A method and system for fault-tolerant control of a few-turn motor based on online parameter identification

Abstract

The application provides a few-turn motor fault-tolerant control method and system based on online parameter identification. The method comprises the following steps: obtaining real-time operation parameters of a motor and preprocessing the real-time operation parameters to obtain effective operation parameters, processing the effective operation parameters by an online parameter identification module to obtain real-time identified motor parameters, then obtaining measurement operation parameters and real-time identified parameters in a preset number and a preset interval period, calculating residual data and comparing the residual data with a preset residual threshold to obtain residual state data, counting the number of continuous residual states, comparing the number of continuous residual states with a preset model state evaluation threshold to obtain a control model state, and updating the control model in the online parameter identification module according to the control model state; thereby, through the acquisition of the effective operation parameters, the determination and counting of the residual state data and the control model state, the few-turn motor fault-tolerant control technology based on online parameter identification is realized.

Description

Technical Field

[0001] This application relates to the field of fault-tolerant control of motors, and more specifically, to a fault-tolerant control method and system for a few-turn motor based on online parameter identification. Background Technology

[0002] In the field of motor control, low-turn motors are widely used in specific scenarios due to their structural characteristics, but their control still faces many technical challenges. Existing low-turn motor control technologies mainly rely on traditional PID control algorithms, which are based on fixed motor parameters and achieve control by adjusting current, voltage, and speed. However, in actual operation, motor parameters change with operating conditions, and traditional PID control cannot adapt to these changes in real time, resulting in a significant decrease in control accuracy.

[0003] In terms of fault-tolerant control, existing methods mostly rely on redundant design or complex fault detection algorithms. Redundant design requires additional hardware, increasing system size and cost; complex fault detection algorithms increase the difficulty of software design, increase system complexity, and are not conducive to engineering applications.

[0004] Meanwhile, due to their inherent structural characteristics, low-turn motors are extremely sensitive to external changes in their parameters; even a slight deviation in the number of turns can lead to significant parameter fluctuations. Traditional control methods struggle to cope with such rapid changes, making them prone to control failures and severely impacting the motor's stable operation and lifespan.

[0005] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0006] The purpose of this application is to provide a fault-tolerant control method and system for a few-turns motor based on online parameter identification. This method can achieve fault-tolerant control technology for a few-turns motor by obtaining effective operating parameters and determining and statistically analyzing residual state data and control model states.

[0007] This application also provides a fault-tolerant control method for a few-turn motor based on online parameter identification, comprising the following steps: The real-time operating parameters of the motor are acquired and preprocessed to obtain effective operating parameters; The online parameter identification module processes the valid operating parameters to obtain real-time identified motor parameters; The system acquires a preset number of preset interval period measurement operation parameters and real-time motor parameters and calculates residual data. The residual data is then compared with a preset residual threshold to obtain residual status data. The number of consecutive residual states is counted, and the number of consecutive residual states is compared with the preset model state evaluation threshold to obtain the control model state; The control model in the online parameter identification module is updated based on the control model status.

[0008] Optionally, in the fault-tolerant control method for a few-turns motor based on online parameter identification described in this application, the step of obtaining the real-time operating parameters of the motor and performing preprocessing to obtain effective operating parameters specifically includes: Acquire the motor's real-time operating parameters, including real-time current data, real-time voltage data, and real-time speed data; The real-time operating parameters are processed by a second-order Butterworth low-pass filter and a moving average algorithm to obtain effective operating parameters, including real-time effective current data, real-time effective voltage data, and real-time effective speed data.

[0009] Optionally, in the fault-tolerant control method for a few-turns motor based on online parameter identification described in this application, the step of processing the effective operating parameters through the online parameter identification module to obtain real-time identified motor parameters specifically includes: The effective operating parameters are identified by the Kalman filter parameter identification model preset in the online parameter identification module to obtain real-time motor parameters, including resistance, inductance, and back electromotive force. Parameter identification includes performing state equation calculations and covariance matrix initialization, which includes the process noise covariance matrix and the measurement noise covariance matrix.

[0010] Optionally, in the fault-tolerant control method for a few-turns motor based on online parameter identification described in this application, the step of obtaining a preset number of preset interval period measurement operating parameters and real-time identification of motor parameters and calculating residual data, and comparing the residual data with a preset residual threshold to obtain residual state data, specifically includes: Real-time residual data is obtained by processing the measured operating parameters and real-time identified motor parameters through a residual calculation model. The real-time residual data is compared with the preset residual threshold to obtain the residual status data; The first threshold and the second threshold are extracted based on the preset residual threshold, and the first threshold is greater than the second threshold; If the real-time residual data is greater than the first threshold or less than the second threshold, the residual status data is abnormal status data. If the real-time residual data is greater than or equal to the second threshold and less than or equal to the second threshold, then the residual status data is normal status data.

[0011] Optionally, in the fault-tolerant control method for a few-turns motor based on online parameter identification described in this application, the step of counting the number of consecutive residual states and comparing the number of consecutive residual states with a preset model state evaluation threshold to obtain the control model state specifically includes: The number of consecutive abnormal states in the residual state data is counted and marked as the number of consecutive abnormal states; The control model state, including whether the model is effective or ineffective, is obtained by comparing the number of consecutive abnormal occurrences with a preset model state evaluation threshold. If the number of consecutive abnormal occurrences is greater than or equal to the preset model state evaluation threshold, then the control model state is model failure. If the number of consecutive abnormal occurrences is less than the preset model state evaluation threshold, then the control model state is considered to be effective.

[0012] Optionally, in the fault-tolerant control method for a few-turns motor based on online parameter identification described in this application, updating the control model in the online parameter identification module according to the control model state specifically includes: Obtain the control model state; if the model is valid, the control model remains unchanged. If the model fails, the weights of the process noise covariance matrix and the measurement noise covariance matrix are adjusted. The process noise covariance matrix is ​​multiplied by a preset increase factor, and the measurement noise covariance matrix is ​​multiplied by a preset decrease factor.

[0013] Secondly, this application provides a fault-tolerant control system for a low-turn motor based on online parameter identification. The system includes a memory and a processor. The memory includes a program for garbage data recycling and solid-state storage optimization. When executed by the processor, the garbage data recycling and solid-state storage optimization program performs the following steps: The real-time operating parameters of the motor are acquired and preprocessed to obtain effective operating parameters; The online parameter identification module processes the valid operating parameters to obtain real-time identified motor parameters; The system acquires a preset number of preset interval period measurement operation parameters and real-time motor parameters and calculates residual data. The residual data is then compared with a preset residual threshold to obtain residual status data. The number of consecutive residual states is counted, and the number of consecutive residual states is compared with the preset model state evaluation threshold to obtain the control model state; The control model in the online parameter identification module is updated based on the control model status.

[0014] Optionally, in the fault-tolerant control system for a low-turn motor based on online parameter identification described in this application, the step of acquiring the real-time operating parameters of the motor and performing preprocessing to obtain effective operating parameters specifically includes: Acquire the motor's real-time operating parameters, including real-time current data, real-time voltage data, and real-time speed data; The real-time operating parameters are processed by a second-order Butterworth low-pass filter and a moving average algorithm to obtain effective operating parameters, including real-time effective current data, real-time effective voltage data, and real-time effective speed data.

[0015] Optionally, in the fault-tolerant control system for a low-turn motor based on online parameter identification described in this application, the step of processing the effective operating parameters through the online parameter identification module to obtain real-time identified motor parameters specifically includes: The effective operating parameters are identified by the Kalman filter parameter identification model preset in the online parameter identification module to obtain real-time motor parameters, including resistance, inductance, and back electromotive force. Parameter identification includes performing state equation calculations and covariance matrix initialization, which includes the process noise covariance matrix and the measurement noise covariance matrix.

[0016] As described above, this application provides a fault-tolerant control method and system for low-turn motors based on online parameter identification. This method obtains and preprocesses real-time motor operating parameters to obtain effective operating parameters. The effective parameters are then processed by an online parameter identification module to obtain real-time identified motor parameters. A preset number of measured operating parameters at preset intervals are acquired along with the real-time identified parameters. Residual data is calculated and compared with a preset residual threshold to obtain residual state data. The number of consecutive residual states is counted and compared with a preset model state evaluation threshold to obtain the control model state. The control model in the online parameter identification module is then updated based on the control model state. Thus, by obtaining effective operating parameters and determining and statistically analyzing residual state data and control model states, fault-tolerant control technology for low-turn motors based on online parameter identification is achieved.

[0017] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a fault-tolerant control method for a few-turns motor based on online parameter identification provided in this application embodiment; Figure 2A flowchart illustrating the acquisition of effective operating parameters for a fault-tolerant control method for a few-turns motor based on online parameter identification, provided in an embodiment of this application. Figure 3 The flowchart illustrates the process of obtaining real-time identified motor parameters in a fault-tolerant control method for a few-turns motor based on online parameter identification, as provided in this application embodiment. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Please refer to Figure 1 , Figure 1 This is a flowchart of a fault-tolerant control method for a few-turns motor based on online parameter identification, as described in some embodiments of this application. This fault-tolerant control method for a few-turns motor based on online parameter identification is used in terminal devices, such as computers and mobile phones. The method includes the following steps: S11. Obtain the real-time operating parameters of the motor and perform preprocessing to obtain valid operating parameters; S12. The effective operating parameters are processed by the online parameter identification module to obtain real-time identified motor parameters; S13. Obtain a preset number of preset interval period measurement operation parameters and real-time motor parameters and calculate residual data, compare the residual data with a preset residual threshold to obtain residual status data; S14. Count the number of consecutive residual states and compare the number of consecutive residual states with the preset model state evaluation threshold to obtain the control model state. S15. Update the control model in the online parameter identification module according to the control model status.

[0023] It should be noted that the real-time operating parameters of the motor are subject to certain instability, i.e., interference factors. In order to better analyze the effective data, the real-time operating parameters need to be processed to obtain effective operating parameters. The effective operating parameters are processed by the online parameter identification module to obtain the real-time identified motor parameters. There will be a certain degree of difference between the real-time identified motor parameters and the actual operating parameters. A preset number of measured operating parameters and real-time identified motor parameters are obtained according to a preset interval period, and residual data is calculated. The residual data can, to some extent, reflect the accuracy of the control model during the identification process. If the residual data exceeds a certain level, it indicates that the error is too large, and the residual state is an abnormal state. The number of consecutive abnormal states is counted. If the number of consecutive abnormal states exceeds a certain number, it indicates that the control model has failed and cannot accurately obtain the real-time identified motor parameters. The control model needs to be updated according to the control model state in order to correctly identify the motor. In this embodiment, the preset interval period can be set to 10ms.

[0024] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the process of obtaining effective operating parameters for a fault-tolerant control method for a low-turn motor based on online parameter identification, as provided in this application embodiment. According to this embodiment, obtaining the real-time operating parameters of the motor and performing preprocessing to obtain effective operating parameters specifically includes: S21. Obtain the real-time operating parameters of the motor, including real-time current data, real-time voltage data, and real-time speed data; S22. The real-time operating parameters are processed by second-order Butterworth low-pass filtering and moving average algorithm to obtain effective operating parameters, including real-time effective current data, real-time effective voltage data and real-time effective speed data.

[0025] It should be noted that the real-time current data includes d / q-axis current. The d / q axes are two orthogonal axes in a rotating coordinate system defined by coordinate transformations (such as Park transform and Clarke transform). They are mainly used to simplify the mathematical model and control logic of AC motors. The d-axis is defined as the axis along the direction of the magnetic flux linkage (or the direction of the excitation magnetic field) of the permanent magnets of the motor rotor, and the q-axis is defined as the axis perpendicular to the d-axis (90° electrical angle away from the d-axis), orthogonal to the direction of the rotor magnetic flux linkage, and mainly affects the output torque of the motor. Based on the real-time speed data, electrical angular velocity and moment of inertia data can be obtained. A second-order Butterworth low-pass filter (cutoff frequency 5kHz) is applied to the real-time operating parameters to suppress high-frequency noise; a moving average algorithm (window length 16 points) is used to eliminate random interference and ensure the stability of parameter identification.

[0026] Please refer to Figure 3 , Figure 3This is a flowchart illustrating the process of obtaining real-time identified motor parameters in a fault-tolerant control method for a few-turns motor based on online parameter identification, as provided in an embodiment of this application. According to this embodiment, the step of processing effective operating parameters through an online parameter identification module to obtain real-time identified motor parameters specifically includes: S31. The effective operating parameters are identified by the Kalman filter parameter identification model preset in the online parameter identification module to obtain real-time motor parameters, including resistance, inductance and back electromotive force. S32. Parameter identification includes performing state equation calculation and covariance matrix initialization. The covariance matrix includes the process noise covariance matrix and the measurement noise covariance matrix.

[0027] It should be noted that, according to the motor's manufacturer's instructions, the permanent magnet flux linkage, d / q axis inductance, stator resistance, viscosity coefficient, and number of pole pairs can be obtained. These parameters, combined with the effective operating parameters, can be used for parameter identification through Kalman filtering. After identification, the covariance matrix is ​​initialized, including the process noise covariance matrix and the measurement noise covariance matrix.

[0028] According to an embodiment of the present invention, the step of obtaining a preset number of preset interval period measurement operating parameters and real-time identification motor parameters and calculating residual data, comparing the residual data with a preset residual threshold to obtain residual state data, specifically includes: Real-time residual data is obtained by processing the measured operating parameters and real-time identified motor parameters through a residual calculation model. The real-time residual data is compared with the preset residual threshold to obtain the residual status data; The first threshold and the second threshold are extracted based on the preset residual threshold, and the first threshold is greater than the second threshold; If the real-time residual data is greater than the first threshold or less than the second threshold, the residual status data is abnormal status data. If the real-time residual data is greater than or equal to the second threshold and less than or equal to the second threshold, then the residual status data is normal status data.

[0029] It should be noted that real-time residual data reflects the deviation between the measured operating parameters and the real-time identified motor parameters. Under normal motor operation (no faults, stable parameters), a large amount of residual data is collected experimentally, and its distribution characteristics (such as standard deviation σ) are statistically analyzed. The threshold is usually set to 3 times the standard deviation (3σ)—this is based on the statistical characteristics of the normal distribution; residuals exceeding this range can be considered "abnormal fluctuations." The calculation method for real-time residual data is as follows: ; in, For real-time residual data, To measure operating parameters, Measurement matrix (preset according to motor model) To identify motor parameters in real time; in this embodiment, the first threshold is 3σ and the second threshold is -3σ.

[0030] According to an embodiment of the present invention, the step of counting the number of consecutive residual states and comparing the number of consecutive residual states with a preset model state evaluation threshold to obtain the control model state specifically includes: The number of consecutive abnormal states in the residual state data is counted and marked as the number of consecutive abnormal states; The control model state, including whether the model is effective or ineffective, is obtained by comparing the number of consecutive abnormal occurrences with a preset model state evaluation threshold. If the number of consecutive abnormal occurrences is greater than or equal to the preset model state evaluation threshold, then the control model state is model failure. If the number of consecutive abnormal occurrences is less than the preset model state evaluation threshold, then the control model state is considered to be effective.

[0031] It should be noted that the control model refers to the motor control model. Occasional anomalies do not necessarily indicate the failure of the control model. Therefore, it is necessary to continuously count the number of anomalies to confirm this. In this embodiment, the preset model state evaluation threshold is set to 5 times.

[0032] According to an embodiment of the present invention, updating the control model in the online parameter identification module based on the control model state specifically includes: Obtain the control model state; if the model is valid, the control model remains unchanged. If the model fails, the weights of the process noise covariance matrix and the measurement noise covariance matrix are adjusted. The process noise covariance matrix is ​​multiplied by a preset increase factor, and the measurement noise covariance matrix is ​​multiplied by a preset decrease factor.

[0033] It should be noted that in this embodiment, if the model fails, the process noise covariance matrix is ​​multiplied by a preset amplification factor of 1.2, which increases the process noise covariance matrix Q and enhances the filtering's ability to track dynamic changes in the system; the measurement noise covariance matrix is ​​multiplied by a preset reduction factor of 0.8, which decreases the measurement noise covariance matrix R, increases the confidence in the measurement data, and accelerates convergence to the true value.

[0034] It is worth mentioning that it also includes: Acquire real-time detection current data and rated current data; The real-time monitored current data is compared with the rated current data; If the real-time monitored current data is greater than or equal to N times the rated current data, and the statistical duration is greater than M milliseconds, then it is determined to be a short circuit fault. If it is a short circuit fault, the corresponding current limiting strategy will be activated, that is, the PWM duty cycle will be reduced to the preset ratio; If the real-time monitored current data is less than p times the rated current data and the statistical duration is greater than q milliseconds, it is determined to be an open circuit fault. If the fault is an open circuit, switch to the backup drive circuit to ensure the motor continues to run.

[0035] It should be noted that when the real-time monitored current data is greater than or equal to N times the rated current data, it indicates that the current is too large. In addition, the duration exceeds the threshold, which can be judged as a short circuit. In this embodiment, N is 150%, M is 5, p is 10%, and q is 10. PWM is pulse width modulation. When a bit short circuit fault is detected, the PWM duty cycle drops to 20%.

[0036] It is worth mentioning that it also includes: A PWM signal with a carrier frequency of 20KHz is used, and the dead time is set to a preset fixed duration; After increasing the DC bus utilization rate to R times by the third harmonic injection method, the feedforward compensation parameter data are obtained, including the rate of change of d / q axis current, stator winding resistance data, d / q axis inductance data, and d / q axis current data. The d / q axis compensation voltage is obtained by processing the feedforward compensation parameter data through a preset feedforward compensation model. The formula for calculating the d / q axis compensation voltage in the feedforward compensation model is as follows: ; in, For d / q axis compensation voltage, For stator winding resistance data, For d / q axis current data, For d / q axis inductance data, Let be the rate of change of the d / q-axis current.

[0037] It should be noted that in motor PWM drive control, dead time refers to a time interval in the same bridge arm of the power inverter during which "neither the upper nor lower transistors are conducting" to prevent the two power switching transistors from conducting simultaneously due to switching delay and causing a short circuit. In this embodiment, the R value is 1.15, and the preset fixed value of the dead time is set to 2µs.

[0038] This invention also discloses a fault-tolerant control system for a few-turns motor based on online parameter identification, comprising a memory and a processor. The memory stores a program for a fault-tolerant control method for a few-turns motor based on online parameter identification. When the processor executes the program for the fault-tolerant control method for a few-turns motor based on online parameter identification, it performs the following steps: The real-time operating parameters of the motor are acquired and preprocessed to obtain effective operating parameters; The online parameter identification module processes the valid operating parameters to obtain real-time identified motor parameters; The system acquires a preset number of preset interval period measurement operation parameters and real-time motor parameters and calculates residual data. The residual data is then compared with a preset residual threshold to obtain residual status data. The number of consecutive residual states is counted, and the number of consecutive residual states is compared with the preset model state evaluation threshold to obtain the control model state; The control model in the online parameter identification module is updated based on the control model status.

[0039] It should be noted that the real-time operating parameters of the motor are subject to certain instability, i.e., interference factors. In order to better analyze the effective data, the real-time operating parameters need to be processed to obtain effective operating parameters. The effective operating parameters are processed by the online parameter identification module to obtain the real-time identified motor parameters. There will be a certain degree of difference between the real-time identified motor parameters and the actual operating parameters. A preset number of measured operating parameters and real-time identified motor parameters are obtained according to a preset interval period, and residual data is calculated. The residual data can, to some extent, reflect the accuracy of the control model during the identification process. If the residual data exceeds a certain level, it indicates that the error is too large, and the residual state is an abnormal state. The number of consecutive abnormal states is counted. If the number of consecutive abnormal states exceeds a certain number, it indicates that the control model has failed and cannot accurately obtain the real-time identified motor parameters. The control model needs to be updated according to the control model state in order to correctly identify the motor. In this embodiment, the preset interval period can be set to 10ms.

[0040] According to an embodiment of the present invention, the step of acquiring the real-time operating parameters of the motor and performing preprocessing to obtain effective operating parameters specifically includes: Acquire the motor's real-time operating parameters, including real-time current data, real-time voltage data, and real-time speed data; The real-time operating parameters are processed by a second-order Butterworth low-pass filter and a moving average algorithm to obtain effective operating parameters, including real-time effective current data, real-time effective voltage data, and real-time effective speed data.

[0041] It should be noted that the real-time current data includes d / q-axis current. The d / q axes are two orthogonal axes in a rotating coordinate system defined by coordinate transformations (such as Park transform and Clarke transform). They are mainly used to simplify the mathematical model and control logic of AC motors. The d-axis is defined as the axis along the direction of the magnetic flux linkage (or the direction of the excitation magnetic field) of the permanent magnets of the motor rotor, and the q-axis is defined as the axis perpendicular to the d-axis (90° electrical angle away from the d-axis), orthogonal to the direction of the rotor magnetic flux linkage, and mainly affects the output torque of the motor. Based on the real-time speed data, electrical angular velocity and moment of inertia data can be obtained. A second-order Butterworth low-pass filter (cutoff frequency 5kHz) is applied to the real-time operating parameters to suppress high-frequency noise; a moving average algorithm (window length 16 points) is used to eliminate random interference and ensure the stability of parameter identification.

[0042] According to an embodiment of the present invention, the step of processing valid operating parameters through an online parameter identification module to obtain real-time identified motor parameters specifically includes: The effective operating parameters are identified by the Kalman filter parameter identification model preset in the online parameter identification module to obtain real-time motor parameters, including resistance, inductance, and back electromotive force. Parameter identification includes performing state equation calculations and covariance matrix initialization, which includes the process noise covariance matrix and the measurement noise covariance matrix.

[0043] It should be noted that, according to the motor's manufacturer's instructions, the permanent magnet flux linkage, d / q axis inductance, stator resistance, viscosity coefficient, and number of pole pairs can be obtained. These parameters, combined with the effective operating parameters, can be used for parameter identification through Kalman filtering. After identification, the covariance matrix is ​​initialized, including the process noise covariance matrix and the measurement noise covariance matrix.

[0044] According to an embodiment of the present invention, the step of obtaining a preset number of preset interval period measurement operating parameters and real-time identification motor parameters and calculating residual data, comparing the residual data with a preset residual threshold to obtain residual state data, specifically includes: Real-time residual data is obtained by processing the measured operating parameters and real-time identified motor parameters through a residual calculation model. The real-time residual data is compared with the preset residual threshold to obtain the residual status data; The first threshold and the second threshold are extracted based on the preset residual threshold, and the first threshold is greater than the second threshold; If the real-time residual data is greater than the first threshold or less than the second threshold, the residual status data is abnormal status data. If the real-time residual data is greater than or equal to the second threshold and less than or equal to the second threshold, then the residual status data is normal status data.

[0045] It should be noted that real-time residual data reflects the deviation between the measured operating parameters and the real-time identified motor parameters. Under normal motor operation (no faults, stable parameters), a large amount of residual data is collected experimentally, and its distribution characteristics (such as standard deviation σ) are statistically analyzed. The threshold is usually set to 3 times the standard deviation (3σ)—this is based on the statistical characteristics of the normal distribution; residuals exceeding this range can be considered "abnormal fluctuations." The calculation method for real-time residual data is as follows: ; in, For real-time residual data, To measure operating parameters, Measurement matrix (preset according to motor model) To identify motor parameters in real time; in this embodiment, the first threshold is 3σ and the second threshold is -3σ.

[0046] According to an embodiment of the present invention, the step of counting the number of consecutive residual states and comparing the number of consecutive residual states with a preset model state evaluation threshold to obtain the control model state specifically includes: The number of consecutive abnormal states in the residual state data is counted and marked as the number of consecutive abnormal states; The control model state, including whether the model is effective or ineffective, is obtained by comparing the number of consecutive abnormal occurrences with a preset model state evaluation threshold. If the number of consecutive abnormal occurrences is greater than or equal to the preset model state evaluation threshold, then the control model state is model failure. If the number of consecutive abnormal occurrences is less than the preset model state evaluation threshold, then the control model state is considered to be effective.

[0047] It should be noted that the control model refers to the motor control model. Occasional anomalies do not necessarily indicate the failure of the control model. Therefore, it is necessary to continuously count the number of anomalies to confirm this. In this embodiment, the preset model state evaluation threshold is set to 5 times.

[0048] According to an embodiment of the present invention, updating the control model in the online parameter identification module based on the control model state specifically includes: Obtain the control model state; if the model is valid, the control model remains unchanged. If the model fails, the weights of the process noise covariance matrix and the measurement noise covariance matrix are adjusted. The process noise covariance matrix is ​​multiplied by a preset increase factor, and the measurement noise covariance matrix is ​​multiplied by a preset decrease factor.

[0049] It should be noted that in this embodiment, if the model fails, the process noise covariance matrix is ​​multiplied by a preset amplification factor of 1.2, which increases the process noise covariance matrix Q and enhances the filtering's ability to track dynamic changes in the system; the measurement noise covariance matrix is ​​multiplied by a preset reduction factor of 0.8, which decreases the measurement noise covariance matrix R, increases the confidence in the measurement data, and accelerates convergence to the true value.

[0050] It is worth mentioning that it also includes: Acquire real-time detection current data and rated current data; The real-time monitored current data is compared with the rated current data; If the real-time monitored current data is greater than or equal to N times the rated current data, and the statistical duration is greater than M milliseconds, then it is determined to be a short circuit fault. If it is a short circuit fault, the corresponding current limiting strategy will be activated, that is, the PWM duty cycle will be reduced to the preset ratio; If the real-time monitored current data is less than p times the rated current data and the statistical duration is greater than q milliseconds, it is determined to be an open circuit fault. If the fault is an open circuit, switch to the backup drive circuit to ensure the motor continues to run.

[0051] It should be noted that when the real-time monitored current data is greater than or equal to N times the rated current data, it indicates that the current is too large. In addition, the duration exceeds the threshold, which can be judged as a short circuit. In this embodiment, N is 150%, M is 5, p is 10%, and q is 10. PWM is pulse width modulation. When a bit short circuit fault is detected, the PWM duty cycle drops to 20%.

[0052] It is worth mentioning that it also includes: A PWM signal with a carrier frequency of 20KHz is used, and the dead time is set to a preset fixed duration; After increasing the DC bus utilization rate to R times by the third harmonic injection method, the feedforward compensation parameter data are obtained, including the rate of change of d / q axis current, stator winding resistance data, d / q axis inductance data, and d / q axis current data. The d / q axis compensation voltage is obtained by processing the feedforward compensation parameter data through a preset feedforward compensation model. The formula for calculating the d / q axis compensation voltage in the feedforward compensation model is as follows: ; in, For d / q axis compensation voltage, For stator winding resistance data, For d / q axis current data, For d / q axis inductance data, Let be the rate of change of the d / q-axis current.

[0053] It should be noted that in motor PWM drive control, dead time refers to a time interval in the same bridge arm of the power inverter during which "neither the upper nor lower transistors are conducting" to prevent the two power switching transistors from conducting simultaneously due to switching delay and causing a short circuit. In this embodiment, the R value is 1.15, and the preset fixed value of the dead time is set to 2µs.

[0054] This invention discloses a fault-tolerant control method and system for a few-turns motor based on online parameter identification. The method involves acquiring and preprocessing real-time operating parameters of the motor to obtain effective operating parameters. An online parameter identification module processes these effective parameters to obtain real-time identified motor parameters. Then, a preset number of measured operating parameters at preset intervals are acquired along with the real-time identified parameters. Residual data is calculated and compared with a preset residual threshold to obtain residual state data. The number of consecutive residual states is counted and compared with a preset model state evaluation threshold to obtain the control model state. The control model in the online parameter identification module is updated based on the control model state. Thus, by acquiring effective operating parameters and determining and statistically analyzing residual state data and control model states, a fault-tolerant control technology for a few-turns motor based on online parameter identification is achieved.

[0055] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0056] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0057] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0058] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A fault-tolerant control method for a low-turn motor based on online parameter identification, characterized in that, include: The real-time operating parameters of the motor are acquired and preprocessed to obtain effective operating parameters; The online parameter identification module processes the valid operating parameters to obtain real-time identified motor parameters; The system acquires a preset number of preset interval period measurement operation parameters and real-time motor parameters and calculates residual data. The residual data is then compared with a preset residual threshold to obtain residual status data. The control model state is obtained by counting the number of consecutive residual states and comparing them with a preset model state evaluation threshold. Specifically, this includes: counting the number of consecutive abnormal state data in the residual state data and marking them as the number of consecutive abnormal states; comparing the number of consecutive abnormal states with a preset model state evaluation threshold to obtain the control model state, including model effectiveness or model failure; if the number of consecutive abnormal states is greater than or equal to the preset model state evaluation threshold, the control model state is model failure; if the number of consecutive abnormal states is less than the preset model state evaluation threshold, the control model state is model effectiveness. The control model in the online parameter identification module is updated according to the control model status. Specifically, the control model status is obtained. If the model is valid, the control model remains unchanged. If the model is invalid, the weights of the process noise covariance matrix and the measurement noise covariance matrix are adjusted. The process noise covariance matrix is ​​multiplied by a preset increase factor, and the measurement noise covariance matrix is ​​multiplied by a preset decrease factor.

2. The fault-tolerant control method for a few-turns motor based on online parameter identification according to claim 1, characterized in that, The process of acquiring the real-time operating parameters of the motor and preprocessing them to obtain valid operating parameters specifically includes: Acquire the motor's real-time operating parameters, including real-time current data, real-time voltage data, and real-time speed data; The real-time operating parameters are processed by a second-order Butterworth low-pass filter and a moving average algorithm to obtain effective operating parameters, including real-time effective current data, real-time effective voltage data, and real-time effective speed data.

3. The fault-tolerant control method for a few-turns motor based on online parameter identification according to claim 2, characterized in that, The process of obtaining real-time identified motor parameters by processing valid operating parameters through an online parameter identification module specifically includes: The effective operating parameters are identified by the Kalman filter parameter identification model preset in the online parameter identification module to obtain real-time motor parameters, including resistance, inductance, and back electromotive force. Parameter identification includes performing state equation calculations and covariance matrix initialization, which includes the process noise covariance matrix and the measurement noise covariance matrix.

4. The fault-tolerant control method for a few-turns motor based on online parameter identification according to claim 3, characterized in that, The process of acquiring a preset number of preset interval period measurement operating parameters and real-time identification motor parameters, calculating residual data, and comparing the residual data with a preset residual threshold to obtain residual status data specifically includes: Real-time residual data is obtained by processing the measured operating parameters and real-time identified motor parameters through a residual calculation model. The real-time residual data is compared with the preset residual threshold to obtain the residual status data; The first threshold and the second threshold are extracted based on the preset residual threshold, and the first threshold is greater than the second threshold; If the real-time residual data is greater than the first threshold or less than the second threshold, the residual status data is abnormal status data. If the real-time residual data is greater than or equal to the second threshold and less than or equal to the second threshold, then the residual status data is normal status data.

5. A fault-tolerant control system for a low-turn motor based on online parameter identification, characterized in that, The system includes a memory and a processor. The memory includes a garbage data recycling and solid-state storage optimization method program. When the garbage data recycling and solid-state storage optimization method program is executed by the processor, it performs the following steps: The real-time operating parameters of the motor are acquired and preprocessed to obtain effective operating parameters; The online parameter identification module processes the valid operating parameters to obtain real-time identified motor parameters; The system acquires a preset number of preset interval period measurement operation parameters and real-time motor parameters and calculates residual data. The residual data is then compared with a preset residual threshold to obtain residual status data. The control model state is obtained by counting the number of consecutive residual states and comparing them with a preset model state evaluation threshold. Specifically, this includes: counting the number of consecutive abnormal state data in the residual state data and marking them as the number of consecutive abnormal states; comparing the number of consecutive abnormal states with a preset model state evaluation threshold to obtain the control model state, including model effectiveness or model failure; if the number of consecutive abnormal states is greater than or equal to the preset model state evaluation threshold, the control model state is model failure; if the number of consecutive abnormal states is less than the preset model state evaluation threshold, the control model state is model effectiveness. The control model in the online parameter identification module is updated according to the control model status. Specifically, the control model status is obtained. If the model is valid, the control model remains unchanged. If the model is invalid, the weights of the process noise covariance matrix and the measurement noise covariance matrix are adjusted. The process noise covariance matrix is ​​multiplied by a preset increase factor, and the measurement noise covariance matrix is ​​multiplied by a preset decrease factor.

6. The fault-tolerant control system for a few-turns motor based on online parameter identification according to claim 5, characterized in that, The process of acquiring the real-time operating parameters of the motor and preprocessing them to obtain valid operating parameters specifically includes: Acquire the motor's real-time operating parameters, including real-time current data, real-time voltage data, and real-time speed data; The real-time operating parameters are processed by a second-order Butterworth low-pass filter and a moving average algorithm to obtain effective operating parameters, including real-time effective current data, real-time effective voltage data, and real-time effective speed data.

7. The fault-tolerant control system for a few-turn motor based on online parameter identification according to claim 6, characterized in that, The process of obtaining real-time identified motor parameters by processing valid operating parameters through an online parameter identification module specifically includes: The effective operating parameters are identified by the Kalman filter parameter identification model preset in the online parameter identification module to obtain real-time motor parameters, including resistance, inductance, and back electromotive force. Parameter identification includes performing state equation calculations and covariance matrix initialization, which includes the process noise covariance matrix and the measurement noise covariance matrix.

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