Filling control method and system based on online parameter identification

Abstract

The invention provides a filling control method and system based on online parameter identification. According to the method, the synchronous error data of each servo shaft in the multi-shaft servo system is acquired, the error time sequence matrix is constructed by the synchronous error data according to the time sequence, and the displacement feedback data of each servo shaft in the multi-shaft servo system is acquired in real time through the high-precision rotary encoder; a dynamic compensation signal is generated according to the deviation between the displacement feedback data and a preset track, online estimation is carried out on mechanical vibration disturbance of the multi-axis servo system based on the error time sequence matrix, a disturbance parameter estimation value is obtained, the dynamic compensation signal and the disturbance parameter estimation value are fused, and a servo synchronous compensation amount is generated; according to the technical scheme, through fusion of real-time parameter identification and dynamic compensation, the filling precision and efficiency are remarkably improved, meanwhile, the manual debugging complexity is reduced, and a reliable solution is provided for high-stability production under multiple working conditions.

Description

Technical Field

[0001] The present application relates to the field of industrial automation control technology, and particularly relates to a filling control method and system based on online parameter identification. Background Art

[0002] With the continuous growth of the automation production requirements in industries such as food, medicine, and daily chemicals, filling equipment is gradually developing towards high speed and high precision. In the scenario of parallel operation of multiple filling heads, the synchronous control accuracy of the servo system directly affects the filling efficiency and product quality. Especially in the field of liquid filling, complex working conditions such as changes in material viscosity, mechanical vibration interference, and temperature fluctuations pose higher requirements for the collaborative control ability of the multi-axis servo system, and it is necessary to achieve micron-level trajectory tracking accuracy and millisecond-level dynamic disturbance suppression ability.

[0003] Existing filling control schemes mainly adopt traditional PID control combined with a feedforward compensation strategy, or a multi-axis synchronization algorithm based on model predictive control (MPC). Some improved schemes introduce fuzzy control or adaptive sliding mode control technology to cope with disturbances by online adjusting control parameters.

[0004] However, the existing schemes still have significant defects. Traditional PID control relies on fixed parameters and is prone to parameter adjustment lag under dynamic disturbances caused by high-speed start-stop of filling heads or material impact, resulting in the accumulation of synchronous errors. Model predictive control is difficult to meet the real-time requirements of the multi-axis filling system due to its high computational complexity, and it relies on an accurate mathematical model, which is prone to model mismatch problems under complex working conditions. Although intelligent algorithms such as fuzzy control can improve robustness, the construction of its rule base and parameter optimization highly depend on expert experience, and the dynamic response speed is insufficient to effectively suppress the phase shift caused by high-frequency mechanical vibration. Summary of the Invention

[0005] The present application provides a filling control method and system based on online parameter identification to solve the problems of synchronous error accumulation caused by parameter adjustment lag of traditional PID control in the prior art, insufficient real-time performance and model mismatch caused by high computational complexity of model predictive control, and the dependence on expert experience for constructing the rule base and insufficient dynamic response speed of intelligent algorithms.

[0006] In a first aspect, the present application provides a filling control method based on online parameter identification, including:

[0007] Obtain the synchronous error data of each servo axis in the multi-axis servo system, and construct an error time series matrix with the synchronous error data according to the time series;

[0008] Real-time collect the displacement feedback data of each servo axis in the multi-axis servo system through a high-precision rotary encoder, and generate a dynamic compensation signal according to the deviation between the displacement feedback data and a preset trajectory;

[0009] Perform an online estimation of the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain an estimated value of the disturbance parameter;

[0010] Fuse the dynamic compensation signal with the estimated value of the disturbance parameter to generate a servo synchronization compensation amount.

[0011] Optionally, performing an online estimation of the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain an estimated value of the disturbance parameter includes:

[0012] Perform a sliding window recursive process on the error time series matrix, and use a covariance matrix update algorithm to calculate the dynamic coupling coefficient between each servo axis in real time;

[0013] Use a frequency-domain separation method based on fast Fourier transform to perform frequency-domain conversion on the dynamic coupling coefficient and the error time series matrix within the current time window, and extract the main frequency band amplitude parameter of mechanical vibration;

[0014] Construct a disturbance parameter weighting factor according to the dynamic coupling coefficient and the main frequency band amplitude parameter, and use the weighting factor to dynamically correct the vibration phase offset of the servo axis to generate an estimated value of the disturbance parameter including amplitude-frequency characteristics and coupling effects.

[0015] Optionally, constructing a disturbance parameter weighting factor according to the dynamic coupling coefficient and the main frequency band amplitude parameter, and using the weighting factor to dynamically correct the vibration phase offset of the servo axis to generate an estimated value of the disturbance parameter including amplitude-frequency characteristics and axis coupling effects includes:

[0016] Generate an amplitude-frequency coupling joint weight coefficient by performing a non-linear superposition operation on the dynamic coupling coefficient and the main frequency band amplitude parameter;

[0017] Perform a time-domain integral correction on the vibration phase offset of the servo axis based on the amplitude-frequency coupling joint weight coefficient, and dynamically associate the vibration phase offset with the vibration energy through an integration operation to generate a corrected vibration phase offset;

[0018] Perform time-frequency domain energy normalization processing on the corrected vibration phase offset and the main frequency band amplitude parameter, and combine the time-domain standard deviation of the error time series matrix to generate an estimated value of the disturbance parameter including amplitude-frequency characteristics and multi-axis coupling effects.

[0019] Optionally, fusing the dynamic compensation signal with the estimated value of the disturbance parameter to generate a servo synchronization compensation amount includes:

[0020] Generate a dynamic weight coefficient according to the frequency-domain energy distribution of the estimated value of the disturbance parameter and the time-domain standard deviation of the error time series matrix;

[0021] Fuse the trajectory tracking error component in the dynamic compensation signal and the mechanical vibration component of the disturbance parameter estimate value through the dynamic weight coefficient to generate a fusion compensation quantity including trajectory deviation suppression and vibration disturbance compensation;

[0022] Based on the real-time synchronization error data of the servo axis, perform phase difference prediction compensation on the fusion compensation quantity, and decompose it into an amplitude compensation component and a phase compensation component;

[0023] Superimpose the amplitude compensation component on the current loop control instruction of the servo axis, and suppress high-frequency noise through frequency domain amplitude limiting filtering; at the same time, inject the phase compensation component into the position loop feedforward channel, and avoid mechanical shock through time domain integral smoothing processing to generate a servo synchronization compensation quantity.

[0024] Optionally, fuse the trajectory tracking error component in the dynamic compensation signal and the mechanical vibration component of the disturbance parameter estimate value through the dynamic weight coefficient to generate a fusion compensation quantity including trajectory deviation suppression and vibration disturbance compensation, including:

[0025] Perform time domain amplitude normalization on the trajectory tracking error component to obtain the normalized trajectory tracking error component;

[0026] Perform frequency domain energy normalization on the mechanical vibration component to obtain the normalized mechanical vibration component;

[0027] According to the real-time working conditions of the filling process, decompose the dynamic weight coefficient into a trajectory tracking weight factor and a vibration suppression weight factor;

[0028] Multiply the normalized trajectory tracking error component by the trajectory tracking weight factor to generate a trajectory compensation quantity;

[0029] Multiply the normalized mechanical vibration component by the vibration suppression weight factor to generate a vibration suppression quantity;

[0030] Perform vector superposition on the trajectory compensation quantity and the vibration suppression quantity to generate a fusion compensation quantity.

[0031] Optionally, it is characterized in that the synchronization error data of each servo axis in the multi-axis servo system is obtained, and the synchronization error data is constructed into an error time series matrix according to the time series, including:

[0032] Obtain the synchronization error data of each servo axis in real time through the position closed-loop feedback signal of the multi-axis servo system, where the synchronization error data includes position deviation, speed deviation, and acceleration deviation;

[0033] Perform sliding window filtering processing on the synchronization error data to generate filtered synchronization error data;

[0034] Based on the timing characteristics of the filling cycle, the filtered synchronous error data is divided into dynamic time windows at preset time intervals;

[0035] Calculate the covariance matrix for the filtered synchronous error data within each dynamic time window, and construct a dynamic error time series matrix according to the calculation results.

[0036] Optionally, the displacement feedback data of each servo axis in the multi-axis servo system is collected in real time through a high-precision rotary encoder, and a dynamic compensation signal is generated based on the deviation between the displacement feedback data and the preset trajectory, including:

[0037] Obtain the original displacement data of each servo axis in real time through a high-precision rotary encoder, and perform sliding window mean filtering on the original displacement data;

[0038] Compare the filtered displacement data with the expected position, speed, and acceleration of the preset trajectory in real time, and generate position deviation, speed tracking deviation, and acceleration tracking deviation according to the comparison results;

[0039] Perform phase lead compensation on the acceleration tracking deviation based on the dynamic response characteristics of the servo axis to generate an acceleration feedforward compensation term;

[0040] Fuse the position deviation, speed tracking deviation, and acceleration feedforward compensation term to generate an initial compensation signal;

[0041] Use a nonlinear correction algorithm to perform amplitude limiting processing on the initial dynamic compensation signal, and generate a dynamic compensation signal after suppressing high-frequency oscillation components.

[0042] In a second aspect, the present application provides a filling control system based on online parameter identification, including:

[0043] A construction module for obtaining the synchronous error data of each servo axis in the multi-axis servo system, and constructing an error time series matrix for the synchronous error data according to the time series;

[0044] An acquisition module for collecting the displacement feedback data of each servo axis in the multi-axis servo system in real time through a high-precision rotary encoder, and generating a dynamic compensation signal based on the deviation between the displacement feedback data and the preset trajectory;

[0045] An estimation module for online estimating the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain a disturbance parameter estimation value;

[0046] A fusion module for fusing the dynamic compensation signal and the disturbance parameter estimation value to generate a servo synchronization compensation amount.

[0047] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a filling control method based on online parameter identification as described in the first aspect above.

[0048] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which when executed by a computer, implements a filling control method based on online parameter identification as described in the first aspect.

[0049] In the embodiment of the present application, by constructing a multi-axis synchronous error time series matrix in real time and combining the dynamic compensation signal generated by high-precision displacement feedback, the collaborative optimization control of the trajectory tracking and mechanical vibration in the filling process is realized. Based on the disturbance estimation mechanism of online parameter identification, the multi-axis coupling effect and vibration characteristics can be dynamically captured, significantly improving the adaptive adjustment ability of the system, effectively reducing the need for manual intervention, and providing reliable guarantee for high-precision filling under complex working conditions.

[0050] Furthermore, through the sliding window recursive processing and covariance matrix update algorithm, the dynamic coupling relationship between servo axes is quantified in real time, and the vibration energy distribution characteristics are accurately extracted by combining frequency domain analysis, providing key parameter support for vibration suppression. This method breaks through the limitations of the traditional offline calibration mode, realizes the online tracking of time-varying disturbance characteristics, greatly improves the pertinence and real-time performance of vibration suppression, and ensures the stability of multi-axis coordinated motion.

[0051] Even further, based on the dynamic weight allocation strategy of frequency domain energy and time domain fluctuation, the trajectory compensation and vibration suppression requirements are intelligently fused, and the accurate decoupling of the compensation component is achieved through phase difference prediction. The dual-channel cooperative control design of the current loop and the position loop takes into account high-frequency noise suppression and mechanical shock mitigation, enabling the system to maintain high-precision trajectory tracking under complex disturbances, while extending the service life of the equipment, and providing a complete control closed-loop solution for high-speed and high-precision filling scenarios.

[0052] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1A flow chart of a filling control method based on online parameter identification provided by the present application is shown;

[0055] Figure 2 A structural schematic diagram of a filling control system based on online parameter identification provided by the present application is shown;

[0056] Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0058] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0059] The researchers found that the existing filling control methods generally have problems such as low trajectory tracking accuracy and delayed disturbance suppression when dealing with high-speed multi-axis collaborative operations due to factors such as mechanical vibration disturbances, multi-axis dynamic coupling effects, and load mutations. Traditional PID control relies on fixed parameters, which leads to the accumulation of synchronization errors. Model predictive control is difficult to meet real-time requirements due to its high computational complexity, and intelligent algorithms cannot adapt to dynamic parameter changes under complex working conditions due to the solidification of the rule base. Based on this, a filling control method based on online parameter identification is provided. This method can capture the multi-axis dynamic coupling characteristics in real time through the error timing matrix, and combines the online estimation of disturbance parameters with the fusion of dynamic compensation signals to achieve collaborative optimization of trajectory tracking and vibration suppression.

[0060] The technical solution of the present application can be applied to high-speed liquid / paste filling equipment and multi-filling head collaborative operation scenarios, and is particularly suitable for the problem of decreased multi-axis synchronization accuracy caused by mechanical resonance and load mutation during the filling of high-viscosity materials.

[0061] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0062] Figure 1 The flowchart of a filling control method based on online parameter identification provided by an embodiment of the present application is as Figure 1 shown, and the method includes:

[0063] Step 101, obtain the synchronous error data of each servo axis in the multi-axis servo system, and construct an error time series matrix with the synchronous error data according to the time series;

[0064] In this step, the synchronous error data refers to the deviation amount of the actual motion parameters (such as position, speed, and acceleration) of each servo axis in the multi-axis servo system relative to the preset trajectory during collaborative operation; the error time series matrix is a multi-dimensional data structure formed by arranging the synchronous error data of each servo axis at different time points in chronological order, and is used to depict the dynamic error distribution and evolution characteristics of the multi-axis system.

[0065] In this embodiment, first, the position, speed, and acceleration data of each servo axis are collected in real time through the closed-loop feedback interface of the multi-axis servo system, and compared with the theoretical value of the preset filling trajectory cycle by cycle to generate the synchronous error data of each axis. Secondly, the sliding window filtering algorithm is used to preprocess the original error data, and the filtering window length is dynamically adjusted according to the mechanical resonance characteristics of the servo axis to filter out high-frequency noise interference. Then, the filtered synchronous error data is structured and arranged according to the time series: taking the servo axis number as the row, the sampling time point as the column, and the error type (position / speed / acceleration) as the depth dimension, a three-dimensional error time series matrix is constructed. Finally, the error time series matrix is time-stamped and aligned with the filling action stage (such as accelerating injection, uniform filling, decelerating stop) to ensure the spatio-temporal correlation between the error data and the actual filling process.

[0066] For example, in the liquid filling scenario, for the servo axes corresponding to 4 filling heads, the position error (such as the deviation between the actual position of the filling head and the preset trajectory), speed error (the deviation between the actual speed and the theoretical value), and acceleration error during multiple sampling cycles are collected in real time. In the constructed error time series matrix, each data point represents a certain type of error value of a certain servo axis at a specific moment (such as the speed deviation of the second axis in the middle stage of the injection stage). Through the matrix structure, the spatio-temporal distribution law of the errors of each axis can be intuitively analyzed (such as the continuous position lag of the third axis during the deceleration stage), providing a data basis for subsequent disturbance parameter estimation.

[0067] Step 102: Real-time collect the displacement feedback data of each servo axis in the multi-axis servo system through a high-precision rotary encoder, and generate a dynamic compensation signal based on the deviation between the displacement feedback data and the preset trajectory;

[0068] In this step, the displacement feedback data refers to the actual displacement of the servo axis obtained in real time through a high-precision rotary encoder, which is used to characterize the real-time spatial position of the filling actuator; the dynamic compensation signal is a correction amount generated based on the deviation between the displacement feedback data and the preset trajectory, which is used to adjust the servo control command online to eliminate the trajectory tracking error.

[0069] In this embodiment, first, based on the constructed error time series matrix, extract the position, speed, and acceleration deviation data of each servo axis at a specific time point. Secondly, real-time collect the actual displacement data of each servo axis through a high-precision rotary encoder, and after sliding window filtering processing, compare it with the expected position, speed, and acceleration of the preset trajectory point by point to generate a multi-dimensional trajectory deviation vector. Then, according to the amplitude and direction of the deviation vector, use an algorithm combining proportional integral and phase lead compensation to dynamically correct the trajectory deviation, and generate a dynamic compensation signal including position compensation, speed compensation, and acceleration feedforward. Finally, inject the dynamic compensation signal into the corresponding control command channel according to the servo axis number to complete real-time error correction.

[0070] For example, in the liquid filling scenario of step 101, for the position deviation of the first axis in a certain filling cycle, obtain its actual displacement data through a high-precision rotary encoder and compare it with the expected position of the preset trajectory. If the actual displacement lags behind the theoretical position, a positive position compensation signal is generated; if the speed tracking is insufficient, a speed compensation component is superimposed; at the same time, according to the phase characteristics of the acceleration deviation, a feedforward compensation term is generated through a lead network. The finally fused dynamic compensation signal is superimposed on the servo control command to drive the first axis to correct the displacement deviation in the next filling cycle, so that its actual motion trajectory converges within the preset theoretical trajectory range, thereby ensuring the filling accuracy.

[0071] Step 103: Online estimate the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain the estimated value of the disturbance parameter;

[0072] In this step, the mechanical vibration disturbance refers to the periodic or transient vibration interference caused by mechanical structure resonance, material impact, or load mutation in the multi-axis servo system during the filling process; the estimated value of the disturbance parameter is a quantitative parameter extracted through frequency domain and time domain analysis of the error time series matrix, including the main vibration frequency band, amplitude distribution, and phase offset, which is used to characterize the dynamic characteristics of mechanical vibration.

[0073] In this embodiment, first, the time-domain error sequences of each servo axis are extracted from the constructed error time-series matrix, and are converted into frequency-domain energy spectra through fast Fourier transform (FFT) to identify the main frequency band of mechanical vibration (such as the 50Hz filling head resonance peak). Secondly, the cross-axis coupling vibration components in the frequency-domain energy spectrum are separated by using the wavelet packet decomposition algorithm (such as the 30Hz secondary resonance caused by the vibration transfer of adjacent filling heads), and the energy ratio and phase offset angle of each frequency band are calculated. Then, the dominant vibration modes are selected based on the energy ratio threshold (such as the frequency band with an energy ratio > 15%), and their frequency, amplitude, and phase parameters are used as the estimated values of the disturbance parameters. Finally, the estimated values of the disturbance parameters are correlated with the filling action phases (such as acceleration, constant speed, deceleration) to generate a mapping relationship table between the vibration characteristics and the process time series.

[0074] For example, in the previous step, the error time-series matrix shows that the 3rd axis has periodic position fluctuations during the filling acceleration phase (t = 0 - 30ms). By analyzing its error sequence through FFT, the main frequency band is identified as 48Hz (corresponding to the mechanical resonance frequency of the filling head), and the secondary frequency band is 22Hz (corresponding to the material flow impact vibration). The wavelet packet decomposition further separates the amplitude of the 48Hz frequency band as 0.12mm and the phase lag as 15°, and the amplitude of the 22Hz frequency band as 0.05mm and the phase lead as 8°. The 48Hz is selected as the dominant vibration mode. The estimated values of the disturbance parameters are marked as {frequency 48Hz, amplitude 0.12mm, phase lag 15°}, which are used for the generation of the dynamic compensation signal in the subsequent steps.

[0075] Step 104, fuse the dynamic compensation signal with the estimated values of the disturbance parameters to generate a servo synchronization compensation amount;

[0076] In this step, the servo synchronization compensation amount is a composite control correction amount generated by fusing the dynamic compensation signal with the estimated values of the disturbance parameters, which is used to synchronously suppress the trajectory tracking error and mechanical vibration interference in the multi-axis servo system, and includes an amplitude compensation component (to suppress high-frequency vibration) and a phase compensation component (to eliminate the multi-axis synchronization time delay).

[0077] In this embodiment, first, a trajectory tracking error compensation component (such as position and velocity compensation terms) is extracted from the dynamic compensation signal generated in step 102, and at the same time, the main frequency band amplitude and phase offset parameters of mechanical vibration are obtained from the estimated values of disturbance parameters in step 103. Secondly, a frequency-domain weighted fusion algorithm is adopted to dynamically adjust the weight ratio of trajectory compensation and vibration suppression according to the proportion of main frequency band energy: when the proportion of high-frequency vibration energy is high, the weight of the vibration suppression term is increased; when the low-frequency trajectory deviation is significant, the priority of the trajectory compensation term is enhanced. Then, the fused compensation amount is spatially allocated according to the servo axis number through a multi-axis collaborative processing module to separate the cross-axis coupling vibration components (such as the vibration transfer interference between adjacent filling heads), ensuring that the compensation amounts of each axis are independent and collaborative. Finally, a servo synchronization compensation amount including amplitude limiting and phase synchronization calibration is generated and injected into the command channel of the multi-axis servo system according to the control period.

[0078] For example, in the liquid filling scenario of step 103, for the 48 Hz mechanical vibration disturbance (amplitude 0.12 mm, phase lag 15°) identified for the third axis, it is fused with the dynamic compensation signal (such as position compensation +0.02 mm, velocity compensation -0.03 mm / s) generated in step 102. According to the proportion of 48 Hz vibration energy (such as 25%), the trajectory compensation weight is dynamically allocated as 70% and the vibration suppression weight is 30% to generate a servo synchronization compensation amount: the trajectory compensation component +0.014 mm (0.02×70%), the vibration suppression component -0.036 mm (0.12×30%), and the phase compensation component (leading 15°) is superimposed. The final compensation amount drives the third axis to correct the position deviation and suppress vibration in the next control period, converging the deviation between the actual trajectory and the preset trajectory from 0.12 mm to ±0.01 mm.

[0079] To solve the problems of low online estimation accuracy of mechanical vibration disturbances in multi-axis servo systems and difficulty in quantifying dynamic coupling effects in the prior art, in some embodiments, according to step 103, an online estimation of the mechanical vibration disturbances of the multi-axis servo system is performed based on the error time series matrix to obtain estimated values of disturbance parameters, including:

[0080] Step 201, perform a sliding window recursive process on the error time series matrix, and use a covariance matrix update algorithm to calculate the dynamic coupling coefficients between each servo axis in real time;

[0081] In this step, the sliding window recursive processing means dividing the error time series matrix into consecutive time windows (such as each 100 ms as a window), and realizing dynamic data processing by recursively updating parameters; the covariance matrix update algorithm means calculating the covariance matrix of the servo axis error data in real time through the recursive least squares method to quantify the dynamic coupling strength between axes (such as the coupling coefficient of axis 1 to axis 2 is 0.85); the dynamic coupling coefficient characterizes the transmission strength of vibration interference between servo axes (the numerical range is [0, 1], 0 means no coupling, and 1 means complete coupling).

[0082] In the embodiment of the present application, first, the position, speed, and acceleration error data of each servo axis within the current time window (such as t = 0 - 100 ms) are extracted from the error time series matrix. Secondly, the covariance matrix is updated using the recursive least squares method, and the dynamic coupling coefficient between axes is calculated. For example, the error covariance between axis 1 and axis 2 is 0.85, and the covariance between axis 3 and axis 4 is 0.92. Finally, a dynamic coupling coefficient matrix is generated.

[0083] Step 202: Perform frequency domain conversion on the dynamic coupling coefficient and the error time series matrix within the current time window by using the amplitude-frequency characteristic separation method based on the fast Fourier transform, and extract the main frequency band amplitude parameter of mechanical vibration;

[0084] The amplitude-frequency characteristic separation method means converting the time domain error data into a frequency domain energy spectrum through the fast Fourier transform (FFT) to separate the main frequency band of mechanical vibration; the main frequency band amplitude parameter refers to the amplitude corresponding to the vibration frequency with an energy ratio exceeding a threshold (such as 15%) (such as 50 Hz corresponding to 0.12 mm).

[0085] In the embodiment of the present application, first, perform FFT transformation on the dynamic coupling coefficient matrix and the error time series data of the current window to generate a frequency domain energy spectrum. Secondly, identify the main frequency band with an energy ratio exceeding the threshold (such as the energy ratio of 48 Hz of axis 3 is 25%), and extract its amplitude parameter (0.12 mm). Finally, associate the main frequency band amplitude parameter with the dynamic coupling coefficient.

[0086] Step 203: Construct a perturbation parameter weighting factor according to the dynamic coupling coefficient and the main frequency band amplitude parameter, and use the weighting factor to dynamically correct the vibration phase offset of the servo axis to generate a perturbation parameter estimation value including amplitude-frequency characteristics and coupling effects;

[0087] The perturbation parameter weighting factor refers to a composite weight combining the dynamic coupling coefficient and the main frequency band amplitude (such as W = αC + βA, α = 0.6, β = 0.4), which is used to quantify the comprehensive influence of vibration energy and inter-axis coupling; the vibration phase offset refers to the lag or lead angle of the vibration waveform relative to the theoretical position (such as a lag of 15°).

[0088] In the embodiment of the present application, first, a dynamic coupling coefficient (such as 0.92 of shaft 3) and a main frequency band amplitude parameter (0.12 mm) are used to generate a combined weight coefficient through non-linear superposition operation. Secondly, the vibration phase offset is corrected by time-domain integration based on the combined weight coefficient. Finally, the corrected phase offset and the main frequency band amplitude parameter are subjected to time-frequency domain energy normalization processing to generate an estimated value of the disturbance parameter.

[0089] In this embodiment, through dynamic coupling analysis, frequency-domain feature separation, and weighting factor correction, accurate online estimation of mechanical vibration disturbances in a multi-axis servo system is achieved, solving the problem that it is difficult to synergistically optimize the coupling effect and frequency-domain features in traditional methods, and significantly improving the filling accuracy and equipment stability.

[0090] In some embodiments, according to step 203, a disturbance parameter weighting factor is constructed based on the dynamic coupling coefficient and the main frequency band amplitude parameter, and the vibration phase offset of the servo axis is dynamically corrected using the weighting factor to generate an estimated value of the disturbance parameter including amplitude-frequency characteristics and axis coupling effects, including:

[0091] Step 301, generating an amplitude-frequency coupling combined weight coefficient by non-linearly superposing the dynamic coupling coefficient and the main frequency band amplitude parameter;

[0092] In this step, the dynamic coupling coefficient is a parameter that quantifies the vibration transfer intensity between servo axes and reflects the tightness of the mutual influence of the multi-axis mechanical structure. The main frequency band amplitude parameter is the amplitude corresponding to the dominant frequency of mechanical vibration extracted by fast Fourier transform, representing the concentration degree of vibration energy in the frequency domain. The non-linear superposition operation is a mathematical method that fuses the dynamic coupling coefficient and the main frequency band amplitude based on a preset weight rule, and is used to generate a comprehensive weight that simultaneously reflects the axis coupling effect and the vibration energy distribution. The amplitude-frequency coupling combined weight coefficient is a composite parameter generated by the above fusion and is used to dynamically adjust the priority of vibration phase correction.

[0093] In the embodiment of the present application, first, a dynamic coupling coefficient (for example, the coupling coefficient between shaft 3 and shaft 4 is 0.92) and the main frequency band amplitude parameter within the current time window (for example, the vibration amplitude of 48 Hz of shaft 3 is 0.12 mm) are obtained from the online estimation module of the multi-axis servo system. Secondly, these two parameters are input into a preset non-linear function (for example, "if the coupling coefficient > 0.8 and the amplitude > 0.1 mm, then the combined weight coefficient is increased to 0.9") to generate an amplitude-frequency coupling combined weight coefficient. Finally, this coefficient is passed to the vibration phase correction module for dynamically adjusting the compensation priority of different servo axes.

[0094] Step 302: Based on the amplitude-frequency coupling joint weight coefficient, perform time-domain integration correction on the vibration phase offset of the servo axis. Through the integration operation, dynamically associate the vibration phase offset with the vibration energy to generate a corrected vibration phase offset.

[0095] In this step, the time-domain integration correction refers to an adjustment method that dynamically associates the vibration phase offset with the vibration energy through integration operations. Its core is to cumulatively adjust the phase correction amount according to the amplitude-frequency coupling joint weight coefficient. The vibration phase offset represents the lag or lead angle of the mechanical vibration waveform relative to the theoretical position (such as a 15-degree lag), and the corrected vibration phase offset is an optimized parameter that matches the vibration energy after integration processing.

[0096] In this embodiment, first, obtain the amplitude-frequency coupling joint weight coefficient (for example, the joint weight coefficient of axis 3 is 0.9). Second, input this coefficient and the original vibration phase offset of the servo axis (for example, the initial phase of axis 3 lags by 15 degrees) into the time-domain integrator, and generate a corrected phase offset (for example, a 7.5-degree lag after correction) according to the preset integration gain (for example, the gain coefficient is 0.5). Finally, bind the corrected phase offset with the main frequency band amplitude parameter (for example, 0.12 mm) to generate the final disturbance parameter including amplitude-frequency characteristics and energy distribution.

[0097] Step 303: Perform time-frequency domain energy normalization processing on the corrected vibration phase offset and the main frequency band amplitude parameter, and combine the time-domain standard deviation of the error time series matrix to generate an estimated disturbance parameter including amplitude-frequency characteristics and multi-axis coupling effects.

[0098] In this step, the time-frequency domain energy normalization processing refers to the process of mapping vibration parameters with different dimensions (such as phase offset and amplitude) to a unified energy evaluation system, and achieving comparability between parameters through weight allocation. The time-domain standard deviation of the error time series matrix is used to quantify the fluctuation degree of the servo axis synchronization error and reflect the stability of trajectory tracking.

[0099] In this embodiment, first, the corrected vibration phase offset (e.g., 7.5 degrees of lag for shaft 3) and the main frequency band amplitude parameter (e.g., 0.12 mm) are respectively normalized: the phase offset is mapped to the [0, 1] interval according to the maximum allowable lag angle (such as ±20 degrees) (7.5 degrees is mapped to 0.375), and the amplitude parameter is normalized to the [0, 1] interval according to the preset threshold (such as 0.2 mm) (0.12 mm is mapped to 0.6). Second, in combination with the time-domain standard deviation of the error time series matrix (e.g., 0.05 mm), a perturbation parameter estimate value is generated through a weighting formula (such as normalized phase weight 0.4, amplitude weight 0.4, standard deviation weight 0.2) (e.g., 0.375×0.4 + 0.6×0.4 + 0.05×0.2 = 0.41). Finally, a perturbation parameter estimate value including the amplitude-frequency characteristic (amplitude 0.6) and the multi-axis coupling effect (phase correction 0.375) is output.

[0100] To solve the problems in the prior art that the construction of the vibration parameter weighting factor depends on empirical rules and the amplitude-frequency coupling effect is difficult to dynamically correct, based on this, in some embodiments, according to step 104, the dynamic compensation signal and the perturbation parameter estimate value are fused to generate a servo synchronization compensation amount, including:

[0101] Step 401, generating a dynamic weight coefficient according to the frequency-domain energy distribution of the perturbation parameter estimate value and the time-domain standard deviation of the error time series matrix;

[0102] In this step, the frequency-domain energy distribution represents the energy proportion of mechanical vibration at different frequencies (e.g., the vibration energy at 48 Hz accounts for 25%), and the time-domain standard deviation reflects the fluctuation degree of the servo axis synchronization error (such as the error fluctuation range is ±0.05 mm). The dynamic weight coefficient is an adjustment parameter generated according to the vibration energy proportion and the error fluctuation degree, and is used to balance the priorities of trajectory compensation and vibration suppression.

[0103] In this embodiment, first, the frequency-domain energy distribution of the perturbation parameter estimate value is analyzed (e.g., the vibration energy at 48 Hz of shaft 3 accounts for 25%), and at the same time, the time-domain standard deviation of the error time series matrix is calculated (e.g., 0.05 mm). Second, when the vibration energy proportion exceeds the threshold (such as 20%), the vibration suppression weight is increased (e.g., weight 0.6), and when the error fluctuation is significant (standard deviation > 0.03 mm), the trajectory compensation weight is increased (e.g., weight 0.7). Finally, a dynamic weight coefficient is generated (e.g., trajectory weight 0.7, vibration weight 0.3).

[0104] Step 402, fusing the trajectory tracking error component in the dynamic compensation signal and the mechanical vibration component of the perturbation parameter estimate value through the dynamic weight coefficient to generate a fusion compensation amount including trajectory deviation suppression and vibration perturbation compensation;

[0105] In this step, the trajectory tracking error component represents the deviation between the actual position of the servo axis and the preset trajectory (such as the position deviation of axis 3 being +0.1 mm), and the mechanical vibration component represents the vibration suppression amount extracted from the estimated value of the disturbance parameter (such as the amplitude being 0.12 mm). The fusion compensation amount is a comprehensive correction amount generated by weighted superposition of the two types of components through dynamic weight coefficients.

[0106] In this embodiment, first, the trajectory tracking error component (such as the position deviation of axis 3 being +0.1 mm) is extracted from the dynamic compensation signal, and the mechanical vibration component (such as the amplitude being 0.12 mm) is extracted from the estimated value of the disturbance parameter. Second, weighting is performed according to the dynamic weight coefficients (trajectory weight 0.7, vibration weight 0.3): the trajectory compensation amount = 0.1 × 0.7 = 0.07 mm, and the vibration suppression amount = 0.12 × 0.3 = 0.036 mm. Finally, the fusion compensation amount (0.07 + 0.036 = 0.106 mm) is generated by superposition.

[0107] Step 403: Based on the real-time synchronization error data of the servo axis, perform phase difference prediction compensation on the fusion compensation amount, and decompose it into an amplitude compensation component and a phase compensation component;

[0108] In this step, the phase difference prediction compensation predicts the action time difference between servo axes according to the real-time synchronization error (such as axis 3 lagging axis 4 by 10 ms). The amplitude compensation component is used to suppress the vibration amplitude, and the phase compensation component is used to eliminate the time delay error.

[0109] In this embodiment, first, based on the real-time synchronization error data of the servo axis (such as the error of axis 3 lagging axis 4 being 0.1 mm), the phase difference (such as lagging by 10 ms) is predicted. Second, the fusion compensation amount is decomposed according to the phase difference: the amplitude compensation component (0.036 mm) is used to suppress vibration, and the phase compensation component (leading by 10 ms) is used to correct the time delay. Finally, the two types of components are output to different control loops.

[0110] Step 404: Superimpose the amplitude compensation component on the current loop control command of the servo axis, and suppress high-frequency noise through frequency domain limiting filtering; at the same time, inject the phase compensation component into the position loop feedforward channel, and avoid mechanical shock through time domain integral smoothing processing to generate a servo synchronization compensation amount;

[0111] In this step, the frequency domain limiting filtering truncates the high-frequency noise of the amplitude compensation component (such as filtering out noise above 100 Hz), and the time domain integral smoothing processing performs a slow change adjustment on the phase compensation component to avoid mechanical shock.

[0112] In this embodiment, first, an amplitude compensation component (0.036 mm) is superimposed on the current loop control command, and high-frequency noise is suppressed through frequency-domain amplitude limiting filtering (e.g., cut-off frequency 100 Hz). Secondly, a phase compensation component (leading by 10 ms) is injected into the position loop feed-forward channel, and the servo axis movement is smoothly adjusted through time-domain integration (e.g., increasing the command by 5% every 10 ms). Finally, a servo synchronization compensation amount is generated to drive multi-axis coordinated movement.

[0113] Through dynamic weight distribution, component fusion, and dual-loop coordinated control, this embodiment achieves an accurate balance between trajectory compensation and vibration suppression, and solves the problem of filling dripping caused by the conflict between trajectory and vibration compensation in traditional single-loop control.

[0114] In order to improve the robustness of the fusion of trajectory and vibration compensation components in the prior art, based on this, in some embodiments, according to what is described in step 402, the trajectory tracking error component and the mechanical vibration component of the disturbance parameter estimate value in the dynamic compensation signal are fused through the dynamic weight coefficient to generate a fusion compensation amount including trajectory deviation suppression and vibration disturbance compensation, including:

[0115] Step 501, perform time-domain amplitude normalization on the trajectory tracking error component to obtain the normalized trajectory tracking error component;

[0116] In this step, time-domain amplitude normalization is a processing process that maps the actual numerical value of the trajectory tracking error component to a standard interval according to a preset range, and is used to eliminate the influence of parameters with different dimensions on the fusion calculation. The normalized trajectory tracking error component represents the relative size of the original error on the standardized scale, which is convenient for subsequent weight distribution and multi-parameter fusion.

[0117] In this embodiment, first, set the maximum allowable deviation range of the trajectory tracking error (e.g., ±0.2 mm). Secondly, map the actual trajectory tracking error component (e.g., the position deviation of axis 3 +0.1 mm) to the [-1, 1] interval in proportion (+0.1 mm corresponds to the normalized value 0.5). Finally, output the normalized trajectory tracking error component (e.g., 0.5) for subsequent fusion calculation with the vibration component.

[0118] Step 502, perform frequency-domain energy normalization on the mechanical vibration component to obtain the normalized mechanical vibration component;

[0119] In this step, frequency-domain energy normalization is a process of converting the amplitude of the mechanical vibration component into a standard proportional value according to the proportion of its main frequency band energy, and is used to quantify the relative intensity of the vibration energy in the overall frequency spectrum. The mechanical vibration component refers to the amplitude of a specific frequency vibration signal extracted through frequency-domain analysis, such as the vibration amplitude of a certain servo axis at a frequency of 48 Hz.

[0120] In this embodiment, first, the original vibration signal of the servo axis is collected by a vibration sensor (for example, the vibration amplitude of axis 3 measured by an acceleration sensor is 0.12 mm), and the signal is subjected to a fast Fourier transform to generate a frequency-domain energy distribution map. Secondly, the main frequency band with the highest energy proportion is identified (for example, the energy of 48 Hz accounts for 25% of the total vibration energy), and the corresponding amplitude parameter (0.12 mm) is extracted. Then, according to the preset reference energy threshold (for example, the energy proportion of the main frequency band being 20% is the reference), the actual amplitude is scaled proportionally to the normalized interval (for example, 0.12 mm corresponds to an energy of 25%, the normalized value is 1.25, and the limit is 1.0). Finally, the normalized mechanical vibration component (for example, 1.0) is output for subsequent fusion calculation with the trajectory error component.

[0121] Step 503: Decompose the dynamic weight coefficient into a trajectory tracking weight factor and a vibration suppression weight factor according to the real-time working conditions of the filling process;

[0122] In this step, the decomposition of the dynamic weight coefficient is a process of reasonably allocating the comprehensive compensation weight to two key control objectives, namely trajectory tracking and vibration suppression, according to the specific operating state of the current filling process. The trajectory tracking weight factor is used to control the following accuracy of the servo axis for the preset trajectory, and the vibration suppression weight factor is used to control the elimination strength of mechanical vibration.

[0123] In this embodiment, first, the current working state is monitored in real time by speed sensors and vibration sensors installed on the filling equipment, including key parameters such as filling speed and vibration amplitude. Secondly, according to the preset weight allocation rule, when it is detected that the vibration amplitude exceeds the safety threshold, the proportion of the vibration suppression weight factor is automatically increased; when the trajectory deviation is large, the trajectory tracking weight factor is correspondingly increased. Then, the system intelligently allocates the dynamic weight coefficient according to the current working condition requirements. For example, 60% of the weight is allocated to vibration suppression and 40% to trajectory tracking in the high-speed filling stage. Finally, the two generated weight factors are passed to the subsequent compensation amount calculation module to ensure that the control system can flexibly adjust the compensation strategy according to actual needs.

[0124] Step 504: Multiply the normalized trajectory tracking error component by the trajectory tracking weight factor to generate a trajectory compensation amount;

[0125] In this step, the trajectory compensation amount is a correction parameter generated by combining the normalized trajectory tracking error component with the trajectory tracking weight factor allocated by the system. This parameter directly determines the amplitude of the trajectory correction that the servo axis needs to perform and is a key intermediate variable for the control system to achieve precise motion.

[0126] In this embodiment, first, obtain the trajectory tracking error component that has completed normalization processing from the data processing module. This value has eliminated the influence of dimension and is within the standard range. At the same time, read the currently calculated trajectory tracking weight factor from the weight allocation module. Then perform a multiplication operation on these two values to obtain the final trajectory compensation amount. For example, when the normalized trajectory error of a certain servo axis is 0.5 and the current trajectory tracking weight factor is 0.4, the system will automatically calculate a trajectory compensation amount of 0.2. This compensation amount will be immediately transmitted to the motion control module, converted into specific motor control commands, and drive the servo axis to perform precise position correction. The entire process is completed within milliseconds, ensuring real-time compensation for motion errors.

[0127] Step 505: Multiply the normalized mechanical vibration component by the vibration suppression weight factor to generate a vibration suppression amount;

[0128] In this step, the vibration suppression amount is a control parameter calculated by combining the normalized mechanical vibration component with the vibration suppression weight factor dynamically allocated by the system. This parameter reflects the current vibration suppression force that needs to be applied and is an important adjustment variable for the control system to achieve stable operation.

[0129] In this embodiment, first, obtain the value of the mechanical vibration component that has completed normalization processing from the data processing module. This value has undergone frequency-domain energy conversion processing. At the same time, obtain the value of the vibration suppression weight factor calculated in real time from the weight allocation module. Then perform a multiplication operation on these two values to generate the final vibration suppression amount. For example, when the normalized vibration component of a certain servo axis is detected to be 1.0 and the current vibration suppression weight factor is 0.6, the system will automatically calculate a vibration suppression amount of 0.6. This suppression amount will be immediately transmitted to the vibration control module, converted into specific suppression commands, and effectively attenuate mechanical vibration by adjusting servo drive parameters. The entire process is carried out in real time, ensuring fast response and precise control of vibration.

[0130] Step 506: Vectorially superimpose the trajectory compensation amount and the vibration suppression amount to generate a fusion compensation amount;

[0131] In this step, the fusion compensation amount is a comprehensive control parameter generated by vectorially superimposing the trajectory compensation amount and the vibration suppression amount in space. This parameter comprehensively considers the position tracking accuracy and the vibration suppression requirement and is the control command that finally acts on the servo system.

[0132] In this embodiment, first, obtain the calculated trajectory compensation amount from the trajectory control module, and this parameter reflects the position deviation that needs to be corrected. At the same time, obtain the currently calculated vibration suppression amount from the vibration control module, and this parameter reflects the vibration amplitude that needs to be eliminated. Then, vectorially superimpose these two parameters according to the spatial motion direction of the servo axis to generate the final fusion compensation amount. For example, when the trajectory compensation amount of a certain servo axis is +0.04 mm (forward correction) and the vibration suppression amount is -0.12 mm (reverse suppression), the system will generate a fusion compensation amount of -0.08 mm. This comprehensive compensation instruction will be immediately transmitted to the servo driver, while optimizing the position tracking and vibration suppression effects. The entire process is completed in real time within the control cycle to ensure the timeliness and accuracy of the system response.

[0133] To solve the problems of large noise interference and lack of spatio-temporal correlation in the synchronous error data in the prior art, based on this, in some embodiments, according to what is described in step 101, obtain the synchronous error data of each servo axis in the multi-axis servo system, and construct an error time series matrix for the synchronous error data according to the time series, including:

[0134] Step 601, obtain the synchronous error data of each servo axis in real time through the position closed-loop feedback signal of the multi-axis servo system, where the synchronous error data includes position deviation, speed deviation, and acceleration deviation;

[0135] In this step, the synchronous error data refers to the real-time deviation data of each moving axis in the multi-axis servo system in three dimensions of position, speed, and acceleration relative to the preset trajectory. These data are continuously collected through the position closed-loop feedback system, providing the basic input for subsequent error analysis and compensation control.

[0136] In this embodiment, first, the actual position signal of the axis is collected in real time through the high-precision encoder installed on each servo motor, and at the same time, the preset theoretical trajectory data is read from the motion controller. Secondly, the position deviation value of each axis is obtained through differential calculation. For example, the actual position of a certain axis lags behind the theoretical position by 0.1 mm. Then, the speed deviation is calculated based on the time series change rate of the position signal. For example, the actual speed is 5 mm / s slower than the command speed. Finally, the acceleration deviation is obtained through the second derivative of the speed signal. For example, the actual acceleration is 2 m / s lower than the command value 2 . All deviation data is updated at a fixed sampling period (such as 1 ms) and transmitted to the central processing unit through the data bus. The entire acquisition process uses hardware-level signal processing to ensure the real-time and accuracy of the data.

[0137] Step 602, perform sliding window filtering processing on the synchronous error data to generate filtered synchronous error data;

[0138] In this step, sliding window filtering is a technique that sets a fixed-length analysis window on the continuously updated error data sequence to smooth the data within the window. This processing method can not only retain the real-time characteristics of the data, but also effectively suppress measurement noise and instantaneous interference.

[0139] In this embodiment, a sliding window containing several sampling points is first set (for example, a data window of 50ms duration corresponds to 50 1ms sampling points). As new error data is continuously input, the window slides forward and always retains the latest continuous data. The position deviation, velocity deviation and acceleration deviation in the window are weighted averaged, respectively, with newer data being given a higher weight. For example, the 50 position deviation values in the window are exponentially weighted to generate smoothed position deviation data. The processed data not only eliminates high-frequency noise interference, but also maintains the true trend of error changes.

[0140] Step 603, based on the timing characteristics of the filling cycle, the filtered synchronization error data is divided into dynamic time windows according to preset time intervals;

[0141] In this step, dynamic time window division is to divide the continuous error data stream into analysis periods that match the filling action according to the periodic characteristics of the filling process. This division method enables the system to adopt differentiated control strategies for different process stages.

[0142] In this embodiment, the process sequence of the filling machine is first analyzed to determine the time nodes of the key stages (such as 200ms for the bottle positioning stage, 300ms for the filling stage, and 150ms for the sealing stage). Then, based on these time characteristics, the filtered synchronous error data stream is automatically divided into corresponding analysis windows. For example, a 300ms analysis window is automatically created at the beginning of the filling stage, and all error data within this period are processed centrally. The data in each window will be marked with the process stage to which it belongs for targeted processing by subsequent modules.

[0143] Step 604, performing covariance matrix calculation on the filtered synchronization error data in each dynamic time window, and constructing a dynamic error time series matrix according to the calculation results;

[0144] In this step, the covariance matrix calculation is a process of quantifying the degree of correlation between the error data of each motion axis in the multi-axis servo system through mathematical methods. The dynamic error timing matrix is a three-dimensional data structure that integrates the time dimension, the coupling relationship between axes, and the error evolution trend, and can fully reflect the dynamic error characteristics of the system.

[0145] In this embodiment, first, multi-axis error data after filtering processing is extracted from the dynamic time window, including position deviation, speed deviation, and acceleration deviation of each axis. Then, for the error data of all sampling points within the window, the covariance value between any two servo-axis errors is calculated, such as the covariance between the X-axis position deviation and the Y-axis speed deviation. Next, these covariance values are arranged in a matrix according to the axis number and error type to construct a covariance matrix reflecting the error correlation characteristics within the current time window. Finally, the covariance matrices of consecutive time windows are stacked in time sequence to form a dynamic error time sequence matrix containing time evolution information.

[0146] To solve the problem of insufficient real-time performance of dynamic compensation caused by the lag of displacement feedback data in the prior art, based on this, in some embodiments, according to step 102, the displacement feedback data of each servo axis in the multi-axis servo system is collected in real time by a high-precision rotary encoder, and a dynamic compensation signal is generated according to the deviation between the displacement feedback data and the preset trajectory, including:

[0147] Step 701, the original displacement data of each servo axis is obtained in real time by a high-precision rotary encoder, and the original displacement data is subjected to sliding window mean filtering processing;

[0148] In this step, the original displacement data refers to the unprocessed axis position pulse signal directly collected by the servo motor through a high-precision rotary encoder. Sliding window mean filtering is a data preprocessing method that eliminates random interference signals by averaging the displacement data within a continuous time period.

[0149] In this embodiment, first, the original displacement pulse signal is collected by an optoelectronic encoder (such as a 23-bit absolute encoder) installed at the rear end of the servo motor at a fixed sampling frequency (such as 1 MHz). After these signals are converted into digital signals through a differential circuit, they are sent to the real-time processing unit. The system sets a sliding window with a width of 50 sampling points (corresponding to a duration of 50 μs), and the window slides forward continuously as new data arrives. The arithmetic mean of the displacement data within the window is taken. For example, 50 original position values (such as 10234, 10237, 10235...) are calculated as a filtered value of 10235.6. The processed data not only retains the true displacement trend but also effectively suppresses abnormal jumps caused by electromagnetic interference or mechanical vibration. The filtering parameters can be dynamically adjusted according to different motion stages. For example, when moving at high speed, the window width is automatically reduced to 20 sampling points to improve the response speed.

[0150] Step 702, the filtered displacement data is compared in real time with the expected position, speed, and acceleration of the preset trajectory, and position deviation, speed tracking deviation, and acceleration tracking deviation are generated according to the comparison results;

[0151] In this step, the expected data of the preset trajectory refers to the ideal motion curve generated by the motion controller according to the process requirements, including the theoretical values of position, speed, and acceleration in three dimensions. Real-time comparative analysis is a process of quantifying the tracking accuracy by performing multi-dimensional differential calculations on the actual motion state and the theoretical trajectory.

[0152] In this embodiment, first, the expected position, speed, and acceleration command values at the current moment are read from the motion controller (such as the position command 50.0 mm, the speed command 100 mm / s, and the acceleration command 5 m / s 2 ). At the same time, the filtered actual displacement data is obtained (such as the current position 50.2 mm). Through real-time calculation, the following are generated: the position deviation (+0.2 mm), the speed tracking deviation (the corresponding deviation -5 mm / s for the actual speed of 95 mm / s), and the acceleration tracking deviation (the corresponding deviation -0.2 m / s 2 for the actual acceleration of 4.8 m / s 2 ). These deviation data are updated at a frequency of 1 kHz and transmitted to the compensation control module through a high-speed data bus. The system adopts a hardware-level parallel computing architecture to ensure that the deviation calculations in all dimensions are completed within microseconds to meet the real-time control requirements.

[0153] Step 703: Based on the dynamic response characteristics of the servo axis, perform phase lead compensation on the acceleration tracking deviation to generate an acceleration feedforward compensation term;

[0154] In this step, phase lead compensation is a pre-compensation technique designed for the dynamic response lag characteristic of the servo system. By injecting a compensation signal in advance, the inherent delay of the system is offset. The acceleration feedforward compensation term is the future compensation amount predicted based on the current acceleration deviation, which is used to improve the dynamic tracking performance of the system.

[0155] In this embodiment, first, the dynamic response characteristics of the servo axis are analyzed (such as it is measured that the acceleration response of axis 3 has an 8 ms lag). According to the lag time and the current acceleration tracking deviation (such as -0.2 m / s 2 ), the compensation amount to be injected in advance is calculated through a pre-compensation algorithm. For example, for a deviation of -0.2 m / s 2 , after considering the system inertia and damping coefficient, a lead compensation term of +0.25 m / s 2 is generated. This compensation term is directly superimposed on the control command through the feedforward channel, so that the actual acceleration can accurately match the command value after 8 ms. The compensation parameters are dynamically adjusted according to the real-time identified system characteristics. For example, the compensation gain is automatically increased during high-speed motion to cope with greater inertial delay.

[0156] Step 704: Perform weighted fusion on the position deviation, speed tracking deviation, and acceleration feedforward compensation term to generate an initial compensation signal;

[0157] In this step, weighted fusion is the process of integrating multi-dimensional error compensation amounts into a unified control instruction through preset weight ratios. The initial compensation signal is a preliminary control quantity that synthesizes the compensation requirements for position, speed, and acceleration, providing a basis for subsequent precise adjustment.

[0158] In this embodiment, first, default ratios of the position deviation weight of 0.6, the speed deviation weight of 0.3, and the acceleration compensation term weight of 0.1 are set. When a position deviation of +0.2 mm is detected, a position compensation component of +0.12 mm (0.2×0.6) is generated; for a speed deviation of -5 mm / s, a speed compensation component of -1.5 mm / s (-5×0.3) is generated; the acceleration feedforward compensation term of +0.25 m / s 2 After being converted into an equivalent displacement of +0.025 mm, a weight value of +0.0025 mm is taken. Then, the three components are superimposed to generate an initial compensation signal of +0.1225 mm. The system dynamically adjusts the weights according to the real-time working conditions. For example, the speed weight is increased to 0.5 during the high-speed stage to ensure dynamic response. All calculations are completed in parallel in the FPGA hardware to ensure that the compensation signal is output within 1 μs.

[0159] Figure 2 The structural schematic diagram of a filling control system based on online parameter identification is provided for the embodiments of the present application, as Figure 2 shown. The system includes:

[0160] A construction module 21, configured to obtain the synchronous error data of each servo axis in the multi-axis servo system, and construct an error time series matrix for the synchronous error data according to the time series;

[0161] An acquisition module 22, configured to collect the displacement feedback data of each servo axis in the multi-axis servo system in real time through a high-precision rotary encoder, and generate a dynamic compensation signal according to the deviation between the displacement feedback data and a preset trajectory;

[0162] An estimation module 23, configured to perform online estimation of the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain an estimated value of the disturbance parameter;

[0163] A fusion module 24, configured to fuse the dynamic compensation signal and the estimated value of the disturbance parameter to generate a servo synchronization compensation amount.

[0164] Figure 2 The described filling control system based on online parameter identification can execute Figure 1A filling control method based on online parameter identification described in the illustrated embodiment, its implementation principle and technical effects will not be elaborated. For a filling control system based on online parameter identification in the above embodiment, the specific ways in which each module and unit perform operations have been described in detail in the embodiment related to this method, and will not be elaborated here.

[0165] In a possible design, Figure 2 A filling control system based on online parameter identification in the illustrated embodiment can be implemented as a computing device, such as Figure 3 as shown, this computing device may include a storage component 31 and a processing component 32;

[0166] The storage component 31 stores one or more computer instructions, wherein, the one or more computer instructions are for the processing component 32 to call and execute.

[0167] The processing component 32 is used for the Figure 1 a filling control method based on online parameter identification in the above

[0168] embodiment. Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for executing the above method.

[0169] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component 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 read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0170] Of course, the computing device will necessarily also include other components, such as input / output interfaces, display components, communication components, etc.

[0171] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module can be an output device, an input device, etc.

[0172] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0173] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0174] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 a filling control method based on online parameter identification shown in the embodiment.

[0175] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0176] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0177] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A filling control method based on online parameter identification, characterized in that Including: Obtain the synchronous error data of each servo axis in the multi-axis servo system, and construct an error time series matrix for the synchronous error data in time series; Collect the displacement feedback data of each servo axis in the multi-axis servo system in real time through a high-precision rotary encoder, and generate a dynamic compensation signal according to the deviation between the displacement feedback data and the preset trajectory; Online estimate the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain a disturbance parameter estimation value; Fuse the dynamic compensation signal and the disturbance parameter estimation value to generate a servo synchronization compensation amount.

2. The method according to claim 1, characterized in that Online estimate the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain a disturbance parameter estimation value, including: Perform a sliding window recursive process on the error time series matrix, and use a covariance matrix update algorithm to calculate the dynamic coupling coefficient between each servo axis in real time; Perform a frequency domain conversion on the dynamic coupling coefficient and the error time series matrix within the current time window by using an amplitude-frequency characteristic separation method based on the fast Fourier transform, and extract the main frequency band amplitude parameter of mechanical vibration; Construct a disturbance parameter weighting factor according to the dynamic coupling coefficient and the main frequency band amplitude parameter, and use the weighting factor to dynamically correct the vibration phase offset of the servo axis to generate a disturbance parameter estimation value including amplitude-frequency characteristics and coupling effects.

3. The method according to claim 2, characterized in that Construct a disturbance parameter weighting factor according to the dynamic coupling coefficient and the main frequency band amplitude parameter, and use the weighting factor to dynamically correct the vibration phase offset of the servo axis to generate a disturbance parameter estimation value including amplitude-frequency characteristics and axis coupling effects, including: Generate an amplitude-frequency coupling joint weight coefficient by performing a non-linear superposition operation on the dynamic coupling coefficient and the main frequency band amplitude parameter; Perform a time domain integral correction on the vibration phase offset of the servo axis based on the amplitude-frequency coupling joint weight coefficient, and dynamically associate the vibration phase offset with the vibration energy through an integration operation to generate a corrected vibration phase offset; Perform a time-frequency domain energy normalization process on the corrected vibration phase offset and the main frequency band amplitude parameter, and combine the time domain standard deviation of the error time series matrix to generate a disturbance parameter estimation value including amplitude-frequency characteristics and multi-axis coupling effects.

4. The method according to claim 1, characterized in that Fuse the dynamic compensation signal and the disturbance parameter estimation value to generate a servo synchronization compensation amount, including: Generate a dynamic weight coefficient according to the frequency domain energy distribution of the disturbance parameter estimation value and the time domain standard deviation of the error time series matrix; Fuse the trajectory tracking error component in the dynamic compensation signal and the mechanical vibration component of the disturbance parameter estimation value through the dynamic weight coefficient to generate a fusion compensation amount including trajectory deviation suppression and vibration disturbance compensation; Based on the real-time synchronous error data of the servo axis, perform a phase difference prediction compensation on the fusion compensation amount, and decompose it into an amplitude compensation component and a phase compensation component; Superimpose the amplitude compensation component on the current loop control instruction of the servo axis, and suppress high-frequency noise through frequency domain amplitude limiting filtering; at the same time, inject the phase compensation component into the position loop feedforward channel, and avoid mechanical shock through time domain integral smoothing processing to generate a servo synchronization compensation amount.

5. The method according to claim 4, characterized in that Fusing the trajectory tracking error component in the dynamic compensation signal and the mechanical vibration component of the disturbance parameter estimate through the dynamic weight coefficient to generate a fusion compensation quantity including trajectory deviation suppression and vibration disturbance compensation, comprising: Performing time-domain amplitude normalization on the trajectory tracking error component to obtain the normalized trajectory tracking error component; Performing frequency-domain energy normalization on the mechanical vibration component to obtain the normalized mechanical vibration component; Decomposing the dynamic weight coefficient into a trajectory tracking weight factor and a vibration suppression weight factor according to the real-time working conditions of the filling process; Multiplying the normalized trajectory tracking error component by the trajectory tracking weight factor to generate a trajectory compensation quantity; Multiplying the normalized mechanical vibration component by the vibration suppression weight factor to generate a vibration suppression quantity; Performing vector superposition on the trajectory compensation quantity and the vibration suppression quantity to generate a fusion compensation quantity.

6. The method according to claim 1, wherein Obtaining the synchronization error data of each servo axis in the multi-axis servo system and constructing an error time series matrix from the synchronization error data according to the time series, comprising: Real-time obtaining the synchronization error data of each servo axis through the position closed-loop feedback signal of the multi-axis servo system, wherein the synchronization error data includes position deviation, speed deviation and acceleration deviation; Performing sliding window filtering on the synchronization error data to generate filtered synchronization error data; Based on the time series characteristics of the filling cycle, dividing the filtered synchronization error data into dynamic time windows at a preset time interval; Calculating the covariance matrix for the filtered synchronization error data within each dynamic time window and constructing a dynamic error time series matrix according to the calculation results.

7. The method according to claim 1, wherein Real-time collecting the displacement feedback data of each servo axis in the multi-axis servo system through a high-precision rotary encoder and generating a dynamic compensation signal according to the deviation between the displacement feedback data and a preset trajectory, comprising: Real-time obtaining the original displacement data of each servo axis through a high-precision rotary encoder and performing sliding window mean filtering on the original displacement data; Comparing the filtered displacement data with the expected position, speed and acceleration of the preset trajectory in real time and generating a position deviation, a speed tracking deviation and an acceleration tracking deviation according to the comparison results; Performing phase lead compensation on the acceleration tracking deviation based on the dynamic response characteristics of the servo axis to generate an acceleration feedforward compensation term; Performing weighted fusion on the position deviation, the speed tracking deviation and the acceleration feedforward compensation term to generate an initial compensation signal; Performing amplitude limiting processing on the initial dynamic compensation signal using a nonlinear correction algorithm to suppress high-frequency oscillation components and generate a dynamic compensation signal.

8. A filling control system based on online parameter identification, characterized in that, Comprising: A construction module for obtaining the synchronization error data of each servo axis in the multi-axis servo system and constructing an error time series matrix from the synchronization error data according to the time series; An acquisition module for real-time collecting the displacement feedback data of each servo axis in the multi-axis servo system through a high-precision rotary encoder and generating a dynamic compensation signal according to the deviation between the displacement feedback data and a preset trajectory; An estimation module for online estimating the mechanical vibration disturbance of the multi-axis servo system based on the error time series matrix to obtain an estimate of the disturbance parameter; A fusion module, configured to fuse the dynamic compensation signal and the disturbance parameter estimation value to generate a servo synchronization compensation amount.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a filling control method based on online parameter identification according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, it implements a filling control method based on online parameter identification according to any one of claims 1 to 7.

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