Debugging method and system for high-precision mechanical and electrical equipment

Abstract

The invention discloses a debugging method and system for high-precision mechanical and electrical equipment, and relates to the technical field of electrical equipment debugging, and the method comprises the steps: deploying a multi-source heterogeneous sensor on the high-precision mechanical and electrical equipment, collecting displacement, temperature, vibration and current signals, and generating multi-dimensional state data through time alignment and filtering processing; time sequence features of error disturbance are extracted based on the multi-dimensional state data, a dynamic composite error representation model is constructed, and a comprehensive positioning deviation estimated value is output; inputting the comprehensive positioning deviation estimated value into an electromechanical coupling virtual model in the high-fidelity digital twin platform, and automatically optimizing to generate an initial control parameter set; driving equipment to execute low-speed reciprocating motion by utilizing the initial control parameter set, acquiring a position response residual error in a reversing stage, identifying friction and hysteresis nonlinear characteristics on line based on the position response residual error, and constructing a feed-forward compensation function; and integrating the feed-forward compensation function and the initial control parameter set into an equipment bottom layer motion controller.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment commissioning technology, and in particular to a commissioning method and system for high-precision mechanical and electrical equipment. Background Technology

[0002] Electrical equipment commissioning technology refers to the technical process of ensuring that the control performance, motion accuracy, stability, and reliability of electromechanical integrated equipment (especially electromechanical systems containing servo drives, motion control, and sensor feedback) meet design specifications after installation or maintenance. This process involves a series of systematic tests, parameter tuning, performance verification, and error compensation. Therefore, improving the intelligence and safety of electrical equipment commissioning using advanced technologies is one of the most pressing issues to be addressed.

[0003] In the field of electrical equipment commissioning, traditional calibration and compensation are mostly based on static or quasi-static assumptions, which cannot characterize the dynamic characteristics of disturbances evolving over time. This causes the accuracy of the equipment to degrade rapidly after long-term operation. Moreover, in micron / nanoscale motion, nonlinear effects such as friction, hysteresis, and dead zone become the main bottlenecks limiting repeatability accuracy. Conventional linear controllers are almost powerless against such structural disturbances, especially prone to creep or overshoot during low-speed commutation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a debugging method for high-precision mechanical and electrical equipment. This solves the problem that traditional calibration and compensation are mostly based on static or quasi-static assumptions, which cannot characterize the dynamic characteristics of disturbances evolving over time. As a result, the accuracy of the equipment degrades rapidly after long-term operation. Furthermore, in micron / nanoscale motion, nonlinear effects such as friction, hysteresis, and dead zones become the main bottlenecks limiting repeatability accuracy. Conventional linear controllers are almost powerless against such structural disturbances, especially prone to creep or overshoot problems in low-speed commutation stages.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a debugging method for high-precision mechanical and electrical equipment, comprising: Deploy multi-source heterogeneous sensors in high-precision mechanical and electrical equipment to collect displacement, temperature, vibration and current signals, and generate multi-dimensional state data through time alignment and filtering; Based on the extraction of time series features of error disturbance from multidimensional state data, a dynamic composite error characterization model is constructed, and a comprehensive positioning deviation estimate is output. The estimated value of the comprehensive positioning deviation is input into the electromechanical coupling virtual model in the high-fidelity digital twin platform, and the initial control parameter set is automatically optimized and generated. The device is driven to perform low-speed reciprocating motion using an initial control parameter set. The position response residual during the commutation phase is collected. Based on the position response residual, the friction and hysteresis nonlinear characteristics are identified online, and a feedforward compensation function is constructed. The feedforward compensation function and the initial control parameter set are integrated into the device's underlying motion controller to form a closed-loop debugging strategy and start the device operation to obtain the tracking error. The disturbance uncertainty index is calculated based on the time evolution characteristics of the tracking error. The parameter update step size of the digital twin model and the correction magnitude of the feedforward compensation function are dynamically adjusted based on the disturbance uncertainty index. The process is iterated until the tracking error converges to within the preset micrometer-level accuracy threshold.

[0007] As a preferred embodiment of the debugging method for high-precision mechanical and electrical equipment described in this invention, the following steps are taken: Deploying multi-source heterogeneous sensors on the high-precision mechanical and electrical equipment to collect displacement, temperature, vibration, and current signals, and generating multi-dimensional state data through time alignment and filtering processing, specifically: High-resolution optical encoders, thermocouples, triaxial accelerometers, and Hall current sensors are installed at the drive end, load end, and key structural support points of the equipment's motion axis, respectively. All sensors are triggered by a unified hardware clock to achieve synchronous sampling and obtain raw displacement signals, temperature signals, vibration signals and current signals; The original signals are subjected to low-pass filtering to suppress high-frequency electromagnetic interference, and the vibration signals are additionally subjected to sliding median filtering to remove transient impact noise. The four types of filtered signals are strictly aligned according to the sampling time to form a synchronous multidimensional state data sequence.

[0008] As a preferred embodiment of the debugging method for high-precision mechanical and electrical equipment described in this invention, the steps of extracting time-series features of error disturbances based on multi-dimensional state data, constructing a dynamic composite error characterization model, and outputting a comprehensive positioning deviation estimate are as follows: The instantaneous positioning error is obtained by calculating the difference between the command position and the filtered displacement signal; Set a fixed-length sliding time window, and extract statistical features from the instantaneous positioning error, temperature signal, vibration signal and current signal within the sliding time window, including the mean error, temperature rise rate, vibration energy and current harmonic distortion rate. The statistical features are combined into a feature vector and input into a nonlinear mapping function, which is implemented by a radial basis neural network, and is used to output a comprehensive positioning error estimate that reflects the current intensity of the composite disturbance. Among them, vibration energy The calculation formula is: ; in, Indicates the duration of the sliding time window. Indicates at time The acquired three-dimensional vibration signal vector, This represents the Euclidean norm of the vector. It characterizes the energy intensity of structural vibration per unit time and is used to quantify the disturbance level caused by mechanical loosening or resonance.

[0009] As a preferred embodiment of the debugging method for high-precision mechanical and electrical equipment described in this invention, the step of inputting the comprehensive positioning deviation estimate into the electromechanical coupling virtual model in the high-fidelity digital twin platform to automatically optimize and generate an initial control parameter set includes the following steps: Establish a multi-physics coupled virtual model in the digital twin platform, which includes the electromagnetic dynamics of the motor, the stiffness of the mechanical transmission chain, the thermal expansion displacement effect, and the structural modal characteristics. The comprehensive positioning deviation estimate is injected as an external disturbance term into the output of the multiphysics coupled virtual model to simulate the dynamic response of the actual equipment under the current operating conditions. Define the vector of control parameters to be optimized, including proportional gain, derivative gain, notch filter center frequency, and trajectory look-ahead window length; A Bayesian optimization algorithm is used to iteratively search in the parameter space to minimize the mean square error between the virtual model output trajectory and the reference trajectory. In each iteration, Bayesian optimization constructs a Gaussian process surrogate model based on historical evaluation results and selects the next most promising parameter combination to reduce error for simulation evaluation; The converged parameter combination is used as the initial control parameter set; The expression for the mean square error J(p) in trajectory tracking is: ; in, This represents the vector of control parameters currently being evaluated. Indicates the reference trajectory. Indicates in the parameter The simulated displacement output by the digital twin model. This represents the total duration of a single test trajectory. Used to measure parameters The virtual tracking performance under these conditions is the objective function of Bayesian optimization.

[0010] As a preferred embodiment of the debugging method for high-precision mechanical and electrical equipment described in this invention, the steps of driving the equipment to perform low-speed reciprocating motion using an initial control parameter set, collecting the position response residual during the commutation phase, identifying friction and hysteresis nonlinear characteristics online based on the position response residual, and constructing a feedforward compensation function are as follows: Download the initial control parameter set to the device's underlying motion controller, and control a single motion axis to perform multiple complete reciprocating strokes in the positive and negative directions at a speed of less than 1 mm per second. The instantaneous positioning error within a fixed time interval before and after each zero-crossing of the velocity is used to form the position response residual sequence of the reversing phase; Based on the position response residual sequence and the corresponding velocity signal during the commutation stage, the internal parameters of the LuGre friction model and the Bouc-Wen hysteresis model are jointly fitted. The recursive least squares method is used to update the model parameters online, ensuring that the compensation function can be adaptively adjusted according to equipment wear or temperature changes; Construct a feedforward compensation function that takes real-time displacement and velocity as input and outputs compensation torque commands; The internal state evolution equation of the LuGre friction and Bouc-Wen hysteresis joint model is as follows: ; in, Indicates the current displacement. Indicates the current speed. Represents the hysteresis internal state variable. , , The hysteresis shape parameters to be identified; Among them, the feedforward compensation torque The calculation formula is: ; in, , , These represent static friction stiffness, damping friction coefficient, and viscous friction coefficient, respectively, all of which are real parameters obtained through online identification; The It is directly superimposed on the control output to counteract structural positioning deviations caused by friction and hysteresis.

[0011] As a preferred embodiment of the debugging method for high-precision mechanical and electrical equipment described in this invention, the step of integrating the feedforward compensation function and the initial control parameter set into the equipment's underlying motion controller to form a closed-loop debugging strategy and start equipment operation to obtain the tracking error includes the following specific steps: A standard feedback control law is configured in the motion controller, and its output is the sum of the proportional and derivative terms; The output of the feedforward compensation function is added to the output of the feedback control law to form a total control command and drive the servo driver. The equipment operates according to a preset standard test trajectory, recording the actual displacement and command position in real time; The difference between the two is calculated as the tracking error, and the complete error sequence of the most recent running cycles is cached.

[0012] As a preferred embodiment of the debugging method for high-precision mechanical and electrical equipment described in this invention, the steps of calculating the disturbance uncertainty index based on the time evolution characteristics of the tracking error, dynamically adjusting the parameter update step size of the digital twin model and the correction amplitude of the feedforward compensation function based on the disturbance uncertainty index, and iteratively executing until the tracking error converges to within a preset micrometer-level accuracy threshold are as follows: For multiple cycles of the cached tracking error sequence, calculate the standard deviation and coefficient of variation within the sliding window; The two are weighted and combined to form a disturbance uncertainty indicator; Two uncertainty thresholds, high and low, are set. When the disturbance uncertainty index is higher than the high threshold, the system is determined to be in a high disturbance state. At this time, the update step size of the control parameters in the digital twin model is reduced and the correction magnitude of the feedforward compensation function parameters is limited. When the disturbance uncertainty index is below the low threshold, the system is determined to be stable. At this time, the update step size and correction magnitude are increased to accelerate the convergence process. Repeatedly perform parameter updates, compensation function corrections, and equipment operation until the maximum tracking error for three consecutive cycles is less than the preset micron-level accuracy threshold; Among them, the disturbance uncertainty index The calculation formula is: ; in, Indicates recent The standard deviation of the tracking error over a period of time reflects the absolute magnitude of the error fluctuation. The coefficient of variation is defined as follows: ,in The average tracking error is used to measure the relative dispersion of the error with respect to its mean. These are preset weighting coefficients used to adjust the proportion of absolute fluctuations and relative dispersion in uncertainty assessment.

[0013] Secondly, the present invention provides a debugging system for high-precision mechanical and electrical equipment, comprising: Multi-source sensing fusion module, dynamic error modeling module, digital twin optimization module, nonlinear feedforward compensation module, closed-loop debugging and execution module, and adaptive iterative control module; The multi-source sensor fusion module is used to deploy optical encoders, thermocouples, triaxial accelerometers and Hall current sensors at the drive end, load end and key structural support points of the device's motion axis. It achieves synchronous sampling through a unified hardware clock and performs low-pass filtering and sliding median filtering on the original displacement, temperature, vibration and current signals to output a time-aligned multidimensional state data sequence. The dynamic error modeling module is used to calculate the instantaneous positioning error based on multidimensional state data. It extracts the mean error, temperature rise rate, vibration energy and current harmonic distortion rate as time series features within the sliding time window, inputs the feature vector into the radial basis neural network to construct a dynamic composite error characterization model, and outputs a comprehensive positioning deviation estimate. The digital twin optimization module is used to establish an electromechanical coupling virtual model in a high-fidelity digital twin platform, which includes motor electromagnetic dynamics, transmission chain stiffness, thermal expansion effect and structural modes. The comprehensive positioning deviation estimate is used as a disturbance input, and the Bayesian optimization algorithm is used to automatically optimize in the control parameter space to generate an initial control parameter set that minimizes the virtual trajectory tracking error. The nonlinear feedforward compensation module is used to drive the device to perform low-speed reciprocating motion using the initial control parameter set, collect the position response residual during the commutation stage, identify the LuGre friction and Bouc-Wen hysteresis model parameters online based on the residual and velocity signal, construct a feedforward compensation function with displacement and velocity as input, and output the compensation torque command. The closed-loop debugging execution module is used to integrate the feedforward compensation function and the initial control parameter set into the device's underlying motion controller to form a closed-loop control strategy that combines feedback and feedforward, driving the device to run along a standard test trajectory and collecting the tracking error between the actual displacement and the commanded position in real time. The adaptive iterative control module is used to calculate the disturbance uncertainty index based on the time evolution characteristics of the tracking error. This index is a weighted fusion of the error standard deviation and the coefficient of variation. Based on this index, the parameter update step size of the digital twin model and the correction amplitude of the feedforward compensation function are dynamically adjusted. The adjustment is conservative when there is high disturbance and the convergence is accelerated when there is low disturbance. The iterative execution continues until the tracking error is stably converged within the preset micron-level accuracy threshold.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the debugging method for high-precision mechanical and electrical equipment as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the debugging method for high-precision mechanical and electrical equipment as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by fusing multi-source heterogeneous sensing data, constructing a dynamic composite error model based on time series features, introducing a high-fidelity digital twin-driven automatic parameter optimization mechanism, embedding nonlinear friction and hysteresis feedforward compensation for micron / nanometer-level motion, and introducing an adaptive iterative control strategy based on disturbance uncertainty indicators, the debugging process of high-precision mechanical and electrical equipment has been transformed from static calibration and manual trial and error to dynamic perception and precise compensation. This effectively shortens the debugging cycle, improves the positioning repeatability and long-term operational stability, and overcomes the problems of high-precision performance caused by composite disturbances such as thermal drift, structural loosening, electromagnetic interference, and nonlinear friction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the debugging method for high-precision mechanical and electrical equipment in Example 1.

[0019] Figure 2 This is a schematic diagram of the debugging system for high-precision mechanical and electrical equipment in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, referring to Figure 1 and Figure 2 This is one embodiment of the present invention, which provides a debugging method for high-precision mechanical and electrical equipment, including the following steps: S1. Deploy multi-source heterogeneous sensors in high-precision mechanical and electrical equipment to collect displacement, temperature, vibration and current signals, and generate multi-dimensional state data through time alignment and filtering.

[0024] Furthermore, high-resolution optical encoders, thermocouples, triaxial accelerometers, and Hall current sensors are installed at the drive end, load end, and key structural support points of the equipment's motion axis, respectively. All sensors are triggered by a unified hardware clock to achieve synchronous sampling and obtain raw displacement, temperature, vibration, and current signals. The raw signals are low-pass filtered to suppress high-frequency electromagnetic interference, and the vibration signal is additionally filtered by sliding median filtering to remove transient impact noise. The four types of filtered signals are strictly aligned according to the sampling time to form a synchronous multidimensional state data sequence.

[0025] It should be noted that by deploying multiple types of high-precision sensors at the drive end, load end, and key support points, and by using a unified hardware clock for synchronous sampling and multi-level filtering, the perception distortion caused by asynchronous signals or noise interference is effectively eliminated. This provides a high-fidelity, highly consistent multi-dimensional state data foundation for subsequent error modeling, and effectively improves the system's sensitivity and reliability to weak composite disturbances.

[0026] In particular, the sensor layout strategy adopted in this step is not a simple stacking, but a targeted placement based on the physical mechanism of disturbance propagation path in high-precision electromechanical systems: the drive end reflects the input characteristics of the motor and transmission chain, the load end is directly related to the execution accuracy, and the support point captures the structural modal response; the three work together to completely reconstruct the dynamic behavior of the device in the spatial and temporal dimensions; the synchronous sampling mechanism fundamentally solves the phase distortion problem caused by the timing misalignment of multi-source signals, while the hierarchical filtering design takes into account the different processing needs of the spectral characteristics of different signals, providing a physically meaningful and high signal-to-noise ratio original data foundation for subsequent modeling, which is a prerequisite for achieving micron-level sensing accuracy.

[0027] S2. Extract time series features of error disturbance based on multidimensional state data, construct a dynamic composite error characterization model, and output a comprehensive positioning deviation estimate.

[0028] Furthermore, the difference between the commanded position and the filtered displacement signal is calculated to obtain the instantaneous positioning error. A fixed-length sliding time window is set, and statistical features are extracted from the instantaneous positioning error, temperature signal, vibration signal, and current signal within the sliding time window, including the mean error, temperature rise rate, vibration energy, and current harmonic distortion rate. The statistical features are combined into a feature vector and input into a nonlinear mapping function, which is implemented by a radial basis function neural network, to output a comprehensive positioning deviation estimate reflecting the current intensity of the combined disturbance. Among these, vibration energy... The calculation formula is: ; in, Indicates the duration of the sliding time window. Indicates at time The acquired three-dimensional vibration signal vector, This represents the Euclidean norm of the vector. It characterizes the energy intensity of structural vibration per unit time and is used to quantify the disturbance level caused by mechanical loosening or resonance.

[0029] It should be noted that by extracting multidimensional statistical features of error disturbances within a sliding time window and introducing radial basis neural networks for nonlinear mapping, the dynamic composite error characterization model can accurately capture the time-varying coupling effects of disturbance sources such as thermal deformation, mechanical loosening, and electromagnetic coupling. This overcomes the shortcomings of traditional static error models that cannot reflect the evolution of disturbances, thus outputting more predictive and representative comprehensive positioning deviation estimates.

[0030] In particular, this step abandons the traditional single modeling paradigm that relies solely on position error, and instead takes a multi-physics coupling perspective, incorporating non-positional quantities such as temperature change rate, vibration energy intensity, and current harmonic distortion into the error characterization system, revealing the implicit impact mechanism of thermo-mechanical-electrical multi-field disturbances on positioning performance; the introduction of a sliding time window enables the model to have memory capabilities, and can identify the cumulative effects and transient changes of disturbances; the selection of the nonlinear mapping function fully considers the interactive complexity between high-dimensional features, avoiding modeling biases caused by simplification assumptions such as linear weighting, so that the comprehensive positioning error estimate not only reflects the current state, but also contains the ability to predict the short-term evolution trend in the future.

[0031] S3. Input the comprehensive positioning deviation estimate into the electromechanical coupling virtual model in the high-fidelity digital twin platform to automatically optimize and generate the initial control parameter set.

[0032] Furthermore, a multi-physics coupled virtual model is established in the digital twin platform, which includes motor electromagnetic dynamics, mechanical transmission chain stiffness, thermal expansion displacement effect, and structural modal characteristics. The comprehensive positioning deviation estimate is injected as an external disturbance term into the output of the multi-physics coupled virtual model to simulate the dynamic response of the actual equipment under the current operating conditions.

[0033] Define the control parameter vector to be optimized, including proportional gain, derivative gain, notch filter center frequency, and trajectory look-ahead window length; use a Bayesian optimization algorithm to iteratively search in the parameter space to minimize the mean square error between the virtual model output trajectory and the reference trajectory; in each iteration, the Bayesian optimization constructs a Gaussian process surrogate model based on historical evaluation results and selects the next most promising parameter combination for simulation evaluation; the converged parameter combination is used as the initial control parameter set; the expression for the trajectory tracking mean square error J(p) is: ; in, This represents the vector of control parameters currently being evaluated. Indicates the reference trajectory. Indicates in the parameter The simulated displacement output by the digital twin model. This represents the total duration of a single test trajectory. Used to measure parameters The virtual tracking performance under these conditions is the objective function of Bayesian optimization.

[0034] It should be noted that by using the comprehensive positioning deviation estimate as a disturbance input into the high-fidelity digital twin model and combining it with Bayesian optimization for automatic parameter optimization, the high cost and high risk caused by repeated trial and error debugging on site are avoided. The method efficiently explores the optimal control strategy in virtual space, significantly shortens the debugging cycle, and ensures that the initial control parameter set has good generalization performance and robustness on real equipment.

[0035] In particular, the construction of the digital twin model strictly follows the first principles of electromechanical systems, integrating equations from multiple fields such as electromagnetics, mechanics, and thermodynamics to ensure a high degree of consistency between the virtual body and the physical entity in terms of dynamic behavior. The measured comprehensive deviation is injected as a disturbance into the virtual output end, rather than simply superimposed on the input side, which more realistically simulates the direct impact path of external disturbances on the system output. The introduction of Bayesian optimization overcomes the shortcomings of traditional grid search or gradient descent in high-dimensional non-convex parameter spaces, which are prone to getting trapped in local optima or having excessively high computational costs. Its intelligent sampling mechanism based on the probabilistic surrogate model can approach the global optimum with very few simulations, greatly improving the efficiency and reliability of virtual debugging.

[0036] S4. Use the initial control parameter set to drive the equipment to perform low-speed reciprocating motion, collect the position response residual during the commutation stage, identify the friction and hysteresis nonlinear characteristics online based on the position response residual, and construct a feedforward compensation function.

[0037] Furthermore, the initial control parameter set is downloaded to the device's underlying motion controller, controlling a single motion axis to perform multiple complete reciprocating strokes in both positive and negative directions at a speed of less than 1 mm / s. Instantaneous positioning errors within a fixed time interval are extracted before and after each zero-crossing speed point to form the position response residual sequence for the commutation phase. Based on the position response residual sequence and the corresponding velocity signal, the internal parameters of the LuGre friction model and the Bouc-Wen hysteresis model are jointly fitted. The model parameters are updated online using a recursive least squares method to ensure that the compensation function can adaptively adjust with equipment wear or temperature changes. A feedforward compensation function is constructed with real-time displacement and velocity as input and compensation torque command as output. The internal state evolution equation of the LuGre friction and Bouc-Wen hysteresis joint model is as follows: ; in, Indicates the current displacement. Indicates the current speed. Represents the hysteresis internal state variable. , , The hysteresis shape parameters to be identified; where, the feedforward compensation torque The calculation formula is: ; in, , , These represent static friction stiffness, damping friction coefficient, and viscous friction coefficient, respectively, all of which are real parameters obtained through online identification; It is directly superimposed on the control output to counteract structural positioning deviations caused by friction and hysteresis.

[0038] It should be noted that by specifically collecting the position response residuals during the reversing phase in low-speed reciprocating motion, and based on this, identifying the parameters of the LuGre friction and Bouc-Wen hysteresis joint model online, accurate modeling of the dominant nonlinear disturbances in micron / nanometer-level motion is achieved; the constructed feedforward compensation function can actively offset structural positioning deviations, effectively improving the repeatability and trajectory tracking smoothness of the equipment under sensitive working conditions such as low speed and reversing.

[0039] In particular, the low-speed reciprocating motion design precisely focuses on the most difficult-to-control reversing region in high-precision equipment—where the speed approaches zero, friction switches from dynamic to static, and the hysteresis effect is most significant, making traditional control prone to creep or overshoot. By specifically extracting the residuals in this section, the dominant component of nonlinear disturbances can be effectively separated, avoiding being masked by the inertia or damping effects of the high-speed section. The joint identification of LuGre and Bouc-Wen models not only characterizes the speed dependence of friction but also captures the path dependence of hysteresis, forming a complete mathematical description of nonlinear disturbances. The online update mechanism of recursive least squares gives the compensation function self-learning ability, enabling it to dynamically evolve with long-term factors such as equipment aging and changes in lubrication conditions, ensuring the long-term effectiveness of the compensation effect.

[0040] S5. Integrate the feedforward compensation function and the initial control parameter set into the device's underlying motion controller to form a closed-loop debugging strategy and start the device operation to obtain the tracking error.

[0041] Furthermore, a standard feedback control law is configured in the motion controller, whose output is the sum of the proportional and derivative terms; the output of the feedforward compensation function is added to the output of the feedback control law to form a total control command and drive the servo driver; the device runs according to a preset standard test trajectory, and the actual displacement and command position are recorded in real time; the difference between the two is calculated as the tracking error, and the complete error sequence of the most recent running cycles is cached.

[0042] It should be noted that the deep integration of feedforward compensation and feedback control to form a closed-loop debugging strategy not only retains the robust suppression capability of feedback control for unmodeled dynamics, but also eliminates known nonlinear disturbances in advance through the feedforward channel. The synergistic effect of the two effectively reduces the tracking error amplitude and overshoot. At the same time, the cached multi-cycle error sequence provides high-quality data support for subsequent adaptive control, ensuring the continuity and traceability of the debugging process.

[0043] In particular, the integration of feedforward and feedback is not a simple signal superposition, but a hierarchical control logic that actively cancels known disturbances and passively suppresses unknown disturbances at the control architecture level. The feedforward channel acts at the front end of the control command, injecting reverse compensation in advance to weaken the nonlinear effects at the source. The feedback loop focuses on closed-loop stability and anti-interference robustness. The two complement each other in the frequency domain: feedforward improves the tracking accuracy in the low and medium frequencies, while feedback ensures high-frequency stability. The cached multi-cycle error sequence is not only used for current evaluation, but also provides historical reference for subsequent uncertainty analysis, enabling the system to have the ability to remember and recognize disturbance patterns, laying the data foundation for intelligent control.

[0044] S6. Calculate the disturbance uncertainty index based on the time evolution characteristics of the tracking error, dynamically adjust the parameter update step size of the digital twin model and the correction amplitude of the feedforward compensation function based on the disturbance uncertainty index, and iterate until the tracking error converges to within the preset micron-level accuracy threshold.

[0045] Furthermore, for the multiple-cycle tracking error sequences in the cache, the standard deviation and coefficient of variation within the sliding window are calculated; these two are then weighted and fused to form a disturbance uncertainty index; two uncertainty thresholds are set, high and low. When the disturbance uncertainty index is higher than the high threshold, the system is determined to be in a high-disturbance state. At this time, the update step size of the control parameters in the digital twin model is reduced, and the correction amplitude of the feedforward compensation function parameters is limited; when the disturbance uncertainty index is lower than the low threshold, the system is determined to be stable. At this time, the update step size and correction amplitude are increased to accelerate the convergence process; parameter updates, compensation function corrections, and equipment operation are repeated until the maximum tracking error for three consecutive cycles is less than the preset micrometer-level accuracy threshold; among them, the disturbance uncertainty index... The calculation formula is: ; in, Indicates recent The standard deviation of the tracking error over a period of time reflects the absolute magnitude of the error fluctuation. The coefficient of variation is defined as follows: ,in The average tracking error is used to measure the relative dispersion of the error with respect to its mean. These are preset weighting coefficients used to adjust the proportion of absolute fluctuations and relative dispersion in uncertainty assessment.

[0046] It should be noted that a disturbance uncertainty index is constructed based on the time evolution characteristics of tracking error, and the parameter update step size and compensation correction magnitude are dynamically adjusted accordingly, so that the debugging strategy has the intelligent characteristics of disturbance perception and strategy self-adaptation; under high disturbance, conservative adjustment is made to avoid oscillation, and under low disturbance, convergence is accelerated to improve efficiency, thereby achieving stable, fast and high-precision adaptive debugging under complex working conditions, ensuring that the long-term operating accuracy is always maintained within the micron threshold.

[0047] In particular, the construction of the disturbance uncertainty index breaks away from the traditional approach of simply relying on the error amplitude. Instead, it starts from the statistical distribution characteristics and considers both the absolute strength (standard deviation) and relative dispersion (coefficient of variation) of error fluctuations, thus more comprehensively characterizing the unpredictability of the system's operating state. The dynamic adjustment mechanism is essentially a risk-aware optimization strategy: when the system exhibits high uncertainty, the learning rate is reduced to prevent over-adjustment from causing oscillations; when the system tends to stabilize, the learning rate is increased to accelerate convergence. This adaptive mechanism, which seeks speed while maintaining stability, makes the debugging process both safe and efficient, and can robustly achieve micron-level accuracy targets in complex and ever-changing actual working conditions.

[0048] This embodiment also provides a debugging system for high-precision mechanical and electrical equipment, including: Multi-source sensing fusion module, dynamic error modeling module, digital twin optimization module, nonlinear feedforward compensation module, closed-loop debugging and execution module, and adaptive iterative control module; The multi-source sensor fusion module is used to deploy optical encoders, thermocouples, triaxial accelerometers and Hall current sensors at the drive end, load end and key structural support points of the equipment's motion axis. It achieves synchronous sampling through a unified hardware clock and performs low-pass filtering and sliding median filtering on the original displacement, temperature, vibration and current signals to output a time-aligned multidimensional state data sequence. The dynamic error modeling module is used to calculate instantaneous positioning error based on multidimensional state data. It extracts the mean error, temperature rise rate, vibration energy and current harmonic distortion rate as time series features within a sliding time window, inputs the feature vectors into a radial basis neural network to construct a dynamic composite error representation model, and outputs a comprehensive positioning deviation estimate. The digital twin optimization module is used to establish an electromechanical coupling virtual model in a high-fidelity digital twin platform, which includes motor electromagnetic dynamics, transmission chain stiffness, thermal expansion effect and structural modes. The comprehensive positioning deviation estimate is used as a disturbance input, and the Bayesian optimization algorithm is used to automatically find the optimal value in the control parameter space to generate the initial control parameter set that minimizes the virtual trajectory tracking error. The nonlinear feedforward compensation module is used to drive the device to perform low-speed reciprocating motion using the initial control parameter set, collect the position response residual during the commutation stage, identify the parameters of LuGre friction and Bouc-Wen hysteresis model online based on the residual and velocity signal, construct the feedforward compensation function with displacement and velocity as input, and output the compensation torque command. The closed-loop debugging and execution module is used to integrate the feedforward compensation function and the initial control parameter set into the device's underlying motion controller to form a closed-loop control strategy that combines feedback and feedforward, driving the device to run along the standard test trajectory and collecting the tracking error between the actual displacement and the commanded position in real time. The adaptive iterative control module is used to calculate the disturbance uncertainty index based on the time evolution characteristics of the tracking error. This index is a weighted fusion of the error standard deviation and the coefficient of variation. Based on this index, the parameter update step size of the digital twin model and the correction magnitude of the feedforward compensation function are dynamically adjusted. It makes conservative adjustments when there is high disturbance and accelerates convergence when there is low disturbance. Iterative execution continues until the tracking error is stably converged within the preset micron-level accuracy threshold.

[0049] This embodiment also provides a computer device applicable to the debugging method for high-precision mechanical and electrical equipment, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the debugging method for high-precision mechanical and electrical equipment as proposed in the above embodiment.

[0050] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0051] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the debugging method for high-precision mechanical and electrical equipment as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0052] In summary, this invention achieves a transformation in the debugging process of high-precision mechanical and electrical equipment from static calibration and manual trial and error to dynamic sensing and precise compensation by fusing multi-source heterogeneous sensing data, constructing a dynamic composite error model based on time series features, introducing a high-fidelity digital twin-driven automatic parameter optimization mechanism, embedding nonlinear friction and hysteresis feedforward compensation for micron / nanometer-level motion, and introducing an adaptive iterative control strategy based on disturbance uncertainty indicators. This effectively shortens the debugging cycle, improves positioning repeatability and long-term operational stability, and overcomes the problems of high-precision performance caused by composite disturbances such as thermal drift, structural loosening, electromagnetic interference, and nonlinear friction.

[0053] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A debugging method for high-precision mechanical and electrical equipment, characterized in that: include: Deploy multi-source heterogeneous sensors in high-precision mechanical and electrical equipment to collect displacement, temperature, vibration and current signals, and generate multi-dimensional state data through time alignment and filtering; Based on the extraction of time series features of error disturbance from multidimensional state data, a dynamic composite error characterization model is constructed, and a comprehensive positioning deviation estimate is output. The estimated value of the comprehensive positioning deviation is input into the electromechanical coupling virtual model in the high-fidelity digital twin platform, and the initial control parameter set is automatically optimized and generated. The device is driven to perform low-speed reciprocating motion using an initial control parameter set. The position response residual during the commutation phase is collected. Based on the position response residual, the friction and hysteresis nonlinear characteristics are identified online, and a feedforward compensation function is constructed. The feedforward compensation function and the initial control parameter set are integrated into the device's underlying motion controller to form a closed-loop debugging strategy and start the device operation to obtain the tracking error. The disturbance uncertainty index is calculated based on the time evolution characteristics of the tracking error. The parameter update step size of the digital twin model and the correction magnitude of the feedforward compensation function are dynamically adjusted based on the disturbance uncertainty index. The process is iterated until the tracking error converges to within the preset micrometer-level accuracy threshold.

2. The debugging method for high-precision mechanical and electrical equipment as described in claim 1, characterized in that: The deployment of multi-source heterogeneous sensors in high-precision mechanical and electrical equipment to collect displacement, temperature, vibration, and current signals, followed by time alignment and filtering to generate multi-dimensional state data, is as follows: High-resolution optical encoders, thermocouples, triaxial accelerometers, and Hall current sensors are installed at the drive end, load end, and key structural support points of the equipment's motion axis, respectively. All sensors are triggered by a unified hardware clock to achieve synchronous sampling and obtain raw displacement signals, temperature signals, vibration signals and current signals; The original signals are subjected to low-pass filtering to suppress high-frequency electromagnetic interference, and the vibration signals are additionally subjected to sliding median filtering to remove transient impact noise. The four types of filtered signals are strictly aligned according to the sampling time to form a synchronous multidimensional state data sequence.

3. The debugging method for high-precision mechanical and electrical equipment as described in claim 2, characterized in that: The specific steps for extracting time-series features of error disturbances based on multidimensional state data, constructing a dynamic composite error characterization model, and outputting a comprehensive positioning deviation estimate are as follows: The instantaneous positioning error is obtained by calculating the difference between the command position and the filtered displacement signal; Set a fixed-length sliding time window, and extract statistical features from the instantaneous positioning error, temperature signal, vibration signal and current signal within the sliding time window, including the mean error, temperature rise rate, vibration energy and current harmonic distortion rate. The statistical features are combined into a feature vector and input into a nonlinear mapping function, which is implemented by a radial basis neural network, and is used to output a comprehensive positioning error estimate that reflects the current intensity of the composite disturbance. Among them, vibration energy The calculation formula is: ; in, Indicates the duration of the sliding time window. Indicates at time The acquired three-dimensional vibration signal vector, This represents the Euclidean norm of the vector. It characterizes the energy intensity of structural vibration per unit time and is used to quantify the disturbance level caused by mechanical loosening or resonance.

4. The debugging method for high-precision mechanical and electrical equipment as described in claim 3, characterized in that: The steps for inputting the comprehensive positioning deviation estimate into the electromechanical coupling virtual model in the high-fidelity digital twin platform to automatically optimize and generate the initial control parameter set are as follows: Establish a multi-physics coupled virtual model in the digital twin platform, which includes the electromagnetic dynamics of the motor, the stiffness of the mechanical transmission chain, the thermal expansion displacement effect, and the structural modal characteristics. The comprehensive positioning deviation estimate is injected as an external disturbance term into the output of the multiphysics coupled virtual model to simulate the dynamic response of the actual equipment under the current operating conditions. Define the vector of control parameters to be optimized, including proportional gain, derivative gain, notch filter center frequency, and trajectory look-ahead window length; A Bayesian optimization algorithm is used to iteratively search in the parameter space to minimize the mean square error between the virtual model output trajectory and the reference trajectory. In each iteration, Bayesian optimization constructs a Gaussian process surrogate model based on historical evaluation results and selects the next most promising parameter combination to reduce error for simulation evaluation; The converged parameter combination is used as the initial control parameter set; The expression for the mean square error J(p) in trajectory tracking is: ; in, This represents the vector of control parameters currently being evaluated. Indicates the reference trajectory. Indicates in the parameter The simulated displacement output by the digital twin model. This represents the total duration of a single test trajectory. Used to measure parameters The virtual tracking performance under these conditions is the objective function of Bayesian optimization.

5. The debugging method for high-precision mechanical and electrical equipment as described in claim 4, characterized in that: The process involves using an initial control parameter set to drive the device to perform low-speed reciprocating motion, collecting the position response residuals during the commutation phase, identifying friction and hysteresis nonlinear characteristics online based on the position response residuals, and constructing a feedforward compensation function. The specific steps are as follows: Download the initial control parameter set to the device's underlying motion controller, and control a single motion axis to perform multiple complete reciprocating strokes in the positive and negative directions at a speed of less than 1 mm per second. The instantaneous positioning error within a fixed time interval before and after each zero-crossing of the velocity is used to form the position response residual sequence of the reversing phase; Based on the position response residual sequence and the corresponding velocity signal during the commutation stage, the internal parameters of the LuGre friction model and the Bouc-Wen hysteresis model are jointly fitted. The recursive least squares method is used to update the model parameters online, ensuring that the compensation function can be adaptively adjusted according to equipment wear or temperature changes; Construct a feedforward compensation function that takes real-time displacement and velocity as input and outputs compensation torque commands; The internal state evolution equation of the LuGre friction and Bouc-Wen hysteresis joint model is as follows: ; in, Indicates the current displacement. Indicates the current speed. Represents the hysteresis internal state variable. , , The hysteresis shape parameters to be identified; Among them, the feedforward compensation torque The calculation formula is: ; in, , , These represent static friction stiffness, damping friction coefficient, and viscous friction coefficient, respectively, all of which are real parameters obtained through online identification; The It is directly superimposed on the control output to counteract structural positioning deviations caused by friction and hysteresis.

6. The debugging method for high-precision mechanical and electrical equipment as described in claim 5, characterized in that: The specific steps for integrating the feedforward compensation function and the initial control parameter set into the device's underlying motion controller to form a closed-loop debugging strategy and start the device operation to obtain the tracking error are as follows: A standard feedback control law is configured in the motion controller, and its output is the sum of the proportional and derivative terms; The output of the feedforward compensation function is added to the output of the feedback control law to form a total control command and drive the servo driver. The equipment operates according to a preset standard test trajectory, recording the actual displacement and command position in real time; The difference between the two is calculated as the tracking error, and the complete error sequence of the most recent running cycles is cached.

7. The debugging method for high-precision mechanical and electrical equipment as described in claim 6, characterized in that: The specific steps are as follows: Calculate the disturbance uncertainty index based on the time evolution characteristics of the tracking error; dynamically adjust the parameter update step size of the digital twin model and the correction magnitude of the feedforward compensation function based on the disturbance uncertainty index; iterate until the tracking error converges to within a preset micrometer-level accuracy threshold. For multiple cycles of the cached tracking error sequence, calculate the standard deviation and coefficient of variation within the sliding window; The two are weighted and combined to form a disturbance uncertainty indicator; Two uncertainty thresholds, high and low, are set. When the disturbance uncertainty index is higher than the high threshold, the system is determined to be in a high disturbance state. At this time, the update step size of the control parameters in the digital twin model is reduced and the correction magnitude of the feedforward compensation function parameters is limited. When the disturbance uncertainty index is below the low threshold, the system is determined to be stable. At this time, the update step size and correction magnitude are increased to accelerate the convergence process. Repeatedly perform parameter updates, compensation function corrections, and equipment operation until the maximum tracking error for three consecutive cycles is less than the preset micron-level accuracy threshold; Among them, the disturbance uncertainty index The calculation formula is: ; in, Indicates recent The standard deviation of the tracking error over a period of time reflects the absolute magnitude of the error fluctuation. The coefficient of variation is defined as follows: ,in The average tracking error is used to measure the relative dispersion of the error with respect to its mean. These are preset weighting coefficients used to adjust the proportion of absolute fluctuations and relative dispersion in uncertainty assessment.

8. A debugging system for high-precision mechanical and electrical equipment, based on the debugging method for high-precision mechanical and electrical equipment according to any one of claims 1 to 7, characterized in that: include: Multi-source sensing fusion module, dynamic error modeling module, digital twin optimization module, nonlinear feedforward compensation module, closed-loop debugging and execution module, and adaptive iterative control module; The multi-source sensor fusion module is used to deploy optical encoders, thermocouples, triaxial accelerometers and Hall current sensors at the drive end, load end and key structural support points of the device's motion axis. It achieves synchronous sampling through a unified hardware clock and performs low-pass filtering and sliding median filtering on the original displacement, temperature, vibration and current signals to output a time-aligned multidimensional state data sequence. The dynamic error modeling module is used to calculate the instantaneous positioning error based on multidimensional state data. It extracts the mean error, temperature rise rate, vibration energy and current harmonic distortion rate as time series features within the sliding time window, inputs the feature vector into the radial basis neural network to construct a dynamic composite error characterization model, and outputs a comprehensive positioning deviation estimate. The digital twin optimization module is used to establish an electromechanical coupling virtual model in a high-fidelity digital twin platform, which includes motor electromagnetic dynamics, transmission chain stiffness, thermal expansion effect and structural modes. The comprehensive positioning deviation estimate is used as a disturbance input, and the Bayesian optimization algorithm is used to automatically optimize in the control parameter space to generate an initial control parameter set that minimizes the virtual trajectory tracking error. The nonlinear feedforward compensation module is used to drive the device to perform low-speed reciprocating motion using the initial control parameter set, collect the position response residual during the commutation stage, identify the LuGre friction and Bouc-Wen hysteresis model parameters online based on the residual and velocity signal, construct a feedforward compensation function with displacement and velocity as input, and output the compensation torque command. The closed-loop debugging execution module is used to integrate the feedforward compensation function and the initial control parameter set into the device's underlying motion controller to form a closed-loop control strategy that combines feedback and feedforward, driving the device to run along a standard test trajectory and collecting the tracking error between the actual displacement and the commanded position in real time. The adaptive iterative control module is used to calculate the disturbance uncertainty index based on the time evolution characteristics of the tracking error. This index is a weighted fusion of the error standard deviation and the coefficient of variation. Based on this index, the parameter update step size of the digital twin model and the correction amplitude of the feedforward compensation function are dynamically adjusted. The adjustment is conservative when there is high disturbance and the convergence is accelerated when there is low disturbance. The iterative execution continues until the tracking error is stably converged within the preset micron-level accuracy threshold.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the debugging method for high-precision mechanical and electrical equipment as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the debugging method for high-precision mechanical and electrical equipment as described in any one of claims 1 to 7.

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