A permanent magnet synchronous motor position parameter real-time identification method, system and medium

Abstract

The application discloses a permanent magnet synchronous motor position parameter real-time identification method and system and a medium, and belongs to the technical field of motor control. The method comprises the following steps: observing the voltage and current of the permanent magnet synchronous motor through a sliding mode observer to obtain an extended back electromotive force signal; filtering the harmonic component of the extended back electromotive force signal by using an adaptive filter to obtain a fundamental signal of the extended back electromotive force signal; and obtaining the motor rotating speed and the rotor position angle of the permanent magnet synchronous motor through a quadrature phase-locked loop of the fundamental signal of the extended back electromotive force signal to generate a control signal of the permanent magnet synchronous motor. The sliding mode observer based on the adaptive filter is applied to the permanent magnet synchronous motor driving system, the application can accurately and timely identify the motor position parameter, improves the dynamic performance of the built-in permanent magnet synchronous motor without a position sensor, and effectively enhances the reliability of the position sensorless control system.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, specifically to a method, system, and medium for real-time identification of position parameters of a permanent magnet synchronous motor. Background Technology

[0002] With advancements in motor technology and applications, AC motors are replacing traditional DC motors as the primary drive system in industrial applications. Among AC motors, built-in permanent magnet synchronous motors are widely used in industrial and household appliances due to their simple structure, small size, light weight, high efficiency, and high power density.

[0003] High-performance control of built-in permanent magnet synchronous motor systems requires real-time acquisition of rotor position information, typically provided by mechanical position sensors. However, the use of mechanical position sensors increases the size of the motor system, reduces its sealing performance and reliability, and imposes stringent environmental requirements. Real-time position parameter identification technology for permanent magnet synchronous motors eliminates the need for mechanical position sensors, utilizing voltage and current signals to acquire rotor position and speed information. This solves a series of problems associated with mechanical position sensors and improves system performance.

[0004] Based on operating speed, real-time position parameter identification techniques for permanent magnet synchronous motors (PMSMs) can be broadly categorized into two types: the first is the high-frequency injection method suitable for zero and low speeds, which utilizes the salient pole characteristics of the motor to obtain rotor position information; the second is the model-based method suitable for medium and high speeds. The model-based method can be further divided into open-loop estimation and closed-loop estimation. Open-loop estimation methods include direct calculation and open-loop flux linkage methods, while closed-loop estimation methods include sliding mode observers, disturbance observers, state observers, model reference adaptive methods, and extended Kalman filters. Among these, the sliding mode observer has been widely studied due to its high robustness to disturbance changes, low sensitivity to parameter variations, and good dynamic performance. However, due to the influence of inverter nonlinearity and magnetic field space harmonics, a large number of harmonics exist in the identified back EMF, leading to harmonic pulsations in the identified rotor position angle and reducing identification accuracy. Therefore, it is essential to propose a real-time position parameter identification method for PMSMs. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, system and medium for real-time identification of position parameters of permanent magnet synchronous motors, which can accurately identify the position parameters of the motor in real time, improve the dynamic performance of built-in permanent magnet synchronous motors without position sensors and effectively enhance the reliability of the control system without position sensors.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for real-time identification of position parameters of a permanent magnet synchronous motor includes:

[0008] S101, the voltage and current of the permanent magnet synchronous motor are observed through a sliding mode observer to obtain the extended back electromotive force signal;

[0009] S102, the harmonic components of the extended back EMF signal are filtered out using an adaptive filter to obtain the fundamental signal of the extended back EMF signal;

[0010] S103 uses the fundamental signal of the extended back EMF signal to obtain the motor speed and rotor position angle of the permanent magnet synchronous motor through an orthogonal phase-locked loop to generate the control signal for the permanent magnet synchronous motor.

[0011] Optionally, the function expression of the sliding mode observer in step S101 is:

[0012]

[0013] In the above formula, and The current identification value in the α,β coordinate system, u α and u β Let L be the voltage of the permanent magnet synchronous motor in the α,β coordinate system. q R is the q-axis inductance. s For stator resistance, and The extended back electromotive force (EMF) in the α,β coordinate system is expressed as the functional expression for obtaining the extended back EMF signal by observing the voltage and current of the permanent magnet synchronous motor using a sliding mode observer:

[0014]

[0015] In the above formula, and Let k be the extended back electromotive force in the α,β coordinate system. SMO For gain, 'a' is a positive constant coefficient. and The current identification value is in the α,β coordinate system. and i represents the current identification value in the α,β coordinate system. α and i β Let be the current in the α,β coordinate system; and the calculation function expression for the voltage of the permanent magnet synchronous motor in the α,β coordinate system is:

[0016]

[0017] In the above formula, u α and u β Let L be the voltage in the α,β coordinate system. q R is the q-axis inductance.s For the stator resistance, p = d / dt is the differential operator, e α and e β Let i be the equivalent back electromotive force in the α,β coordinate system. α and i β Let be the current in the α,β coordinate system. The expression for the calculation function of the equivalent back electromotive force in the α,β coordinate system is:

[0018]

[0019] In the above formula, λ f For the permanent magnet flux linkage of a permanent magnet synchronous motor, L d For d-axis inductance, λ a =λ f +(L d -L q )i d For effective flux linkage, ω e Let θ be the motor speed. e This is the rotor position angle of the motor.

[0020] Optionally, the adaptive filter in step S102 includes:

[0021] An α-axis adaptive filtering unit is used to filter the input α-axis extended back EMF signal. After filtering out harmonic components, the fundamental signal of the extended back electromotive force signal along the α axis is obtained.

[0022] The β-axis adaptive filtering unit is used to filter the input β-axis extended back EMF signal. After filtering out harmonic components, the fundamental signal of the extended back electromotive force signal along the β axis is obtained.

[0023] The α-axis adaptive filtering unit and the β-axis adaptive filtering unit have the same structure and both include:

[0024] The fifth harmonic observer is used to observe error signals to obtain the fifth harmonic back electromotive force.

[0025] The seventh harmonic observer is used to observe the error signal to obtain the seventh harmonic back electromotive force. The difference between the sum of the fifth harmonic back electromotive force, the seventh harmonic back electromotive force, and the fundamental signal of the output extended back electromotive force signal and the input extended back electromotive force signal constitutes the error signal.

[0026] The fundamental frequency observer is used to observe the error signal and the rotor frequency to obtain the fundamental signal of the extended back electromotive force signal output.

[0027] Optionally, the fifth harmonic observer, the seventh harmonic observer, and the fundamental frequency observer have the same structure, and each includes a filtering module and a coefficient adjustment module based on the gradient descent method. The transfer function of the filtering module is:

[0028]

[0029] In the above formula, G i (z) is the transfer function of the filtering module, Z is the transform function, and k r ω is the resonant gain, ω0 is the resonant angular frequency, ω c Let z be the cutoff frequency, z be a complex variable, and T be the frequency. s The sampling period.

[0030] Optionally, the coefficient adjustment module based on gradient descent method processes the input signal as follows:

[0031] S201, the output sinusoidal signal x of the filter module i The signal (n) and the error signal are superimposed and then amplified.

[0032] S202, the amplified signal and the coefficients W updated online using the gradient descent method are... i Summation of (n+1);

[0033] S203, the summation result obtained by passing the output of the open-loop module is compared with the sinusoidal signal x output by the filter module. i (n) The two signals are superimposed and used as the fundamental signal of the extended back electromotive force signal output, and at the same time as the input of the gradient descent method.

[0034] Optionally, the coefficient W updated online in step S202 using the gradient descent method i The expression for the computation function of (n+1) is:

[0035] W i (n+1)=W i (n)+με(n)x i (n)

[0036] In the above formula, W i ε(n) is the input to the gradient descent method, μ is the learning rate parameter, ε(n) is the error signal, and x... i (n) represents the output sinusoidal signal of the filter module.

[0037] Optionally, the function expression of the transfer function of the open-loop module in step S203 is:

[0038]

[0039] In the above formula, μ is the learning rate parameter, and C is the output sinusoidal signal x of the filtering module. iThe amplitude of (n), ω0 is the resonant angular frequency, and T is the output sinusoidal signal x of the filter module. i The period of (n).

[0040] Optionally, step S103 includes:

[0041] S301, the orthogonal phase-locked loop calculates the rotor position angle error according to the following formula:

[0042]

[0043] In the above formula, and These are the fundamental signals of the extended back electromotive force signals along the α and β axes, respectively. This is the identification value for the rotor position angle;

[0044] S302, the quadrature phase-locked loop identifies the rotor position angle in real time according to the following formula:

[0045]

[0046] In the above formula, Here, is the identification value of the rotor position angle, s is the Laplace operator, and k is the value of the rotor position angle. p and k i The coefficients of the quadrature phase-locked loop (PLL) are... This represents the error in the rotor position angle;

[0047] S303, the motor speed is obtained by differentiating the identified value of the rotor position angle.

[0048] In addition, the present invention provides a real-time identification system for position parameters of a permanent magnet synchronous motor, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the real-time identification method for position parameters of the permanent magnet synchronous motor.

[0049] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, the computer program being programmed or configured by a microprocessor to execute the real-time identification method for position parameters of the permanent magnet synchronous motor.

[0050] Compared with the prior art, the present invention has the following main advantages:

[0051] 1. This invention applies an adaptive filtering-based sliding mode observer to a permanent magnet synchronous motor drive system. First, the invention obtains the extended back electromotive force (EMF) signal by inputting voltage and current in the sliding mode observer coordinate system. Then, an adaptive filter based on gradient descent is used to filter out harmonic components in the back EMF, outputting the fundamental frequency information of the extended back EMF. The motor speed and rotor position angle are identified in real time through a quadrature phase-locked loop. The identified speed value is applied to the speed loop, sliding mode observer, and adaptive filter, while the identified rotor position angle value is fed back to the coordinate transformation module. This invention can accurately identify motor position parameters in real time, improving the dynamic performance of sensorless operation of the built-in permanent magnet synchronous motor and effectively enhancing the reliability of the sensorless control system.

[0052] 2. The present invention includes using an adaptive filter based on gradient descent to filter out harmonic components in the back electromotive force, which significantly reduces the impact of harmonic errors on rotor position angle and speed identification and improves identification accuracy. Attached Figure Description

[0053] Figure 1 This is a block diagram illustrating the control principle of the method in an embodiment of the present invention.

[0054] Figure 2 This is a block diagram illustrating the control principle of the sliding mode observer and the orthogonal phase-locked loop in an embodiment of the present invention.

[0055] Figure 3 This is a block diagram illustrating the control principle of the adaptive filter in an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram illustrating the principle of online coefficient updating using the gradient descent method in an embodiment of the present invention.

[0057] Figure 5 The Bode plots of the adaptive filter under different learning rates are shown in the embodiments of the present invention. Detailed Implementation

[0058] like Figure 1 As shown, the real-time identification method for position parameters of a permanent magnet synchronous motor in this embodiment includes:

[0059] S101, the voltage and current of the permanent magnet synchronous motor are observed through a sliding mode observer to obtain the extended back electromotive force signal;

[0060] S102, the harmonic components of the extended back EMF signal are filtered out using an adaptive filter to obtain the fundamental signal of the extended back EMF signal;

[0061] S103 uses the fundamental signal of the extended back EMF signal to obtain the motor speed and rotor position angle of the permanent magnet synchronous motor through a quadrature phase-locked loop (PLL) to generate the control signal for the permanent magnet synchronous motor. See also... Figure 1It can be seen that the q-axis current parameter is obtained by subtracting the motor speed from the reference value of the motor speed and then controlling it through PI. The difference between the q-axis current parameter and the actual q-axis current is obtained by controlling it through PI to obtain the q-axis voltage reference value. The difference between the d-axis current parameter and the actual d-axis current is obtained by controlling it through PI to obtain the d-axis voltage reference value. The q-axis voltage reference value and the d-axis voltage reference value are transformed into the voltage in the α,β coordinate system. Then, after being modulated by SVPWM, the voltage is input to control the converter of the permanent magnet synchronous motor to control the operation of the permanent magnet synchronous motor.

[0062] In this embodiment, the function expression of the sliding mode observer in step S101 is:

[0063]

[0064] In the above formula, and The current identification value in the α,β coordinate system, u α and u β Let L be the voltage of the permanent magnet synchronous motor in the α,β coordinate system. q R is the q-axis inductance. s For stator resistance, and The extended back electromotive force in the α,β coordinate system is as follows: Figure 2 As shown, the functional expression for obtaining the extended back electromotive force signal by observing the voltage and current of the permanent magnet synchronous motor through a sliding mode observer is:

[0065]

[0066] In the above formula, and Let k be the extended back electromotive force in the α,β coordinate system. SMO For gain, 'a' is a positive constant coefficient. and The current identification value is in the α,β coordinate system. and i represents the current identification value in the α,β coordinate system. α and i β Let be the current in the α,β coordinate system; and the calculation function expression for the voltage of the permanent magnet synchronous motor in the α,β coordinate system is:

[0067]

[0068] In the above formula, u α and u β Let L be the voltage in the α,β coordinate system. q R is the q-axis inductance. s For the stator resistance, p = d / dt is the differential operator, e α and eβ Let i be the equivalent back electromotive force in the α,β coordinate system. α and i β Let be the current in the α,β coordinate system. The expression for the calculation function of the equivalent back electromotive force (equivalent back electromotive force model) in the α,β coordinate system is:

[0069]

[0070] In the above formula, λ f For the permanent magnet flux linkage of a permanent magnet synchronous motor, L d For d-axis inductance, λ a =λ f +(L d -L q )i d For effective flux linkage, ω e Let θ be the motor speed. e This is the rotor position angle of the motor.

[0071] The extended back electromotive force (EMF) in the α,β coordinate system output by the sliding mode observer is input into an adaptive filter to obtain the fundamental signal of the back EMF identification value. For example... Figure 3 As shown, the adaptive filter in step S102 of this embodiment includes:

[0072] An α-axis adaptive filtering unit is used to filter the input α-axis extended back EMF signal. After filtering out harmonic components, the fundamental signal of the extended back electromotive force signal along the α axis is obtained.

[0073] The β-axis adaptive filtering unit is used to filter the input β-axis extended back EMF signal. After filtering out harmonic components, the fundamental signal of the extended back electromotive force signal along the β axis is obtained.

[0074] In this embodiment, the α-axis adaptive filtering unit and the β-axis adaptive filtering unit have the same structure and both include:

[0075] The fifth harmonic observer is used to observe error signals to obtain the fifth harmonic back electromotive force.

[0076] The seventh harmonic observer is used to observe the error signal to obtain the seventh harmonic back electromotive force. The difference between the sum of the fifth harmonic back electromotive force, the seventh harmonic back electromotive force, and the fundamental signal of the output extended back electromotive force signal and the input extended back electromotive force signal constitutes the error signal.

[0077] The fundamental frequency observer is used to observe the error signal and the rotor frequency to obtain the fundamental signal of the extended back electromotive force signal output.

[0078] As can be seen from the above description, the α-axis adaptive filtering unit and the β-axis adaptive filtering unit have a recursive structure, which can adjust the coefficients online according to the changes in the input signal, and do not depend on parameter changes, thereby improving the performance and robustness of the built-in permanent magnet synchronous motor sensorless control system.

[0079] Taking the α-axis as an example, the adaptive filter has a three-layer structure, corresponding to the fundamental frequency, the fifth harmonic, and the seventh harmonic, respectively, and converts the back electromotive force... As the input signal, the fundamental frequency component of the back electromotive force was identified. As the output signal, the transfer function from the input signal to the error signal is:

[0080]

[0081] In the above formula, H i (z) It consists of the transfer function of the α-axis filtering module and the coefficient adjustment module based on gradient descent. In this embodiment, the fifth harmonic observer, the seventh harmonic observer, and the fundamental frequency observer have the same structure and all include a filtering module and a coefficient adjustment module based on gradient descent. The transfer function of the filtering module is:

[0082]

[0083] In the above formula, G i (z) is the transfer function of the filtering module, Z is the transform function, and k r ω is the resonant gain, ω0 is the resonant angular frequency, ω c Let z be the cutoff frequency, z be a complex variable, and T be the frequency. s The sampling period.

[0084] In this embodiment, the coefficient adjustment module based on gradient descent method processes the input signal as follows:

[0085] S201, the output sinusoidal signal x of the filter module i The signal (n) and the error signal are superimposed and then amplified.

[0086] S202, the amplified signal and the coefficients W updated online using the gradient descent method are... i Summation of (n+1);

[0087] S203, the summation result obtained by passing the output of the open-loop module is compared with the sinusoidal signal x output by the filter module. i (n) The two signals are superimposed and used as the fundamental signal of the extended back electromotive force signal output, and at the same time as the input of the gradient descent method.

[0088] Taking the fundamental frequency along the α-axis as an example, the transfer function G of the filter module corresponding to the fundamental frequency along the α-axis 1α The output signal x α1(n) is a sine sequence, which can be represented as:

[0089]

[0090] In the above formula, C and φ represent the frequency of the sinusoidal sequence. Given the amplitude and phase angle at a given point, the error signal ε α for:

[0091]

[0092] In the above formula, w iα (n) represents the coefficients updated online using the gradient descent method.

[0093] In this embodiment, the value function J(n) is constructed as follows:

[0094]

[0095] Where, ε α (n) is the error signal, W α (n),X α (n) is the vector at time n:

[0096] X α (n)=[x 1α (n)x 5α (n)x 7α (n)]

[0097] W α (n)=[w 1α (n)w 5α (n)w 7α (n)]

[0098] In the above formula, x 1α (n), x 5α (n) and x 7α (n) represent the 1st, 5th, and 7th harmonic signals, respectively, and w 1α (n), w 5α (n) and w 7α (n) represent the coefficients of the 1st, 5th, and 7th harmonic signals, respectively. Vector W is updated using the gradient descent method. α (n), making W α (n) reaches its minimum value, and the formula is:

[0099]

[0100] In the above formula, The gradient is used as the denominator. Therefore, the coefficient W updated online via gradient descent in step S202 of this embodiment can be obtained. i The expression for the computation function of (n+1) is:

[0101] W i (n+1)=W i (n)+με(n)x i (n)

[0102] In the above formula, W i ε(n) is the input to the gradient descent method, μ is the learning rate parameter, ε(n) is the error signal, and x... i (n) represents the output sinusoidal signal of the filter module.

[0103] The function expression of the transfer function of the open-loop module in step S203 of this embodiment is as follows:

[0104]

[0105] In the above formula, μ is the learning rate parameter, and C is the output sinusoidal signal x of the filtering module. i The amplitude of (n), ω0 is the resonant angular frequency, and T is the output sinusoidal signal x of the filter module. i The period of (n). Therefore, the adaptive filter with a three-layer structure in this embodiment, from the input signal e fα (z) to output signal transfer function F 1,5,7 (z) is:

[0106]

[0107] In the above formula, H1(z) is the transfer function of the open-loop module of the fundamental frequency observer.

[0108] like Figure 2 As shown, step S103 in this embodiment includes:

[0109] S301, the orthogonal phase-locked loop calculates the rotor position angle error according to the following formula:

[0110]

[0111] In the above formula, and These are the fundamental signals of the extended back electromotive force signals along the α and β axes, respectively. This is the identification value for the rotor position angle;

[0112] S302, the quadrature phase-locked loop identifies the rotor position angle in real time according to the following formula:

[0113]

[0114] In the above formula, Here, is the identification value of the rotor position angle, s is the Laplace operator, and k is the value of the rotor position angle. p and k i The coefficients of the quadrature phase-locked loop (PLL) are... This represents the error in the rotor position angle;

[0115] S303 differentiates the identified rotor position angle value to obtain the motor speed. The speed is then identified in real-time using a quadrature phase-locked loop. and rotor position angle This enables dual closed-loop control of the built-in permanent magnet synchronous motor.

[0116] Figure 4 To illustrate the basic principle of the gradient descent method used in this embodiment, from... Figure 4 It can be seen that the value function J(n) and the coefficient vector W α The relationship between (n): W α As the value function J(n) moves along the negative gradient direction of the value function J(n), the value function J(n) gradually decreases. After multiple iterations, the value function J(n) reaches its minimum value. Figure 5 This is the Bode plot of the adaptive filter under different learning rates in this embodiment. Figure 5 It can be seen that the learning rate can change the bandwidth of the adaptive filter, and different learning rates will affect the convergence speed and harmonic suppression accuracy of the adaptive filter. Therefore, a suitable learning rate should be chosen to achieve a balance between convergence speed and harmonic suppression accuracy. The amplitude of the adaptive filter's transfer function is 1 at the fundamental frequency, and negative at the frequencies corresponding to the fifth and seventh harmonics, thus exhibiting a strong suppression effect on the fifth and seventh harmonics.

[0117] In addition, the present invention provides a real-time identification system for position parameters of a permanent magnet synchronous motor, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the real-time identification method for position parameters of the permanent magnet synchronous motor.

[0118] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, the computer program being programmed or configured by a microprocessor to execute the real-time identification method for position parameters of the permanent magnet synchronous motor.

[0119] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time identification of position parameters of a permanent magnet synchronous motor, characterized in that, include: S101 uses a sliding mode observer to observe the voltage and current of the permanent magnet synchronous motor to obtain the extended back electromotive force signal: In the above formula, and for α,β Extended back electromotive force in coordinate system k SMO For gain, a Positive constant coefficients and for α,β Current identification value in coordinate system and for α,β Current in a coordinate system; S102, the harmonic components of the extended back EMF signal are filtered out using an adaptive filter to obtain the fundamental signal of the extended back EMF signal; S103, the fundamental signal of the extended back EMF signal is obtained through an orthogonal phase-locked loop to obtain the motor speed and rotor position angle of the permanent magnet synchronous motor in order to generate the control signal of the permanent magnet synchronous motor; The adaptive filter in step S102 includes: α Axis adaptive filtering unit, used for input... α Extended back EMF signal of the shaft After filtering out harmonic components, we obtain α The fundamental signal of the extended back electromotive force signal of the shaft ; β Axis adaptive filtering unit, used for input... β Extended back EMF signal of the shaft After filtering out harmonic components, we obtain β The fundamental signal of the extended back electromotive force signal of the shaft ; The α Axis adaptive filtering unit and β The axis-adaptive filtering units have the same structure and all include: The fifth harmonic observer is used to observe error signals to obtain the fifth harmonic back electromotive force. The seventh harmonic observer is used to observe the error signal to obtain the seventh harmonic back electromotive force. The difference between the sum of the fifth harmonic back electromotive force, the seventh harmonic back electromotive force, and the fundamental signal of the output extended back electromotive force signal and the input extended back electromotive force signal constitutes the error signal. The fundamental frequency observer is used to observe the error signal and the rotor frequency to obtain the fundamental signal of the extended back electromotive force signal output. The fifth harmonic observer, seventh harmonic observer, and fundamental frequency observer have the same structure and all include a filtering module and a coefficient adjustment module based on gradient descent. The transfer function of the filtering module is: In the above formula, This is the transfer function of the filtering module. For transformation function, For resonant gain, It is the resonant angular frequency. Let z be the cutoff frequency, and z be a complex variable. The sampling period; The coefficient adjustment module based on gradient descent performs the following processing on the input signal: S201, the output sinusoidal signal of the filter module The signal and the error signal are superimposed and then amplified. S202, the amplified signal and coefficients updated online using the gradient descent method. Summation; S203, the summation result obtained through the open-loop module is compared with the sinusoidal signal output from the filter module. The two signals are superimposed to form the fundamental signal of the extended back electromotive force signal output, and simultaneously serve as the input to the gradient descent method. The coefficients updated online in step S202 using gradient descent The expression for the computation function is: In the above formula, As the input for gradient descent, The learning rate parameter, For error signals, This is the output sinusoidal signal of the filtering module.

2. The method for real-time identification of position parameters of a permanent magnet synchronous motor according to claim 1, characterized in that, The function expression for the sliding mode observer in step S101 is: In the above formula, and for α,β Current identification value in coordinate system and For permanent magnet synchronous motors in α,β Voltage in coordinate system for q Shaft inductor, For stator resistance, and for α,β Extended back electromotive force in coordinate system; and permanent magnet synchronous motor in α,β The expression for calculating voltage in the coordinate system is: In the above formula, and for α,β Voltage in coordinate system for q Shaft inductor, For stator resistance, For differential operators, and for α,β Equivalent back electromotive force in coordinate system and for α,β Current in coordinate system α,β The expression for the equivalent back electromotive force in the coordinate system is: In the above formula, The permanent magnet flux linkage of a permanent magnet synchronous motor. for d Shaft inductor, For effective magnetic flux, This refers to the motor speed. This is the rotor position angle of the motor.

3. The method for real-time identification of position parameters of a permanent magnet synchronous motor according to claim 1, characterized in that, The function expression of the transfer function of the open-loop module in step S203 is: In the above formula, The learning rate parameter, C The output sinusoidal signal of the filter module amplitude, It is the resonant angular frequency. T The output sinusoidal signal of the filter module The cycle.

4. The method for real-time identification of position parameters of a permanent magnet synchronous motor according to claim 1, characterized in that, Step S103 includes: S301, The orthogonal phase-locked loop calculates the rotor position angle error according to the following formula: In the above formula, and They are respectively α、β The fundamental signal of the extended back electromotive force signal of the shaft. This is the identification value for the rotor position angle; S302, the quadrature phase-locked loop identifies the rotor position angle in real time according to the following formula: In the above formula, Let be the identification value of the rotor position angle, and s be the Laplace operator. and The coefficients of the quadrature phase-locked loop (PLL) are... This represents the error in the rotor position angle; S303, the motor speed is obtained by differentiating the identified value of the rotor position angle.

5. A real-time position parameter identification system for a permanent magnet synchronous motor, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to execute the real-time identification method for position parameters of a permanent magnet synchronous motor according to any one of claims 1 to 4.

6. A computer-readable storage medium storing a computer program, characterized in that, The computer program is used to be programmed or configured by a microprocessor to execute the real-time identification method for position parameters of a permanent magnet synchronous motor according to any one of claims 1 to 4.

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