A stability analysis method and system for torque motor drive system

Abstract

The present invention discloses a stability analysis method and system for a torque motor drive system, and specifically relates to the technical field of torque motor drive. The method gradually increases and decreases the time delay in a simulation environment to obtain response change data of a controller output and a motor drive system input, evaluates the response sensitivity of the controller under different time delay conditions, and evaluates the response rate stability of the motor drive system in combination with a load mutation scenario. The real-time performance of the controller response to the motor drive system is evaluated through a machine learning model. When the real-time performance is low, an abnormality detection mechanism is used to identify unstable time periods, and controller parameters are dynamically adjusted, thereby effectively reducing oscillation, over-modulation and instability problems caused by time lag effects, improving the stability and reliability of the motor drive system in high-speed or precision control applications, and significantly improving the real-time response capability of the controller in a dynamic environment to avoid system loss of control or damage.

Description

Technical Field

[0001] The present invention relates to the technical field of torque motor drive, and in particular to a stability analysis method and system for a torque motor drive system. Background Art

[0002] The stability analysis of torque motor drive system refers to the study of how to keep the system stable during operation, especially under different loads, speeds, and environmental conditions, to avoid undesirable vibrations, loss of control, or other problems. As a motor that can generate large torque, the drive system of the torque motor usually requires precise control strategies to ensure stable operation of the system and avoid performance degradation or equipment damage caused by system instability.

[0003] In stability analysis, researchers will evaluate the dynamic response of the motor drive system, focusing on the relationship between the motor's speed, torque output and load, and model and analyze factors that may cause instability (such as controller parameters, external disturbances, system delays, etc.). Through mathematical modeling, simulation and experimental verification, the control strategy can be optimized to ensure that the system can work stably under various working conditions and improve overall performance and reliability.

[0004] The prior art has the following deficiencies:

[0005] When the motor drive system is running at high speed, nonlinear time lag effects may occur due to time delays in signal transmission, calculation and execution. Traditional control strategies (such as PID controllers) usually assume that the system delay is constant and can be optimized by adjusting parameters. However, when there is uncertainty in the system delay, or the delay itself is variable, it may cause predictive control to fail and feedback lag effects caused by delays may occur. This time lag effect is particularly evident in dynamic systems and is often ignored in conventional simulation and modeling. In addition, due to the existence of time lag, the controller cannot respond to changes in the motor and load in a timely manner, resulting in control signal lag, which in turn causes oscillation, overshoot or unstable response. This time lag effect can become very complicated under certain special conditions and even cause the system to completely lose control. For high-speed motors or precision control applications, this nonlinear time lag can be a fatal problem, resulting in irreversible equipment damage. Summary of the invention

[0006] The object of the present invention is to provide a stability analysis method and system for a torque motor drive system to address the deficiencies in the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a stability analysis method for a torque motor drive system, comprising the following steps:

[0008] S1: In a simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded;

[0009] S2: Based on the acquired response change data, analyze the control accuracy of the controller under different time delay conditions, determine the oscillation degree of the controller output signal, and evaluate the response sensitivity of the controller when facing different motor drive system delay changes;

[0010] S3: When the controller has high response sensitivity, the speed and torque change rate data of the motor drive system are collected in the simulation environment by simulating the sudden change of the motor load;

[0011] S4: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation amplitude of the response rate of the motor drive system under the condition of rapid load change, and evaluate the response rate stability of the motor drive system when facing load changes;

[0012] S5: Evaluate the real-time performance of the controller in responding to the dynamics of the motor drive system according to the response sensitivity of the controller to the time delay changes of different motor drive systems and the response rate stability of the motor drive system to the load changes, and classify it into different real-time performance levels according to the evaluation results, and perform corresponding processing;

[0013] S6: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

[0014] Preferably, in S2, the controller output signal and the motor response signal are subjected to Fourier transform to obtain a signal spectrum, and then the signal oscillation abnormality index is generated after analysis. The method for obtaining the signal oscillation abnormality index is:

[0015] Get the time series data y(m) of the controller output signal and the motor response signal, which represents the value of the controller output signal at time m. The sampling period is , perform fast Fourier transform on the time domain signal y(m) to obtain the frequency domain representation Y(f) of the signal. The formula of fast Fourier transform is: ;in: is the frequency domain signal at the frequency point The complex value at , j is the imaginary unit, is the kernel function of Fourier transform, N is the number of sampling points of the signal; calculate the amplitude spectrum of the frequency domain signal, the amplitude spectrum The calculation formula is: ;in, is a frequency domain signal The module is: ;in, and are the real and imaginary parts of the Fourier transform result respectively;

[0016] Calculate the energy ratio of the signal in the high-frequency area and set a frequency threshold , then the high frequency energy Calculated as: ;in, It is the square of the spectrum amplitude, representing the energy of each frequency component, and calculating the energy proportion of the low-frequency area. Calculated as: ; Combine the energy proportion of high frequency and low frequency to express the oscillation degree of the signal , the expression is: ; Calculate the signal oscillation anomaly index based on the oscillation degree ratio, the expression is: ; In the formula, is the signal oscillation anomaly index, where is a sliding window size.

[0017] Preferably, in S4, a response rate fluctuation index is generated after analyzing the response rate fluctuation amplitude of the motor drive system under the condition of rapid load change. The response rate fluctuation index is obtained by:

[0018] Collect the response data of the motor drive system under rapid load change conditions, specifically the change of speed ω(t) and torque T(t) over time, and calculate the rate of change of the signal and torque change rate, the expressions are: ;in, is the motor speed at time t, Indicates the rate of change of speed; ; Where T(t) is the output torque of the motor at time t, is the rate of change of torque; construct the time series X(t), calculate the mean μ of the series, and then calculate its deviation series : ; then calculate the cumulative deviation: ; Where S(N) is the cumulative deviation of the time series to time point N. In each time window, the range is calculated, that is, the difference between the maximum and minimum values ​​of the cumulative sum: ; Calculate the standard deviation σ of the original data X(t): Calculate the R / S ratio based on the calculation results of the range and standard deviation: ; Take the logarithm of the R / S ratio with respect to N and plot a log-log plot: ; Then the straight line in the graph is fitted by the least squares method, and the inverse of the slope H is the response rate fluctuation index, which is expressed as: , ; Where EQ is the response rate fluctuation index.

[0019] Preferably, in S5, the signal oscillation anomaly index and the response rate fluctuation index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the real-time value label of the controller's response to the dynamics of the motor drive system as the prediction target, and takes minimizing the sum of the prediction errors of the real-time value labels of all controllers' responses to the dynamics of the motor drive system as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The real-time value of the controller's response to the dynamics of the motor drive system is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0020] Preferably, the acquired real-time value of the controller responding to the dynamics of the motor drive system is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the real-time value of the controller responding to the dynamics of the motor drive system is compared with the first standard threshold and the second standard threshold respectively;

[0021] If the real-time value of the controller responding to the dynamics of the motor drive system is greater than the second standard threshold, it means that the real-time of the controller responding to the dynamics of the motor drive system is high, and a high real-time response signal is generated, and the real-time of the controller responding to the dynamics of the motor drive system is classified as a high real-time response level;

[0022] If the real-time value of the controller responding to the dynamics of the motor drive system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the real-time response of the controller to the dynamics of the motor drive system is general, and a medium real-time response signal is generated, and the real-time response of the controller to the dynamics of the motor drive system is classified as a medium real-time response level;

[0023] If the real-time value of the controller's response to the motor drive system dynamics is less than the first standard threshold, it means that the real-time response of the controller to the motor drive system dynamics is low. At this time, a low real-time response signal is generated, and the real-time response of the controller to the motor drive system dynamics is divided into a low real-time response level.

[0024] Preferably, in S6, when the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormal degree of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated, specifically:

[0025] When the real-time performance of the controller responding to the dynamics of the motor drive system is low, that is, the real-time performance value of the controller responding to the dynamics of the motor drive system generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the real-time performance values ​​of the controller responding to the dynamics of the motor drive system generated within a subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a data set is established, and the standard deviation of the data set is calculated, that is, the abnormal degree value of the real-time performance of the controller responding to the dynamics of the motor drive system is calculated;

[0026] The calculated real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is compared with the real-time abnormality degree value reference threshold preset according to historical data. If the real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is greater than or equal to the real-time abnormality degree value reference threshold, it means that the real-time abnormality degree of the motor drive system dynamics is high. At this time, a warning signal is generated and it is divided into an unstable time period of the motor drive system; if the real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is less than the real-time abnormality degree value reference threshold, it means that the real-time abnormality degree of the motor drive system dynamics is low, and no warning signal is generated.

[0027] Preferably, in the case of a high degree of real-time abnormality, the parameters of the controller need to be dynamically adjusted, including: increasing the feedback gain of the controller, ,integral and differential In the parameters, to speed up the controller response speed, the formula for adjusting the gain is: , ; Among them, α, β, and γ are gain adjustment coefficients calculated according to the abnormality degree value, and α>1, β>1, γ>1, which are used to enhance the adjustment ability of the controller.

[0028] The present invention also provides a stability analysis system for a torque motor drive system, including a time delay test module, a response sensitivity evaluation module, a load mutation simulation module, a response rate analysis module, a real-time evaluation and grading module, and an anomaly detection module:

[0029] Delay test module: In the simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded;

[0030] Response sensitivity evaluation module: Based on the acquired response change data, the control accuracy of the controller under different delay conditions is analyzed, the oscillation degree of the controller output signal is determined, and the response sensitivity of the controller when facing different motor drive system delay changes is evaluated;

[0031] Load mutation simulation module: When the controller has high response sensitivity, the speed and torque change rate data of the motor drive system are collected in the simulation environment by simulating the sudden change of the motor load;

[0032] Response rate analysis module: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation range of the response rate of the motor drive system under rapid load changes, and evaluate the response rate stability of the motor drive system when facing load changes;

[0033] Real-time evaluation and grading module: Based on the response sensitivity of the controller to the time delay changes of different motor drive systems and the response rate stability of the motor drive system to load changes, the real-time performance of the controller in responding to the dynamics of the motor drive system is evaluated, and the real-time performance is divided into different real-time performance levels according to the evaluation results, and corresponding processing is performed;

[0034] Abnormality detection module: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

[0035] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0036] 1. The present invention collects data on controller output and motor response signals by gradually increasing and decreasing the delay test in a simulation environment, and combines Fourier transform and dynamic analysis methods to evaluate the response sensitivity, oscillation degree and response rate fluctuation of the controller under load mutation of the motor drive system. By combining the signal oscillation abnormality index and the response rate fluctuation index, the machine learning model is used to predict the dynamic real-time performance of the controller, and the time period with low real-time performance is dynamically adjusted, thereby optimizing the parameters of the controller to avoid over-adjustment, oscillation or unstable response of the system under high-speed operation or complex working conditions, and ensure the smooth operation of the system.

[0037] 2. The present invention can monitor the operating status of the motor drive system in real time by establishing a dynamic real-time evaluation system, generate a warning signal when an abnormality is detected, and adjust the controller parameters in time to improve the system response speed and prevent the feedback lag effect caused by delays. When the degree of real-time abnormality is high, the feedback gain is automatically increased, the proportional, integral, and differential parameters are adjusted, and the adjustment ability of the system is improved, thereby effectively improving the stability and reliability of the motor drive system in a high-speed, precision control environment. Through this innovative solution, the present invention significantly improves the stability of the motor drive system in a complex dynamic environment and avoids the problem of system loss of control or equipment damage caused by time lag. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 The figure is a flow chart of the method of the present invention.

[0040] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0042] Example 1, please refer to Figure 1 and Figure 2 As shown, the stability analysis method for a torque motor drive system described in this embodiment includes the following steps:

[0043] S1: In a simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded;

[0044] S2: Based on the acquired response change data, analyze the control accuracy of the controller under different time delay conditions, determine the oscillation degree of the controller output signal, and evaluate the response sensitivity of the controller when facing different motor drive system delay changes;

[0045] S3: When the controller has high response sensitivity, the speed and torque change rate data of the motor drive system are collected in the simulation environment by simulating the sudden change of the motor load;

[0046] S4: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation amplitude of the response rate of the motor drive system under the condition of rapid load change, and evaluate the response rate stability of the motor drive system when facing load changes;

[0047] S5: Evaluate the real-time performance of the controller in responding to the dynamics of the motor drive system according to the response sensitivity of the controller to the time delay changes of different motor drive systems and the response rate stability of the motor drive system to the load changes, and classify it into different real-time performance levels according to the evaluation results, and perform corresponding processing;

[0048] S6: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

[0049] In S1, in the simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded, specifically:

[0050] To build an accurate simulation model of the motor drive system, the model should include important components such as the motor, controller, load, sensor, and driver. In this model, the physical characteristics of the motor (such as torque-speed characteristics, motor inertia, torque constant, etc.) and the control algorithm of the system (such as PID control or other types of controllers) should be accurately modeled.

[0051] The controller is responsible for adjusting the operating state of the motor according to the changes in the system input and output. Common controllers include PID controllers: PID controllers are the most common feedback control method and are widely used in motor control. Its model is: ; Where e(t) is the error (the difference between the target speed and the actual speed), u(t) is the control signal, , , is the proportional, integral, and differential gain. In the simulation model, the delay mainly exists in the signal transmission, calculation, and execution process. As needed, an adjustable delay parameter can be introduced in the signal channel or control loop. Specifically, a simulated delay can be inserted between the input and output of the controller. The test of gradually increasing and decreasing the delay includes: starting from no delay, gradually increasing the delay in the system until a predetermined maximum value is reached (for example, from 0 ms to hundreds of milliseconds or seconds), simulating various possible delay conditions in the system. The delay can be divided into fixed delay and random delay: simulating more complex real-world situations, introducing random fluctuations in the delay in the system, and observing how the system responds to unpredictable changes in the delay. After each delay adjustment, record the output signal of the controller and the response changes of the input of the motor drive system. The specific parameters recorded include: the speed change of the motor, the torque output of the motor, the output signal of the controller (such as PWM signal or other control variables), the change of the motor load, and the dynamic response time of the system, such as the time required for the motor to reach a steady state.

[0052] During the test, the delay needs to be adjusted continuously to simulate different load and environmental conditions and record the system's response. This will help researchers identify phenomena such as oscillation, overshoot, and unstable response of the system under different delay conditions.

[0053] S2: Based on the acquired response change data, analyze the control accuracy of the controller under different time delay conditions, determine the oscillation degree of the controller output signal, and evaluate the response sensitivity of the controller when facing different motor drive system delay changes.

[0054] First, through simulation testing, obtain the output signal of the controller and the input response change data of the motor system under different delay conditions (such as a delay range from short to long). Record the control signal sent by the controller to the motor driver, usually a current or voltage command signal. Record important response parameters such as the actual speed, output torque, and load condition of the motor. Gradually adjust the delay in the simulation and record the change in delay and its impact on the controller response.

[0055] Due to the existence of time delay, the collected control signal and motor response data may have noise or discontinuous changes. Therefore, it is necessary to pre-process the data, including: using a low-pass filter or a moving average filter to smooth the data and remove high-frequency noise. Ensure that the time axis of the control signal and the response signal are aligned to take into account the asynchrony of the time delay effect.

[0056] The goal of control accuracy analysis is to evaluate how the controller maintains the desired performance of the motor under different delay conditions. Specific analysis methods include:

[0057] Calculate the control error: the difference between the target value and the actual output. Common error indicators include: or ; Average the error over the entire simulation time period to reflect the long-term accuracy of the controller. Analyze the change in the maximum error under different delay conditions to determine whether the controller can maintain a low error under extreme delay conditions. Evaluate the volatility of the error, especially under large delays, the error may fluctuate or increase periodically, which manifests as system oscillation. Calculate the standard deviation of the error, which indicates the amplitude of error fluctuation. The larger the standard deviation, the worse the control accuracy and the more severe the oscillation.

[0058] Oscillation is one of the common manifestations of feedback hysteresis caused by time delay. Especially when the control system does not respond to the input signal in time, the controller may over-correct, causing system oscillation. To determine the degree of oscillation, the following methods can be used:

[0059] Perform Fourier transform on the controller output signal and the motor response signal to obtain the spectrum of the signal. Oscillations usually appear as an enhancement of a specific frequency, so spectrum analysis can help determine whether there are periodic oscillations. By analyzing the main frequency and amplitude of the signal, you can understand the severity of the oscillation. If the oscillation frequency is close to the natural frequency of the system, it may cause resonance and system instability.

[0060] Perform Bode plot analysis on the closed-loop system of the controller to evaluate the gain and phase margin of the system under different time delay conditions. A small gain margin indicates that the system is highly sensitive to disturbances and prone to oscillation. A small phase margin means that the system is prone to oscillation, and too small a phase margin may lead to a decrease in system stability.

[0061] Through time domain signal analysis, observe the overshoot and oscillation period of the controller output signal and the motor response. The larger the overshoot and the more unstable the oscillation period, the more unstable the system response. Analyze the time required for the system to reach a stable state from the initial state. When the time delay is too large, the system may take longer to stabilize or fail to converge.

[0062] After Fourier transforming the controller output signal and the motor response signal, the signal spectrum is analyzed to generate the signal oscillation anomaly index, which is used to evaluate the response sensitivity of the controller when facing different motor drive system delay changes. The signal oscillation anomaly index is obtained as follows:

[0063] Get the time series data y(m) of the controller output signal and the motor response signal, which represents the value of the controller output signal at time m. The sampling period is (That is, the sampling interval of the signal in the time domain). Perform a fast Fourier transform on the time domain signal y(m) to obtain the frequency domain representation of the signal Y(f). The formula for the fast Fourier transform is: ;in: is the frequency domain signal at the frequency point The complex value at , j is the imaginary unit, is the kernel function of Fourier transform, N is the number of sampling points of the signal, is the sampling period.

[0064] Calculate the amplitude spectrum of the frequency domain signal, which can be used to determine the intensity of each frequency component. The calculation formula is: ;in, is a frequency domain signal The module is: ;in, and are the real and imaginary parts of the Fourier transform result respectively;

[0065] Calculates the fraction of energy in the high frequency region of a signal. Usually, high frequency components represent rapid changes in oscillation, which can appear as overshoot or ringing, especially in control systems. Set a frequency threshold (For example, the frequency below the system's natural frequency or noise frequency is considered low frequency), then the high-frequency energy It can be calculated as: ;in, It is the square of the spectrum amplitude and represents the energy of each frequency component. High frequency parts are usually the source of system overmodulation or oscillation. Calculate the energy percentage of the low frequency area. Low frequency components usually represent the steady-state response of the system, while low frequency components may help control the stability of the signal. Low frequency energy Calculated as: ;so, It reflects the stability of the system.

[0066] The oscillation degree of the signal is expressed by combining the energy proportion of high frequency and low frequency. , the expression is: ; Calculate the signal oscillation anomaly index based on the oscillation degree ratio, the expression is: ; In the formula, is the signal oscillation anomaly index, where It is a sliding window size used to smooth the fluctuation of the shock anomaly index.

[0067] The larger the signal oscillation anomaly index is, the more sensitive the controller is to the time delay changes of the motor drive system. This is because a higher oscillation anomaly index indicates that the energy proportion of the high-frequency components in the signal increases, which usually means that there is a strong oscillation in the system. When the delay changes greatly, the controller fails to adjust the output in time, resulting in a lag in the control signal, causing system instability or oscillation, and the oscillation anomaly index will naturally rise. Therefore, when the signal oscillation anomaly index is large, it usually means that the controller overreacts to the feedback of the system under the delay change, and the system is prone to over-adjustment or oscillation, reflecting that the controller is very sensitive to the delay change and may need to be adjusted or optimized.

[0068] On the contrary, the smaller the signal oscillation anomaly index is, the lower the controller's response sensitivity to delay changes is. A smaller oscillation anomaly index means that the signal has fewer high-frequency components, the system's response is relatively stable, and the controller can stably track changes in the motor drive system without causing excessive oscillations. At this time, the controller is more adaptable to delay changes, and can effectively suppress the instability caused by delays through appropriate control strategies, ensuring that the system maintains stable operation under load changes or external disturbances. Therefore, when the signal oscillation anomaly index is small, it means that the controller has a lower response sensitivity and can better cope with the effects caused by delays.

[0069] S3: When the response sensitivity of the controller is high, in the simulation environment, the change rate data of the speed and torque of the motor drive system are collected by simulating the sudden change of the motor load.

[0070] In the simulation environment, the operation of the motor drive system is simulated and load mutations are artificially introduced. Load mutations refer to sudden changes in load, such as a rapid increase or decrease in load, or a sharp change in load characteristics. Load mutations may cause drastic fluctuations in the speed and torque of the motor, especially when the control system does not respond in time or the control strategy is not appropriate. This scenario is usually used to verify the stability of the system under dynamic conditions and the adaptability of the controller.

[0071] Load changes can be simulated by:

[0072] Increase or decrease load value: Sudden changes in load torque, such as increasing the load from a smaller value to a larger value, or vice versa, and observe the response of the motor and control system.

[0073] Temporal characteristics of mutation: The time span of simulated load change can be an instantaneous mutation or a gradual change, but the speed must be fast enough to test the dynamic response capability of the control system.

[0074] During the simulation of load mutation, the focus is on collecting the speed and torque change rate data of the motor drive system. Speed ​​and torque are the core outputs of the motor drive system and reflect the system's response to load changes.

[0075] Speed ​​change rate: The rate at which the motor speed changes over time, indicating how quickly the motor responds to load changes. The speed change rate can be obtained by calculating the time derivative of the motor speed signal: ;in, is the motor speed at time t, Indicates the rate of change of speed. Torque change rate: the rate at which the motor output torque changes over time, indicating the motor's response speed to load changes. The torque change rate can be obtained by calculating the time derivative of the torque signal: ; Where T(t) is the output torque of the motor at time t, is the rate of change of torque. By collecting these change rate data, the response speed of the motor drive system under load mutation and the stability of the system can be monitored in real time.

[0076] S4: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation amplitude of the response rate of the motor drive system under rapid load change conditions, and evaluate the response rate stability of the motor drive system when facing load changes.

[0077] Preprocess the collected data on the rate of change of motor speed and torque. If there is noise or outliers in the data, filtering methods (such as low-pass filtering) can be used to remove high-frequency noise and retain the main trend of change. In order to more accurately observe the response fluctuations of the system, methods such as sliding average can be used to smooth the data and reduce the interference of instantaneous fluctuations.

[0078] Perform differential or numerical derivative calculations on the time series data of motor speed ω(t) and torque T(t) to obtain their rate of change and By analyzing the rate of change data, the fluctuation amplitude of the system response when the load changes can be quantified. A common method is to calculate the standard deviation, maximum, minimum and peak value of the response rate to evaluate the stability and transition process of the system.

[0079] The standard deviation measures the degree of dispersion of data points. The larger the standard deviation of the response rate, the more unstable the system response is and the greater the fluctuation is. The expressions are: , ;in, and are the mean values ​​of the speed and torque change rates, respectively, N is the total number of data points, and are the standard deviations of the speed and torque change rates respectively.

[0080] The peak value refers to the maximum or minimum value of the speed or torque change rate, which can reflect the most violent fluctuation in the system response. If the peak value is too large, it may indicate that the system is over-modulated or oscillating. The expressions are: , ; In the formula, and are the peak values ​​of speed and torque change rates respectively.

[0081] The oscillation period of the system is calculated by analyzing the rate of change signals of speed and torque. The shorter the oscillation period, the faster the system responds, but if it is too short, it may cause overshoot or unstable oscillation. The oscillation period can be determined by looking for periodic fluctuations in the signal. Common methods are to identify periodic changes through zero crossing method or peak detection method.

[0082] By calculating the standard deviation, peak value and oscillation period of the speed and torque change rate data, the overall evaluation index of the response fluctuation amplitude can be obtained. Based on these data, it can be judged whether the system responds smoothly under load mutation conditions or whether there is excessive response fluctuation.

[0083] Response stability: If the standard deviation is small and the peak value does not change much, it means that the system can respond smoothly to load changes, the controller adjustment effect is good, and the system can maintain stability when the load changes rapidly.

[0084] Unstable response: If the standard deviation is large, the peak value is too high, or there is a strong periodic fluctuation, it means that the system is too sensitive to load changes and may over-adjust or oscillate. At this time, the stability of the system is poor, and it is necessary to optimize the control strategy or introduce measures to suppress oscillation.

[0085] The response rate fluctuation index is generated by analyzing the response rate fluctuation amplitude of the motor drive system under rapid load change conditions to evaluate the response rate stability of the motor drive system when facing load changes. The response rate fluctuation index is obtained as follows:

[0086] Collect the response data of the motor drive system under rapid load change conditions, specifically the change of speed (ω(t)) and torque (T(t)) over time, and calculate the rate of change of the signal and torque change rate, the expressions are: ;in, is the motor speed at time t, Indicates the rate of change of speed; ; Where T(t) is the output torque of the motor at time t, is the rate of change of torque; construct a time series X(t) (in the present invention, the speed or torque change rate), calculate the mean μ of the sequence, and then calculate its deviation sequence : ; then calculate the cumulative deviation: ; Where S(N) is the cumulative deviation of the time series to time point N. In each time window, calculate the range, that is, the difference between the maximum and minimum values ​​of the cumulative sum: ; Calculate the standard deviation σ of the original data X(t): Calculate the R / S ratio based on the calculation results of the range and standard deviation: ; Take the logarithm of the R / S ratio with respect to N and plot a log-log plot: ; Then the straight line in the graph is fitted by the least squares method, and the inverse of the slope H is the response rate fluctuation index, which is expressed as: , ; Where EQ is the response rate fluctuation index.

[0087] The larger the response rate fluctuation index, the poorer the response rate stability of the motor drive system when facing load changes. A larger fluctuation index usually means that the system response has strong inertia or persistence, which may cause overshoot, oscillation or hysteresis effects. When the system inertia is strong, load changes may cause large response fluctuations, and the system takes longer to stabilize. This phenomenon is particularly dangerous in high-speed motors and precision control situations, which may cause system instability or even equipment damage.

[0088] On the contrary, the smaller the response rate fluctuation index, the better the response rate stability of the motor drive system. A smaller fluctuation index usually means that the system is more sensitive to load changes and can respond quickly to load changes, avoiding oscillations and instability caused by excessive lag or inertia. Although this highly sensitive response helps to quickly adjust the motor drive system, if it is overly sensitive, it may also lead to frequent small adjustments, resulting in unstable control strategies. Therefore, in practical applications, it is necessary to balance the stability and sensitivity of the response rate according to the specific situation.

[0089] S5: Evaluate the real-time performance of the controller in responding to the dynamics of the motor drive system based on the response sensitivity of the controller to different motor drive system delay changes and the response rate stability of the motor drive system to load changes. Divide it into different real-time levels based on the evaluation results and perform corresponding processing.

[0090] The signal oscillation anomaly index and the response rate fluctuation index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the real-time value label of the controller responding to the dynamics of the motor drive system as the prediction target, and takes minimizing the sum of the prediction errors of the real-time value labels of all controllers responding to the dynamics of the motor drive system as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The real-time value of the controller responding to the dynamics of the motor drive system is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0091] The method for obtaining the real-time value of the controller's response to the dynamics of the motor drive system is to obtain the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: ; In the formula, is the output function of the model, OAE is the signal oscillation anomaly index, EQ is the response rate fluctuation index, It is the real-time value of the controller's response to the dynamics of the motor drive system.

[0092] The obtained real-time value of the controller responding to the dynamics of the motor drive system is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the real-time value of the controller responding to the dynamics of the motor drive system is compared with the first standard threshold and the second standard threshold respectively;

[0093] If the real-time value of the controller responding to the dynamics of the motor drive system is greater than the second standard threshold, it means that the real-time of the controller responding to the dynamics of the motor drive system is high, and a high real-time response signal is generated, and the real-time of the controller responding to the dynamics of the motor drive system is classified as a high real-time response level;

[0094] If the real-time value of the controller responding to the dynamics of the motor drive system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the real-time response of the controller to the dynamics of the motor drive system is general, and a medium real-time response signal is generated, and the real-time response of the controller to the dynamics of the motor drive system is classified as a medium real-time response level;

[0095] If the real-time value of the controller's response to the motor drive system dynamics is less than the first standard threshold, it means that the real-time response of the controller to the motor drive system dynamics is low. At this time, a low real-time response signal is generated, and the real-time response of the controller to the motor drive system dynamics is divided into a low real-time response level.

[0096] High real-time response level (real-time value > second standard threshold): When the controller's response is very real-time, it means that the controller can quickly adapt to changes in the motor drive system. In this case, the current control strategy can be maintained or slightly optimized. Due to the rapid response of the system, it is usually not necessary to make major adjustments, only slight compensation or enhance the robustness of the existing strategy to ensure that the system remains stable under high dynamic load changes.

[0097] Medium real-time response level (first standard threshold ≤ real-time value ≤ second standard threshold): The real-time response of the controller is average, indicating that the system has a certain response lag. At this time, it may be necessary to adjust the controller parameters (such as gain value, feedback loop, etc.) appropriately to improve the response speed and stability. For this medium real-time situation, you can use an adaptive control strategy or perform temporary optimization to ensure that the system does not overshoot or oscillate.

[0098] Low real-time response level (real-time value < first standard threshold): When the real-time response of the controller is low, it means that the system is slow to respond, which may cause serious control lag, oscillation or instability. In this case, it is necessary to solve the problem by dynamically adjusting the control strategy or introducing a stronger compensation mechanism. Common treatment methods include: increasing the feedback gain of the controller. Introducing more sophisticated delay compensation algorithms (such as predictive control, robust control, etc.). Adopting a multi-mode control strategy to switch different control modes (for example, gain adjustment, nonlinear control, etc.) according to the real-time level.

[0099] S6: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

[0100] When the real-time performance of the controller's response to the motor drive system dynamics is low, that is, the real-time performance value of the controller's response to the motor drive system dynamics generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the real-time performance values ​​of the controller's response to the motor drive system dynamics generated within a subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a data set is established, and the standard deviation of the data set is calculated, that is, the abnormality degree value of the real-time performance of the controller's response to the motor drive system dynamics is calculated.

[0101] The calculated real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is compared with the real-time abnormality degree value reference threshold preset according to historical data. If the real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is greater than or equal to the real-time abnormality degree value reference threshold, it means that the real-time abnormality degree of the motor drive system dynamics is high. At this time, a warning signal is generated and it is divided into an unstable time period of the motor drive system; if the real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is less than the real-time abnormality degree value reference threshold, it means that the real-time abnormality degree of the motor drive system dynamics is low, and no warning signal is generated.

[0102] In the case of high real-time abnormality, the controller parameters need to be dynamically adjusted to improve the response real-time and stability, including:

[0103] Increase the feedback gain of the controller, especially in the proportional ,integral and differential In the parameters, to speed up the controller response speed, the formula for adjusting the gain is: , , ; Among them, α, β, and γ are gain adjustment coefficients calculated according to the abnormality degree value, and α>1, β>1, γ>1, which are used to enhance the adjustment ability of the controller.

[0104] For higher delays (especially in the case of low dynamic response), the lag effect can be reduced by introducing model predictive control (MPC) or delay compensation technology. This method predicts the future behavior of the motor drive system and responds in advance. In the event of a large anomaly, you can switch to a control strategy with a higher response speed (such as gain scheduling, robust control, or adaptive control) and adjust the control strategy in real time according to different load conditions. Using an adaptive control algorithm, the control parameters are automatically adjusted in real time according to the dynamic response of the motor drive system. This method adjusts the controller's gain, filter and other parameters according to the fluctuation of the degree of real-time anomaly, so that it can respond quickly and stabilize when the system becomes unstable.

[0105] In this embodiment, by gradually increasing and decreasing the delay of the motor drive system in the simulation environment, recording and analyzing the response changes of the controller output and the motor input, the control accuracy and response sensitivity of the controller under different delay conditions are evaluated. When the response sensitivity of the controller is high, by simulating the sudden change of the motor load, the speed and torque change rate data of the motor drive system are collected to analyze the response rate stability of the system under rapid load changes. Combined with the response sensitivity of the controller and the load response stability of the motor system, the real-time performance of the dynamic response of the controller is evaluated and divided into different real-time performance levels. If the real-time performance is low, the degree of real-time abnormality is further evaluated, and it is determined whether to generate an early warning signal based on the historical data reference threshold. If the abnormality is high, the controller parameters are dynamically adjusted for the unstable time period to ensure the stability and efficient response of the motor drive system.

[0106] Embodiment 2, a stability analysis system for a torque motor drive system described in this embodiment includes a delay test module, a response sensitivity evaluation module, a load mutation simulation module, a response rate analysis module, a real-time evaluation and grading module, and an anomaly detection module:

[0107] Delay test module: In the simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded;

[0108] Response sensitivity evaluation module: Based on the acquired response change data, the control accuracy of the controller under different delay conditions is analyzed, the oscillation degree of the controller output signal is determined, and the response sensitivity of the controller when facing different motor drive system delay changes is evaluated;

[0109] Load mutation simulation module: When the controller has high response sensitivity, the speed and torque change rate data of the motor drive system are collected in the simulation environment by simulating the sudden change of the motor load;

[0110] Response rate analysis module: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation range of the response rate of the motor drive system under rapid load changes, and evaluate the response rate stability of the motor drive system when facing load changes;

[0111] Real-time evaluation and grading module: Based on the response sensitivity of the controller to the time delay changes of different motor drive systems and the response rate stability of the motor drive system to load changes, the real-time performance of the controller in responding to the dynamics of the motor drive system is evaluated, and the real-time performance is divided into different real-time performance levels according to the evaluation results, and corresponding processing is performed;

[0112] Abnormality detection module: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

[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] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0115] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0116] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. A stability analysis method for a torque motor drive system, characterized in that: The following steps are involved: S1: In a simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded; S2: Based on the acquired response change data, analyze the control accuracy of the controller under different time delay conditions, determine the oscillation degree of the controller output signal, and evaluate the response sensitivity of the controller when facing different motor drive system delay changes; S3: When the controller response sensitivity is high, in the simulation environment, the speed and torque change rate data of the motor drive system are collected by simulating the sudden change of the motor load; S4: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation amplitude of the response rate of the motor drive system under the condition of rapid load change, and evaluate the response rate stability of the motor drive system when facing load changes; S5: Evaluate the real-time performance of the controller in responding to the dynamics of the motor drive system according to the response sensitivity of the controller to the time delay changes of different motor drive systems and the response rate stability of the motor drive system to the load changes, and classify it into different real-time performance levels according to the evaluation results, and perform corresponding processing; S6: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

2. The stability analysis method for a torque motor drive system according to claim 1, characterized in that: In S2, the controller output signal and the motor response signal are subjected to Fourier transform to obtain the signal spectrum, which is analyzed to generate the signal oscillation abnormality index. The method for obtaining the signal oscillation abnormality index is as follows: Get the time series data y(m) of the controller output signal and the motor response signal, which represents the value of the controller output signal at time m. The sampling period is , perform fast Fourier transform on the time domain signal y(m) to obtain the frequency domain representation Y(f) of the signal. The formula of fast Fourier transform is: ;in: is the frequency domain signal at the frequency point The complex value at , j is the imaginary unit, is the kernel function of Fourier transform, N is the number of sampling points of the signal; calculate the amplitude spectrum of the frequency domain signal, the amplitude spectrum The calculation formula is: ;in, is a frequency domain signal The module is: ;in, and are the real and imaginary parts of the Fourier transform result respectively; Calculate the energy ratio of the signal in the high-frequency area and set a frequency threshold , then the high frequency energy Calculated as: ;in, It is the square of the spectrum amplitude, representing the energy of each frequency component, and calculating the energy proportion of the low-frequency area. Calculated as: ; Combine the energy proportion of high frequency and low frequency to express the oscillation degree of the signal , the expression is: ; Calculate the signal oscillation anomaly index based on the oscillation degree ratio, the expression is: ; In the formula, is the signal oscillation anomaly index, where is a sliding window size.

3. The stability analysis method for a torque motor drive system according to claim 2, characterized in that: In S4, the response rate fluctuation amplitude of the motor drive system under the condition of rapid load change is analyzed to generate a response rate fluctuation index. The response rate fluctuation index is obtained by: Collect the response data of the motor drive system under rapid load change conditions, specifically the change of speed ω(t) and torque T(t) over time, and calculate the rate of change of the signal and torque change rate, the expressions are: ;in, is the motor speed at time t, Indicates the rate of change of speed; ; Where T(t) is the output torque of the motor at time t, is the rate of change of torque; construct the time series X(t), calculate the mean μ of the series, and then calculate its deviation series : ; then calculate the cumulative deviation: ; Where S(N) is the cumulative deviation of the time series to time point N. In each time window, the range is calculated, that is, the difference between the maximum and minimum values ​​of the cumulative sum: ; Calculate the standard deviation σ of the original data X(t): Calculate the R / S ratio based on the calculation results of the range and standard deviation: ; Take the logarithm of the R / S ratio with respect to N and plot a log-log plot: ; Then the straight line in the graph is fitted by the least squares method, and the inverse of the slope H is the response rate fluctuation index, which is expressed as: , ; Where EQ is the response rate fluctuation index.

4. The stability analysis method for a torque motor drive system according to claim 3, characterized in that: In S5, the signal oscillation anomaly index and the response rate fluctuation index are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of the machine learning model. The machine learning model uses each group of comprehensive feature vectors to predict the real-time value label of the controller responding to the dynamics of the motor drive system as the prediction target, and takes minimizing the sum of the prediction errors of the real-time value labels of all controllers responding to the dynamics of the motor drive system as the training target. The machine learning model is trained until the sum of the prediction errors converges and the model training is stopped. The real-time value of the controller responding to the dynamics of the motor drive system is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

5. The stability analysis method for a torque motor drive system according to claim 4, characterized in that: The obtained real-time value of the controller responding to the dynamics of the motor drive system is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the real-time value of the controller responding to the dynamics of the motor drive system is compared with the first standard threshold and the second standard threshold respectively; If the real-time value of the controller responding to the dynamics of the motor drive system is greater than the second standard threshold, it means that the real-time of the controller responding to the dynamics of the motor drive system is high, and a high real-time response signal is generated, and the real-time of the controller responding to the dynamics of the motor drive system is classified as a high real-time response level; If the real-time value of the controller responding to the dynamics of the motor drive system is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the real-time response of the controller to the dynamics of the motor drive system is general, and a medium real-time response signal is generated, and the real-time response of the controller to the dynamics of the motor drive system is classified as a medium real-time response level; If the real-time value of the controller's response to the motor drive system dynamics is less than the first standard threshold, it means that the real-time response of the controller to the motor drive system dynamics is low. At this time, a low real-time response signal is generated, and the real-time response of the controller to the motor drive system dynamics is divided into a low real-time response level.

6. The stability analysis method for a torque motor drive system according to claim 1, characterized in that: In S6, when the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormal degree of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated, specifically: When the real-time performance of the controller responding to the dynamics of the motor drive system is low, that is, the real-time performance value of the controller responding to the dynamics of the motor drive system generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the real-time performance values ​​of the controller responding to the dynamics of the motor drive system generated within a subsequent fixed time period that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a data set is established, and the standard deviation of the data set is calculated, that is, the abnormal degree value of the real-time performance of the controller responding to the dynamics of the motor drive system is calculated; The calculated real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is compared with the real-time abnormality degree value reference threshold preset according to historical data. If the real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is greater than or equal to the real-time abnormality degree value reference threshold, it means that the real-time abnormality degree of the motor drive system dynamics is high. At this time, a warning signal is generated and it is divided into an unstable time period of the motor drive system; if the real-time abnormality degree value of the controller responding to the dynamics of the motor drive system is less than the real-time abnormality degree value reference threshold, it means that the real-time abnormality degree of the motor drive system dynamics is low, and no warning signal is generated.

7. A stability analysis method for a torque motor drive system according to claim 6, characterized in that: In the case of high real-time abnormality, the controller parameters need to be dynamically adjusted, including: increasing the feedback gain of the controller, ,integral and differential In the parameters, to speed up the controller response speed, the formula for adjusting the gain is: , , ; Among them, α, β, and γ are gain adjustment coefficients calculated according to the abnormality degree value, and α>1, β>1, γ>1, which are used to enhance the adjustment ability of the controller.

8. A stability analysis system for a torque motor drive system, used to implement a stability analysis method for a torque motor drive system according to any one of claims 1 to 7, characterized in that: It includes delay test module, response sensitivity assessment module, load mutation simulation module, response rate analysis module, real-time assessment and grading module and anomaly detection module: Delay test module: In the simulation environment, the motor drive system model is tested by gradually increasing and decreasing the delay, and the response change data of the controller output and the motor drive system input are recorded; Response sensitivity evaluation module: Based on the acquired response change data, the control accuracy of the controller under different time delay conditions is analyzed, the oscillation degree of the controller output signal is determined, and the response sensitivity of the controller is evaluated when facing different motor drive system delay changes; Load mutation simulation module: When the controller has high response sensitivity, the speed and torque change rate data of the motor drive system are collected in the simulation environment by simulating the sudden change of the motor load; Response rate analysis module: Analyze the collected data on the speed and torque change rates of the motor drive system, determine the fluctuation range of the response rate of the motor drive system under rapid load changes, and evaluate the response rate stability of the motor drive system when facing load changes; Real-time evaluation and grading module: Based on the response sensitivity of the controller to the time delay changes of different motor drive systems and the response rate stability of the motor drive system to load changes, the real-time performance of the controller in responding to the dynamics of the motor drive system is evaluated, and the real-time performance is divided into different real-time performance levels according to the evaluation results, and corresponding processing is performed; Abnormality detection module: When the real-time performance of the controller in responding to the dynamics of the motor drive system is low, the abnormality of the real-time performance of the controller in responding to the dynamics of the motor drive system within a fixed time period is evaluated. If the abnormality is high, it is divided into an unstable time period of the motor drive system, and the controller parameters within the unstable time period are dynamically adjusted.

Images