Parameter on-line identification method of permanent magnet synchronous motor based on double kalman filter

Abstract

The present application relates to the technical field of motor control, and particularly relates to a parameter online identification method of a permanent magnet synchronous motor based on double Kalman filtering. The method comprises the following steps: S1: constructing a high-frequency voltage small signal injection model, processing the high-frequency voltage small signal injection model by using a discrete Fourier algorithm, and obtaining an identification formula of inductance and resistance; S2: based on the identification formula of inductance and resistance, constructing an identification state equation of resistance and flux linkage considering a current differential term, and splitting the identification state equation of resistance and flux linkage into a state filter and a parameter filter; S3: using the state filter and the parameter filter to correspondingly estimate state quantities and parameter quantities of a system to be identified, using a double Kalman filtering algorithm to perform parallel decoupling operation on the estimation results of the system to be identified, and online identifying resistance and flux linkage parameters. The present application can realize motor starting in the case that the initial value of the controller parameter is zero by reasonably arranging the identification sequence of the parameters.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and in particular to an online parameter identification method for a permanent magnet synchronous motor based on dual Kalman filtering. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in electric vehicles, electric ships, and other electric equipment due to their significant advantages in operating efficiency, speed regulation performance, and control precision. However, during actual operation, factors such as temperature and operating current cause stator inductance, stator resistance, and permanent magnet flux linkage parameters to frequently deviate from their nominal values, resulting in a mismatch between controller parameters and actual parameters, severely impacting control precision and stability. Traditional DC bias signal injection methods are affected by system operating conditions and are sensitive to external noise and vibration, limiting the accuracy of online parameter identification in practical applications. Summary of the Invention

[0003] In view of this, the present invention aims to provide an online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering, to solve the problem that traditional DC bias signal injection methods are affected by system operating conditions and are sensitive to external noise and vibration, thus limiting the accuracy of online parameter identification in practical applications. The present invention can achieve accurate online parameter identification under multiple operating conditions; it is insensitive to initial parameter values ​​and converges quickly under any initial value condition; it also achieves good identification results in applications with significant external noise and vibration; and by rationally arranging the parameter identification sequence, it can enable motor starting when the controller parameters are initially zero. This invention contributes a new approach to achieving accurate online parameter identification for permanent magnet synchronous motors.

[0004] To achieve the above objectives, the technical solution created by this invention is implemented as follows:

[0005] An online parameter identification method for a permanent magnet synchronous motor based on dual Kalman filtering specifically includes the following steps:

[0006] S1: Construct a high-frequency voltage small-signal injection model, and use the discrete Fourier algorithm to process the high-frequency voltage small-signal injection model to obtain the identification formulas for inductance and resistance.

[0007] S2: Based on the identification formulas of inductance and resistance, construct the identification state equations of resistance and magnetic flux considering the current differential term, and decompose the identification state equations of resistance and magnetic flux into state filters and parameter filters.

[0008] S3: Use state filters and parameter filters to estimate the state and parameter quantities of the system to be identified, and use the dual Kalman filter algorithm to perform parallel decoupling operations on the estimation results of the system to be identified, and identify the resistance and flux linkage parameters online.

[0009] Furthermore, in step S1, the expression for the high-frequency voltage small-signal injection model is:

[0010] ;

[0011] in, The initial phase angle of the high-frequency voltage small signal. To respond to the initial phase angle of the current signal, sin represents the sine operation. The resistance identification value under the high-frequency voltage small-signal injection model. The inductance identification value under the high-frequency voltage small-signal injection model. The angular frequency of a small high-frequency voltage signal. This represents the amplitude of a small high-frequency voltage signal. The amplitude of the response current signal is given, and t is time.

[0012] Furthermore, the formulas for identifying inductance and resistance are as follows:

[0013] ;

[0014] in, The inductance identification value under the high-frequency voltage small-signal injection model. This represents the amplitude of a small high-frequency voltage signal. The angular frequency of a small high-frequency voltage signal. In response to the amplitude of the current signal, The initial phase angle of the high-frequency voltage small signal. In response to the initial phase angle of the current signal, The resistance identification value is given under the high-frequency voltage small-signal injection model.

[0015] Furthermore, in step S2, the expression for the identification state equation considering the current differential term, resistance, and magnetic flux is:

[0016] ;

[0017] in, For state variables, For input variables, For output variables, for k The state variable at time t, for k Input variables at time, fork Output variables at time 10:00 for k The state variable at time +1 for k The state equation at time 10:00 for k The output equation at time t, To identify the resistance, To identify the magnetic chain, for d shaft current, for q shaft current, for d shaft voltage, for q shaft voltage, T The superscript is used for the transpose operation. for k The resistance of the time-of-flight identification, for k Real-time identification of magnetic chains, The inductance identification value under the high-frequency voltage small-signal injection model. The sampling period of the current loop. Electric angular velocity, for k Moment d shaft current, for k Moment q shaft current, for k Moment d shaft voltage, for k Moment q Shaft voltage.

[0018] Furthermore, in step S3, the parameter quantities of the system to be identified are estimated using a parameter filter:

[0019] ;

[0020] in, Let k be the prior values ​​of the parameters at time k. Let be the posterior value of the parameter at time k-1. Let be the prior value of the parameter covariance matrix at time k. Let be the posterior value of the parameter covariance matrix at time k-1. The system noise array is a parametric filter.

[0021] Furthermore, in step S3, the state variables of the system to be identified are estimated using a state filter:

[0022] ;

[0023] in, Let k be the prior value of the state variable at time k. The posterior value of the state variable at time k-1. Let k be the prior values ​​of the parameters at time k. Let k be the input variable at time k-1. Let be the state covariance matrix at time k. Let be the Jacobian matrix of the state equation with respect to the state variables. Let be the posterior value of the state covariance matrix at time k-1. Let be the transpose of the Jacobian matrix of the state equation with respect to the state variables. The system noise array is a state filter.

[0024] Furthermore, the calculation formula used for parallel decoupling of the estimation results of state variables and parameter variables using the dual Kalman filter algorithm is as follows:

[0025] ;

[0026] in, It is the parameter Kalman gain. Let be the prior value of the parameter covariance matrix at time k. Let be the transpose of the complete derivative of the parametric filter. The complete derivative of the parametric filter. The measurement noise array for the parameter filter. Let be the posterior value of the parameter covariance matrix at time k. Let k be the prior values ​​of the parameters at time k. Let k be the posterior value of the parameter at time k. for k The predicted residual at time.

[0027] Furthermore, the complete derivative of the parametric filter The solution formula is:

[0028] ;

[0029] Where d represents the differentiation operation with respect to the corresponding physical quantity. The posterior value of the state variable at time k-1. The posterior value of the parameter. Let h() be the Jacobian matrix of the output equation with respect to the state variables. Let f be the prior state value at time k, and f() be the state equation. Let be the posterior value of the parameter at time k-1. Let k be the input variable at time k-1. These are the posterior values ​​of the state variables. These are the prior values ​​of the state variables.

[0030] Furthermore, k Predicted residuals at time The calculation formula is:

[0031] ;

[0032] in, for k Output variables at time 10:00 yes k The output covariance matrix at time t. Let h be the posterior value of the state variable at time k, and h() be the output equation. The output equation is the Jacobian matrix for the state variables. Let be the prior value of the state covariance matrix at time k. This is the transpose of the Jacobian matrix of the output equation with respect to the state variables. The measurement noise array is for the state filter. For state Kalman gain, It is a unit array.

[0033] Furthermore, step S1 also includes constructing a predictive current controller model based on an extended state observer:

[0034] ;

[0035] in, for k Complex vector reference voltage at time t, for k The complex vector reference current at time t. , and These are the inductance, resistance, and flux linkage parameters of the predictive current controller. It is the sampling period of the current loop. Electric angular velocity, represent k Complex vector current estimation at time +1 for k Complex vector estimation lumped perturbation at time +1.

[0036] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0037] This invention presents an online parameter identification method for a permanent magnet synchronous motor based on dual Kalman filtering. It completes the identification process for resistance and flux linkage by decomposing the identification state equation into a state filter and a parameter filter, and using parallel decoupling operations to achieve online identification of resistance and flux linkage. Notably, because the dual Kalman filter of this invention has global convergence, it is insensitive to initial parameter values ​​and can converge quickly under arbitrary initial conditions. Furthermore, the noise immunity of the dual Kalman filter enables this invention to achieve good identification results even in applications with significant external noise and vibration. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0039] Figure 1 A schematic flowchart of the online parameter identification method for permanent magnet synchronous motor based on dual Kalman filtering as described in the embodiments of the present invention;

[0040] Figure 2 The high-frequency voltage small signal and response current signal vector phase diagram described in the embodiments of the present invention;

[0041] Figure 3 The flowchart of resistance and flux identification based on dual Kalman filters described in the embodiments of the present invention;

[0042] Figure 4 An overall architecture diagram of the online parameter identification method for permanent magnet synchronous motors described in the embodiments of the present invention;

[0043] Figure 5 The online inductor identification effect diagram based on the high-frequency voltage small-signal injection model described in the embodiment of the present invention is shown.

[0044] Figure 6 The diagram shows the effect of resistance and flux identification based on dual Kalman filters in the embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0047] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0050] like Figure 1 As shown, this invention proposes an online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering, specifically including the following steps: S1: Construct a high-frequency voltage small-signal injection model, and process the high-frequency voltage small-signal injection model using the Discrete Fourier algorithm to obtain identification formulas for inductance and resistance; S2: Based on the identification formulas for inductance and resistance, construct identification state equations for resistance and flux linkage considering the current differential term, and decompose the identification state equations for resistance and flux linkage into state filters and parameter filters; S3: Use the state filters and parameter filters to estimate the state variables and parameter variables of the system to be identified, and use the dual Kalman filtering algorithm to perform parallel decoupling operations on the estimation results of the system to be identified, and identify the resistance and flux linkage parameters online.

[0051] It should be noted that this invention first establishes a predictive current controller using an extended state observer in a permanent magnet synchronous motor. dThe first step involves injecting a high-frequency voltage small signal into the shaft and using the Discrete Fourier Transform algorithm to identify the inductance and resistance parameters online. The second step involves establishing an identification state equation (specifically, an identification state equation for resistance and flux linkage) considering the current differential term. This equation is then decomposed into a state filter and a parameter filter, and a dual Kalman filter algorithm is used for parallel decoupling operations to identify the resistance and flux linkage parameters online.

[0052] Furthermore, the amplitude of the high-frequency voltage small signal is selected as 0.01 times the rated voltage, i.e., 0.22V, and the frequency is selected as 1000Hz.

[0053] For surface-mounted permanent magnet synchronous motors, the discrete current loop model obtained using the Euler discretization method is as follows:

[0054] (1.1);

[0055] In the formula: represent k The complex vector voltage at time t satisfies ,in and Represent k Moment d shaft voltage and q shaft voltage, j Represents the imaginary unit; represent k The complex vector current at time +1 satisfies = ,in, and Represent k +1 time d shaft current and q shaft current; represent k The complex vector current at time t satisfies = ,in and Represent k Moment d shaft current and q shaft current; It is the sampling period of the current loop. , and These represent the actual inductance, resistance, and flux linkage parameters, respectively. It is electric angular velocity.

[0056] Introducing a linearly extended state observer to estimate and compensate dq Lumped disturbance of the axis, using express k The complex vector estimate of the current at time t satisfies = ,in and Represent k Time estimation d shaft current and estimation q Axis current. The observation law is designed as follows:

[0057] (1.2);

[0058] In the formula: represent k The complex vector estimated current at time +1 satisfies = ,in, and Represent k +1 time d Shaft estimation current and q Shaft estimation current, and Represent k Time and k Complex vector estimation lumped perturbation at time +1; It is the current estimation error, which satisfies ; , and These represent the inductance, resistance, and flux linkage parameters of the predictive current controller, respectively. and These represent the two gain coefficients of the extended state observer.

[0059] Configure gain coefficient and By aligning the poles of the characteristic polynomial in equation (1.2), we obtain the predictive current controller model based on the extended state observer:

[0060] (1.3);

[0061] In the formula: represent k The complex vector reference voltage at time t satisfies = ,in, and Represent k Moment d Shaft reference voltage and q Shaft reference voltage; represent k The complex vector reference current at time t satisfies = ,in and Representk Moment d Shaft reference current and q Shaft reference current. Considering the inverter's one-cycle delay, k- Complex vector reference voltage at time 1 and k Complex vector voltage at time t same.

[0062] As can be seen from equation (1.3), the parameters of the predictive current controller... , and Mismatch can affect the accuracy of the predictive current controller model, leading to the inability to maintain ideal predictive current control performance. This can manifest as abnormal phenomena such as undershoot or transient drop in current step response. Therefore, it is necessary to identify the inductance, resistance, and flux linkage parameters online.

[0063] This invention uses d Online inductance identification using the shaft high-frequency voltage small-signal injection method. dq The vector phase diagram of the shaft high-frequency voltage small signal and response current signal is as follows: Figure 2 As shown, Figure 2 middle It is the angular frequency of a small high-frequency voltage signal. and These are the amplitudes of the high-frequency voltage small signal and the response current signal, respectively. The phase difference is caused by the delay of the predictor current controller, which can be determined by the delay time. and total period of injected signal Sure: , and These are the amplitudes of the high-frequency voltage small signal and the response current signal, respectively, after considering the time delay. From Figure 2 The voltage equation based on the high-frequency voltage small-signal injection model can be obtained:

[0064] (1.4);

[0065] In the formula: and It is the initial phase angle between the high-frequency voltage small signal and the response current signal, and sin represents the sine operation. and These represent the resistance and inductance identification values ​​under the high-frequency voltage small-signal injection model, respectively.

[0066] The amplitude and phase of the high-frequency voltage small signal and the response current signal are extracted using the Discrete Fourier Transform algorithm, while keeping the injected signal frequency constant. Constantly, combined with equation (1.4), we obtain the identification formulas for inductance and resistance:

[0067] (1.5);

[0068] In the formula: cos represents cosine operation. As can be seen from equation (1.5), the identification process of inductance and resistance is only related to the amplitude, phase and frequency of the high-frequency voltage small signal and the response current signal, and is not affected by the basic voltage model of the permanent magnet synchronous motor. Therefore, it can effectively identify the inductance and resistance parameters under zero speed and zero torque conditions.

[0069] The method based on the high-frequency voltage small-signal injection model can simultaneously achieve multi-condition identification of inductors and resistors. However, as can be seen from equation (1.5), the denominator of the resistor identification formula does not have... The identification accuracy may be low due to the current differential term. Therefore, this invention considers the current differential term and re-establishes the identification state equations for resistance and magnetic flux as follows:

[0070] (1.6);

[0071] In the formula: , and These are state variables, input variables, and output variables. , and Represent k The state variables, input variables, and output variables at any given time. represent k The state variable at time +1 and Represent k The state equation and output equation at time t. and These represent the identification resistance and identification flux based on the differential model method, respectively. , , and Represent d shaft current, q shaft current, d shaft voltage and q Shaft voltage, superscript " T " indicates the transpose operation. and Represent k The identification resistance and magnetic flux are based on the differential model method. The state is determined based on equation (1.6). Decomposed into state variables and parameter quantity The Jacobian matrix for solving the state variables is given as follows:

[0072] (1.7);

[0073] In the formula: and These represent the Jacobian matrices of the state equation and the output equation with respect to the state variables, respectively. , Represent k- 1 moment and k The state quantity at any given time. yes k The number of parameters at time, yes k- Input variables at time 1; diacritics " ”,“ "" represents the posterior and prior values ​​of the corresponding physical quantities, respectively.

[0074] The state variables and parameters are estimated using state filters and parameter filters respectively, and discretized using the first-order Euler method. The parallel decoupling operation process in the dual Kalman filter is as follows:

[0075] (1) Parameter prediction

[0076] (1.8);

[0077] In the formula: yes k- The number of parameters at time 1 and Represent k Time and k- The parameter covariance matrix at time 1, It is the system noise array of the parametric filter.

[0078] (2) State Reconstruction

[0079] (1.9);

[0080] In the formula: and Represent k Time and k- The state covariance matrix at time 1 It is the system noise array of the state filter.

[0081] (3) Measurement update

[0082] (1.10);

[0083] In the formula: yes k The predicted residual at time, yes k The output covariance matrix at time t. It is the measurement noise array of the state filter. It is the state Kalman gain. Representative unit array.

[0084] (4) Calculation of the complete derivative

[0085] (1.11);

[0086] In the formula: d This represents the differentiation operation with respect to the corresponding physical quantity. It is the complete derivative of the parametric filter.

[0087] (5) Parameter correction

[0088] (1.12)

[0089] In the formula: It is the parameter Kalman gain. It is the measurement noise array of the parameter filter.

[0090] The calculation flowcharts for equations (1.8) to (1.12) are as follows: Figure 3 As shown, the identification results of resistance and flux linkage are included in k Parameters at time In the posterior values.

[0091] like Figure 4 As shown, inductance is identified based on a high-frequency voltage small-signal injection model, and resistance and flux linkage are identified based on a dual Kalman filter. Figure 4 In this context, PI represents the proportional-integral controller of the speed loop. The specific process is as follows: the current generates a reference voltage via a predictive current controller. This invention injects a high-frequency voltage small signal into the reference voltage to obtain the actual voltage. After inverse Park transform and vector modulation, the actual voltage is output as three-phase current via the inverter. The three-phase current is then subjected to Park transform and Clark transform to obtain... dq The shaft current is filtered out using a band-stop filter to remove the current signal at the frequency of the injected signal, preventing feedback. dq The shaft current has an impact.

[0092] The actual voltage is extracted using a bandpass filter. dq The components of the small signal with the same frequency as the shaft current and the injected small signal are used. The amplitude and phase of the high-frequency voltage small signal and the response current signal are extracted using the discrete Fourier algorithm and substituted into equation (1.5) to achieve inductor identification.

[0093] Based on the dual Kalman filter, combining equations (1.6)-(1.12) from the actual voltage and dq Identify resistance and flux linkage in shaft current.

[0094] like Figure 5As shown, the present invention injects d The shaft high-frequency voltage signal has a frequency of 1000Hz and an amplitude of 0.01 times the per-unit value. It increases the motor speed to 1000 r / min in 1 second, further increases it to 1500 r / min in 1.5 seconds, and suddenly applies the rated load torque in 2 seconds. As can be seen, despite changes in motor speed and load torque, the inductance curve does not show significant shifts or fluctuations, indicating that the present invention is also applicable to complex and changing operating conditions and transient motor conditions.

[0095] like Figure 6 As shown, Figure 6 Experimental conditions and Figure 5 Similar to the previous method, but with Gaussian white noise of a certain amplitude applied to the current input signal, it can be seen that even if the initial value of the estimated parameters is zero, it can quickly converge to the nominal value within 0.1s, indicating that the resistance and flux linkage identification method proposed in this invention has good convergence and convergence speed. The Gaussian white noise did not affect the correct convergence of resistance and flux linkage, indicating that the resistance and flux linkage identification method proposed in this invention has good noise resistance characteristics. Although the motor speed and load torque change, the identification resistance and flux linkage curves do not show obvious shifts or fluctuations, indicating that this invention is also applicable to complex changing operating conditions and motor transient situations.

[0096] In summary, the online parameter identification method for permanent magnet synchronous motors proposed in this invention can achieve accurate online parameter identification under complex and changing operating conditions, and has good convergence, convergence speed, and noise resistance. This invention helps to provide a new approach for achieving accurate online parameter identification of permanent magnet synchronous motors.

[0097] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0098] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for online parameter identification of a permanent magnet synchronous motor based on dual Kalman filtering, characterized in that: Specifically, the steps include the following: S1: Construct a high-frequency voltage small-signal injection model, and use the discrete Fourier algorithm to process the high-frequency voltage small-signal injection model to obtain the identification formulas for inductance and resistance. S2: Based on the identification formulas of inductance and resistance, construct the identification state equations of resistance and magnetic flux considering the current differential term, and decompose the identification state equations of resistance and magnetic flux into state filters and parameter filters. S3: Use state filters and parameter filters to estimate the state and parameter quantities of the system to be identified, and use the dual Kalman filter algorithm to perform parallel decoupling operations on the estimation results of the system to be identified, and identify the resistance and flux linkage parameters online.

2. The online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering according to claim 1, characterized in that: In step S1, the expression for the high-frequency voltage small-signal injection model is: ； in, The initial phase angle of the high-frequency voltage small signal. To respond to the initial phase angle of the current signal, sin represents the sine operation. The resistance identification value under the high-frequency voltage small-signal injection model. The inductance identification value under the high-frequency voltage small-signal injection model. The angular frequency of a small high-frequency voltage signal. This represents the amplitude of a small high-frequency voltage signal. The amplitude of the response current signal is given by t, which is time.

3. The online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering according to claim 1, characterized in that: The formulas for identifying inductors and resistors are: ； in, The inductance identification value under the high-frequency voltage small-signal injection model. This represents the amplitude of a small high-frequency voltage signal. The angular frequency of a small high-frequency voltage signal. In response to the amplitude of the current signal, The initial phase angle of the high-frequency voltage small signal. In response to the initial phase angle of the current signal, The resistance identification value is given under the high-frequency voltage small-signal injection model.

4. The online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering according to claim 1, characterized in that: In step S2, the expression for the identification state equation considering the current differential term, resistance, and flux linkage is: ； in, For state variables, For input variables, For output variables, for k The state variable at time t, for k Input variables at time, for k Output variables at time 10:00 for k The state variable at time +1 for k The state equation at time t, for k The output equation at time t, To identify the resistance, To identify the magnetic chain, for d shaft current, for q shaft current, for d shaft voltage, for q shaft voltage, T The superscript is used for the transpose operation. for k The resistance of the time-of-flight identification, for k Real-time identification of magnetic chains, The inductance identification value under the high-frequency voltage small-signal injection model. The sampling period of the current loop. Electric angular velocity, for k Moment d shaft current, for k Moment q shaft current, for k Moment d shaft voltage, for k Moment q Shaft voltage.

5. The online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering according to claim 1, characterized in that: In step S3, the parameter quantities of the system to be identified are estimated using a parameter filter: ； in, Let k be the prior values ​​of the parameters at time k. Let be the posterior value of the parameter at time k-1. Let be the prior value of the parameter covariance matrix at time k. Let be the posterior value of the parameter covariance matrix at time k-1. The system noise array is a parametric filter.

6. The online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering according to claim 5, characterized in that: In step S3, the state variables of the system to be identified are estimated using a state filter: ； in, Let k be the prior value of the state variable at time k. The posterior value of the state variable at time k-1. Let k be the prior values ​​of the parameters at time k. Let k be the input variable at time k-1. Let be the state covariance matrix at time k. Let be the Jacobian matrix of the state equation with respect to the state variables. Let be the posterior value of the state covariance matrix at time k-1. Let be the transpose of the Jacobian matrix of the state equation with respect to the state variables. The system noise array is a state filter.

7. The online parameter identification method for permanent magnet synchronous motors based on dual Kalman filtering according to claim 6, characterized in that: The calculation formula used for parallel decoupling of the estimation results of state variables and parameter variables using the dual Kalman filter algorithm is as follows: ； in, It is the parameter Kalman gain. Let be the prior value of the parameter covariance matrix at time k. Let be the transpose of the complete derivative of the parametric filter. The complete derivative of the parametric filter. The measurement noise array for the parameter filter. Let be the posterior value of the parameter covariance matrix at time k. Let k be the prior values ​​of the parameters at time k. Let k be the posterior value of the parameter at time k. for k The predicted residual at time.

8. The online parameter identification method for a permanent magnet synchronous motor based on dual Kalman filtering according to claim 7, characterized in that: Complete derivative of a parametric filter The solution formula is: ； Where d represents the differentiation operation with respect to the corresponding physical quantity. The posterior value of the state variable at time k-1. The posterior value of the parameter. Let h() be the Jacobian matrix of the output equation with respect to the state variables. Let f be the prior state value at time k, and f() be the state equation. Let be the posterior value of the parameter at time k-1. Let k be the input variable at time k-1. These are the posterior values ​​of the state variables. These are the prior values ​​of the state variables.

9. The online parameter identification method for a permanent magnet synchronous motor based on dual Kalman filtering according to claim 7, characterized in that: k Predicted residuals at time The calculation formula is: ； in, for k Output variables at time 10:00 yes k The output covariance matrix at time t. Let h be the posterior value of the state variable at time k, and h() be the output equation. This is the Jacobian matrix of the output equation for the state variables. Let be the prior value of the state covariance matrix at time k. This is the transpose of the Jacobian matrix of the output equation with respect to the state variables. The measurement noise array is for the state filter. For state Kalman gain, It is a unit array.

10. The online parameter identification method for a permanent magnet synchronous motor based on dual Kalman filtering according to claim 1, characterized in that: Step S1 also includes constructing a predictive current controller model based on an extended state observer: ； in, for k Complex vector reference voltage at time t, for k The complex vector reference current at time t. , and These are the inductance, resistance, and flux linkage parameters of the predictive current controller. It is the sampling period of the current loop. Electric angular velocity, represent k Complex vector current estimation at time +1 for k Complex vector estimation lumped perturbation at time +1.

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