A high-speed and high-precision multi-axis parallel control system for electromechanical servo

Abstract

The present invention discloses a high-speed and high-precision multi-axis parallel control system for electromechanical servo, which specifically relates to the field of multi-axis parallel control systems. It is used to solve the problem of asynchronous feedback gain mismatch in multi-axis parallel control systems by integrating four core units: feedback monitoring, coupling analysis, gain adjustment, and priority allocation. First, the synchronization of feedback data of each axis is monitored in real time, and potential synchronization problems are identified and quantified in a timely manner. Subsequently, an accurate gain mismatch warning index is generated through coupling analysis to guide the system to perform adaptive dynamic gain adjustment to ensure that the gain parameters are highly matched with the actual operating status of each axis. Finally, by optimizing the allocation of control priorities, the coordinated and efficient operation of the multi-axis system in a complex dynamic environment is achieved. The invention significantly improves the stability, response speed, and resource utilization efficiency of the multi-axis collaborative control system, ensuring the reliability and accuracy of the system under variable operating conditions.

Description

Technical Field

[0001] The present invention relates to the field of multi-axis parallel control systems, and more specifically, to a high-speed and high-precision multi-axis parallel control system for electromechanical servo. Background Art

[0002] Multi-axis parallel control is a key technology in modern industrial automation and precision manufacturing, and is widely used in robot control, CNC machine tools, industrial automation equipment and other fields. In a multi-axis parallel control system, multiple axes or servo motors need to run simultaneously and accurately coordinate their motion trajectories to achieve complex processing, assembly or operation tasks. In order to ensure that each axis maintains consistency during operation, the system relies on precise feedback mechanisms and synchronous control algorithms. However, with the increasing complexity of control tasks and environmental uncertainties, multi-axis systems face challenges in maintaining high precision and high response speed, especially when coordinating multiple motion axes, the synchronization and stability of the system are particularly important.

[0003] In practical applications, multi-axis systems are often affected by differences in sensor accuracy and response speed in the feedback loops of each axis, which leads to asynchronous feedback gain mismatch problems. Asynchronous feedback gain mismatch means that in a multi-axis system, due to the asynchronous feedback signals of each axis, insufficient sensor accuracy or inconsistent response speed, the gain parameters calculated by the feedback data in the error compensation module cannot accurately match the dynamic characteristics of each axis. This problem can cause deviations in the servo control module when performing adjustments, especially in multi-axis synchronous operation, which is more likely to cause a decrease in control accuracy of some axes and system instability.

[0004] Existing technologies mainly rely on fixed gain parameters or simple adaptive control algorithms, which are difficult to deal with complex dynamic changes and external disturbances in multi-axis systems. These technologies are insufficient in dealing with error accumulation and real-time differences in the feedback loop between multiple axes, which often leads to poor performance of the system under high dynamic conditions, especially in synchronous operation, where the gain adjustment of some axes lags or is excessive, leading to a decrease in control accuracy and system instability. These deficiencies limit the application and expansion of existing multi-axis parallel control systems in complex industrial environments.

[0005] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a high-speed and high-precision multi-axis parallel control system for electromechanical servo, which realizes dynamic gain adjustment and optimization control in a multi-axis parallel control system by integrating a feedback monitoring unit, a coupling analysis unit, a gain adjustment unit and a priority allocation unit. The feedback monitoring unit effectively identifies and quantifies the potential synchronization problems in the system by capturing the time-frequency synchronization of the feedback data of each axis in real time, ensuring the coordination and stability of the system in a complex dynamic environment. The coupling analysis unit generates an accurate gain mismatch warning index by comprehensively analyzing the phase coupling relationship and gain response characteristics between the axes, providing real-time guidance for the gain adjustment of the system. The gain adjustment unit further ensures that the gain parameters are highly matched with the actual operating status of each axis through a dynamic adjustment mechanism, significantly improving the response speed and control accuracy of the system. The priority allocation unit optimizes the control priority allocation of the multi-axis system on the basis of dynamic adjustment, improves the resource utilization efficiency, and ensures the unified control and efficient operation of the system in a complex operating environment. Through the collaborative work of these units, the present invention can effectively avoid the problem of asynchronous feedback gain mismatch, comprehensively improve the overall performance and reliability of the multi-axis collaborative control system, and solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A high-speed and high-precision multi-axis parallel control system for electromechanical servo includes a feedback monitoring unit, a coupling analysis unit, a gain adjustment unit and a priority allocation unit.

[0009] The feedback monitoring unit monitors the feedback data of each axis in real time, constructs the time-frequency fusion synchronization coefficient between each axis through the time domain correlation and frequency domain phase difference analysis results, and transmits the time-frequency fusion synchronization coefficient to the coupling analysis unit.

[0010] The coupling analysis unit obtains the time-frequency cross-coupling index and dynamic gain adaptation factor by extracting the phase coupling relationship and gain response characteristics of the time-frequency fusion synchronization coefficient, constructs the feedback gain mismatch warning index, quantifies the mismatch risk of the gain parameters between the axes, and transmits the feedback gain mismatch warning index and mismatch risk results to the gain adjustment unit.

[0011] The gain adjustment unit calculates the gain adjustment reference value of each axis based on the feedback gain mismatch warning index, adjusts the gain parameters to match the operating status of each axis, and implements dynamic gain adjustment to adjust the gain parameters of each axis in real time, and transmits the dynamic gain parameters to the priority allocation unit.

[0012] The priority allocation unit combines the dynamically adjusted gain parameters with the feedback data synchronization evaluation results to reallocate the control priority of each axis and optimize the coordination of multi-axis collaborative operation.

[0013] In a preferred embodiment, the operation process of the feedback monitoring unit includes the following:

[0014] S1.1, each axis collects real-time feedback data through sensors, the data type includes position ,speed , acceleration , the collection frequency of feedback data is and sent to the central control module through a unified data transmission channel; each data point is attached with a timestamp to identify the specific time of data collection, and the timestamp is marked as ,in Indicates the axis number, Indicates the time when the data was collected.

[0015] S1.2, each axis data acquisition module maintains time synchronization with the central control module, adopts a global synchronous clock mechanism to achieve the establishment of a unified time base; the timestamp of each time point is synchronized by a synchronization correction factor After correction, the corrected timestamp expression is: ;in, is the corrected timestamp to ensure time synchronization. It is obtained by accurately measuring the clock deviation of each axis. The calculation formula is: ;in, is the time deviation of the current data point, is the maximum time error allowed by the system, is the frequency of data collection.

[0016] S1.3, the feedback data position, velocity, and acceleration are transformed nonlinearly to generate new feature vectors; the feature vectors are constructed using the following nonlinear mapping formula:

[0017] ;

[0018] ;

[0019] ;

[0020] in, , , is the position, velocity, and acceleration feature vector after nonlinear transformation, , , , , , is the nonlinear mapping coefficient.

[0021] S1.4, in the constructed feature space, the feedback data is converted from the time domain to the frequency domain through discrete Fourier transform to identify the frequency components and phase information in the data; the frequency domain features are calculated by the following formula: ;in, For the The frequency domain characteristics of the axis, is the frequency variable, is the number of data points.

[0022] Next, calculate the phase difference matrix between each axis , the formula is as follows: ; .

[0023] S1.5, construct the time-frequency fusion synchronization coefficient between each axis through the time domain correlation and frequency domain phase difference analysis results , the formula is: ;in, is the element in the time domain correlation matrix, and The upper and lower limits of the frequency range.

[0024] In a preferred embodiment, the operation process of the coupling analysis unit includes the following:

[0025] S2.1, time-frequency cross-coupling index generation logic:

[0026] S2.1.1, perform a preliminary time-frequency analysis on the feedback data of each axis, take the time-frequency fusion synchronization coefficient as the basic data, perform short-time Fourier transform on it, and generate a time-frequency matrix ;in, Indicates time, Indicates frequency; formula: ;in, is a time window function used to extract The local signal characteristics at time is the discrete time point of the time-frequency fusion synchronization coefficient, is the corresponding frequency.

[0027] S2.1.2, based on the time-frequency matrix, calculate the phase difference matrix between different time and frequency points ,formula: ;in, and For different moments, and For different frequency points.

[0028] S2.1.3, using the calculated phase difference matrix, construct the time-frequency cross-coupling matrix , used to represent the interaction intensity between the feedback signals of each axis in the time-frequency domain, formula: ;in, and represent small offsets in time and frequency, respectively. Represents the amplitude information of the signal in the time-frequency matrix, Used to convert phase difference into coupling strength indicator.

[0029] S2.1.4, based on the time-frequency cross-coupling matrix, generate the time-frequency cross-coupling index , the time-frequency cross-coupling index is obtained by integrating the time-frequency cross-coupling matrix over the entire frequency range.

[0030] In a preferred embodiment, the operation process of the coupling analysis unit further includes the following:

[0031] S2.2, dynamic gain adaptation factor generation logic:

[0032] S2.2.1, based on the time-frequency fusion synchronization coefficient, the preliminary gain response of the system is analyzed. First, the feedback data is analyzed in the frequency domain to generate the gain response spectrum matrix ,in represents the time window, Indicates frequency; in each time window, analyze the frequency characteristics of the gain response, extract the key frequency components, and form a basic data matrix; formula: ;in, is the discrete time point of the time-frequency fusion synchronization coefficient, is the corresponding frequency.

[0033] S2.2.2, based on the gain response spectrum matrix, calculate the frequency gain offset matrix , used to measure the degree of gain response deviation in the frequency domain; formula: ;in, and They represent small offsets in time and frequency respectively, and calculate the variation amplitude of the gain response in the frequency space within adjacent time windows.

[0034] S2.2.3, based on the frequency gain offset matrix, construct the gain response gradient matrix , used to quantify the gradient of gain response in frequency space; formula: .

[0035] S2.2.4, based on the gain response gradient matrix, generate a dynamic gain adaptation factor , the dynamic gain adaptation factor is obtained by normalizing the gain gradient matrix in the frequency domain.

[0036] In a preferred embodiment, the operation process of the coupling analysis unit further includes the following:

[0037] S2.3, construct a phase-gain collaborative mapping model to collaboratively map the phase information of the time-frequency cross-coupling index with the dynamic gain adaptation factor; identify the potential risk of gain mismatch by analyzing the mapping relationship between these two indicators in multidimensional space; specifically, map the phase component of the time-frequency cross-coupling index to the change curve of the gain response to generate a collaborative mapping matrix to capture the impact of phase fluctuations on the gain parameters; finally, through the singular value decomposition of the mapping matrix, the singular values ​​obtained represent the main correlation patterns between the phase component and the gain response; perform nonlinear transformation on the singular values, and amplify the key patterns that affect the gain mismatch risk by exponentially smoothing each singular value, and then accumulate the processed singular values ​​to generate a feedback gain mismatch warning index.

[0038] After the feedback gain mismatch warning index is generated, it is compared with the warning threshold; when the feedback gain mismatch warning index is greater than or equal to the warning threshold, it indicates that there is a significant mismatch in the gain parameters in the system, and an adjustment signal is generated at this time; on the contrary, when the feedback gain mismatch warning index is less than the warning threshold, it indicates that the matching of the gain parameters is within a reasonable range, and the system can continue to operate according to the current settings without the need to adjust the gain parameters.

[0039] In a preferred embodiment, the operation process of the gain adjustment unit includes the following:

[0040] S3.1, after the adjustment signal is obtained, the gain adjustment reference value is calculated for each axis based on the feedback gain mismatch warning index; first, the feedback gain mismatch warning index of each axis is compared with the standard response model of the system, which reflects the gain distribution and dynamic response characteristics of each axis under ideal conditions; through comparative analysis, the gain adjustment reference value of each axis is calculated, and the formula is as follows: ;in, Indicates the gain adjustment reference value, is the initial gain parameter, is the system dynamic adjustment coefficient, is the feedback gain mismatch warning index, is the time-frequency fusion synchronization coefficient.

[0041] S3.2, after the gain adjustment reference value is calculated, it is dynamically fused with the real-time feedback data, which includes the current position, speed, acceleration and the gain adjustment result of the previous moment; the feedback data is predicted and corrected through the Kalman filter fusion algorithm, and is comprehensively processed with the calculated gain adjustment reference value. The formula is as follows: ;in, is the dynamic gain parameter, is the Kalman gain, which adjusts the weight of the current feedback data and the gain adjustment result of the previous moment. is the gain parameter of the previous moment.

[0042] S3.3, the gain parameters after dynamic fusion will be applied to the actual control process of each axis; in each control cycle, the servo control module will obtain the updated dynamic gain parameters and apply them to the control signal calculation of each axis to ensure that the motion state of each axis is highly consistent with the expected trajectory; the servo control signal calculation formula is as follows: ;in, is the control signal, is the error signal, that is, the difference between the target position and the actual position.

[0043] S3.4, after the gain parameter is adjusted, the feedback data of each axis is continuously monitored, and adaptive adjustment is performed based on the latest feedback gain mismatch warning index; the adaptive adjustment is based on the recursive least squares method to update the gain parameter estimation online, and the formula is as follows: ;in, is the optimized gain parameter, To adjust the step size, and are the actual response and the expected response, respectively.

[0044] In a preferred embodiment, the operation process of the priority allocation unit includes the following:

[0045] S4.1, after completing the dynamic adjustment of the gain parameters of each axis, conduct a comprehensive analysis of the synchronization evaluation results of the adjusted gain parameters and the feedback data; specifically, compare the dynamic gain parameters of each axis with its corresponding time-frequency fusion synchronization coefficient to evaluate whether the feedback signal after gain adjustment matches the synchronization requirements of the system.

[0046] S4.2, after confirming the synchronization of feedback data, calculate the basis for the control priority allocation of each axis based on the synchronization index and dynamic gain parameters of each axis; analyze the importance of each axis in the current operating state, factors include the time-frequency cross-coupling intensity and dynamic gain response speed of each axis, and generate a control priority matrix through multi-dimensional comprehensive analysis to quantify the priority level of each axis in the current control task.

[0047] S4.3, according to the control priority matrix, the control priority of each axis will be reallocated.

[0048] S4.4, after completing the redistribution of control priorities, the multi-axis collaborative control strategy is optimized.

[0049] The technical effects and advantages of the high-speed and high-precision multi-axis parallel control system for electromechanical servo of the present invention are as follows:

[0050] 1. By running the feedback monitoring unit, the system can effectively capture and analyze the synchronization of the feedback data of each axis in the time domain and frequency domain, and quantify the level of synchronization in the form of time-frequency fusion synchronization coefficient. The real-time update and monitoring of this coefficient enables the system to promptly identify potential synchronization problems between axes and make adjustments based on these problems to ensure the coordination and stability of the system in a complex operating environment. In addition, the application of the time-frequency fusion synchronization coefficient not only improves the control accuracy of the system under dynamic operating conditions, but also provides an important data basis for subsequent analysis and optimization, thereby significantly improving the overall performance and reliability of the multi-axis collaborative control system.

[0051] 2. Through the operation of the coupling analysis unit, the gain mismatch risk in the multi-axis parallel control system can be effectively identified and quantified. By comprehensively utilizing the time-frequency cross-coupling index and the dynamic gain adaptation factor, the gain parameter matching between the axes is comprehensively analyzed from the two key dimensions of phase coordination and gain dynamic adaptability. The application of the phase-gain collaborative mapping model makes the generation of the gain mismatch warning index more accurate, which can sensitively capture the potential mismatch risk of the gain parameters in the system and provide real-time adjustment guidance. Through this process, the stability and responsiveness of the system are significantly improved, especially in complex dynamic environments. The system can more accurately adjust the gain parameters of each axis to ensure the efficient operation of multi-axis collaborative control, thereby promoting the improvement of the overall system performance.

[0052] 3. By running the gain adjustment unit, the system can monitor and adjust the gain parameters of each axis in real time to make it highly matched with the actual operating status of the system. The dynamic adjustment mechanism not only improves the response speed of the system in complex dynamic environments, but also ensures the overall coordination of the multi-axis system. The adaptive adjustment mechanism further optimizes the gain parameter setting and reduces the risk of gain mismatch, thereby significantly improving the stability and control accuracy of the system.

[0053] 4. After running the priority allocation unit, the system can reallocate the control priority of each axis based on the dynamic adjustment of the gain parameters to ensure the high coordination of the multi-axis system during operation. By optimizing the allocation of control priorities, the system can use resources more effectively and improve overall operating efficiency, while reducing control delays and resource waste caused by improper priority allocation. Unified control of the multi-axis system in a complex operating environment can be achieved, significantly improving the overall performance and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a structural schematic diagram of a high-speed and high-precision multi-axis parallel control system for electromechanical servo of the present invention;

[0055] Figure 2 The present invention is a schematic diagram of the steps of a priority allocation unit of a high-speed and high-precision multi-axis parallel control system for electromechanical servo. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Example 1

[0058] Figure 1 The invention provides a high-speed and high-precision multi-axis parallel control system for electromechanical servo, comprising: a feedback monitoring unit, a coupling analysis unit, a gain adjustment unit and a priority allocation unit;

[0059] The feedback monitoring unit monitors the feedback data of each axis in real time, constructs the time-frequency fusion synchronization coefficient between each axis through the time domain correlation and frequency domain phase difference analysis results, and transmits the time-frequency fusion synchronization coefficient to the coupling analysis unit.

[0060] The coupling analysis unit obtains the time-frequency cross-coupling index and dynamic gain adaptation factor by extracting the phase coupling relationship and gain response characteristics of the time-frequency fusion synchronization coefficient, constructs the feedback gain mismatch warning index, quantifies the mismatch risk of the gain parameters between the axes, and transmits the feedback gain mismatch warning index and mismatch risk results to the gain adjustment unit.

[0061] The gain adjustment unit calculates the gain adjustment reference value of each axis based on the feedback gain mismatch warning index, adjusts the gain parameters to match the operating status of each axis, and implements dynamic gain adjustment to adjust the gain parameters of each axis in real time, and transmits the dynamic gain parameters to the priority allocation unit.

[0062] The priority allocation unit combines the dynamically adjusted gain parameters with the feedback data synchronization evaluation results to reallocate the control priority of each axis and optimize the coordination of multi-axis collaborative operation.

[0063] In a multi-axis parallel control system, ensuring the synchronization and coordination of feedback data from each axis is crucial to the overall stability of the system. Since the feedback data from each axis comes from different sensors and modules, factors such as time asynchrony, data transmission delay, and signal noise may cause inconsistencies in the feedback data in the time and frequency domains, thus affecting the precise control and coordination of the system. By accurately synchronizing and nonlinearly transforming the feedback data of each axis and analyzing the correlation of the data in the time and frequency domains, the synchronization level between the axes can be effectively identified and quantified. The introduction of the time-frequency fusion synchronization coefficient provides the system with an innovative way to comprehensively evaluate the coordination of feedback data in both time and frequency dimensions, which helps to discover potential synchronization problems that may cause system instability in advance.

[0064] The operation process of the feedback monitoring unit includes the following:

[0065] S1.1, each axis collects real-time feedback data through high-precision sensors. The data types include position ,speed , acceleration The collection frequency of these feedback data is , and sent to the central control module through a unified data transmission channel. Each data point is attached with an accurate timestamp to identify the specific time of data collection. The timestamp is marked as ,in Indicates the axis number, Indicates the time when the data was collected.

[0066] S1.2, each axis data acquisition module keeps time synchronization with the central control module, and adopts a hardware-level global synchronization clock mechanism to achieve the establishment of a unified time base. The timestamp of each time point is synchronized by the synchronization correction factor After correction, the corrected timestamp expression is: ;in, is the corrected timestamp to ensure time synchronization. It is obtained by accurately measuring the clock deviation of each axis. The calculation formula is: ; is the time deviation of the current data point, is the maximum time error allowed by the system, The data collection frequency is eliminated by correcting the timestamp to eliminate the time asynchrony problem caused by clock deviation.

[0067] S1.3, the feedback data position, velocity, and acceleration are transformed nonlinearly to generate new feature vectors to enhance the correlation between the data of each axis. The feature vector is constructed by the following nonlinear mapping formula:

[0068] ;

[0069] ;

[0070] ;

[0071] in, , , is the position, velocity, and acceleration feature vector after nonlinear transformation, , , , , , is the nonlinear mapping coefficient, which is set according to the dynamic characteristics of the system and determined through experiments to ensure that the feature space after data transformation can capture the complex interaction relationship between the axes.

[0072] S1.4, in the constructed feature space, the feedback data is converted from the time domain to the frequency domain through discrete Fourier transform (DFT) to identify the frequency components and phase information in the data. The frequency domain features are calculated by the following formula: ;in, For the The frequency domain characteristics of the axis, is the frequency variable, is the number of data points. Next, calculate the phase difference matrix between each axis , the formula is as follows: ; , which reflects the phase difference of each axis at different frequencies. Combined with the time domain correlation analysis, a comprehensive correlation matrix between the axes is constructed for further synchronization index calculation.

[0073] S1.5, construct the time-frequency fusion synchronization coefficient between each axis through the time domain correlation and frequency domain phase difference analysis results , the formula is: ; is the element in the time domain correlation matrix, and The time-frequency fusion synchronization coefficient reflects the synchronization of the feedback data between the axes in the time domain and the frequency domain.

[0074] As time goes by, the time-frequency fusion synchronization coefficient needs to be updated in real time and its change trend needs to be analyzed. By monitoring the fluctuations, possible synchronization problems between axes can be identified, and data changes at key moments can be recorded to provide a basis for subsequent analysis.

[0075] The time-frequency fusion synchronization coefficient is used to indicate the comprehensive synchronization level of the feedback data of each axis in the time domain and frequency domain, reflecting the coordination degree between the axes in the two dimensions of time and frequency. The larger the coefficient value, the higher the synchronization and consistency of the feedback data between the axes in the time domain and frequency domain, reflecting that the axes of the system have strong coordination and stability during operation; on the contrary, the smaller the coefficient value, the poorer the synchronization of the feedback data between the axes, the larger the phase difference and time inconsistency, which may indicate that the system has potential synchronization problems and coordination errors in multi-axis collaborative control, which requires further analysis and correction.

[0076] By running the feedback monitoring unit, the system can effectively capture and analyze the synchronization of the feedback data of each axis in the time domain and frequency domain, and quantify the level of synchronization in the form of a time-frequency fusion synchronization coefficient. The real-time update and monitoring of this coefficient enables the system to promptly identify potential synchronization problems between the axes and make adjustments based on these problems to ensure the coordination and stability of the system in a complex operating environment. In addition, the application of the time-frequency fusion synchronization coefficient not only improves the control accuracy of the system under dynamic operating conditions, but also provides an important data basis for subsequent analysis and optimization, thereby significantly improving the overall performance and reliability of the multi-axis collaborative control system.

[0077] The operation process of the coupling analysis unit includes the following:

[0078] In a multi-axis parallel control system, the matching of gain parameters is crucial to the stability and coordination of the system. As the complexity of the system operating environment increases, factors such as external interference, load fluctuations, and dynamic changes will cause gain mismatches between axes, resulting in system performance degradation and even operational failures. In order to effectively identify and quantify this gain mismatch risk, it is necessary to deeply analyze the dynamic correlation and response characteristics between axes from multiple dimensions. The interaction in the time-frequency domain is one of the most complex and far-reaching interactions in a multi-axis system. The time-frequency cross-coupling index can reveal the complex coupling relationship of the feedback signals of each axis in the time and frequency domains, especially the phase mismatch and synchronization problems, which directly affect the gain coordination of the system. At the same time, the dynamic gain adaptation factor quantifies the dynamic adaptability and adjustment sensitivity of the gain parameters by analyzing the gain response changes of the system in the frequency domain, and can accurately reflect the adaptation of the gain parameters in actual operation. Therefore, the comprehensive use of the time-frequency cross-coupling index and the dynamic gain adaptation factor can comprehensively quantify the mismatch risk of the gain parameters between axes from the two key dimensions of phase coordination and gain dynamic adaptability, thereby providing a scientific basis for the stable operation of the system.

[0079] In a complex multi-axis parallel control system, the interaction between the feedback signals of each axis in the time and frequency domains is crucial to the overall stability and coordination of the system. Traditional synchronization analysis usually only focuses on the correlation of a single dimension of time or frequency, and it is difficult to fully capture the complex coupling relationship of the multi-axis system in dynamic operation. As an innovative analysis tool, the time-frequency cross-coupling index combines the interaction between the time and frequency domains. By quantifying the coupling strength between different time windows and frequency points, it provides a new perspective for in-depth insight into the complex interaction relationship within the system. This index is particularly suitable for analyzing the risk of feedback gain mismatch in a system in a highly dynamic environment and identifying potential instability factors caused by time-frequency interaction.

[0080] S2.1, time-frequency cross-coupling index generation logic:

[0081] S2.1.1, perform a preliminary time-frequency analysis on the feedback data of each axis, take the time-frequency fusion synchronization coefficient as the basic data, perform short-time Fourier transform (STFT) on it, and generate a time-frequency matrix .in, Indicates time, Indicates frequency. Formula: ;in, is a time window function used to extract The local signal characteristics at time is the discrete time point of the time-frequency fusion synchronization coefficient, The time-frequency matrix generated by this formula provides the basis for subsequent time-frequency cross-coupling analysis.

[0082] S2.1.2, based on the time-frequency matrix, calculate the phase difference matrix between different time and frequency points , which reflects the phase change in the time-frequency domain and reveals the dynamic coupling relationship between the axes. Formula: ;in, and For different moments, and for different frequency points. This formula calculates Moment and Frequency and Moment and The phase difference between frequencies provides a quantitative description of the dynamic coupling relationship between time and frequency.

[0083] S2.1.3, using the calculated phase difference matrix, construct the time-frequency cross-coupling matrix , which is used to represent the interaction strength between the feedback signals of each axis in the time-frequency domain. This matrix combines the amplitude information and phase difference information in the time-frequency matrix to generate a strength index of cross-coupling. Formula: ;in, and represent small offsets in time and frequency, respectively. Represents the amplitude information of the signal in the time-frequency matrix, Used to convert phase difference into coupling strength indicator.

[0084] S2.1.4, based on the time-frequency cross-coupling matrix, generate the time-frequency cross-coupling index , which is used to quantify the time-frequency coupling strength of the entire system at a given moment. The time-frequency cross-coupling index is obtained by integrating the time-frequency cross-coupling matrix over the entire frequency range. Formula: ;in, and are the lower and upper limits of the frequency range respectively. Through the integration operation, the time-frequency cross-coupling index The coupling strengths at different frequencies are combined to obtain the overall time-frequency coupling strength index.

[0085] By introducing the time-frequency cross-coupling index, the deep coupling relationship in the time-frequency domain of the multi-axis system can be effectively revealed, helping to identify complex interaction problems that are difficult to detect with traditional analysis methods. The index can accurately quantify the coupling strength between axes in the time-frequency domain, providing a powerful tool support for real-time monitoring and early warning of feedback gain mismatch. It not only improves the stability and responsiveness of the system in a high-dynamic environment, but also lays a data foundation for further optimizing the multi-axis collaborative control strategy, thereby promoting the improvement of the overall performance of the system.

[0086] In a multi-axis parallel control system, the gain adjustment of each axis directly affects the stability and responsiveness of the system. Traditional gain adjustment methods are usually based on fixed parameter settings and are difficult to adapt to dynamically changing operating environments. With the influence of complex factors such as external interference and load changes during system operation, the gain parameters need to have sufficient dynamic adaptability to cope with frequently changing operating conditions. As an innovative analysis tool, the dynamic gain adaptation factor quantifies the change gradient of the system gain parameters in the frequency dimension through a detailed analysis of the gain response spectrum, providing a new perspective to evaluate the dynamic adaptability of the gain parameters. This factor can effectively capture the changing characteristics of the gain response in a dynamic environment, laying a theoretical foundation for achieving more precise gain control.

[0087] S2.2, dynamic gain adaptation factor generation logic:

[0088] S2.2.1, based on the time-frequency fusion synchronization coefficient, the preliminary gain response of the system is analyzed. First, the feedback data is analyzed in the frequency domain to generate the gain response spectrum matrix ,in represents the time window, Indicates frequency. In each time window, the frequency characteristics of the gain response are analyzed, key frequency components are extracted, and a basic data matrix is ​​formed. Formula: ;in, is the discrete time point of the time-frequency fusion synchronization coefficient, The generated gain response spectrum matrix reflects the response characteristics of the system gain at different frequencies.

[0089] S2.2.2, based on the gain response spectrum matrix, calculate the frequency gain offset matrix , which is used to measure the degree of gain response deviation in the frequency domain. By comparing the changes in gain response at different times, the sensitivity of system gain to frequency changes and the consistency of response are analyzed. Formula: ;in, and Represents the small offset in time and frequency respectively, and calculates the change amplitude of the gain response in the frequency space within the adjacent time window. This matrix reveals the dynamic offset characteristics of the gain response when the frequency changes.

[0090] S2.2.3, based on the frequency gain offset matrix, construct the gain response gradient matrix , which is used to quantify the change gradient of the gain response in the frequency space. By calculating the gradient of the frequency response in different directions, the dynamic adaptability of the gain is analyzed. Formula: ; The formula represents the gain response in time and frequency The spatial partial derivative product is used to reflect the changing rate and direction of the gain response in different frequency and time dimensions, revealing the dynamic adaptability of the gain parameters.

[0091] S2.2.4, based on the gain response gradient matrix, generate a dynamic gain adaptation factor , which is used to characterize the adaptability of the system gain parameters in a dynamic environment. By normalizing the gain gradient matrix in the frequency domain, an adaptation factor that can reflect the sensitivity of the system gain adjustment is obtained. Formula: In the formula, by normalizing the gain gradient matrix, the dynamic gain adaptation factor can comprehensively consider the change rate and amplitude of the gain at different frequencies, thereby characterizing the dynamic adaptability of the system's gain.

[0092] The introduction of a dynamic gain adaptation factor enables the system to monitor and evaluate the dynamic changes of gain parameters in different frequency and time dimensions in real time, ensuring that gain adjustment can adapt to complex operating environments. This factor helps identify potential mismatch problems in gain parameters by quantifying the change gradient of the gain response, thereby improving the stability and response speed of the system under high dynamic load conditions. Through this innovative gain adaptation mechanism, the overall performance of the system is significantly enhanced, especially when dealing with external interference and nonlinear load changes, the gain parameters of each axis can be adjusted more accurately, achieving more efficient multi-axis collaborative control.

[0093] S2.3, construct a phase-gain collaborative mapping model to collaboratively map the phase information of the time-frequency cross-coupling index with the dynamic gain adaptation factor. By analyzing the mapping relationship between these two indicators in multidimensional space, the potential risk of gain mismatch is identified. Specifically, the phase component of the time-frequency cross-coupling index is mapped to the change curve of the gain response to generate a collaborative mapping matrix to capture the impact of phase fluctuations on the gain parameters. Finally, through the singular value decomposition (SVD) of the mapping matrix, the singular values ​​obtained represent the main correlation patterns between the phase component and the gain response. These singular values ​​are nonlinearly transformed, and the key patterns that affect the gain mismatch risk are amplified by exponentially smoothing each singular value. These processed singular values ​​are then accumulated to generate a feedback gain mismatch warning index. This feedback gain mismatch warning index can sensitively capture the potential mismatch risk of the gain parameters in the system, thereby providing real-time gain adjustment guidance for the system.

[0094] The feedback gain mismatch warning index is used to indicate the mismatch risk of gain parameters between axes, reflecting the gain mismatch that may occur in the system during multi-axis collaborative control. The index provides an in-depth analysis of the adaptability and coordination of system gain parameters by quantifying the degree of complex correlation between phase components and gain responses. When the warning index is high, it indicates that there is a significant mismatch between the gain parameters of each axis, which may lead to a decrease in the stability and response performance of the system; when the warning index is low, it means that the gain parameters are well matched between the axes and the system is in a relatively stable state.

[0095] After the feedback gain mismatch warning index is generated, it is compared with the warning threshold. When the feedback gain mismatch warning index is greater than or equal to the warning threshold, it indicates that there is a significant mismatch in the gain parameters in the system. At this time, an adjustment signal is generated to indicate that the gain parameters of each axis should be readjusted to restore the stability and coordination of the system. On the contrary, when the feedback gain mismatch warning index is less than the warning threshold, it means that the matching of the gain parameters is within a reasonable range, and the system can continue to operate according to the current settings without adjusting the gain parameters. This comparison process can monitor the gain matching status of the system in real time and ensure that the system works in an efficient and stable operating state.

[0096] Through the operation of the coupling analysis unit, the gain mismatch risk in the multi-axis parallel control system can be effectively identified and quantified. By comprehensively utilizing the time-frequency cross-coupling index and the dynamic gain adaptation factor, the gain parameter matching between the axes is comprehensively analyzed from the two key dimensions of phase coordination and gain dynamic adaptability. The application of the phase-gain collaborative mapping model makes the generation of the gain mismatch warning index more accurate, which can sensitively capture the potential mismatch risk of the gain parameters in the system and provide real-time adjustment guidance. Through this process, the stability and responsiveness of the system are significantly improved, especially in complex dynamic environments. The system can more accurately adjust the gain parameters of each axis to ensure the efficient operation of multi-axis collaborative control, thereby promoting the improvement of the overall system performance.

[0097] In a multi-axis parallel control system, the dynamic adjustment of gain parameters directly affects the response speed and stability of the system. As the complexity of the system operating environment increases, gain mismatch between axes may lead to system performance degradation or even failure. By accurately calculating the gain adjustment reference value and dynamically adjusting it in combination with real-time feedback data, it can ensure that the gain parameters of each axis always match the current operating status, thereby achieving efficient and stable multi-axis collaborative control.

[0098] The operation process of the gain adjustment unit includes the following:

[0099] S3.1, after the adjustment signal is obtained, the gain adjustment reference value needs to be calculated for each axis based on the feedback gain mismatch warning index. First, the feedback gain mismatch warning index of each axis is compared with the standard response model of the system. The standard response model reflects the gain distribution and dynamic response characteristics of each axis under ideal conditions. Through comparative analysis, the gain adjustment reference value of each axis is calculated, and the formula is as follows: ;in, Indicates the gain adjustment reference value, is the initial gain parameter, is the system dynamic adjustment coefficient, is the feedback gain mismatch warning index, is the time-frequency fusion synchronization coefficient. This step is to ensure that the direction and amplitude of the gain adjustment match the actual operating status of each axis.

[0100] S3.2, after calculating the gain adjustment reference value, dynamically fuse it with the real-time feedback data to ensure that the gain adjustment can adapt to the immediate state of the system. The real-time feedback data includes the current position, speed, acceleration, and the gain adjustment result of the previous moment. By introducing a fusion algorithm based on Kalman filtering, the feedback data is predicted and corrected, and comprehensively processed with the calculated gain adjustment reference value. The formula is as follows: ;in, is the dynamic gain parameter, is the Kalman gain, which adjusts the weight of the current feedback data and the gain adjustment result of the previous moment. is the gain parameter of the previous moment. This step ensures that the gain adjustment can respond to the state change of the system in real time to avoid over-adjustment or delayed response.

[0101] S3.3, the gain parameters after dynamic fusion will be applied to the actual control process of each axis. In each control cycle, the servo control module will obtain the updated dynamic gain parameters and apply them to the control signal calculation of each axis to ensure that the motion state of each axis is highly consistent with the expected trajectory. The servo control signal calculation formula is as follows: ;in, is the control signal, is the error signal, i.e., the difference between the target position and the actual position. The goal of this step is to achieve synchronous adjustment of the gain parameters of each axis to ensure the accuracy and stability of multi-axis coordinated control.

[0102] S3.4, after the gain parameters are adjusted, the feedback data of each axis is continuously monitored, and adaptive adjustments are made based on the latest feedback gain mismatch warning index. Through the adaptive adjustment mechanism, the system can gradually optimize the gain parameters to make them more in line with actual needs. The adaptive adjustment is based on the recursive least squares method (RLS), which updates the parameter estimation of the system online, and the formula is as follows: ;in, is the optimized gain parameter, To adjust the step size, and The actual response and expected response are respectively. This step ensures that the system remains stable and efficient under high dynamic load conditions through continuous optimization.

[0103] By running the gain adjustment unit, the system can monitor and adjust the gain parameters of each axis in real time to make it highly matched with the actual operating status of the system. The dynamic adjustment mechanism not only improves the response speed of the system in complex dynamic environments, but also ensures the overall coordination of the multi-axis system. The adaptive adjustment mechanism further optimizes the gain parameter setting and reduces the risk of gain mismatch, thereby significantly improving the stability and control accuracy of the system.

[0104] In a multi-axis system, the reasonable allocation of control priorities is crucial to the overall coordination and efficiency of the system. With the dynamic adjustment of the gain parameters of each axis, the system needs to re-evaluate the control priority of each axis to ensure that resources can be optimally allocated when multiple axes work together. By combining the dynamic gain parameters with the feedback data synchronization evaluation results, the system can accurately reallocate control priorities, further optimize the multi-axis collaborative control strategy, and achieve efficient and unified control of the system.

[0105] The operation process of the priority allocation unit includes the following:

[0106] S4.1, after completing the dynamic adjustment of the gain parameters of each axis, conduct a comprehensive analysis of the synchronization evaluation results of these adjusted gain parameters and feedback data. Specifically, compare the dynamic gain parameters of each axis with its corresponding time-frequency fusion synchronization coefficient to evaluate whether the feedback signal after gain adjustment matches the synchronization requirements of the system. This process aims to confirm whether the adjusted gain parameters effectively improve the coordination between the axes and identify possible weaknesses or anomalies in synchronization.

[0107] S4.2, after confirming the synchronization of feedback data, will calculate the basis for the control priority allocation of each axis based on the synchronization index and dynamic gain parameters of each axis. Analyze the importance of each axis in the current operating state, considering factors including the time-frequency cross-coupling strength of each axis, the dynamic gain response speed, and the load changes and external disturbances in the real-time operating environment. Through multi-dimensional comprehensive analysis, the system generates a control priority matrix, which is used to quantify the priority level of each axis in the current control task.

[0108] The time-frequency cross-coupling strength is an indicator used to quantify the interaction strength between axes in the time and frequency domains. The specific calculation process is as follows: First, the system extracts the coupling matrix between the axes from the previously generated time-frequency cross-coupling index. This matrix represents the interaction strength of each axis in different frequency and time windows. By normalizing the matrix, its numerical range is limited to a certain interval, which is convenient for comparison with other indicators. Next, the system calculates the maximum coupling strength between the axes to identify which pair of axes has the strongest interaction in the current control task. The final value of the time-frequency cross-coupling strength will be used as a key factor in the control priority allocation to evaluate whether the operation of a certain axis will significantly affect the control effect of other axes.

[0109] The dynamic gain response speed reflects the response time of each axis to the change of the control signal after the system adjusts the gain parameters. The calculation process is as follows: First, the system measures the time required for the feedback signal to reach the new set value after the gain parameters are adjusted. This period of time is defined as the response time. To improve the accuracy of the calculation, the system records the response time in multiple consecutive control cycles and analyzes this data. By calculating the average response time and the change in response time of each axis under different operating conditions, the dynamic gain response speed can be accurately quantified. Finally, the system sorts the dynamic gain response speed of each axis to determine which axes have an advantage in response speed, thereby providing a basis for the allocation of control priorities.

[0110] S4.3, according to the control priority matrix, the system will reallocate the control priority of each axis. In a multi-axis system, the reallocation of control priority means adjusting the allocation ratio of control resources and computing resources for each axis. For example, the system may allocate more processing time and resources to axes with higher priorities to ensure that they can respond to control commands faster and more accurately. At the same time, axes with lower priorities will moderately reduce resource allocation based on actual needs, but still maintain basic control capabilities. This dynamic adjustment of resource allocation can be achieved through an optimized scheduling algorithm to ensure the coordination and efficiency of the overall operation of the system.

[0111] S4.4, after completing the redistribution of control priorities, the system will optimize the multi-axis collaborative control strategy. At this point, the control priority of each axis has been determined, and the system needs to ensure that each axis can work efficiently and collaboratively according to the new priority in actual operation. To this end, the system will coordinate the control instructions between the axes so that the actions of the high-priority axes can guide or adapt to the operations of the low-priority axes, and ultimately achieve unified control between multiple axes. The specific optimization process may include adjusting the motion trajectory of each axis to make it more coordinated in space and time to avoid conflicts and interference. At the same time, the system will monitor the operating status of each axis in real time and adjust the control strategy in time to deal with possible abnormal situations or external disturbances.

[0112] After running the priority allocation unit, the system can reallocate the control priority of each axis based on the dynamic adjustment of the gain parameters to ensure the high coordination of the multi-axis system during operation. By optimizing the allocation of control priorities, the system can use resources more effectively and improve overall operating efficiency, while reducing control delays and resource waste caused by improper priority allocation. Unified control of the multi-axis system in a complex operating environment can be achieved, significantly improving the overall performance and stability of the system.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0115] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-speed and high-precision multi-axis parallel control system for electromechanical servo, characterized in that: include: feedback monitoring unit, coupling analysis unit, gain adjustment unit and priority allocation unit; The feedback monitoring unit monitors the feedback data of each axis in real time, constructs the time-frequency fusion synchronization coefficient between each axis through the time domain correlation and frequency domain phase difference analysis results, and transmits the time-frequency fusion synchronization coefficient to the coupling analysis unit; The coupling analysis unit extracts the phase coupling relationship and gain response characteristics of the time-frequency fusion synchronization coefficient, obtains the time-frequency cross-coupling index and the dynamic gain adaptation factor, constructs the feedback gain mismatch warning index, quantifies the mismatch risk of the gain parameters between the axes, and transmits the feedback gain mismatch warning index and the mismatch risk results to the gain adjustment unit; The gain adjustment unit calculates the gain adjustment reference value of each axis based on the feedback gain mismatch warning index, adjusts the gain parameter to match the operation status of each axis, and implements dynamic gain adjustment to adjust the gain parameter of each axis in real time, and transmits the dynamic gain parameter to the priority allocation unit; The priority allocation unit combines the dynamically adjusted gain parameters with the feedback data synchronization evaluation results to reallocate the control priority of each axis and optimize the coordination of multi-axis collaborative operation.

2. A high-speed, high-precision, multi-axis parallel control system for electromechanical servo according to claim 1, characterized in that: The operation process of the feedback monitoring unit includes the following: S1.1, each axis collects real-time feedback data through sensors, the data type includes position ,speed , acceleration , the collection frequency of feedback data is and sent to the central control module through a unified data transmission channel; each data point is attached with a timestamp to identify the specific time of data collection, and the timestamp is marked as ,in Indicates the axis number, Indicates the time of data collection; S1.2, each axis data acquisition module maintains time synchronization with the central control module, adopts a global synchronous clock mechanism to achieve the establishment of a unified time base; the timestamp of each time point is synchronized by a synchronization correction factor After correction, the corrected timestamp expression is: ;in, is the corrected timestamp to ensure time synchronization. It is obtained by accurately measuring the clock deviation of each axis. The calculation formula is: ;in, is the time deviation of the current data point, is the maximum time error allowed by the system, is the frequency of data collection; S1.3, the feedback data position, velocity, and acceleration are transformed nonlinearly to generate new feature vectors; the feature vectors are constructed using the following nonlinear mapping formula: ； ； ； in, , , is the position, velocity, and acceleration feature vector after nonlinear transformation, , , , , , is the nonlinear mapping coefficient; S1.4, in the constructed feature space, the feedback data is converted from the time domain to the frequency domain through discrete Fourier transform to identify the frequency components and phase information in the data; the frequency domain features are calculated by the following formula: ;in, For the The frequency domain characteristics of the axis, is the frequency variable, is the number of data points; Next, calculate the phase difference matrix between each axis , the formula is as follows: ; ; S1.5, construct the time-frequency fusion synchronization coefficient between each axis through the time domain correlation and frequency domain phase difference analysis results , the formula is: ;in, is the element in the time domain correlation matrix, and The upper and lower limits of the frequency range.

3. A high-speed, high-precision, multi-axis parallel control system for electromechanical servo according to claim 2, characterized in that: The operation process of the coupling analysis unit includes the following: S2.1, time-frequency cross-coupling index generation logic: S2.1.1, perform a preliminary time-frequency analysis on the feedback data of each axis, take the time-frequency fusion synchronization coefficient as the basic data, perform short-time Fourier transform on it, and generate a time-frequency matrix ;in, Indicates time, Indicates frequency; formula: ;in, is a time window function used to extract The local signal characteristics at time is the discrete time point of the time-frequency fusion synchronization coefficient, is the corresponding frequency; S2.1.2, based on the time-frequency matrix, calculate the phase difference matrix between different time and frequency points ,formula: ;in, and For different moments, and For different frequency points; S2.1.3, using the calculated phase difference matrix, construct the time-frequency cross-coupling matrix , used to represent the interaction intensity between the feedback signals of each axis in the time-frequency domain, formula: ;in, and represent small offsets in time and frequency, respectively. Represents the amplitude information of the signal in the time-frequency matrix, Used to convert phase difference into coupling strength index; S2.1.4, based on the time-frequency cross-coupling matrix, generate the time-frequency cross-coupling index , the time-frequency cross-coupling index is obtained by integrating the time-frequency cross-coupling matrix over the entire frequency range.

4. A high-speed and high-precision multi-axis parallel control system for electromechanical servo according to claim 3, characterized in that: The operation process of the coupling analysis unit also includes the following: S2.2, dynamic gain adaptation factor generation logic: S2.2.1, based on the time-frequency fusion synchronization coefficient, the preliminary gain response of the system is analyzed. First, the feedback data is analyzed in the frequency domain to generate the gain response spectrum matrix ,in represents the time window, Indicates frequency; in each time window, analyze the frequency characteristics of the gain response, extract the key frequency components, and form a basic data matrix; formula: ;in, is the discrete time point of the time-frequency fusion synchronization coefficient, is the corresponding frequency; S2.2.2, based on the gain response spectrum matrix, calculate the frequency gain offset matrix , used to measure the degree of gain response deviation in the frequency domain; formula: ;in, and They represent small offsets in time and frequency respectively, and calculate the variation of the gain response in the frequency space within adjacent time windows; S2.2.3, based on the frequency gain offset matrix, construct the gain response gradient matrix , used to quantify the gradient of gain response in frequency space; formula: ; S2.2.4, based on the gain response gradient matrix, generate a dynamic gain adaptation factor , the dynamic gain adaptation factor is obtained by normalizing the gain gradient matrix in the frequency domain.

5. A high-speed and high-precision multi-axis parallel control system for electromechanical servo according to claim 4, characterized in that: The operation process of the coupling analysis unit also includes the following: S2.3, constructing a phase-gain co-mapping model to co-map the phase information of the time-frequency cross-coupling index with the dynamic gain adaptation factor; By analyzing the mapping relationship between these two indicators in multidimensional space, the potential risk of gain mismatch is identified; specifically, the phase component of the time-frequency cross-coupling index is mapped to the change curve of the gain response to generate a collaborative mapping matrix to capture the impact of phase fluctuations on the gain parameters; finally, through the singular value decomposition of the mapping matrix, the singular values ​​obtained represent the main correlation mode between the phase component and the gain response; the singular values ​​are nonlinearly transformed, and the key modes that affect the gain mismatch risk are amplified by exponentially smoothing each singular value, and then the processed singular values ​​are accumulated to generate a feedback gain mismatch warning index; After the feedback gain mismatch warning index is generated, it is compared with the warning threshold; When the feedback gain mismatch warning index is greater than or equal to the warning threshold, it indicates that there is a significant mismatch in the gain parameters in the system, and an adjustment signal is generated at this time; On the contrary, when the feedback gain mismatch warning index is less than the warning threshold, it means that the matching of the gain parameters is within a reasonable range, and the system can continue to operate according to the current settings without adjusting the gain parameters.

6. A high-speed and high-precision multi-axis parallel control system for electromechanical servo according to claim 5, characterized in that: The operation process of the gain adjustment unit includes the following: S3.1, after the adjustment signal is obtained, the gain adjustment reference value is calculated for each axis based on the feedback gain mismatch warning index; first, the feedback gain mismatch warning index of each axis is compared with the standard response model of the system, which reflects the gain distribution and dynamic response characteristics of each axis under ideal conditions; through comparative analysis, the gain adjustment reference value of each axis is calculated, and the formula is as follows: ;in, Indicates the gain adjustment reference value, is the initial gain parameter, is the system dynamic adjustment coefficient, is the feedback gain mismatch warning index, is the time-frequency fusion synchronization coefficient; S3.2, after the gain adjustment reference value is calculated, it is dynamically fused with the real-time feedback data, which includes the current position, speed, acceleration and the gain adjustment result of the previous moment; the feedback data is predicted and corrected through the Kalman filter fusion algorithm, and is comprehensively processed with the calculated gain adjustment reference value. The formula is as follows: ;in, is the dynamic gain parameter, is the Kalman gain, which adjusts the weight of the current feedback data and the gain adjustment result of the previous moment. is the gain parameter of the previous moment; S3.3, the gain parameters after dynamic fusion will be applied to the actual control process of each axis; in each control cycle, the servo control module will obtain the updated dynamic gain parameters and apply them to the control signal calculation of each axis to ensure that the motion state of each axis is highly consistent with the expected trajectory; the servo control signal calculation formula is as follows: ;in, is the control signal, is the error signal, i.e., the difference between the target position and the actual position; S3.4, after the gain parameter is adjusted, the feedback data of each axis is continuously monitored, and adaptive adjustment is performed based on the latest feedback gain mismatch warning index; the adaptive adjustment is based on the recursive least squares method to update the gain parameter estimation online, and the formula is as follows: ;in, is the optimized gain parameter, To adjust the step size, and are the actual response and the expected response, respectively.

7. A high-speed, high-precision, multi-axis parallel control system for electromechanical servo according to claim 6, characterized in that: The operation process of the priority allocation unit includes the following: S4.1, after completing the dynamic adjustment of the gain parameters of each axis, comprehensively analyze the synchronization evaluation results of the adjusted gain parameters and feedback data; specifically, compare the dynamic gain parameters of each axis with its corresponding time-frequency fusion synchronization coefficient to evaluate whether the feedback signal after gain adjustment matches the synchronization requirements of the system; S4.2, after confirming the synchronization of feedback data, the basis for allocating control priorities of each axis will be calculated based on the synchronization index and dynamic gain parameters of each axis; the importance of each axis in the current operating state will be analyzed, including the time-frequency cross-coupling strength and dynamic gain response speed of each axis. Through multi-dimensional comprehensive analysis, a control priority matrix will be generated to quantify the priority level of each axis in the current control task; S4.3, reallocating the control priority of each axis according to the control priority matrix; S4.4, after completing the redistribution of control priorities, the multi-axis collaborative control strategy is optimized.

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