturbation, and combining it with the future control input provided by the optimized trajectory model, a 5-step (5-millisecond) simulation was performed. Simulation results show that the predicted Y-direction position deviation at the 5th millisecond will decrease from the current 0.03 milliradians to 0.02 milliradians. The simulation output data reveals the convergence trend of the error.

[0086] In step S1604, if the deviation between the simulated output data and the preset accuracy requirement verification standard is less than the preset simulation verification threshold, then the control strategy is confirmed to be effective based on the simulated output data, and a stable control signal is generated.

[0087] It should be noted that the deviation between the simulated output data and the preset accuracy requirement verification standard is the Euclidean distance between the deviations in the x and y directions. The preset accuracy requirement verification standard is the absolute accuracy required by the manufacturing process, for example, requiring an X-direction deviation of less than 2.0 micrometers and a Y-direction deviation of less than 4.0 micrometers. The preset simulation verification threshold is a typical noise fluctuation range determined by statistical analysis of historical deviation data. Historical trajectory deviation data generated from at least 1000 hours of operation is collected, and the cleaned x-direction and y-direction deviation data are calculated using the 95th percentile of the historical deviation data to ensure coverage of most stable operating conditions. The specific value can be adjusted according to the system accuracy requirements. The calculation results show that in 95% of the historical operating data, the x-direction deviation is less than 1.9 micrometers and the y-direction deviation is less than 3.8 micrometers. The Euclidean distance between the simulated output data (predicted future deviation) and the accuracy requirement verification standard (maximum allowable deviation) is calculated. If this distance is less than the simulation verification threshold, it indicates that the prediction result not only meets the accuracy requirements but also has a sufficiently high stability margin. The system then confirms the effectiveness of the control strategy and directly generates a stable control signal based on the simulated output data. In this step, the parameters of the optimized trajectory model remain unchanged to ensure the stability of the control decision basis.

[0088] In one embodiment, the simulated output data needs to be compared with a preset accuracy requirement verification standard. The standard specifies that the deviation in the y-direction should be less than 4.0 micrometers and the deviation in the x-direction should be less than 2.0 micrometers. If the simulated output data has a y-direction deviation of 3.0 micrometers and an x-direction deviation of 1.0 micrometer, its combined deviation from the accuracy standard is less than the simulated verification threshold (2.0 micrometers), thus meeting the verification requirement, and parameter optimization is triggered. The parameter mapping method converts the deviation data into trajectory parameter adjustment instructions, such as adjusting the path smoothness from 0.5 to 0.45 and the maximum angular acceleration from 15000 rad / s² to 14000 rad / s², generating stable control signal data.

[0089] In step S17, the environmental interference characteristics are calibrated using the stabilization control signal, and the real-time position signal is enhanced and smoothed to obtain an enhanced and filtered position signal, including: S1701, Based on the stable control signal, analyze the environmental interference characteristics of the filtered position signal to obtain an interference feature dataset; S1702, if the deviation between the interference feature dataset and the preset noise model is greater than the preset dynamic adjustment threshold, then the filtered position signal is enhanced and smoothed to obtain an enhanced filtered signal. S1703, For the enhanced filtered signal, the stable control signal is adjusted using signal calibration parameters to obtain the enhanced filtered position signal.

[0090] In step S1701, based on the stable control signal, the environmental interference characteristics of the filtered position signal are analyzed to obtain an interference feature dataset.

[0091] It should be noted that interference may originate from vibration or electromagnetic noise in the processing environment. By analyzing the signal frequency components using Fast Fourier Transform (FFT), abnormal peaks are identified as interference characteristics.

[0092] In one embodiment, analysis revealed periodic interference in the y-direction signal at a frequency of 500 Hz, possibly caused by vibrations from a nearby fan, generating an interference feature dataset. This data records information such as the frequency and amplitude of the interference, providing a basis for subsequent filtering.

[0093] In step S1702, if the deviation between the interference feature dataset and the preset noise model is greater than the preset dynamic adjustment threshold, then the filtered position signal is enhanced and smoothed to obtain an enhanced filtered signal.

[0094] It should be noted that the preset noise model characterizes the system's background noise level under normal operating conditions without strong external interference. This is achieved by running the system continuously for a relatively long period, such as 24 hours, in a favorable environment, collecting noise data from the position signals. The statistical characteristics of this model are described by the mean μ and standard deviation σ of the noise amplitude, calculated using the following formula: ; Where N is the total number of historical noise data samples, Let be the amplitude of the i-th noise sample. The dynamically adjusted threshold is the boundary for determining whether the current interference is "abnormal". Based on the above noise model, and assuming that the noise roughly follows a normal distribution, the dynamically adjusted threshold is set to . This threshold covers approximately 99.7% of the normally distributed data range. For example, when... , The dynamic adjustment threshold is 0.095 micrometers. If the deviation is less than this threshold, such as 0.1 micrometers, a Kalman filter is triggered for enhanced smoothing. The Kalman filter combines historical motion states with current observations to optimize position estimation. The state equation is as follows: The observation equation is .in Let k be the state vector of the system at time k, containing the position. and speed ; Here is the state transition matrix. To control the input matrix, For system control input (acceleration). The noise is a process noise that follows a zero-mean Gaussian distribution. Let be the process noise covariance matrix. To observe the noise, it follows a zero-mean Gaussian distribution with a covariance matrix of R. The units of Q and R must be consistent with the units of the state vectors (position and velocity), and are determined through system identification experiments. Specific methods include collecting the system's input and output data under specific operating conditions, and then fitting the data using algorithms such as least squares method and maximum likelihood estimation to obtain Q and R that truly reflect the statistical characteristics of the system noise.

[0095] Kalman filtering is implemented using the following recursive formula: State prediction: ; Observation Update: ; The process noise covariance matrix Q and the observation noise covariance matrix R are determined through statistical analysis of historical operating data or system identification experiments. Specifically, they can be obtained by collecting the system's free response data without control input and calculating the covariance of the state estimation error.

[0096] In one embodiment, the system's preset noise model parameters are μ = 0.02 μm and σ = 0.025 μm. The calculated dynamic adjustment threshold is 0.095 μm. The initial denoised signal is x = 12.3 μm and y = 5.7 μm. Analysis of the current interference feature dataset reveals a deviation of 0.10 μm from the preset noise model. Since 0.10 μm is greater than 0.095 μm, the system determines that abnormal interference exists and immediately initiates Kalman filtering for enhanced smoothing. Based on the above Kalman filter model, the process noise covariance is set. With an observation noise covariance R=0.5, the enhanced filtered signal (x=12.2 μm, y=5.65 μm) is obtained after adjustments through state prediction and observation update steps. This method effectively fuses multi-source information, improving signal smoothness and accuracy.

[0097] In step S1703, for the enhanced filtered signal, the stabilization control signal is adjusted using signal calibration parameters to obtain the enhanced filtered position signal.

[0098] It should be noted that, for enhanced filtered signals, signal calibration parameters are used to adjust the stabilization control signal. Calibration parameters may include position deviation correction coefficients or current adjustment factors.

[0099] In one embodiment, the enhanced filtered signal shows a y-direction deviation of 0.05 milliradians. The calibration parameters map this deviation to a drive current adjustment command, for example, adjusting the y-axis drive current from 1.5A to 1.45A, generating an enhanced filtered position signal. This adjustment ensures that the laser beam maintains a stable trajectory on the processing plane.

[0100] In step S18, the enhanced filtered position signal is compared with the expected position of the optimized trajectory model. If the deviation value is greater than a preset trajectory deviation detection threshold, the extraction of the offset vector data and the generation of the offset compensation requirement signal are repeated to obtain the final stable control signal, including: S1801, Calculate the deviation between the enhanced filtered position signal and the expected position of the optimized trajectory model to obtain the offset vector dataset; S1802, if the central deviation value of the offset vector data is greater than the preset trajectory deviation detection threshold, then the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated, and the stabilization control signal is adjusted to obtain the final stabilization control signal.

[0101] In step S1801, the deviation between the enhanced filtered position signal and the expected position of the optimized trajectory model is calculated to obtain the offset vector dataset.

[0102] It should be noted that the deviation between the enhanced filtered position signal and the optimized trajectory model is calculated using Euclidean distance.

[0103] In one embodiment, the optimized trajectory model's expected position at a certain moment is x = 15.0 mradians and y = 8.5 mradians, while the enhanced filtered signal is x = 15.2 mradians and y = 8.44 mradians. The Euclidean distance algorithm calculates a deviation of approximately 0.22 mradians. After applying the conversion coefficient k, the deviation of 0.22 mradians corresponds to a displacement deviation of 22 micrometers.

[0104] In step S1802, if the central deviation value of the offset vector data is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated, and the stabilization control signal is adjusted to obtain the final stabilization control signal.

[0105] It should be noted that the trajectory deviation detection threshold here still uses the calculation formula from the above steps, specifically for this galvanometer system. micrometer, , If D = 2 micrometers, then the preset trajectory deviation detection threshold is 7.97 micrometers. If the deviation value is greater than the preset trajectory deviation detection threshold, for example, a deviation value of 22 micrometers, then the offset vector is extracted using the least squares method. The least squares method uses a linear regression model, and the fitted model is... Where x is the time series index and y is the corresponding deviation value. This is achieved by minimizing the weighted sum of squared residuals. To solve for the parameters and Where n is the number of data points used for fitting, typically the 15 most recent sampling points. The weighting coefficients are calculated using exponential weighting. ,in The weight decay rate of historical data in the weighted least squares method is determined, and its selection is directly related to the closed-loop bandwidth or equivalent time constant of the system. Specifically, the system's unit step response settling time (e.g., the time required to reach 95% of the steady-state value) is first obtained through system identification or calculated from motor parameters. The number of data points n used for fitting (e.g., 15) should cover a time window roughly equivalent to the unit step response settling time. The value of should be chosen so that the weight decays to a certain extent at the beginning of the time window when i=1. An effective setting rule is to let That is, the weight of the oldest data in the window decays to 5%. From this, we can deduce... For n=15, the calculation is as follows: Therefore, it is usually taken as 0.2. For systems with faster response, a larger value such as 0.3 can be used, while for systems with slower response, a smaller value such as 0.1 can be used to give more weight to recent data.

[0106] The formula for solving the parameters is as follows ; Based on the slope obtained from the fitting and intercept It can predict the systemic offset at the current moment. The offset direction is determined. The least squares method is used to analyze multiple deviation data points to fit the offset direction and magnitude. Based on the offset vector dataset, a linear interpolation method is used to generate a compensation requirement signal. If the deviation value in the offset vector dataset obtained after iteration is less than a preset trajectory deviation detection threshold, such as 7.97 micrometers, the repeated execution process is stopped, and the stabilization control signal is adjusted to obtain the final stabilization control signal. Alternatively, if the number of iterations exceeds a preset value, such as 10 times, or if the deviation oscillation increases, the iteration is terminated and an alarm is triggered.

[0107] In one embodiment, the attenuation factor is set using the deviation data from the most recent 15 sampling points. Three consecutive deviation values ​​of 5 μm, 5.05 μm, and 5.8 μm were obtained. Using the weighted least squares method described above, an offset vector dataset was obtained, indicating a systematic offset of approximately 5.42 μm in the y-direction of the galvanometer. For this 5.42 μm y-direction offset, a series of compensation values ​​were generated by gradually transitioning from the current value to the target compensation value over five control cycles using linear interpolation. This process progressively adjusted the galvanometer's control signal. The compensation requirement signal indicated a need to reduce the y-axis drive current by 0.15 A. The adjusted stable control signal corrected the current from 1.5 A to 1.35 A. This method ensures a smooth transition in the control signal, avoiding galvanometer oscillations caused by abrupt changes.

[0108] In summary, this invention discloses a motion control method for a laser galvanometer, including the acquisition and preprocessing of real-time position signals to effectively filter out environmental noise interference; the identification of complex trajectory segments and calculation of offset vectors through trajectory deviation detection thresholds to achieve precise positioning of key points; the generation of compensation commands by combining historical motion data to achieve real-time dynamic compensation of offsets; the triggering of trajectory model optimization through model update condition thresholds to achieve adaptive adjustment of the control strategy; the further improvement of the system's anti-interference capability through predictive simulation and enhanced smoothing processing; and the formation of a complete feedback closed loop through iterative control to ensure the stability of the output signal. This invention, through multi-level deviation processing and iterative optimization, effectively solves the problem of insufficient real-time offset detection and compensation capabilities of existing laser galvanometers at key points of complex trajectories, providing a highly reliable and high-precision motion control solution for precision laser processing.

[0109] Reference Figure 2 The second embodiment of the present invention provides a motion control system for a laser galvanometer, comprising: The signal acquisition and preprocessing module acquires real-time position signals and performs preprocessing to obtain filtered position signals. The trajectory analysis and offset detection module calculates the deviation between the filtered position signal and the preset trajectory model. If the deviation is greater than the preset trajectory deviation detection threshold, it identifies complex trajectory segments and calculates offset vector data. The constraint judgment and compensation decision module generates an offset compensation demand signal based on the offset vector data; The data fusion and instruction generation module processes the offset compensation requirement signal by combining historical motion data, generates an adjustment instruction sequence to drive the motion control device, and obtains the compensated motion parameter data. The model verification and adaptive update module compares the compensated motion parameter data with the collected actual position feedback results. If the feedback deviation is less than the preset model update condition threshold, the parameters of the preset trajectory model are updated based on the historical trajectory features to obtain the optimized trajectory model. The prediction simulation and steady-state control module, based on the target trajectory provided by the optimized trajectory model, performs prediction simulation on the current motion state reflected by the filtered position signal. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated. The environmental calibration and signal enhancement module calibrates the environmental interference characteristics through the stable control signal and enhances and smooths the real-time position signal to obtain the enhanced and filtered position signal. The iterative control and feedback closed-loop module compares the enhanced filtered position signal with the optimized trajectory model. If the deviation value is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated to obtain the final stable control signal.

[0110] It should be noted that the motion control system for a laser galvanometer provided in this embodiment of the invention is used to execute all the process steps of the motion control method for a laser galvanometer in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0111] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a motion control program for a laser galvanometer. When the processor executes the computer program, it implements the steps described in the various laser galvanometer motion control method embodiments above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments, such as the environmental calibration and signal enhancement module.

[0112] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0113] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0114] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0115] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0116] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0117] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0118] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for controlling the motion of a laser galvanometer, characterized in that, include: Real-time position signals are acquired and preprocessed to obtain filtered position signals; The deviation between the filtered position signal and the preset trajectory model is calculated. If the deviation is greater than the preset trajectory deviation detection threshold, complex trajectory segments are identified and offset vector data is calculated. Generate an offset compensation requirement signal based on the offset vector data; The offset compensation demand signal is processed by combining historical motion data to generate an adjustment command sequence to drive the motion control device and obtain the compensated motion parameter data. Compare the compensated motion parameter data with the actual position feedback results. If the feedback deviation is less than the preset model update condition threshold, then update the parameters of the preset trajectory model based on the historical trajectory features to obtain the optimized trajectory model. Based on the target trajectory provided by the optimized trajectory model, the current motion state reflected by the filtered position signal is predicted and simulated. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated. The environmental interference characteristics are calibrated using the stabilized control signal, and the real-time position signal is enhanced and smoothed to obtain an enhanced and filtered position signal. The enhanced and filtered position signal is compared with the expected position of the optimized trajectory model. If the deviation value is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated to obtain the final stable control signal.

2. The motion control method for a laser galvanometer according to claim 1, characterized in that, Real-time position signals are acquired and preprocessed to obtain filtered position signals, including: The position detection device acquires real-time position signals from the motion control device at a preset signal acquisition frequency and performs preliminary cleaning to obtain cleaned position signals. The cleaned position signal is subjected to noise smoothing filtering to obtain the filtered position signal.

3. The motion control method for a laser galvanometer according to claim 1, characterized in that, The deviation between the filtered position signal and the preset trajectory model is calculated. If the deviation is greater than the preset trajectory deviation detection threshold, complex trajectory segments are identified and offset vector data is calculated, including: The deviation between the filtered position signal and the preset trajectory model is calculated to obtain the deviation data; If the deviation value data is greater than the preset trajectory deviation detection threshold, the deviation value data is analyzed to determine the complex trajectory segment and obtain the complex trajectory segment data. The offset vector of the complex trajectory segment data is calculated to obtain the offset vector data.

4. The motion control method for a laser galvanometer according to claim 1, characterized in that, Generate an offset compensation requirement signal based on the offset vector data, including: Displacement deviation is extracted from the offset vector data and the displacement components are analyzed to obtain displacement deviation data; If the displacement deviation data exceeds the performance limit of the drive system, a constraint over-limit flag is generated, and constraint over-limit data is obtained. The constraint over-limit flag in the constraint over-limit data is mapped to the preset control parameters to generate the initial offset compensation requirement signal, thus obtaining the initial compensation signal data. The initial compensation signal data is vector-adjusted and optimized to obtain the offset compensation requirement signal.

5. The motion control method for a laser galvanometer according to claim 1, characterized in that, The offset compensation demand signal is processed by combining historical motion data to generate an adjustment command sequence to drive the motion control device, resulting in compensated motion parameter data, including: Historical motion data is acquired, trajectory features are extracted from it, path patterns are analyzed, and trajectory feature data is obtained. The deviation components between the offset compensation demand signal and the trajectory feature data are processed to obtain deviation analysis data; The deviation analysis data is mapped to preset control parameters to obtain adjustment command sequence data; The motion control device is driven by the adjustment instruction sequence data to adjust the operating parameters and obtain the compensated motion parameters.

6. The motion control method for a laser galvanometer according to claim 1, characterized in that, Comparing the compensated motion parameter data with the collected actual position feedback results, if the feedback deviation is less than a preset model update condition threshold, the parameters of the preset trajectory model are updated based on historical trajectory features to obtain an optimized trajectory model, including: The actual position feedback result of the galvanometer is collected; The difference between the actual position feedback result and the expected position corresponding to the compensated motion parameter data is calculated to obtain the first fused deviation data; The first fused deviation data is compared with a preset model update condition threshold. If the first fused deviation data is less than the preset model update condition threshold, deviation judgment data is obtained. Based on the deviation judgment data, trajectory features and velocity distribution data are extracted from historical trajectory data, and the parameters in the preset trajectory model are updated to obtain the updated parameter data. Based on the updated parameter data, the preset trajectory model is adjusted to generate an optimized trajectory model.

7. The motion control method for a laser galvanometer according to claim 1, characterized in that, Based on the target trajectory provided by the optimized trajectory model, the current motion state reflected by the filtered position signal is predicted and simulated. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated, including: Based on the optimized trajectory model, historical motion data is acquired and cleaned to obtain cleaned historical data. Based on the filtered position signal and the cleaned historical data, the deviation between the current motion state and the expected state of the optimized trajectory model is calculated to obtain the second fused deviation data. The development trend of the second fused deviation data is predicted and simulated to generate simulation output data; If the deviation between the simulated output data and the preset accuracy requirement verification standard is less than the preset simulation verification threshold, then the control strategy is confirmed to be effective based on the simulated output data, and a stable control signal is generated.

8. The motion control method for a laser galvanometer according to claim 1, characterized in that, The environmental interference characteristics are calibrated using the stabilized control signal, and the real-time position signal is enhanced and smoothed to obtain an enhanced and filtered position signal, including: Based on the stable control signal, the environmental interference characteristics of the filtered position signal are analyzed to obtain an interference feature dataset. If the deviation between the interference feature dataset and the preset noise model is greater than the preset dynamic adjustment threshold, then the filtered position signal is enhanced and smoothed to obtain an enhanced filtered signal. For the enhanced filtered signal, the stabilization control signal is adjusted using signal calibration parameters to obtain the enhanced filtered position signal.

9. The motion control method for a laser galvanometer according to claim 1, characterized in that, Compare the enhanced filtered position signal with the expected position of the optimized trajectory model. If the deviation value is greater than a preset trajectory deviation detection threshold, repeat the process of extracting the offset vector data and generating the offset compensation requirement signal to obtain the final stable control signal, including: The deviation between the enhanced filtered position signal and the expected position of the optimized trajectory model is calculated to obtain the offset vector dataset; If the central deviation value of the offset vector data is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated, and the stabilization control signal is adjusted to obtain the final stabilization control signal.

10. A motion control system for a laser galvanometer, characterized in that, include: The signal acquisition and preprocessing module acquires real-time position signals and performs preprocessing to obtain filtered position signals. The trajectory analysis and offset detection module calculates the deviation between the filtered position signal and the preset trajectory model. If the deviation is greater than the preset trajectory deviation detection threshold, it identifies complex trajectory segments and calculates offset vector data. The constraint judgment and compensation decision module generates an offset compensation demand signal based on the offset vector data; The data fusion and instruction generation module processes the offset compensation requirement signal by combining historical motion data, generates an adjustment instruction sequence to drive the motion control device, and obtains the compensated motion parameter data. The model verification and adaptive update module compares the compensated motion parameter data with the collected actual position feedback results. If the feedback deviation is less than the preset model update condition threshold, the parameters of the preset trajectory model are updated based on the historical trajectory features to obtain the optimized trajectory model. The prediction simulation and steady-state control module, based on the target trajectory provided by the optimized trajectory model, performs prediction simulation on the current motion state reflected by the filtered position signal. If the simulation output meets the preset accuracy requirement verification standard, a stable control signal is generated. The environmental calibration and signal enhancement module calibrates the environmental interference characteristics through the stable control signal and enhances and smooths the real-time position signal to obtain the enhanced and filtered position signal. The iterative control and feedback closed-loop module compares the enhanced filtered position signal with the expected position of the optimized trajectory model. If the deviation value is greater than the preset trajectory deviation detection threshold, the process of extracting the offset vector data and generating the offset compensation requirement signal is repeated to obtain the final stable control signal.

Images