Performance parameter testing method and device of servo steering engine and medium

Abstract

The invention discloses a servo steering engine performance parameter testing method and device and a medium, and relates to the technical field of industrial control, and the method comprises the steps: collecting the real-time feedback data of a servo steering engine, adjusting the frequency range and the frequency step length of a sweep frequency through employing a genetic algorithm, and generating a sweep frequency parameter; calculating the amplitude of the frequency sweep instruction and the amplitude of the steering engine operation feedback data based on the performance fitting data set, obtaining an amplitude-frequency characteristic curve, selecting a bandwidth frequency point when the amplitude is reduced to a specified proportion according to the amplitude-frequency characteristic curve, and generating a phase-frequency characteristic curve; analyzing the phase frequency response of the target frequency point according to the phase frequency characteristic curve, judging whether the phase requirement is met or not, obtaining the bandwidth qualification state, evaluating the overall performance of the servo steering engine, and generating a performance evaluation report. According to the invention, the automation and flexibility of the test process are improved, and the test efficiency and performance of the industrial control system are further optimized.

Description

Technical Field

[0001] This invention relates to the field of industrial control technology, and in particular to a method, equipment and medium for testing the performance parameters of a servo motor. Background Technology

[0002] Servo motors are widely used in automation control, especially in precision motion control, robotics, and aircraft, playing a crucial role in industrial control systems. To ensure the precise control performance of servo motors, frequency response analysis is typically required. This type of analysis often employs a frequency sweep method, gradually adjusting the frequency of the control signal to monitor the servo motor's response characteristics. In this way, the amplitude-frequency characteristics, phase-frequency characteristics, and bandwidth of the servo motor can be evaluated, thereby assessing and optimizing its performance and ultimately improving the overall performance of the industrial control system.

[0003] Traditional methods have certain limitations in parameter adjustment and testing efficiency. The setting of the sweep frequency range and step size is usually empirical and lacks the ability to adapt and adjust. This may lead to redundancy or omission in the testing of certain frequency ranges, affecting the accuracy of the test results. Summary of the Invention

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

[0005] Therefore, this invention provides a method for testing the performance parameters of a servo motor, which solves the problem of insufficient adaptive adjustment of frequency parameters.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for testing the performance parameters of a servo motor, comprising: acquiring real-time feedback data from the servo motor, and using a genetic algorithm to adjust the frequency range and frequency step size of the sweep frequency to generate sweep parameters; the servo motor generating a sweep command based on the sweep parameters and executing a frequency scan to obtain servo motor operation feedback data, sending the servo motor operation feedback data to a host computer to generate a test dataset; performing piecewise fitting processing on the test dataset to obtain piecewise feedback data, and using a support vector regression algorithm to perform nonlinear fitting on the piecewise feedback data to generate a performance fitting dataset; calculating the amplitude of the sweep command and the amplitude of the servo motor operation feedback data based on the performance fitting dataset to obtain an amplitude-frequency characteristic curve, and selecting the bandwidth frequency point where the amplitude drops to a specified proportion based on the amplitude-frequency characteristic curve to generate a phase-frequency characteristic curve; analyzing the phase-frequency response at the target frequency point based on the phase-frequency characteristic curve to determine whether it meets the phase requirements, obtaining the bandwidth qualification status, evaluating the overall performance of the servo motor, and generating a performance evaluation report.

[0008] As a preferred embodiment of the performance parameter testing method for the servo motor described in this invention, the steps of collecting real-time feedback data from the servo motor and using a genetic algorithm to adjust the frequency range and frequency step size of the sweep frequency to generate sweep parameters are as follows:

[0009] Collect real-time feedback data from the servo motor, initialize the sweep frequency range and frequency step size, and set the initial sweep parameters;

[0010] The initial frequency sweep parameters are input into the genetic algorithm for evaluation to obtain evaluation data;

[0011] Based on the evaluation data, the initial sweep frequency parameters are screened for fitness and adjusted. The sweep frequency parameters are then generated through single-point crossover and uniform variation.

[0012] In a preferred embodiment of the performance parameter testing method for the servo motor described in this invention, the servo motor generates a frequency sweep command based on the frequency sweep parameters and performs a frequency scan to obtain servo motor operation feedback data. The specific steps are as follows:

[0013] The frequency sweep parameters are passed to the servo motor, the frequency range and step size information in the frequency sweep parameters are parsed, the frequency sweep command is obtained, and the frequency sweep is gradually adjusted according to the frequency sweep command to generate frequency sweep execution data.

[0014] By combining the frequency sweep execution data with the real-time operating status of the servo motor, servo motor operating status data is obtained. The servo motor operating status data is then cleaned, filtered, and timestamped to obtain servo motor operating feedback data.

[0015] As a preferred embodiment of the servo motor performance parameter testing method of the present invention, the specific steps for sending servo motor operation feedback data to a host computer to generate a test dataset are as follows:

[0016] Organize the servo motor operation feedback data, group it by frequency point and add timestamps to generate a real-time operation feedback dataset;

[0017] Remove outliers from the real-time feedback dataset and process missing data to generate a test dataset.

[0018] As a preferred embodiment of the performance parameter testing method for the servo motor described in this invention, the steps of performing piecewise fitting processing on the test dataset to obtain piecewise feedback data, and using a support vector regression algorithm to perform nonlinear fitting on the piecewise feedback data to generate a performance fitting dataset are as follows.

[0019] The test dataset is divided into several segments according to a preset frequency range, and multiple segments of raw data are obtained. Nonlinear fitting is performed using the frequency of each segment of raw data and the servo motor operation feedback data to obtain segmented feedback data.

[0020] The support vector regression algorithm is used to perform nonlinear fitting with the frequency of each segment of feedback data as the independent variable and the servo motor operation feedback data as the dependent variable to obtain the fitting curve. The fitting curve is then interpolated and predicted to generate a performance fitting dataset.

[0021] As a preferred embodiment of the performance parameter testing method for the servo motor described in this invention, the steps for calculating the amplitude of the sweep command and the amplitude of the servo motor operation feedback data based on the performance fitting dataset to obtain the amplitude-frequency characteristic curve are as follows:

[0022] Based on the performance fitting dataset, the amplitude of the frequency sweep command and the amplitude of the servo operation feedback data are calculated respectively to generate an amplitude dataset;

[0023] The difference between the sweep frequency command amplitude and the servo motor operation feedback data amplitude is calculated based on the amplitude dataset, and the trend of amplitude change with frequency is analyzed by curve fitting method, and the amplitude-frequency characteristic curve is plotted.

[0024] As a preferred embodiment of the performance parameter testing method for the servo motor described in this invention, the specific steps for generating the phase frequency response curve by selecting the bandwidth frequency point where the amplitude drops to a specified proportion based on the amplitude-frequency response curve are as follows.

[0025] Identify the bandwidth frequency point where the amplitude drops to a preset amplitude drop ratio from the amplitude-frequency response curve;

[0026] Using the bandwidth frequency point as the bandwidth of the servo motor, phase analysis is performed on the amplitude of the operating data within the bandwidth range of the servo motor to generate a phase-frequency characteristic curve.

[0027] As a preferred embodiment of the performance parameter testing method for the servo motor described in this invention, the steps of analyzing the phase frequency response at the target frequency point based on the phase frequency characteristic curve, determining whether it meets the phase requirements, obtaining the bandwidth qualification status, evaluating the overall performance of the servo motor, and generating a performance evaluation report are as follows.

[0028] The phase value of the target frequency point is extracted from the phase frequency response curve and compared with the phase standard. The phase deviation is calculated, and it is determined whether the phase deviation is within the allowable range. The phase coincidence state is then generated.

[0029] By combining the phase coincidence state with the amplitude dataset, the overall performance of the servo motor is evaluated, and a performance evaluation report is generated.

[0030] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the servo motor performance parameter testing method as described in the first aspect of the present invention.

[0031] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the servo motor performance parameter testing method as described in the first aspect of the present invention.

[0032] The beneficial effects of this invention are as follows: By collecting real-time feedback data from servo motors and using a genetic algorithm to adjust the frequency range and step size of the sweep frequency, optimized sweep parameters are generated, thereby achieving dynamic adaptive adjustment of the sweep parameters and improving the accuracy and efficiency of sweep frequency testing. Through fitness screening and parameter adjustment using the genetic algorithm, the optimal sweep parameters can be automatically generated, ensuring the optimization of the servo motor test coverage and frequency step size. The beneficial effect of this step is that the optimized sweep parameters can accurately reflect the servo motor performance, improve the automation and flexibility of the testing process, and further optimize the testing efficiency and performance of industrial control systems. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the performance parameter testing method for servo motors.

[0035] Figure 2 The flowchart for generating frequency sweep parameters.

[0036] Figure 3 A flowchart for fitting performance data.

[0037] Figure 4 This is a flowchart for performance evaluation.

[0038] Figure 5 A comparison chart of the phase frequency characteristics of servo motors under different sweep frequency parameters. Detailed Implementation

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0042] Reference Figures 1-5 As one embodiment of the present invention, this embodiment provides a method for testing the performance parameters of a servo motor, including the following steps:

[0043] S1. Collect real-time feedback data from the servo motor and use a genetic algorithm to adjust the frequency range and frequency step size of the sweep frequency to generate sweep parameters.

[0044] It should be noted that existing methods rely on experience or setting the range and step size of the sweep frequency. The selection of these parameters largely depends on experience or simple trial and error, lacking the ability to automatically adjust for specific situations. This may lead to test redundancy or omissions in certain frequency ranges, thus affecting the efficiency and accuracy of the test.

[0045] This invention utilizes a genetic algorithm to automatically adjust the range and step size of the frequency sweep. Based on fitness evaluation of feedback data, the genetic algorithm intelligently optimizes the frequency range and step size, ensuring that the test covers the critical frequency bands of servo performance while avoiding redundant testing. This method improves testing efficiency and enhances the test's adaptability and accuracy.

[0046] S1.1 Collect real-time feedback data from the servo motor, initialize the sweep frequency range and frequency step size, and set the initial sweep parameters.

[0047] It should be noted that real-time feedback data from the servo motor is collected, and its operating status, including key information such as speed and load, is monitored in real time through sensors. The initial sweep frequency range and step size are determined based on the servo motor's operating characteristics and the target test frequency band, ensuring coverage of all servo motor response frequencies during testing. The frequency step size determines the precision and resolution of the frequency scan. An appropriate step size is selected based on the servo motor's response characteristics to balance testing efficiency and accuracy, and initial sweep parameters are set, including the frequency range and step size, and then transmitted to the servo motor. This initialization process ensures the sweep parameters have good adaptability, allowing subsequent sweep tests to proceed smoothly and generating the final sweep parameters.

[0048] S1.2 Input the initial frequency sweep parameters into the genetic algorithm for evaluation and obtain evaluation data.

[0049] It should be noted that the genetic algorithm evaluates the fitness of the initial frequency sweep parameters, generates new candidate solutions using crossover and mutation operations, and finds the optimal combination of frequency sweep range and step size through multiple generations of iterative optimization. The fitness of each candidate solution is judged by evaluating its impact on the servo performance in actual testing. For example, the evaluation process analyzes whether the frequency sweep range and step size can effectively cover the servo's response frequency band and improve the servo's testing accuracy. Based on the evaluation results, the genetic algorithm selects the most fitness-oriented frequency sweep parameters and performs crossover and mutation operations to generate evaluation data.

[0050] It should also be noted that a genetic algorithm is an optimization algorithm that simulates the processes of natural selection and biological evolution, solving problems by simulating biological selection and gene mutation. Its basic process includes population initialization, selection, crossover, and mutation. A set of candidate solutions (i.e., the population) is randomly generated, each representing a potential solution to the problem. Through fitness evaluation, candidate solutions with high fitness are selected as the basis for reproduction. Crossover combines two candidate solutions into a new solution, and mutation randomly modifies some solutions to increase population diversity. Through multiple generations of iterative evolution, the genetic algorithm gradually approaches the optimal solution to the problem.

[0051] like Figure 5 This paper presents a comparison of the phase-frequency response characteristics of servo motors under different sweep frequency parameter strategies, using an overview at the top and a magnified view at the bottom. The orange dashed line in the figure corresponds to the phase response curve obtained under a fixed sweep frequency condition, while the blue solid line corresponds to the phase response curve obtained after adaptively adjusting the sweep frequency range and step size using a genetic algorithm. The overview shows that both sweep frequency methods can reflect the overall trend of phase change with frequency across the entire frequency band. Within the key frequency range marked by the red dashed rectangle, the magnified view further reveals the differences between the two methods in the details of phase change. The marking of the points of greatest difference shows that the adaptive sweep frequency method has a higher sampling density in the frequency band where phase change is more sensitive, making the phase response curve more continuous and smooth. This is beneficial for accurately capturing the phase hysteresis and change patterns in the dynamic characteristics of the servo motor, thereby improving the accuracy and stability of phase-frequency characteristic testing.

[0052] S1.3. Based on the evaluation data, the initial sweep frequency parameters are screened for fitness and adjusted. The sweep frequency parameters are then generated through single-point crossover and uniform variation.

[0053] It should be noted that the test results of each set of sweep parameters are compared with the ideal response characteristics of the servo to check whether the frequency range fully covers the servo's operating frequency band and whether the frequency step size can accurately capture the performance changes of the servo. Key indicators such as the amplitude-frequency response and phase-frequency response of the servo under the sweep parameters are calculated to assess whether they meet the requirements. For example, if the servo's frequency response is more stable, the bandwidth is wider, and it exhibits small errors within the set frequency range under a certain set of sweep parameters, then this set of sweep parameters can be considered to have high fitness. Through this comparison and calculation, sweep parameters that provide the best servo performance can be identified. During the fitness screening process, various indicators in the evaluation data (such as frequency range coverage, test accuracy, etc.) are compared with the servo performance standards and ideal response characteristics to screen out sweep parameters that meet the requirements. For example, a bandwidth frequency point where the amplitude drops to a preset amplitude drop ratio is selected as the sweep parameter that meets the performance requirements. The screened sweep parameters are adjusted to optimize the servo's performance; the frequency range and step size are fine-tuned to more accurately match the servo's response characteristics. New sweep frequency parameters are generated by using single-point crossover and uniform mutation operations. Single-point crossover exchanges some information between two candidate sweep frequency parameters to generate new sweep frequency parameters.

[0054] S2. The servo motor generates a frequency sweep command based on the frequency sweep parameters and executes a frequency scan to obtain servo motor operation feedback data. The servo motor operation feedback data is then sent to the host computer to generate a test dataset.

[0055] S2.1. Input the frequency sweep parameters into the servo motor, parse the frequency range and step size information in the frequency sweep parameters, obtain the frequency sweep command, and gradually adjust the frequency scan according to the frequency sweep command to generate frequency sweep execution data.

[0056] It should be noted that the frequency range determines the starting and ending frequencies of the frequency sweep test, while the frequency step size determines the level of detail in the frequency scan. After parsing the sweep parameters, the servo motor generates a sweep command based on the frequency range and step size information. This sweep command includes the process of gradually changing the frequency from the starting frequency to the ending frequency. The servo motor will then gradually adjust the frequency scan according to the sweep command. During each adjustment, the frequency will increase or decrease by the set step size to ensure the continuity and accuracy of the scan process. The servo motor's response data at each frequency point is recorded in real time and used to generate sweep execution data.

[0057] S2.2 Combine the frequency sweep execution data with the real-time operating status of the servo motor to obtain servo motor operating status data, and perform data cleaning, filtering and timestamp marking on the servo motor operating status data to obtain servo motor operating feedback data.

[0058] It should be noted that combining the frequency sweep execution data with the real-time operating status of the servo motor ensures that the servo motor operating status data corresponding to each frequency point is integrated into a complete feedback dataset, including key parameters such as servo motor speed, load, and temperature. This data accurately reflects the servo motor's performance at each frequency point. The feedback dataset undergoes data cleaning to remove outliers. For example, by setting upper and lower limits, data points far from the normal range are removed. Filtering algorithms are used to smooth the data to remove high-frequency noise, ensuring data accuracy and stability. A timestamp is added to each data point to ensure that each data point is associated with its corresponding time node, facilitating subsequent time-series analysis and generating servo motor operation feedback data.

[0059] S2.3 Organize the servo motor operation feedback data, group it by frequency point and add timestamps to generate a real-time operation feedback dataset.

[0060] It should be noted that the servo motor operation feedback data is organized by grouping the servo motor operation status data corresponding to each frequency point according to the frequency point. Each group of data represents the operating status of the servo motor at a specific frequency, including feedback information such as speed, load, and temperature. A timestamp is added to each group of data to identify the specific time corresponding to each data point, ensuring the timeliness and accuracy of the data. Adding timestamps helps identify the order and response characteristics of the data in subsequent analysis and provides a real-time reference. Through these steps, a real-time operation feedback dataset is generated.

[0061] S2.4 Remove outliers from the real-time running feedback dataset and process missing data to generate a test dataset.

[0062] It should be noted that outliers that may exist in the real-time feedback dataset are removed. Outlier identification is accomplished by using upper and lower limits; any value exceeding these limits is considered an outlier. Outlier removal helps ensure the accuracy and representativeness of the data, thus preventing data that does not reflect reality from affecting subsequent analysis. Missing data usually exists in the form of null values ​​and needs to be imputed using interpolation methods. Interpolation methods can predict missing values ​​based on the trend of adjacent data points, ensuring data integrity. After outlier removal and missing data processing, the result will be a cleaned and imputed test dataset.

[0063] S3. Perform piecewise fitting on the test dataset to obtain piecewise feedback data, and use the support vector regression algorithm to perform nonlinear fitting on the piecewise feedback data to generate a performance fitting dataset.

[0064] It should be noted that existing methods process feedback data through linear fitting or simple curve fitting techniques. These methods cannot fully capture the complex nonlinear relationships in the data, which may lead to inaccurate fitting results and thus affect the accurate evaluation of servo performance.

[0065] This invention utilizes a support vector regression (SVR) algorithm to perform nonlinear fitting on piecewise feedback data. SVR constructs a highly adaptive nonlinear model, enabling it to more accurately fit complex variation patterns in the data, thereby generating a higher-precision performance fitting dataset. This method effectively improves fitting accuracy and optimizes the accuracy of servo motor performance evaluation.

[0066] S3.1 Divide the test dataset into several segments according to a preset frequency range, obtain multiple segments of raw data, and use the frequency of each segment of raw data and the servo motor operation feedback data to perform nonlinear fitting to obtain segmented feedback data.

[0067] It should be noted that the test dataset is divided into several segments according to a preset frequency range. Each segment contains feedback information from the servo motor at different frequency points. Based on the frequency range division rules, the data in the test dataset is further segmented into multiple smaller segments, each corresponding to a specific frequency interval. By using frequency as the independent variable and the servo motor operation feedback data as the dependent variable, a non-linear relationship between frequency and servo motor operation feedback data is fitted. This process can reveal the performance variation patterns of the servo motor at different frequencies and obtain segmented feedback data.

[0068] It should also be noted that the frequency range is typically set based on the servo's operating characteristics and testing requirements. In practical applications, the preset frequency range usually considers the servo's maximum and minimum response frequencies to ensure coverage of all operating frequencies under normal operating conditions. This range can be determined by analyzing the servo's frequency response characteristics and experimental data, and typically includes a wide frequency range to ensure a comprehensive evaluation of the servo's dynamic performance and responsiveness.

[0069] The division rule divides the entire frequency range into several smaller intervals based on a preset frequency range, in order to more accurately evaluate the performance of the servo at different frequencies. The division rule is determined based on the frequency variation pattern and the servo's response characteristics. For example, the frequency range can be evenly divided according to the frequency step size, or the division precision can be adjusted according to key characteristics of the frequency response (such as frequency bands with significant amplitude changes) to ensure that the data in each interval can fully reflect the dynamic response characteristics of the servo.

[0070] S3.2. The support vector regression algorithm is used to perform nonlinear fitting with the frequency of each segment of feedback data as the independent variable and the servo motor operation feedback data as the dependent variable to obtain the fitting curve. The fitting curve is then interpolated and predicted to generate a performance fitting dataset.

[0071] It should be noted that the support vector regression algorithm uses the frequency of each segment of feedback data as the independent variable and the servo motor operation feedback data as the dependent variable. The relationship between the frequency data and the servo motor operation feedback data is mapped to a high-dimensional space. The optimal regression relationship is found in this space so that frequency changes can accurately predict the servo motor response. Based on the fitted results, a fitted curve is generated, reflecting the non-linear relationship between frequency and servo motor operation feedback data. The fitted curve is then extended using interpolation methods to predict the servo motor response at other uncollected data points within the frequency range. This process supplements missing data by estimating the changing trends between frequency points, generating a more complete performance fitting dataset.

[0072] S4. Calculate the amplitude of the sweep frequency command and the amplitude of the servo operation feedback data based on the performance fitting dataset, obtain the amplitude-frequency characteristic curve, and select the bandwidth frequency point when the amplitude drops to a specified ratio according to the amplitude-frequency characteristic curve to generate the phase-frequency characteristic curve.

[0073] S4.1 Based on the performance fitting dataset, calculate the amplitude of the frequency sweep command and the amplitude of the servo operation feedback data respectively, and generate an amplitude dataset.

[0074] It should be noted that, based on the performance fitting dataset, the amplitude of the frequency sweep command and the amplitude of the servo motor operation feedback data are calculated separately. For the amplitude of the frequency sweep command, the amplitude component is extracted from the frequency sweep command data at each frequency point; the amplitude reflects the strength of the frequency sweep signal at each frequency point. The amplitude corresponding to the frequency point is extracted from the servo motor feedback data; the amplitude represents the servo motor's response strength at a specific frequency. The amplitude of the frequency sweep command at each frequency point is compared with the amplitude of the servo motor operation feedback data, and the comparison results are compiled to generate an amplitude dataset.

[0075] S4.3 Calculate the difference between the sweep frequency command amplitude and the servo motor operation feedback data amplitude based on the amplitude dataset, and use the curve fitting method to analyze the trend of amplitude change with frequency, and draw the amplitude-frequency characteristic curve.

[0076] It should be noted that, for each frequency point within the frequency range, the amplitude of the sweep command and the amplitude of the servo feedback data are obtained respectively. The difference between the two is then calculated; this difference represents the deviation between the sweep command and the actual servo response. Using frequency as the independent variable and the difference between the sweep command amplitude and the servo feedback data amplitude as the dependent variable, curve fitting is performed on the difference data to generate a smooth curve. This fitting process eliminates data fluctuations at individual frequency points, obtaining a smooth relationship between amplitude and frequency. By plotting the relationship between frequency and amplitude differences, an amplitude-frequency characteristic curve is generated.

[0077] S4.4 Identify the bandwidth frequency point from the amplitude-frequency characteristic curve where the amplitude drops to the preset amplitude drop ratio.

[0078] It should be noted that, based on the amplitude at each frequency point in the amplitude-frequency response curve, the amplitude reduction ratio is determined. This ratio is usually expressed as the ratio to the maximum amplitude, for example, reducing to 70% or 50% of the maximum amplitude. Along the amplitude-frequency response curve, the amplitude change at each frequency point is checked, and a ratio is set, such as reducing the amplitude to 70% of the maximum amplitude. Starting from the initial frequency point of the amplitude-frequency response curve, the amplitude at each frequency point is checked, and the corresponding frequency point is recorded as the bandwidth frequency point.

[0079] S4.5. Using the bandwidth frequency point as the bandwidth of the servo motor, perform phase analysis on the amplitude of the operating data within the bandwidth range of the servo motor to generate a phase frequency characteristic curve.

[0080] It should be noted that combining the frequency sweep execution data with the servo motor's real-time operating status data, and matching the servo motor's operating status data corresponding to each frequency point, allows for a comprehensive understanding of the servo motor's performance at different frequencies. The servo motor operating status data includes key parameters such as speed, load, and temperature. Data cleaning is performed on the servo motor operating status data to remove abnormal data, such as noise data exceeding reasonable values. A filtering algorithm is used to smooth the data, removing high-frequency noise and making the data more stable. A timestamp is added to each data point to ensure that each data point is consistent with its corresponding time node, thereby ensuring the time sequence of the data and generating a servo motor operating feedback dataset.

[0081] S5. Analyze the phase frequency response at the target frequency point based on the phase frequency characteristic curve, determine whether it meets the phase requirements, obtain the bandwidth qualification status, evaluate the overall performance of the servo motor, and generate a performance evaluation report.

[0082] S5.1 Extract the phase value of the target frequency point from the phase frequency characteristic curve and compare it with the phase standard, calculate the phase deviation, determine whether the phase deviation is within the allowable range, and generate the phase coincidence state.

[0083] It should be specified that the position of the target frequency point on the phase-frequency response curve is determined, and the phase value of that frequency point is read from the curve. The extracted phase value is compared with a phase standard, which is usually set according to the servo's design requirements or theoretically desired phase. The phase deviation, i.e., the difference between the phase value of the target frequency point and the phase standard, is calculated. By calculating the phase deviation, the degree of deviation between the actual phase and the ideal phase at the target frequency point can be determined. It is then determined whether the phase deviation is within the allowable range, which is usually determined based on the servo's operating tolerance and performance requirements. If the phase deviation is within the allowable range, it indicates that the phase meets the requirements, and a phase compliance status is generated.

[0084] The expression for calculating the phase deviation is:

[0085] ;

[0086] in, For frequency Phase deviation below; For frequency The actual measured phase value; For frequency The theoretical expected phase value is calculated based on the ideal response characteristics and frequency response model of the servo motor.

[0087] S5.2 Combine the phase coincidence state with the amplitude dataset to evaluate the overall performance of the servo motor and generate a performance evaluation report.

[0088] It should be noted that the phase coincidence status at each frequency point is paired with the corresponding amplitude data to ensure that each frequency point contains both phase coincidence and amplitude information. This allows for the evaluation of the overall performance of the servo motor. By comprehensively analyzing the phase coincidence status and amplitude datasets, it can be determined whether the servo motor's response at different frequencies meets the predetermined performance requirements. For example, the number of phase coincidence statuses and the amplitude variation trend at each frequency point can be calculated. This data can then be used to evaluate the servo motor's performance stability and response accuracy within the operating frequency band. If the phase coincidence status and amplitude at most frequency points meet the requirements, it indicates that the overall performance of the servo motor is good, and a performance evaluation report is generated.

[0089] This embodiment also provides a computer device applicable to the performance parameter testing method of a servo motor, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the performance parameter testing method of the servo motor as proposed in the above embodiment.

[0090] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0091] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the performance parameter testing method for a servo motor as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0092] In summary, this invention achieves dynamic adaptive adjustment of the sweep frequency parameters by: collecting real-time feedback data from the servo motor and using a genetic algorithm to adjust the frequency range and step size of the sweep frequency, thereby generating optimized sweep frequency parameters and improving the accuracy and efficiency of the sweep frequency test; through fitness screening and parameter adjustment using the genetic algorithm, the optimal sweep frequency parameters can be automatically generated, ensuring the optimization of the servo motor test coverage and frequency step size; the beneficial effect of this step is that the optimized sweep frequency parameters can accurately reflect the servo motor performance, improve the automation and flexibility of the testing process, and further optimize the testing efficiency and performance of industrial control systems.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for testing the performance parameters of a servo motor, characterized in that: include, Real-time feedback data from the servo motor is collected, and a genetic algorithm is used to adjust the frequency range and frequency step size of the sweep frequency to generate sweep parameters. The servo motor generates a frequency sweep command based on the frequency sweep parameters and executes a frequency scan to obtain servo motor operation feedback data. The servo motor operation feedback data is then sent to the host computer to generate a test dataset. The test dataset is segmented for fitting to obtain segmented feedback data. Support vector regression algorithm is then used to perform nonlinear fitting on the segmented feedback data to generate a performance fitting dataset. The amplitude of the sweep command and the amplitude of the servo operation feedback data are calculated based on the performance fitting dataset to obtain the amplitude-frequency characteristic curve. The bandwidth frequency point when the amplitude drops to a specified ratio is selected according to the amplitude-frequency characteristic curve to generate the phase-frequency characteristic curve. Analyze the phase frequency response at the target frequency point based on the phase frequency characteristic curve, determine whether it meets the phase requirements, obtain the bandwidth qualification status, evaluate the overall performance of the servo motor, and generate a performance evaluation report.

2. The servo motor performance parameter testing method as described in claim 1, characterized in that: The process involves acquiring real-time feedback data from the servo motor and using a genetic algorithm to adjust the frequency range and frequency step size of the sweep frequency to generate sweep parameters. The specific steps are as follows: Collect real-time feedback data from the servo motor, initialize the sweep frequency range and frequency step size, and set the initial sweep parameters; The initial frequency sweep parameters are input into the genetic algorithm for evaluation to obtain evaluation data; Based on the evaluation data, the initial sweep parameters are screened for fitness and adjusted. The sweep parameters are then generated through single-point crossover and uniform mutation.

3. The servo motor performance parameter testing method as described in claim 2, characterized in that: The servo motor generates a frequency sweep command based on the frequency sweep parameters and executes a frequency scan to obtain servo motor operation feedback data. The specific steps are as follows. The frequency sweep parameters are passed to the servo motor, the frequency range and step size information in the frequency sweep parameters are parsed, the frequency sweep command is obtained, and the frequency sweep is gradually adjusted according to the frequency sweep command to generate frequency sweep execution data. By combining the frequency sweep execution data with the real-time operating status of the servo motor, servo motor operating status data is obtained. The servo motor operating status data is then cleaned, filtered, and timestamped to obtain servo motor operating feedback data.

4. The servo motor performance parameter testing method as described in claim 3, characterized in that: The specific steps for sending the servo motor operation feedback data to the host computer to generate a test dataset are as follows. Organize the servo motor operation feedback data, group it by frequency point and add timestamps to generate a real-time operation feedback dataset; Remove outliers from the real-time feedback dataset and process missing data to generate a test dataset.

5. The servo motor performance parameter testing method as described in claim 4, characterized in that: The process of segmenting the test dataset for fitting, obtaining segmented feedback data, and then using a support vector regression algorithm to perform nonlinear fitting on the segmented feedback data to generate a performance fitting dataset is detailed below. The test dataset is divided into several segments according to a preset frequency range, and multiple segments of raw data are obtained. Nonlinear fitting is performed using the frequency of each segment of raw data and the servo motor operation feedback data to obtain segmented feedback data. The support vector regression algorithm is used to perform nonlinear fitting with the frequency of each segment of feedback data as the independent variable and the servo motor operation feedback data as the dependent variable to obtain the fitting curve. The fitting curve is then interpolated and predicted to generate a performance fitting dataset.

6. The servo motor performance parameter testing method as described in claim 5, characterized in that: The amplitude of the sweep command and the amplitude of the servo motor operation feedback data are calculated based on the performance fitting dataset to obtain the amplitude-frequency characteristic curve. The specific steps are as follows: Based on the performance fitting dataset, the amplitude of the frequency sweep command and the amplitude of the servo operation feedback data are calculated respectively to generate an amplitude dataset; The difference between the sweep frequency command amplitude and the servo motor operation feedback data amplitude is calculated based on the amplitude dataset, and the trend of amplitude change with frequency is analyzed by curve fitting method, and the amplitude-frequency characteristic curve is plotted.

7. The servo motor performance parameter testing method as described in claim 6, characterized in that: The step of selecting the bandwidth frequency point where the amplitude decreases to a specified proportion based on the amplitude-frequency response curve and generating the phase-frequency response curve is as follows: Identify the bandwidth frequency point where the amplitude drops to a preset amplitude drop ratio from the amplitude-frequency response curve; Using the bandwidth frequency point as the bandwidth of the servo motor, phase analysis is performed on the amplitude of the operating data within the bandwidth range of the servo motor to generate a phase-frequency characteristic curve.

8. The servo motor performance parameter testing method as described in claim 7, characterized in that: The steps for analyzing the phase frequency response at the target frequency point based on the phase frequency characteristic curve, determining whether it meets the phase requirements, obtaining the bandwidth qualification status, evaluating the overall performance of the servo motor, and generating a performance evaluation report are as follows. The phase value of the target frequency point is extracted from the phase frequency response curve and compared with the phase standard. The phase deviation is calculated, and it is determined whether the phase deviation is within the allowable range. The phase coincidence state is then generated. By combining the phase coincidence state with the amplitude dataset, the overall performance of the servo motor is evaluated, and a performance evaluation report is generated.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the servo motor performance parameter testing method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the performance parameter testing method for the servo motor according to any one of claims 1 to 8.

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