Motor observer parameter adaptive tuning method and related equipment

Abstract

The invention provides a motor observer parameter adaptive tuning method and related equipment, and relates to the technical field of electric digital data processing. The method comprises the following steps: firstly, constructing a PI parameter mathematical model to provide a basis for subsequent calculation; core parameters such as motor inductance, real-time current, stator voltage and target rotating speed are obtained, and it is ensured that the calculation basis fits the actual state of the motor; then, on the basis of the core parameters, reactive power of the motor is calculated through a PI parameter mathematical model, and the rotating speed and a voltage correction instruction are estimated; adjusting the running state of the motor according to the voltage correction instruction; and the adjustment step is repeated until the absolute value of the reactive power is smaller than a preset reactive power deviation threshold value and the deviation between the estimated rotating speed and the target rotating speed is smaller than a preset rotating speed deviation threshold value, and self-adaptive adjustment and optimization of the motor observer parameters are completed. According to the method, the problem that traditional curing parameters cannot adapt to dynamic changes of the motor is avoided, and the matching degree of the observer parameters and the actual running state of the motor is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of electrical digital data processing technology, and in particular to a method and related equipment for adaptive tuning of motor observer parameters. Background Technology

[0002] In fields such as industrial drives, smart homes, and new energy equipment, multi-motor collaborative operation scenarios (such as multi-station drive of production lines and multi-joint control of robots) are becoming increasingly common. To reduce costs and simplify structures, motor control schemes without physical sensors (without encoders or Hall elements) have become the mainstream choice. This involves estimating state variables such as rotor position and speed through observers to replace traditional sensors and achieve closed-loop control.

[0003] Currently, in sensorless motor control, the optimization of observer parameters mainly adopts a scheme of single motor calibration plus parameter solidification. Specifically, for a specific motor, a set of optimal PI parameters is determined through offline testing and written into the controller, which is then directly called during runtime. Some improvement schemes will make limited parameter corrections based on a single operating condition (such as a fixed load), but the correction logic is based on preset empirical formulas and does not relate to core parameters such as real-time motor current and voltage.

[0004] However, existing solutions have significant drawbacks in multi-motor scenarios without physical sensors. The observer relies on motor parameters to estimate state variables, and the parameter differences between different motors can cause the pre-stored parameters to fail. Especially when multiple motors are used together, the parameters of each motor need to be calibrated offline separately. This is not only cumbersome, but also cannot cope with dynamic changes such as motor aging and parameter drift, making it difficult to meet the convenience and adaptability requirements of multi-motor mixed scenarios. Summary of the Invention

[0005] This application provides a method and related equipment for adaptive tuning of motor observer parameters, which enables adaptive adjustment of motor observer parameters based on real-time core parameters of the motor and operating conditions, and can meet the needs of multi-motor collaborative operation without offline calibration.

[0006] In a first aspect, this application provides an adaptive tuning method for motor observer parameters, applied to a motor observer tuning system. The method includes: constructing a PI parameter mathematical model; obtaining core motor parameters, including motor inductance, real-time current, stator voltage, and target speed; calculating the motor's reactive power, estimated speed, and voltage correction command based on the core motor parameters and the PI parameter mathematical model; adjusting the motor according to the voltage correction command; repeating the above adjustment steps until the absolute value of the reactive power is less than a preset reactive power deviation threshold, and the deviation between the estimated speed and the target speed is less than a preset speed deviation threshold.

[0007] By adopting the above technical solution, a PI parameter mathematical model is first constructed to provide a computational foundation for optimization. Then, core parameters such as motor inductance and real-time current are obtained to ensure that the calculations are closely aligned with the actual state of the motor. Based on the model and core parameters, reactive power, estimated speed, and voltage correction commands are calculated, which accurately reflect motor operating deviations. After adjusting the motor according to the commands, the operation is repeated until the deviation meets the threshold. The entire process revolves around dynamic adjustment of real-time parameters, avoiding the problem that traditional fixed parameters cannot adapt to the dynamic changes of the motor. This significantly improves the matching degree between the observer parameters and the actual operating state of the motor, ensuring stable motor operation.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of constructing the PI parameter mathematical model includes: taking the reactive power balance of the motor as the core, and within a preset range of the optimal efficiency point, the reactive power... The optimal efficiency point, after linearization, is defined as when the reactive power equals zero. The linearization formula is as follows: ;in This refers to the actual rotational speed. To estimate the rotational speed, This is the sensitivity coefficient. The calculation formula is: ;in and This represents the stator current component of the motor in a two-phase stationary coordinate system. and This represents the current component of the motor stator current in the rotating coordinate system. Let the inductance of the motor be denoted as ; assuming the initial speed of the motor is 0, the closed-loop dynamic equation of the phase-locked loop is obtained: ;in, This is the proportionality coefficient in the mathematical model of the PI parameter. These are the integral coefficients in the mathematical model of the PI parameter. For the Laplace operator, Indicates the reactive power The integral operation is used to eliminate steady-state error; the linearized formula is substituted into the closed-loop dynamic equation to obtain the closed-loop transfer function.

[0009] By adopting the above technical solution, with reactive power balance as the core, reactive power is linearized near the optimal efficiency point (reactive power is 0), simplifying calculations while closely aligning with the high-efficiency operation scenario of the motor. The sensitivity coefficient, combined with current components and inductance, accurately correlates the motor's electrical characteristics. Determining the initial speed as 0 yields the closed-loop dynamic equation; PI parameters and integral operations are introduced to eliminate steady-state errors, and then substituted into the linearization formula to obtain the closed-loop transfer function. Each step is interconnected, making the PI parameter mathematical model more closely aligned with the motor control logic, providing reliable model support for subsequent accurate calculations and optimization, and improving the accuracy of parameter calculations.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of substituting the linearization formula into the closed-loop dynamic equation to obtain the closed-loop transfer function includes: substituting the linearization formula into the closed-loop dynamic equation to obtain the following closed-loop transfer function: After simplification, the closed-loop transfer function is: .

[0011] By adopting the above technical solution, the linearization formula is substituted into the closed-loop dynamic equation to obtain a preliminary transfer function, which is then rearranged into a standard form. This process more clearly integrates the relationship between reactive power and speed deviation, as well as the role of PI parameters, into the transfer function, making the mathematical logic of motor speed estimation clearer. The standard closed-loop transfer function facilitates subsequent comparison and equation-solving with the ideal transfer function, laying the foundation for deriving the PI parameter calculation formula. Simultaneously, it makes the entire parameter calculation process more logical and operable, improving the efficiency of parameter solving.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after substituting the linearization formula into the closed-loop dynamic equation to obtain the closed-loop transfer function, the method further includes: obtaining the ideal transfer function based on preset performance requirements for the motor. ,in This is the noise immunity coefficient of the motor. Let be the desired closed-loop time constant; combining the closed-loop transfer function and the ideal transfer function, we obtain the equilibrium equation: Let the numerators and denominators on both sides be... If the linear term and the constant term are equal, then the formula for calculating the PI parameter is obtained: ; .

[0013] By adopting the above technical solution, the ideal transfer function, including the noise immunity factor and the desired closed-loop time constant, is first determined based on the motor performance requirements, thus clarifying the optimization objective. Then, the previously obtained closed-loop transfer function is combined with the equation, and the PI parameter calculation formula is derived by ensuring the equality of corresponding terms on both sides. This process directly transforms the motor performance requirements into specific PI parameters, making the parameter settings more closely aligned with actual application scenarios. This ensures both the motor's noise immunity and the closed-loop response speed requirements, improving the targeting and effectiveness of parameter optimization.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the reactive power of the motor, estimating the speed, and voltage correction commands based on the core parameters of the motor using the PI parameter mathematical model includes: obtaining the core parameters of the motor and its operating conditions, the operating conditions including at least the noise immunity factor and the motor load; and obtaining the noise immunity factor of the motor by using a parameter determination model based on the core parameters of the motor and the operating conditions. and the expected closed-loop time constant The transfer parameter determination model is pre-built using deep learning based on multiple sets of motor core parameters and operating condition samples labeled with noise immunity coefficients and expected closed-loop time constants. The motor's PI parameters are then calculated using this PI parameter calculation formula. and ; Calculate the electric angular velocity of the motor The specific formula is as follows: in For the target speed, This refers to the number of pole pairs of the motor; the reactive power of the motor is calculated in real time. The specific formula is as follows: ;in , For the stator voltage , Axial components, Given the motor inductance; based on the reactive power and the closed-loop dynamic equation, calculate the speed correction. And obtain the estimated rotational speed. ; Calculate the rotor integral angle The specific formula is as follows: ;in This represents the cumulative running time from the moment the motor starts to the current moment.

[0015] By adopting the above technical solution, after obtaining the core parameters and operating conditions, a transfer parameter determination model built with deep learning is used to accurately obtain the noise immunity coefficient and the expected closed-loop time constant. The PI parameters are calculated using the PI parameter formula, and then the electric angular velocity, reactive power, estimated speed, and rotor integral angle are calculated using specific formulas. Each calculation step is closely linked, fully utilizing real-time data and the advantages of deep learning models to ensure that the calculation results more accurately reflect the motor state, providing a reliable basis for subsequent adjustments and improving the accuracy of parameter calculation and speed estimation.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the reactive power of the motor, estimating the speed, and voltage correction commands based on the core parameters of the motor using the PI parameter mathematical model further includes: acquiring target current data, which includes the target excitation current and target torque current in the rotating coordinate system; and converting the real-time current in the two-phase stationary coordinate system into a rotating current component in the rotating coordinate system based on the rotor integral angle. and The current deviation between the rotating current component and the target current data is calculated, and the back electromotive force compensation in the rotating coordinate system is calculated based on the estimated rotational speed. The back electromotive force compensation is proportional to the estimated rotational speed and is used to correct the voltage command to counteract the induced voltage generated by the rotation of the magnetic field. The voltage correction command in the rotating coordinate system is generated by combining the current deviation and the back electromotive force compensation.

[0017] By adopting the above technical solution, after acquiring the target current data, the current components are converted based on the rotor integral angle, and the current deviation is accurately compared. The back electromotive force compensation is calculated in conjunction with the estimated speed to offset the influence of induced voltage. Finally, a voltage correction command is generated by combining the deviation and the compensation. This process takes into account both the influence of current deviation and magnetic field-induced voltage, making the voltage correction command more comprehensive and accurate. This allows for more effective adjustment of the motor's operating state, reducing errors and improving motor control precision.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the process of repeating the above adjustment steps further includes: real-time monitoring of the current change rate in a two-phase stationary coordinate system, wherein the current change rate is the ratio of the absolute value of the difference between the real-time current of the current control cycle and the real-time current of the previous control cycle to the real-time current of the previous control cycle; when the current change rate exceeds a preset current change threshold, the update cycle of the PI parameter is reduced according to a set cycle adjustment ratio to improve the dynamic response speed of parameter adjustment; when the current change rate is less than the set change ratio of the preset current change threshold for n consecutive control cycles, the update cycle of the PI parameter is restored.

[0019] By adopting the above technical solution, the current change rate is monitored in real time. When the change rate exceeds the threshold, the PI parameter update cycle is reduced to accelerate the dynamic response, and the cycle is restored after continuous stabilization. This mechanism can flexibly adjust the parameter update frequency according to the dynamic changes of motor current, respond quickly when the motor state fluctuates, and avoid frequent adjustments that waste resources when stable. It balances dynamic response speed and parameter stability, improving the adaptability and efficiency of parameter optimization during motor operation.

[0020] In a second aspect, this application provides a motor observer tuning system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the motor observer tuning system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a motor observer tuning system, cause the motor observer tuning system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product that, when run on a motor observer tuning system, causes the motor observer tuning system to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting the technical means of "constructing a PI parameter mathematical model, combining real-time acquired core parameters such as motor inductance and current to calculate reactive power, estimate speed and voltage correction commands, and then cyclically adjusting until the deviation meets the standard", the technical problems of existing technologies that require separate offline parameter calibration for multiple motors and cannot cope with motor aging and parameter drift are effectively solved. This enables the observer parameters to dynamically adapt to the real-time status of the motor, meeting the convenience and adaptability requirements of multi-motor scenarios without offline calibration, and ensuring the technical effect of stable motor control.

[0024] 2. By adopting the technical approach of "combining the core parameters of the motor with the operating conditions, obtaining the noise immunity coefficient and the expected closed-loop time constant through the transfer parameter determination model constructed by deep learning, and then calculating key quantities such as PI parameters, electric angular velocity, and reactive power according to the formula", the technical problems of parameter correction relying on preset empirical formulas and being out of touch with the real-time parameters and operating conditions of the motor in the existing technology are effectively solved. This achieves the technical effect of accurately matching the calculation of key parameters with the actual operating state of the motor, improving the accuracy of speed estimation and parameter optimization, and adapting to the dynamic changes of the motor.

[0025] 3. By adopting the technical means of "converting the current component based on the rotor integral angle, calculating the current deviation and the back electromotive force compensation amount that changes with the estimated speed, and combining the two to generate a voltage correction command", the technical problems of voltage commands not taking into account the influence of back electromotive force and insufficient adjustment accuracy in the existing technology are effectively solved. Thus, the voltage correction command can offset the magnetic field rotation induced voltage, accurately compensate for the current deviation, and significantly improve the motor control accuracy and operation stability. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the adaptive tuning method for motor observer parameters in an embodiment of this application. Figure 2 This is another flowchart illustrating the adaptive tuning method for motor observer parameters in this application embodiment; Figure 3 This is a schematic diagram of the physical device structure of a motor observer tuning system in an embodiment of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0029] For ease of understanding, the method provided in this implementation is described in process below. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating the adaptive tuning method for motor observer parameters in an embodiment of this application.

[0030] S101. Construct a mathematical model for PI parameters; Among them, the PI parameter mathematical model refers to the model using a proportionality coefficient. and integral coefficient With this as its core, a mathematical model is constructed by combining the principle of reactive power balance of motors with the dynamic characteristics of phase-locked loops. This model is used to calculate the core parameters of the motor with key state quantities such as speed and reactive power, and serves as the core logical carrier for achieving adaptive parameter tuning.

[0031] This step is executed after the motor observer tuning system is started but before acquiring the core motor parameters. It is the foundational model building stage of the entire adaptive tuning process. Its applicable scenarios cover all motor control scenarios without physical sensors, and it is particularly suitable for scenarios involving multiple motors (such as multi-station drive in production lines) and motor aging or parameter drift (such as inductance changes after long-term operation). It can solve the problem of poor adaptability of traditional fixed parameter schemes under dynamic operating conditions. Specifically, the model building process is divided into four core stages, which will be described in detail in subsequent steps S201-S203, and will not be repeated here.

[0032] S102. Obtain the core parameters of the motor, including motor inductance, real-time current, stator voltage and target speed; Among them, motor inductance (L) is the ability of the motor windings to resist changes in current. It is an inherent electrical parameter of the motor and is determined by design factors such as the number of winding turns, core material, and air gap length. It is used to calculate the sensitivity coefficient c. Real-time current refers to the stator winding current collected in real time by sensors during motor operation, including the α-axis current in a two-phase stationary coordinate system. and β-axis current d-axis current in rotating coordinate system and q-axis current The current is used to reflect the real-time load status of the motor. For example, when the motor is running under rated load, the real-time current may be stable at around 12A. The stator voltage refers to the voltage applied across the stator windings of the motor, which is the electrical energy input that drives the motor. It also includes the α-axis voltage. and β-axis voltage The stator voltage is dynamically adjusted according to changes in motor speed and load. For example, to make the motor reach a speed of 1500 r / min, the stator voltage may need to be adjusted to 380V. The target speed refers to the expected operating speed that the motor needs to reach. It is determined by the actual application scenario (such as the conveyor speed of an assembly line or the rotation speed of a robot joint) and is the target reference for motor control. For example, in the spin-drying process of a washing machine, the target speed may be set to 1200 r / min.

[0033] The system first clarifies the source and method of collecting core parameters: for motor inductance, it prioritizes reading the rated parameters of the motor at the time of manufacture (such as "stator inductance: 5mH" marked on the motor nameplate) as the initial value. If the system has an online inductance identification function (such as an identification algorithm based on voltage and current equations), it will combine the real-time collected voltage and current data to dynamically correct the inductance value. For real-time current, it is acquired by a current sensor (such as a Hall current sensor) connected in series in the stator winding. The sensor must support simultaneous acquisition of two-phase current, and the sampling frequency must be no less than 1kHz (to ensure coverage of the motor control cycle, such as a control cycle of 1ms). The acquired analog current signal is converted into a digital signal by an AD converter and then transmitted to the system's signal processing module. The module preprocesses the signal and finally outputs a smooth signal. , , , data; For the stator voltage, it is acquired by a voltage sensor connected in parallel across the stator windings. The sensor range must match the motor's rated voltage (e.g., a sensor with a range of 0-500V is selected for a 380V motor). The acquired voltage signal is also converted by an analog-to-digital converter and filtered to obtain... , data; The target speed is issued by the upper control unit of the system (such as PLC or industrial controller). The issued command includes the speed value (such as "1500 r / min") and the speed holding time (such as "run continuously for 30 minutes"). If the upper control unit does not issue a command, the system will default to calling the preset target speed (such as the motor rated speed).

[0034] S103. Based on the core parameters of the motor, calculate the reactive power, estimate the speed and voltage correction command of the motor through the mathematical model of the PI parameters; Among them, the estimated rotational speed The estimated motor speed is calculated using a PI parameter mathematical model and is used to replace the actual speed in scenarios without physical sensors. It serves as a feedback signal for closed-loop control, and its accuracy directly affects the motor speed control effect. For example, when the actual speed is 1500 r / min, the estimated speed needs to be controlled within the range of 1500 r / min ± 2 r / min. The voltage correction command is a control signal generated based on the current deviation and back EMF compensation, used to adjust the stator voltage of the motor. It includes d-axis voltage commands and q-axis voltage commands in the rotating coordinate system, used to correct voltage deviations during motor operation and ensure that the speed tracks the target value. For example, when the motor load increases and the speed decreases, the voltage correction command will increase the stator voltage and increase the motor torque.

[0035] Specifically, the calculation process is divided into three core stages: The first stage is PI parameter calculation: obtaining the motor's core parameters and operating conditions, which include at least the noise immunity factor and motor load; based on these core parameters and operating conditions, the motor's noise immunity factor is obtained by determining the model through parameter transfer. and the expected closed-loop time constant The transfer parameter determination model is pre-built using deep learning based on multiple sets of motor core parameters and operating condition samples labeled with noise immunity coefficients and expected closed-loop time constants. The motor's PI parameters are then calculated using this PI parameter calculation formula. and More specifically, the system first acquires the core parameters of the motor, which may include stator voltage, multiple target speeds, and operating conditions (noise immunity β, motor load rate). This data is then input into the parameter determination model. The model, through a trained neural network (e.g., 10 neurons in the input layer, 2 hidden layers, and 2 neurons in the output layer), outputs β and τ under the current operating conditions. For example, given the input "L=5mH, =8A、 =7A, load rate 70%, n=1500r / min", output β=1.2, τ=0.05s; then calculate the sensitivity coefficient c, and substitute it into L and the real-time current component (e.g. =8A、 =7A, through the formula =5×10⁻ 3 ×(8 2 +7 2 = 0.565H·A 2 Finally, the PI parameter is calculated using the formula. , Substituting β=1.2, c=0.565, and τ=0.05s, we calculate... ≈2.12、 ≈42.48, PI parameter configuration complete.

[0036] The second stage involves reactive power estimation, speed estimation, and rotor integral angle calculation: calculating the motor's electrical angular velocity. The specific formula is as follows: ,in For the target speed, This represents the number of pole pairs of the motor. Real-time calculation of the motor's reactive power The specific formula is as follows: ,in , For the stator voltage , Axial components, The inductance of the motor; Based on the reactive power and the closed-loop dynamic equation, calculate the speed correction. And obtain the estimated rotational speed. ; Calculate the rotor integral angle The specific formula is as follows: ,in This represents the cumulative running time from the moment the motor starts to the current moment.

[0037] The third stage is voltage correction command calculation: acquiring target current data, which includes the target excitation current and target torque current in the rotating coordinate system; based on the rotor integral angle, converting the real-time current in the two-phase stationary coordinate system into the rotating current component in the rotating coordinate system. and The process involves calculating the current deviation between the rotating current component and the target current data, and then calculating the back electromotive force (EMF) compensation in the rotating coordinate system based on the estimated rotational speed. This back EMF compensation is proportional to the estimated rotational speed and is used to correct the voltage command to counteract the induced voltage generated by the rotating magnetic field. The voltage correction command in the rotating coordinate system is generated by combining the current deviation and the back EMF compensation. The back EMF compensation is a voltage compensation value set to counteract the induced voltage (i.e., back EMF) generated in the stator windings by the change in the magnetic field when the motor rotor rotates. This value changes with the estimated motor speed; the higher the speed, the greater the back EMF, and the greater the required back EMF compensation. Its function is to prevent the actual motor current from deviating from the target current due to the back EMF, ensuring stable motor operation.

[0038] More specifically, firstly, target current data is acquired. This data setting needs to be combined with the motor's operating requirements (such as the desired output torque and the magnetic field requirements corresponding to the target speed). This is typically preset or issued in real-time by the system's upper-level control unit based on the actual application scenario (such as load size and production cycle requirements). Secondly, a current coordinate system transformation is performed. Since the real-time acquired current is based on a two-phase stationary coordinate system, while the motor's magnetic field and torque control are more easily and precisely adjusted in a rotating coordinate system, the calculated rotor integral angle (which reflects the rotor's current rotational position, ensuring the converted current is synchronized with the rotor's rotational state) is used to convert the real-time current in the two-phase stationary coordinate system into rotating current components in the rotating coordinate system. The transformation process must ensure angle matching so that the converted current components accurately correspond to the magnetic field control and torque control dimensions in the rotating coordinate system. Thirdly, the current deviation and back EMF compensation are calculated. The current deviation calculation requires subtracting the rotating current components in the rotating coordinate system from the corresponding target excitation current and target torque current. The calculations obtain the current deviations in both the magnetic field control and torque control dimensions, clarifying the gap between the current and the target value. The calculation of the back EMF compensation amount requires the estimated motor speed. Since higher speeds generate stronger back EMF from the rotor rotation, which partially offsets the input voltage and causes a current decrease, the compensation amount is calculated based on the correlation between speed and back EMF to offset the current's influence, ensuring that subsequent voltage adjustments effectively correct the current. Finally, a voltage correction command is generated. After calculating the current deviation and back EMF compensation amount, the two are combined. For the current deviation, the system calculates the voltage adjustment portion used to correct the deviation based on preset control logic (such as proportional regulation logic; the larger the deviation, the larger the corresponding voltage adjustment). This voltage adjustment portion is then superimposed on the back EMF compensation amount to obtain the final voltage correction command in the rotating coordinate system. This command considers both offsetting the back EMF's influence and specifically correcting the current deviation, ensuring that after adjustment, the motor current gradually approaches the target current, thereby achieving precise control of speed and torque.

[0039] S104. Adjust the motor according to the voltage correction command; First, the voltage correction command needs to be parsed and converted. Since the generated voltage correction command is a digital signal based on a rotating coordinate system, and the motor drive module (such as an inverter) typically needs to receive voltage control signals in a two-phase stationary coordinate system or a three-phase coordinate system, the system must first convert the voltage correction command in the rotating coordinate system into a voltage signal format recognizable by the drive module through inverse coordinate transformation (such as Park inverse transformation, which combines the rotor integral angle to convert the rotating coordinate system voltage into a two-phase stationary coordinate system voltage). Simultaneously, the digital signal is converted into an analog signal (this step is unnecessary if the drive module supports digital input), ensuring the command format is correct. The input requirements of the drive module must be matched. Secondly, the drive module performs voltage adjustment. After receiving the converted voltage control signal, the motor drive module (such as a three-phase inverter based on IGBT) changes the magnitude and phase of the voltage applied to the motor stator winding by adjusting the on and off times of the internal power switching devices (such as IGBTs) (i.e., PWM pulse width modulation). For example, when the voltage correction command requires an increase in the voltage of the corresponding torque control, the drive module will increase the PWM pulse width of the corresponding phase winding, increase the average voltage of that phase, and thus increase the torque current component in the stator current, thereby increasing the electromagnetic torque of the motor.

[0040] S105. Repeat the above adjustment steps until the absolute value of the reactive power is less than the preset reactive power deviation threshold, and the deviation between the estimated speed and the target speed is less than the preset speed deviation threshold.

[0041] Among them, the preset speed deviation threshold refers to the critical value set in advance by the system to determine whether the motor speed has reached the expected control accuracy, and is determined according to the accuracy requirements of the application scenario.

[0042] The core logic of the process is "loop judgment - iterative adjustment": First, deviation data acquisition and calculation: After completing a voltage adjustment, the system collects the adjusted core parameters of the motor (including real-time current and stator voltage) in real time, recalculates the current reactive power and estimated speed based on the PI parameter mathematical model, and then calculates the "absolute value of reactive power" and the "deviation between estimated speed and target speed" respectively. For example, if the calculated reactive power after adjustment is 8Var, its absolute value is 8Var, the target speed is 1500r / min, the estimated speed is 1480r / min, and the speed deviation is 20r / min; Second, dual-condition judgment: The system compares the calculated "absolute value of reactive power" with the "preset reactive power deviation threshold", and simultaneously checks the "rotation speed deviation threshold". The system compares the "speed deviation" with the "preset speed deviation threshold". Only when both conditions are met simultaneously (i.e., the absolute value of reactive power < the preset reactive power deviation threshold, and the speed deviation < the preset speed deviation threshold) is the motor considered to have reached a stable operating state and the repeated adjustment is stopped. If either condition is not met (e.g., the absolute value of reactive power meets the standard but the speed deviation exceeds the standard, or vice versa), the system is considered to need to continue adjusting. Finally, the adjustment process is executed cyclically. If the system is considered to need to continue adjusting, it will re-trigger the "obtain motor core parameters" step (at this time, the latest adjusted parameters are obtained). Based on the new parameters, the reactive power is recalculated, the speed and voltage correction instructions are estimated, and the voltage adjustment is executed again, forming a closed loop of "adjustment-detection-re-adjustment" until both deviation conditions are met. During the cycle, the system records the trend of deviation changes for each adjustment (e.g., the absolute value of reactive power gradually decreases from 20Var to 3Var, and the speed deviation gradually decreases from 50r / min to 2r / min). If the deviation is found to have no significant decrease or repeated fluctuations for a long time (e.g., 10 consecutive control cycles), the abnormal diagnosis logic will be triggered to check whether there are faults in the parameter acquisition, model calculation or drive module, so as to ensure the effectiveness and reliability of the cycle adjustment.

[0043] In some embodiments, within each control cycle (e.g., 1ms), the real-time current of the α-axis and β-axis in a two-phase stationary coordinate system is acquired, the absolute value of the difference between the current and the current of the previous cycle is calculated, and then divided by the current value of the corresponding axis in the previous cycle to obtain the current change rate of the α-axis and β-axis (the maximum of the two is taken as the current change rate of the current cycle). If the current change rate is greater than the preset current change threshold (e.g., 10%), it is determined to be a violent current fluctuation, and the PI parameter update cycle is immediately reduced by the set cycle adjustment ratio (e.g., 50%) (e.g., from 50ms to 25ms) to make the parameter update more frequent and respond quickly to current changes. If the current change rate is less than the preset current change threshold × the set change ratio (e.g., 10% × 50% = 5%) for n consecutive control cycles (e.g., 5 cycles), it is determined that the current has returned to stability, and the PI parameter update cycle is restored to the default value. This shortens the update cycle when the current changes drastically, allowing the PI parameters to track changes in motor status in real time. This avoids speed estimation errors or system oscillations caused by parameter update lag, and enables the PI parameter update cycle to be dynamically adapted to the actual operating status of the motor, thus balancing control accuracy and system efficiency.

[0044] In the above embodiment, a PI parameter mathematical model with reactive power balance as the core and incorporating the dynamic characteristics of a phase-locked loop is first constructed to provide a precise logical carrier for parameter calculation. Then, core parameters such as motor inductance and current are acquired in real time to ensure that the calculation is based on reality. Subsequently, reactive power, estimated speed, and voltage correction commands are calculated based on the model and core parameters to accurately reflect the operating deviation. After adjusting the motor according to the commands, the process is iterated until the deviation meets the standard, forming a closed-loop logic of "model construction - parameter acquisition - calculation adjustment - iterative optimization". Therefore, the observer parameters can be dynamically adapted to the real-time state of the motor without manual offline calibration. This effectively solves the problems in the prior art where multiple motors need to be calibrated offline separately, cannot cope with motor aging and parameter drift, and have poor parameter adaptability under dynamic conditions. This achieves the convenience and adaptability of multi-motor collaborative work in scenarios without physical sensors, significantly improves the accuracy of motor speed estimation, operating stability, and dynamic response speed, while reducing the debugging complexity and maintenance cost of multi-motor systems.

[0045] Based on the above, the following is a more detailed description of the process provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the adaptive tuning method for motor observer parameters in this application.

[0046] S201. Taking the reactive power balance of the motor as the core, within the preset range of the optimal efficiency point, the reactive power... Perform linearization; Among them, motor reactive power balance refers to the stable and equal state between the reactive power used to establish and maintain the magnetic field and the reactive power compensated by the system during motor operation. The core is to optimize the energy utilization efficiency of the motor's magnetic field and avoid increased motor losses and decreased efficiency due to excessive or insufficient reactive power. The optimal efficiency point refers to the operating condition where the motor's reactive power is equal to 0. At this time, there is no excess energy consumption in the motor's magnetic field, and the efficiency of converting electrical energy into mechanical energy is the highest. For example, a three-phase asynchronous motor has zero reactive power at a load rate of 70% and a speed of 1500 r / min. This operating condition is the optimal efficiency point of the motor. The preset range of the optimal efficiency point refers to the range around the optimal efficiency point that allows for small fluctuations in reactive power. This range is determined based on the motor's rated power and operating stability requirements. For example, for a 10kW motor, the reactive power absolute value can be set to a range of less than 5Var to ensure that the motor efficiency remains at a high level when operating within this range. Reactive power refers to the power used by the motor to establish the magnetic field during operation. It does not perform external work, and its value directly reflects the balance state of the motor's magnetic field. When the reactive power deviates from 0, the motor efficiency will decrease accordingly. Linearization processing refers to the process of converting the nonlinear relationship between reactive power and speed deviation into a linear mathematical expression within a preset range of the optimal efficiency point. The purpose is to simplify the calculation of the subsequent PI parameter mathematical model and improve the real-time performance and accuracy of the model. For example, within the preset range, the nonlinear change of reactive power with speed deviation can be approximated as a linear relationship of "reactive power = sensitivity coefficient × speed deviation".

[0047] The optimal efficiency point refers to the point where the reactive power is equal to 0. The linearization formula is as follows: ,in This refers to the actual rotational speed. To estimate the rotational speed, The sensitivity coefficient is derived using the following formula: Let the rotational speed deviation be Δw = - (The difference between the actual rotational speed and the estimated rotational speed reflects the error in speed estimation.) Near the optimal efficiency point (A=0), the deviation in the rotational speed of the magnetic field (caused by Δw) directly induces an "extra induced voltage" in the stator winding, which in turn generates an "extra reactive current," ultimately manifesting as fluctuations in reactive power A. According to the law of electromagnetic induction, this fluctuation satisfies an "approximately linear relationship": A∝Δw, i.e., A=k・Δw (k is the proportionality coefficient).

[0048] This sensitivity coefficient The calculation formula is: ,in and This represents the stator current component of the motor in a two-phase stationary coordinate system. and This represents the current component of the motor stator current in the rotating coordinate system. This is the inductance of the motor.

[0049] The expression for the sensitivity coefficient c is derived as follows: To clarify the specific form of the proportional coefficient k, it is necessary to analyze it in conjunction with the electrical parameters of the motor (inductance L, stator current components): The energy formula for the stator magnetic field of an electric motor is: E = ½ × L × I 2 (L is the stator inductance, and I is the effective value of the stator current).

[0050] Reactive power is the rate of change of magnetic field energy, i.e., A∝dE / dt. The change in current I is related to the rotational speed deviation Δw (Δw causes a deviation in the rotational speed of the magnetic field, which in turn causes a change in the induced current).

[0051] Further consider coordinate system transformation (in motor control, a two-phase stationary coordinate system αβ and a rotating coordinate system dq are commonly used, and the current components satisfy the "amplitude conservation"): Effective value of stator current in a two-phase stationary coordinate system = ; Effective value of stator current in rotating coordinate system = ; Based on the orthogonality of coordinate transformation, = (The current amplitude remains unchanged during coordinate system transformation), therefore 2 + 2 = 2 + 2 .

[0052] Based on the relationship between magnetic field energy and reactive power, the reactive power fluctuation caused by the speed deviation Δw is proportional to the sum of the squares of the inductance L and the current. Therefore, the proportionality coefficient k can be defined as the sensitivity coefficient c, and: c = L·( 2 + 2 )=L·( 2 + 2 ) Substituting the linear relationship A = k × Δw from step 1, we finally get: A ≈ c × ( - ).

[0053] Specifically, the core objective of motor reactive power balance is first defined. The system determines the optimal efficiency point (reactive power = 0) by calling motor design parameters (such as rated power, rated voltage, and stator inductance) and combining them with offline test data (such as reactive power-efficiency curves under different operating conditions). This point serves as the benchmark for subsequent linearization processing, ensuring that the processed model is built around the optimal efficiency objective. Secondly, a preset range for the optimal efficiency point is defined. Based on the motor's efficiency tolerance during actual operation (such as allowing efficiency to drop by no more than 5% from its maximum value), the system analyzes the correlation between reactive power and efficiency to determine the boundaries of the preset range. For example, if the motor efficiency drops by more than 5% when reactive power exceeds ±5Var, the preset range is set to "reactive power ∈ [-5Var, 5Var]". This range must also match the motor's common operating conditions (such as the motor operating for more than 80% of its time within this range). To avoid frequent failures in linearization due to an excessively narrow range, the system performs reactive power linearization. Within a preset range, multiple sets of "speed deviation - reactive power" sample data (speed deviation being the difference between the actual and estimated speed) are collected. The sample data are fitted using the least squares method to obtain a linearized mathematical expression. This expression uses speed deviation as the independent variable and reactive power as the dependent variable, while simultaneously calculating the sensitivity coefficient (the slope of the fitted line). The magnitude of the sensitivity coefficient is related to the motor inductance and stator current components (verified by the formula "sensitivity coefficient = motor inductance × (sum of squares of current components in the two-phase stationary coordinate system)"). This ensures that the linearization relationship reflects the inherent electrical characteristics of the motor. For example, after fitting, "reactive power ≈ 0.6 × speed deviation" (sensitivity coefficient is 0.6). This expression will subsequently be used to construct the closed-loop dynamic equation of the phase-locked loop, simplifying the speed estimation calculation process.

[0054] S202. Determine the initial speed of the motor as 0, and obtain the closed-loop dynamic equation of the phase-locked loop; An initial speed of 0 means that the rotor speed is 0 at the moment of motor startup (when the motor is not powered on or has just been powered on). This is the default initial state during the motor startup phase and the initial condition for constructing the phase-locked loop (PLL) closed-loop dynamic equation. This ensures that the equation describes the speed change process from the motor's stationary state. The PLL is a control module used to track the actual speed of the motor. By comparing the deviation between the actual speed and the estimated speed, it adjusts the estimated speed to gradually approach the actual speed. Its core function is to achieve accurate speed tracking and avoid excessive speed deviation that could lead to motor instability.

[0055] First, the rationality of setting the initial motor speed to 0 is determined. The system is based on the physical characteristics of motor startup (the rotor is stationary when the motor is not powered on, and the speed starts to rise from 0 when powered on) and the initial state assumption in the absence of physical sensors (the speed cannot be obtained before startup, so the default initial speed is 0). It is clarified that setting the initial speed to 0 is the initial condition that conforms to the actual operating conditions, avoiding deviations from reality due to incorrect initial conditions. Second, the working principle of the phase-locked loop (PLL) is analyzed. The PLL consists of three parts: speed deviation detection, PI regulator, and speed estimator. The deviation detection module calculates the difference between the actual speed and the estimated speed (speed deviation), which is converted into reactive power through linearization (i.e., the linearized relationship obtained in S201). The PI regulator receives the reactive power signal and generates the speed adjustment amount through proportional operation (fast response to deviation) and integral operation (eliminating steady-state error). The speed estimator updates the estimated speed according to the speed adjustment amount, forming a closed loop of "deviation detection-adjustment-estimation update". Finally, the closed-loop dynamic equation of the PLL is derived. Based on the input-output relationship of the PI regulator and the linearized relationship between reactive power and speed deviation, the following equation is constructed: The Laplace operator is used to convert the integral operation in the time domain into a mathematical expression in the complex frequency domain, simplifying the solution of the equation. "Integral coefficient / Laplace operator" represents the integral operation on reactive power, ensuring that the system has no steady-state error (e.g., when the speed deviation persists, the integral term will accumulate adjustment amount and gradually eliminate the deviation). The variables in the equations are all complex frequency domain variables, which facilitates the subsequent analysis of the system's dynamic characteristics (such as stability and response speed) through the transfer function.

[0056] S203. Substitute the linearization formula into the closed-loop dynamic equation to obtain the closed-loop transfer function; The closed-loop transfer function is ; After simplification, the final closed-loop transfer function is: .

[0057] S204. Based on the preset performance requirements of the motor, the ideal transfer function is obtained, and the closed-loop transfer function and the ideal transfer function are combined to obtain the balance equation. Among them, the ideal transfer function refers to the complex frequency domain mathematical expression that is constructed based on the preset performance requirements and can perfectly reflect the desired dynamic characteristics of the motor. Its numerator and denominator structure is determined by the noise immunity coefficient and the desired closed-loop time constant, and is used as a comparison benchmark to guide the design of actual PI parameters.

[0058] This step should be carried out according to the logic of "quantification of performance requirements → construction of ideal transfer function → simultaneous establishment of transfer functions": First, the preset motor performance requirements are quantified. The system determines two key performance parameters based on the application scenario: noise immunity coefficient and expected closed-loop time constant. The noise immunity coefficient is used to measure the system's ability to suppress noise. The larger the noise immunity coefficient value, the stronger the system's noise immunity (such as the smaller the impact of grid voltage fluctuations and current sensor noise on speed estimation). The expected closed-loop time constant is used to measure the system's dynamic response speed. The smaller the expected closed-loop time constant value, the faster the system response (such as the shorter the time from motor start-up to reaching the target speed). The specific steps for obtaining the noise immunity coefficient and expected closed-loop time constant have been described in detail in S103, and will not be repeated here.

[0059] Secondly, an ideal transfer function is constructed based on the quantified performance parameters. The structure of the ideal transfer function must match the order of the closed-loop transfer function (both are in the form of a first-order numerator and a second-order denominator) to ensure that the parameters can be solved by comparing the coefficients after combining the equations. Based on the preset performance requirements, the ideal transfer function is derived as follows: ,in This is the noise immunity coefficient of the motor. The desired closed-loop time constant; Among them, the molecule " "This reflects the driving effect of the noise immunity figure on the output, in the denominator." "This reflects the constraints imposed by the desired closed-loop time constant and noise immunity on the system's dynamic characteristics (the coefficient of the linear term 's' in the denominator is determined by...") and The constant term is jointly determined by... (Decision). Finally, by simultaneously solving the closed-loop transfer function and the ideal transfer function, we obtain the equilibrium equation: This equation binds the actual system characteristics to the desired characteristics, providing mathematical constraints for the subsequent solution of the PI parameters.

[0060] S205, let the numerators and denominators on both sides be... If the linear term and the constant term are equal, then the formula for calculating the PI parameter is obtained.

[0061] First, break down the transfer function structure: each transfer function consists of two parts, a numerator and a denominator, and the numerator and denominator can be further broken down into "terms containing the rate of change of rotational speed (linear term)" and "fixed terms not containing the rate of change of rotational speed (constant term)".

[0062] Next, establish the coefficient correspondence: for the two transfer functions to be completely equal, the following conditions must be met: "the coefficient of the linear term in the left numerator equals the coefficient of the linear term in the right numerator," "the constant term in the left numerator equals the constant term in the right numerator," "the coefficient of the linear term in the left denominator equals the coefficient of the linear term in the right denominator," and "the constant term in the left denominator equals the constant term in the right denominator." These four equations constitute the constraints for solving the PI parameter.

[0063] By solving the system of equations and eliminating other variables, we can obtain: ; .

[0064] In this embodiment, reactive power is linearized within a preset range of optimal efficiency point, clarifying the linear correlation between reactive power and speed deviation. Next, the initial motor speed is determined to be 0, and a phase-locked loop (PLL) closed-loop dynamic equation is constructed, establishing the dynamic relationship between estimated speed, reactive power, and PI parameters. The linearized formula is then substituted into the closed-loop dynamic equation to obtain the closed-loop transfer function, quantifying the signal transmission characteristics from actual speed to estimated speed. Subsequently, an ideal transfer function is constructed based on preset motor performance requirements, and the closed-loop transfer function is combined to obtain the balance equation, binding the actual system characteristics with the desired performance. Finally, by making the linear terms and constant terms in the numerator and denominator of both sides of the balance equation equal, the following is derived: The PI parameter calculation formula establishes a direct correlation between PI parameters and the core parameters and performance requirements of the motor. Therefore, it can adaptively calculate suitable PI parameters based on the motor's real-time core parameters (such as inductance and current) and dynamic operating conditions (such as load changes and noise interference), eliminating the need for manual offline calibration. This effectively solves the problems of poor parameter adaptability, motor aging / parameter drift leading to the failure of pre-stored parameters, and lag in dynamic operating condition response in traditional multi-motor mixed-use scenarios. As a result, it enables adaptive parameter tuning in sensorless motor control scenarios, improving the accuracy of motor speed estimation, dynamic response speed, and noise immunity, while reducing the debugging complexity and maintenance cost of multi-motor systems.

[0065] The motor observer tuning system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a motor observer tuning system in an embodiment of this application.

[0066] It should be noted that, Figure 3 The structure of the motor observer tuning system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0067] like Figure 3As shown, the motor observer tuning system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0068] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0069] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0070] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0071] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0072] Specifically, the motor observer tuning system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the adaptive tuning method of motor observer parameters provided in the above embodiment.

[0073] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the motor observer tuning system described in the above embodiments; or it may exist independently and not assembled into the motor observer tuning system. The storage medium carries one or more computer programs that, when executed by a processor of the motor observer tuning system, cause the motor observer tuning system to implement the adaptive tuning method for motor observer parameters provided in the above embodiments.

[0074] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0075] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An adaptive tuning method for motor observer parameters, applied to a motor observer tuning system, characterized in that, The method includes: constructing a mathematical model for PI parameters; Obtain the core parameters of the motor, including motor inductance, real-time current, stator voltage, and target speed; Based on the core parameters of the motor, the reactive power, estimated speed, and voltage correction command of the motor are calculated through the PI parameter mathematical model. Adjust the motor according to the voltage correction command; Repeat the above adjustment steps until the absolute value of the reactive power is less than the preset reactive power deviation threshold, and the deviation between the estimated speed and the target speed is less than the preset speed deviation threshold.

2. The method according to claim 1, characterized in that, The steps for constructing the PI parameter mathematical model include: With the reactive power balance of the motor as the core, the reactive power is adjusted within a preset range of the optimal efficiency point. Linearization is performed, and the optimal efficiency point is defined as when the reactive power equals 0. The linearization formula is as follows: ； in This refers to the actual rotational speed. To estimate the rotational speed, The sensitivity coefficient is... The calculation formula is: ； in and This represents the stator current component of the motor in a two-phase stationary coordinate system. and This represents the current component of the motor stator current in the rotating coordinate system. The inductance of the motor; With the initial speed of the motor set to 0, the closed-loop dynamic equation of the phase-locked loop is obtained: ； in, The proportionality coefficient in the mathematical model of the PI parameter is... These are the integral coefficients in the mathematical model of the PI parameters. For the Laplace operator, This indicates the reactive power. The integral operation is used to eliminate steady-state error; Substituting the linearization formula into the closed-loop dynamic equation yields the closed-loop transfer function.

3. The method according to claim 2, characterized in that, The step of substituting the linearization formula into the closed-loop dynamic equation to obtain the closed-loop transfer function includes: Substituting the linearization formula into the closed-loop dynamic equation, the following closed-loop transfer function is obtained: ； After simplification, the closed-loop transfer function is: 。 4. The method according to claim 2 or 3, characterized in that, After substituting the linearization formula into the closed-loop dynamic equation to obtain the closed-loop transfer function, the method further includes: The ideal transfer function is obtained based on the preset performance requirements of the motor: ,in This is the noise immunity coefficient of the motor. The desired closed-loop time constant; Combining the closed-loop transfer function and the ideal transfer function, we obtain the equilibrium equation: ； Let the numerators and denominators on both sides be... If the linear term and the constant term are equal, then the formula for calculating the PI parameter is obtained: ； 。 5. The method according to claim 1, characterized in that, The step of calculating the motor's reactive power, estimating the speed, and voltage correction commands based on the motor's core parameters and using the PI parameter mathematical model includes: Obtain the core parameters and operating conditions of the motor, including at least the noise immunity factor and the motor load; Based on the core parameters of the motor and the operating conditions, the noise immunity coefficient of the motor is obtained by determining the model through the transfer parameters. and the expected closed-loop time constant The transmission parameter determination model is constructed in advance using deep learning based on multiple sets of motor core parameters and operating condition samples labeled with noise immunity coefficients and expected closed-loop time constants. Calculate the PI parameters of the motor according to the PI parameter calculation formula. and ; Calculate the electric angular velocity of the motor The specific formula is as follows: ,in For the target speed, This represents the number of pole pairs of the motor. Real-time calculation of the motor's reactive power The specific formula is as follows: ； in , For the stator voltage , Axial components, The inductance of the motor; Based on the reactive power and the closed-loop dynamic equation, calculate the speed correction. And obtain the estimated rotational speed. ; Calculate the rotor integral angle The specific formula is as follows: ； in This represents the cumulative running time from the moment the motor starts to the current moment.

6. The method according to claim 1 or 5, characterized in that, The step of calculating the reactive power, estimating the speed, and voltage correction command of the motor based on the core parameters of the motor and using the PI parameter mathematical model further includes: Acquire target current data, which includes target excitation current and target torque current in a rotating coordinate system; Based on the rotor integral angle, the real-time current in the two-phase stationary coordinate system is converted into a rotating current component in the rotating coordinate system. and ; The current deviation between the rotating current component and the target current data is calculated, and the back electromotive force compensation in the rotating coordinate system is calculated based on the estimated rotational speed. The back electromotive force compensation is proportional to the estimated rotational speed and is used to correct the voltage command to counteract the induced voltage generated by the rotation of the magnetic field. The voltage correction command in the rotating coordinate system is generated by combining the current deviation and the back electromotive force compensation.

7. The method according to claim 1, characterized in that, The process of repeating the above adjustment steps also includes: The current change rate is monitored in real time in a two-phase stationary coordinate system. The current change rate is the absolute value of the difference between the real-time current of the current control cycle and the real-time current of the previous control cycle, and the ratio of the real-time current of the previous control cycle to the real-time current of the previous control cycle. When the current change rate exceeds the preset current change threshold, the update cycle of the PI parameter is reduced according to the set cycle adjustment ratio to improve the dynamic response speed of parameter adjustment. When the rate of change of the current is less than the set change ratio of the preset current change threshold for n consecutive control cycles, the update cycle of the PI parameter is restored.

8. A motor observer tuning system, characterized in that, The motor observer tuning system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the motor observer tuning system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is run on the motor observer tuning system, the motor observer tuning system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the motor observer tuning system, the motor observer tuning system performs the method as described in any one of claims 1-7.

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