Permanent magnet synchronous motor control method based on mechanical parameter self-identification technology

Abstract

The invention relates to a permanent magnet synchronous motor control method based on a mechanical parameter self-identification technology, and the method comprises the following steps: 1, building a motor observation model under the condition of considering disturbance, and designing a linear expansion state observer and a linear active disturbance rejection controller according to the motor observation model; 2, according to a total disturbance observation formula obtained by the linear expansion state observer, calculating an identification algorithm of the viscous friction coefficient and the rotational inertia of the motor; step 3, suppressing medium-high frequency disturbance by adopting a quasi-resonance controller; and 4, substituting the parameters obtained by the identification algorithm into the linear active disturbance rejection controller. According to the method, on-line identification of the viscous friction coefficient and the rotational inertia of the motor is realized on the premise that the permanent magnet synchronous motor maintains constant-speed operation by using the disturbance observation characteristic of the linear expansion state observer; error values caused by periodic torque pulsation in an identification result are eliminated through the quasi-resonance controller and the linear active disturbance rejection controller, and the identification accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a permanent magnet synchronous motor control method based on mechanical parameter self-identification technology. Background Art

[0002] In recent years, permanent magnet synchronous motor (PMSM) has been widely used in servo systems due to its advantages such as high efficiency, high power density, and high torque. However, during the operation of the motor, there are not only unknown external disturbances, but also its own electrical and mechanical parameters will change due to changes in the operating environment. Therefore, how to ensure that the permanent magnet synchronous motor can still achieve high-precision and high-efficiency operation in the face of these internal and external disturbances has become a hot topic of research today.

[0003] As a nonlinear control algorithm with low dependence on model parameters, the anti-disturbance control technology is widely used in permanent magnet synchronous motor control. The inaccuracy of the motor model parameters will be observed and compensated by the extended state observer together with the external disturbance as part of the internal disturbance. This compensation requires increasing the observer bandwidth to maintain the original controller performance, which will undoubtedly increase the burden on the observer. The prior art proposes to identify the motor parameters first, and then substitute the identification results into the controller to reduce the observer bandwidth, but the addition of the identification algorithm increases the redundancy of the system and increases the manufacturing cost. Summary of the invention

[0004] The present invention provides a permanent magnet synchronous motor control method based on mechanical parameter self-identification technology to solve the above technical problems.

[0005] In order to solve the above technical problems, the present invention provides a permanent magnet synchronous motor control method based on mechanical parameter self-identification technology, comprising the following steps:

[0006] Step 1: Establish a motor observation model under disturbance consideration, and design a linear extended state observer and a linear active disturbance rejection controller based on the motor observation model;

[0007] Step 2: Calculate the identification algorithm of the viscous friction coefficient and the moment of inertia of the motor according to the total disturbance observation formula obtained by the linear extended state observer;

[0008] Step 3: Use a quasi-resonant controller to suppress medium and high frequency disturbances;

[0009] Step 4: Substitute the parameters obtained by the identification algorithm into the linear active disturbance rejection controller.

[0010] Preferably, the mechanical equation of the motor observation model is:

[0011]

[0012] Among them, T e represents electromagnetic torque; J0 is the initial value of the motor's moment of inertia; ΔJ is the difference between the actual value and the initial value of the motor's moment of inertia, ω rd is the DC component of the speed; ω r Represents the mechanical angular velocity of the rotor; Δω r is the harmonic component of the speed; T L represents the load torque; B0 represents the initial value of the viscous friction coefficient; ΔB represents the difference between the initial value and the actual value of the viscous friction coefficient.

[0013] Preferably, the linear extended state observer is:

[0014]

[0015] Among them, z1, z2, are the estimated values ​​of x1, x2, and y respectively; l1 and l2 are the observer adjustment parameters; P is the number of magnetic poles of the motor, ψ f is the rotor flux.

[0016] Preferably, the adjustment parameters l1 and l2 of the linear extended state observer are related to the bandwidth ω of the linear extended state observer. eso The relationship between them is:

[0017]

[0018] Preferably, the control rate of the linear active disturbance rejection controller is:

[0019]

[0020] Among them, k p are the controller parameters of the linear active disturbance rejection controller; r is the expected value of the system input.

[0021] Preferably, the total disturbance observation formula is:

[0022]

[0023] Preferably, the viscous friction coefficient is calculated based on the total disturbance as:

[0024]

[0025] make:

[0026]

[0027] In K1, the linear extended state observer is used to calculate ωr and f are identified, and the identification results are z1 and z2 respectively.

[0028] Preferably, in K2, the identification value is:

[0029]

[0030] in,

[0031] Preferably, the identified value of the moment of inertia is:

[0032]

[0033] Preferably, the transfer function of the quasi-resonant controller is:

[0034]

[0035] Among them, k r is the resonance gain; ω0 is the resonance angular frequency; ω c is the resonance bandwidth.

[0036] Compared with the prior art, the permanent magnet synchronous motor control method based on mechanical parameter self-identification technology provided by the present invention has the following advantages:

[0037] 1. The present invention utilizes the disturbance observation characteristics of the linear extended state observer itself, and realizes the online identification of the motor's viscous friction coefficient and moment of inertia under the premise of maintaining constant speed operation of the permanent magnet synchronous motor, thereby reducing the redundancy of the system;

[0038] 2. The present invention eliminates the error value caused by periodic torque pulsation in the identification result through a quasi-resonance-auto-disturbance rejection controller, thereby improving the accuracy of identification and enhancing the control performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a design structure diagram of a linear extended state observer in a specific implementation mode of the present invention;

[0040] Figure 2 It is an equivalent diagram of a speed loop based on a quasi-proportional resonant controller in a specific implementation mode of the present invention;

[0041] Figure 3 A Bode diagram of a speed loop in a specific embodiment of the present invention;

[0042] Figure 4 A block diagram of a permanent magnet synchronous motor control system in a specific embodiment of the present invention;

[0043] Figure 5a and 5b They are respectively the harmonic suppression front in a specific embodiment of the present inventionK2 Analysis diagram of

[0044] Figure 6a and 6b They are respectively after harmonic suppression in a specific embodiment of the present invention. K2 Analysis diagram of

[0045] Figure 7 Comparison of identification diagrams for suppressing the front and rear viscous friction coefficient B in a specific embodiment of the present invention;

[0046] Figure 8 Comparison of identification diagrams for suppressing the front and rear moment of inertia J in a specific embodiment of the present invention;

[0047] Fig. 9 The speed response curves of the motor under the traditional auto-disturbance rejection control and the quasi-resonance-auto-disturbance rejection control of the present invention are shown. DETAILED DESCRIPTION

[0048] In order to describe the technical solution of the above invention in more detail, specific embodiments are listed below to demonstrate the technical effects; it should be emphasized that these embodiments are used to illustrate the present invention but not to limit the scope of the present invention.

[0049] The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology provided by the present invention comprises the following steps:

[0050] Step 1: Considering the existence of periodic and non-periodic disturbances in the operation of the permanent magnet synchronous motor control system, a motor observation model considering disturbances is established, and a linear extended state observer (LESO) and a linear active disturbance rejection controller are designed based on the motor observation model;

[0051] Step 2: Calculate the identification algorithm of the viscous friction coefficient and the moment of inertia of the motor according to the total disturbance observation formula obtained by the linear extended state observer;

[0052] Step 3: Analyze the anti-disturbance performance of the traditional ADRC and use a quasi-resonant controller to suppress medium and high frequency disturbances, thereby improving the identification accuracy of the motor parameters;

[0053] Step 4: Substitute the parameters obtained by the identification algorithm into the linear active disturbance rejection controller to improve the control performance of the active disturbance rejection controller.

[0054] The present invention realizes identification of viscous friction coefficient and moment of inertia by extracting disturbance information from an extended state observer, and combines a quasi-resonant controller with an anti-disturbance controller, thereby improving identification accuracy and control performance.

[0055] The present invention takes the surface-mounted permanent magnet synchronous motor as the research object, and the voltage equation established in the dq axis rotating coordinate system is:

[0056]

[0057] where u d 、u q Respectively represent the stator voltage of dq axis; i d 、i q Respectively represent the stator current of dq axis; ψ f represents the rotor flux; ω e Indicates the rotor electrical angular velocity; L s Represents the stator equivalent inductance; R s is the phase resistance of the stator winding.

[0058] The electromagnetic torque equation and motion equation of the permanent magnet synchronous motor can be expressed as:

[0059]

[0060] Where T e Represents electromagnetic torque; T L represents load torque; J represents moment of inertia; B represents viscous friction coefficient; ω r It represents the mechanical angular velocity of the rotor; P represents the number of pole pairs of the motor.

[0061] There are non-periodic and periodic disturbances in the permanent magnet synchronous motor control system. Among them, non-periodic disturbances are mainly caused by sudden changes in the motor's load torque and changes in the motor's parameters; periodic disturbances are mainly caused by factors such as cogging torque harmonics, motor flux harmonics, current measurement error harmonics, and inverter output harmonics. The harmonic frequency caused by periodic disturbances is mainly 6nω e , which will eventually act on the speed loop to cause harmonic speed, the specific form is as follows:

[0062]

[0063] Among them, ω rd is the DC component of the speed; Δω r is the harmonic component of the speed; ω rn is the amplitude of the speed harmonic component.

[0064] At the same time, considering that the moment of inertia and viscous friction coefficient may change during the operation of the motor, combining equations (2) and (3), the mechanical equation with disturbance is finally obtained:

[0065]

[0066] Among them, J0 is the initial value of the motor moment of inertia; ΔJ is the difference between the actual value and the initial value of the motor moment of inertia; B0 represents the initial value of the viscous friction coefficient; ΔB represents the difference between the initial value and the actual value of the viscous friction coefficient.

[0067] Formula (4) is used as the observation model of the linear extended state observer. At the same time, in order to facilitate the design of the observer, it is necessary to transform Formula (4) into an integral series system form, which is transformed into:

[0068]

[0069] Where f is the total disturbance that needs to be observed.

[0070] Let x1 = ω r , x2=f,u=i q , y=x1, and transform equation (5) into:

[0071]

[0072] in,

[0073] According to formula (6), the linear extended state observer is designed as:

[0074]

[0075] Among them, z1, z2, are the estimated values ​​of x1, x2, and y respectively; l1 and l2 are the observer adjustment parameters. The structure diagram of LESO is shown in Figure 1 shown.

[0076] The adjustment parameters l1, l2 of the linear extended state observer and the bandwidth ω of the linear extended state observer eso The relationship between them is:

[0077]

[0078] As long as we choose a suitable observer bandwidth ω eso , the extended state observer can track the system state variables accurately in real time.

[0079] The control rate of the linear active disturbance rejection controller is finally designed as:

[0080]

[0081] Among them, k p are the controller parameters of the linear active disturbance rejection controller; r is the expected value of the system input.

[0082] The use of accurate motor mechanical parameters can improve the performance of the linear active disturbance rejection controller. It can be seen from equation (5) that the disturbance term observed by the linear extended state observer includes the motor's viscous friction coefficient B and moment of inertia J. Therefore, the mechanical parameter identification can be achieved through the linear extended state observer.

[0083] Transform the total disturbance formula in equation (5):

[0084]

[0085] According to formula (10), the viscous friction coefficient B can be calculated. In which, let:

[0086]

[0087] The following is a detailed analysis of K1 and K2 respectively.

[0088] In K1, J0 and T L is the known quantity of the system, ω r and f are the unknown quantities of the system. Through LESO, we can know the ω of the system r and f are used for identification, and the identification results are z1 and z2 respectively. However, the LESO observed value and the actual value are not completely equal. LESO itself has the characteristics of a low-pass filter. When the observed result converges to the actual value, it will filter out some high-frequency disturbance signals. Therefore, replacing f with the observed disturbance z2 in equation (10) will not only not affect the identification result of B, but also improve the identification accuracy.

[0089] In K2, ΔJ is the unknown quantity of the system. From formula (3), we can see that Z p The DC component is 0, and the harmonic component is mainly 6n times the motor fundamental frequency. Therefore, K2 will not cause The identification results are biased, but it will affect The recognition accuracy is as follows. And as ΔJ increases, The recognition accuracy will gradually decrease. Since ΔJ is unknown, in order to improve The recognition accuracy can only be improved by reducing Z p The jitter, along with Z p The amplitude tends to 0, The recognition accuracy of is also gradually improved. Therefore, formula (10) can be transformed into:

[0090]

[0091] in, is the identification value of the viscous friction coefficient B.

[0092] Then the total disturbance formula in equation (5) is reshaped as follows:

[0093]

[0094] In formula (12), the denominator The DC component is 0. To avoid the denominator being 0, integrate both the numerator and denominator on the right side of the equation:

[0095]

[0096] Formula (13) integrates the actual value of the disturbance, and the disturbance z2 observed by LESO has part of the high-frequency signal filtered out due to the characteristics of low-pass filtering. From the above analysis of K2, it can be seen that the filtered high-frequency signal contains the information of the motor moment of inertia J, so z2 cannot be directly substituted into formula (13). According to formula (5), the disturbance value containing the complete moment of inertia information can be obtained The calculation formula is as follows:

[0097]

[0098] Will and Substituting into equation (14) at the same time, the identification value of the moment of inertia is obtained as follows:

[0099]

[0100] From equation (11), it can be seen that the periodic speed harmonic suppression capability of the ADRC directly determines the accuracy of parameter identification. According to equations (6) and (7), the state error equation of the linear extended state observer can be obtained:

[0101]

[0102] Among them, e1=z1-x1; e2=z2-x2.

[0103] Converting equations (7) and (16) to the frequency domain, we can obtain the transfer function G of the disturbance observation value: z2 (s) and the transfer function of the disturbance estimation error G e2 (s) is:

[0104]

[0105] By G z2 From the expression of (s), we can see that LESO is equivalent to a second-order low-pass filter in the frequency domain, so it can achieve error-free estimation of DC or slowly changing disturbances. However, when the disturbance has frequent periodic fluctuations, the disturbance estimation error will not converge to a stable value.

[0106] Then calculate G e2 (s) Amplitude-frequency response:

[0107]

[0108] For a frequency of ω h , the amplitude is A m The sinusoidal disturbance x2 = A m sin(ω h t), the disturbance estimation error e2 of LESO is still a sinusoidal signal, and its amplitude is:

[0109]

[0110] It can be seen from formula (19) that the frequency ω of the disturbance signal h When the frequency of the disturbance signal ω is low, the error amplitude is greatly attenuated and the disturbance estimation accuracy is high. h When the error amplitude is high, the LESO cannot effectively estimate the disturbance. At the same time, for disturbances of the same frequency, increasing the bandwidth of the LESO can effectively reduce the disturbance estimation error. However, too high a bandwidth will cause the ADRC technology to lose its noise suppression capability in the low frequency band.

[0111] Traditional anti-disturbance control can effectively suppress low-frequency disturbance signals; however, it cannot suppress disturbances in the medium and high frequency bands due to inaccurate estimation. In this regard, the present invention introduces a resonant controller, which uses the characteristic of infinite gain at the resonant frequency to suppress the medium and high frequency disturbance harmonics in the motor.

[0112] The transfer function of the ideal resonant controller is:

[0113]

[0114] Among them, k r is the resonance gain; ω0 is the resonance angular frequency.

[0115] It can be seen from equation (20) that the ideal resonant controller only works on the resonant angular frequency at ω0. However, in the actual system operation process, due to the errors in the sampling circuit and the accuracy limitations of the digital control system, the actual fundamental frequency of the motor will fluctuate around its ideal value, so it is difficult for the ideal resonant controller to achieve a good harmonic suppression effect. Therefore, by improving equation (20), we can get:

[0116]

[0117] Among them, ω c is the resonance bandwidth.

[0118] Equation (21) is an improved quasi-resonant controller. Although the high-frequency harmonic suppression capability of the controller is deteriorated, the suppressible harmonic frequency range is increased.

[0119] Combining equations (6) and (9), we can obtain the speed loop form equivalent to a first-order pure integral link:

[0120]

[0121] The quasi-resonant controller is connected in parallel with the proportional link in the equivalent speed loop to obtain a quasi-proportional resonant controller:

[0122]

[0123] The equivalent block diagram of the speed loop based on the quasi-proportional resonant controller is as follows: Figure 2 As shown. Figure 2 The closed-loop transfer function of the equivalent speed loop can be obtained as:

[0124]

[0125] Take ω0=100π, k p =10,ω c =1, k r =100.

[0126] According to formula (24), the Bode diagram of the speed loop can be obtained as follows: Figure 3 As shown. Figure 3 It can be seen that the signal amplitude near 50HZ is large and the phase lag is small, which can achieve the suppression of harmonic signals near the frequency of 50HZ. Therefore, the combination of the quasi-resonant controller and the linear anti-disturbance control controller can achieve the suppression of low-frequency disturbances of the motor and specified medium and high frequency disturbances. The quasi-resonant-anti-disturbance control can not only improve the steady-state performance of the system, but also improve the accuracy of the extended state observer in identifying the motor parameters. The specific system block diagram is as follows Figure 4 shown.

[0127] In order to verify the feasibility of the method proposed in this invention, a simulation model based on Figure 4 The specific simulation parameters of the motor are shown in Table 1.

[0128] Table 1 Permanent magnet synchronous motor simulation experiment parameters

[0129] parameter Numeric <![CDATA[Stator resistance R s / Ω]]> 0.96 <![CDATA[Stator inductance L s / H]]> 0.0085 <![CDATA[Rotor flux linkage ψ f / Wb]]> 0.1827 <![CDATA[Moment of inertia J / (kg·m 2 )]]> 0.003 Pole pair number P 4 Motor speed n / (r / min) 1000 Viscous friction coefficient B 0.0001

[0130] Before mechanical parameter identification, first verify the rationality of the above analysis of K2, and set appropriate resonant controller parameters according to the simulation results of K2. The waveform of K2 can be obtained by running the traditional vector control system, and the main harmonic components of K2 can be obtained by Fourier series analysis of the waveform (where the fundamental frequency ω is obtained according to the motor speed and pole pair number, that is, ω = 66.67Hz). The simulation results are as follows Figure 5a and 5b shown.

[0131] Depend on Figure 5a and 5b It can be seen that under traditional vector control, the DC component of K2 is 0, and the AC component is mainly composed of the 6nth harmonic of the motor fundamental frequency. Among them, the 6th harmonic component is the highest, accounting for 978.91% of the fundamental frequency; the 12th harmonic component accounts for 427.48% of the fundamental frequency.

[0132] Under the action of the quasi-resonant-auto-disturbance rejection controller, the suppressed K2 waveform and Fourier change analysis diagram can be obtained, as shown in Figure 6a and 6b shown.

[0133] Depend on Figure 6a and 6b It can be seen that under the action of the quasi-resonant controller, the fluctuation amplitude of K2 decreased by 70%. At the same time, the 6th harmonic component accounted for 45.77% of the fundamental wave, a decrease of 933.14%; the 12th harmonic component accounted for 111.37% of the fundamental wave, a decrease of 316.11%.

[0134] The viscous friction coefficient B and the moment of inertia J are identified according to formula (11) and formula (15). The traditional ADRC control system and the quasi-resonant ADRC control system proposed in this application are used for parameter identification. The identification results are shown in Figure 1. Figure 7 and Figure 8 shown.

[0135] Figure 7 The dashed line is the identification diagram of the viscous friction coefficient B before suppression, and the solid line is the identification diagram of the viscous friction coefficient B after suppression. It can be seen that through formula (11), the viscous friction coefficient B before and after suppression can converge to the true value. However, the identification error value of B before suppression is 120%; the identification error value of B after suppression is reduced to 30%.

[0136] Figure 8 The dashed line is the identification diagram of the moment of inertia J before suppression, and the solid line is the identification diagram of the moment of inertia J after suppression. It can be seen that through formula (15), the moment of inertia J before and after suppression can converge to the true value. However, the identification error value of J before suppression is 0.06%; after suppression, J The identification error value is reduced to 0.03%.

[0137] After obtaining the identification results, the motor target speed is set to 1000r / min. Substitute the identification results of the viscous friction coefficient B and the moment of inertia J into the quasi-resonant-ADRC and compare them with the traditional ADRC (i.e., the mechanical parameters are unknown) to obtain the motor speed response curve, as shown in the figure. Fig. 9 shown.

[0138] Fig. 9 The dashed line is the speed response curve of the motor under the traditional anti-disturbance control, and the initial value of the moment of inertia is set to 2 / 3 of the actual value; the solid line is the speed response curve of the new anti-disturbance controller after the identified mechanical parameters are substituted. It can be seen that the motor speed has an overshoot of 0.6% under the traditional control; after adopting the new controller, the system shortens the adjustment time and achieves no overshoot.

[0139] In summary, the permanent magnet synchronous motor control method based on mechanical parameter self-identification technology provided by the present invention includes the following steps: Step 1: Considering that there are periodic and non-periodic disturbances in the operation process of the permanent magnet synchronous motor control system, a motor observation model considering disturbances is established, and a linear extended state observer and a linear anti-disturbance control controller are designed according to the motor observation model; Step 2: According to the total disturbance observation formula obtained by the linear extended state observer, an identification algorithm of the viscous friction coefficient and the moment of inertia of the motor is calculated; Step 3: The anti-disturbance performance of the traditional anti-disturbance control controller is analyzed, and a quasi-resonant controller is used to suppress medium and high frequency disturbances, thereby improving the identification accuracy of the motor parameters; Step 4: Substitute the parameters obtained by the identification algorithm into the linear anti-disturbance control controller to improve the control performance of the anti-disturbance control controller. The present invention realizes the identification of the viscous friction coefficient and the moment of inertia by extracting the disturbance information in the extended state observer. And the quasi-resonant controller is combined with the anti-disturbance control controller, and the identification accuracy and control performance are improved at the same time.

[0140] Obviously, those skilled in the art can make various changes and modifications to the invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the invention fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A permanent magnet synchronous motor control method based on mechanical parameter self-identification technology, characterized in that: The steps include: Step 1: Establish a motor observation model under disturbance consideration, and design a linear extended state observer and a linear active disturbance rejection controller based on the motor observation model; Step 2: Calculate the identification algorithm of the viscous friction coefficient and the moment of inertia of the motor according to the total disturbance observation formula obtained by the linear extended state observer; Step 3: Use a quasi-resonant controller to suppress medium and high frequency disturbances; Step 4: Substitute the parameters obtained by the identification algorithm into the linear active disturbance rejection controller.

2. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 1, characterized in that: The mechanical equation of the motor observation model is: Among them, T e represents electromagnetic torque; J0 is the initial value of the motor's moment of inertia; ΔJ is the difference between the actual value and the initial value of the motor's moment of inertia, ω rd is the DC component of the speed; ω r Represents the mechanical angular velocity of the rotor; Δω r is the harmonic component of the speed; T L represents the load torque; B0 represents the initial value of the viscous friction coefficient; ΔB represents the difference between the initial value and the actual value of the viscous friction coefficient.

3. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 2 is characterized in that: The linear extended state observer is: Among them, z1, z2, are the estimated values ​​of x1, x2, and y respectively; l1 and l2 are the observer adjustment parameters; P is the number of magnetic poles of the motor, ψ f is the rotor flux.

4. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 3 is characterized in that: The adjustment parameters l1, l2 of the linear extended state observer and the bandwidth ω of the linear extended state observer eso The relationship between them is:

5. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 4 is characterized in that: The control rate of the linear active disturbance rejection controller is: Among them, k p are the controller parameters of the linear active disturbance rejection controller; r is the expected value of the system input.

6. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 5, characterized in that: The total disturbance observation formula is:

7. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 6 is characterized in that: The viscous friction coefficient is calculated based on the total disturbance as: make: In K1, the linear extended state observer is used to calculate ω r and f are identified, and the identification results are z1 and z2 respectively.

8. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 7 is characterized in that: In K2, the identification value is: in, 9. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 8, characterized in that: The identified value of the moment of inertia is:

10. The permanent magnet synchronous motor control method based on mechanical parameter self-identification technology according to claim 9, characterized in that: The transfer function of the quasi-resonant controller is: Among them, k r is the resonance gain; ω0 is the resonance angular frequency; ω c is the resonance bandwidth.

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