Permanent magnet synchronous motor position decoupling online parameter identification method based on extended Kalman filtering

Abstract

The invention relates to a permanent magnet synchronous motor position decoupling online parameter identification method based on extended Kalman filtering, and belongs to the technical field of permanent magnet synchronous motor control. The method comprises the following steps: respectively establishing permanent magnet synchronous motor voltage equations in a static coordinate system under a zero vector and an effective vector; voltage information is directly read according to the on-off state of the power device, and the on-off state information is substituted into alpha-beta axis conversion to carry out nonlinear compensation on the voltage; obtaining harmonic information of the stator current according to the equation; changing the equation to obtain an equation set which is decoupled from the position of the rotor and contains independent information; and establishing a state equation and an observation equation of an extended Kalman filtering algorithm according to the equation set under high-frequency current injection or a dynamic working condition, and carrying out online parameter identification. According to the method, the permanent magnet synchronous motor model irrelevant to the rotor position is established, parameter identification is carried out through extended Kalman filtering, and accurate identification of online parameters under the dynamic working condition is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of permanent magnet synchronous motor control, and relates to a position decoupling online parameter identification method for a permanent magnet synchronous motor based on extended Kalman filtering. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) have been widely used in the servo motor field due to their significant advantages, including high efficiency, high power density, and excellent control performance. PMSMs play a key role in numerous applications, such as industrial automated production lines, robotic joint drives, and aerospace equipment. However, accurate model parameters are indispensable for achieving high-precision control and excellent dynamic response. Only with accurate model parameters can the motor ensure stable and efficient operation under various operating conditions, meeting the stringent performance requirements of different application scenarios.

[0003] Traditional permanent magnet synchronous motor parameter identification methods primarily rely on voltage equations established in the dq-axis coordinate system. The dq-axis coordinate system is a commonly used coordinate system for motor analysis. By converting the motor's three-phase stationary coordinate system into a dq-axis rotating coordinate system, the motor's mathematical model can be greatly simplified, facilitating the analysis and calculation of various motor parameters. In practice, the voltage injection method is often used to provide a sufficient number of linearly independent equations for parameter identification. The voltage injection method injects a specific voltage signal into the motor windings to stimulate the motor's response, thereby obtaining data such as the motor's voltage and current. This data contains information about the motor parameters. Through processing and analysis, multiple linearly independent equations can be obtained. Finally, statistical methods are used to calculate the various motor parameters. Statistical methods can process and analyze large amounts of experimental data, solving for motor parameters such as resistance, inductance, and flux linkage using algorithms such as least squares and maximum likelihood estimation.

[0004] However, this method has certain limitations. Specifically, the dq-axis voltages in the equations are not actual measurements of the motor terminal voltages, but rather estimates obtained by applying power device compensation to the reference voltage output by the control system's current loop. Furthermore, these estimates exhibit a strong correlation with the measured motor position.

[0005] Rotor position errors are ubiquitous in all aspects of permanent magnet synchronous servo control systems. These errors negatively impact the accuracy of parameter identification using traditional methods. For example, when rotor position errors exist, the dq-axis voltages estimated based on the erroneous position information will also deviate. This, in turn, causes the motor parameters calculated using statistical methods to deviate from their true values, reducing parameter identification accuracy. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an online parameter identification method for permanent magnet synchronous motor position decoupling based on extended Kalman filtering, which divides the voltage into zero vector and effective vector according to the action of the voltage vector, extracts the current differential of the motor under the action of different vectors, utilizes the harmonic information contained therein to improve the independent information contained in the equation group, decouples the rotor position from the model, and finally identifies the nonlinear equations through the extended Kalman filtering method to achieve accurate identification of the electrical parameters of the permanent magnet synchronous motor.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for online parameter identification of position decoupling of a permanent magnet synchronous motor based on an extended Kalman filter comprises the following steps:

[0009] Establish the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under zero vector and effective vector respectively;

[0010] The voltage information is directly read according to the switching state of the power device of the permanent magnet synchronous motor, and the switching state information is substituted into the αβ axis transformation to perform nonlinear compensation on the voltage.

[0011] The harmonic information of the stator current is obtained according to the permanent magnet synchronous motor voltage equation in the stationary coordinate system under the zero vector and the effective vector;

[0012] By changing the permanent magnet synchronous motor voltage equation in the stationary coordinate system under the zero vector and the effective vector, a set of equations containing independent information for decoupling the rotor position is obtained;

[0013] When injecting high-frequency current or in dynamic working conditions, the state equation and observation equation of the extended Kalman filter algorithm are established according to the above equations, and online parameter identification is performed through the extended Kalman filter algorithm.

[0014] Furthermore, the zero vector and effective vector are established as follows to write the permanent magnet synchronous motor voltage equation in the stationary coordinate system:

[0015]

[0016] Where u α_z ,u β_z are the zero vector voltages of the α and β axes, i α_z ,i β_z are the zero vector currents of α and β axes respectively; u α_a ,u β_a are the effective vector voltages on the α and β axes, i α_a ,i β_a are the effective vector currents of α and β axes respectively; R s is the stator resistance, L sis the stator inductance, p=d / dt is the differential operator, ω e is the electrical angular velocity of the motor rotor, ψ f is the permanent magnet flux, θ e Indicates the rotor electrical angle.

[0017] Furthermore, the switching state of the power device is substituted into the equation group after the αβ axis transformation and expressed as:

[0018]

[0019] Where u a ,u b ,u c Represent the three-phase voltage, u ab ,u ac ,u bc Represent the three-phase line voltage, V dc Indicates bus voltage, S a ,S b ,S c Respectively represent the switching status of the three-phase bridge arms.

[0020] Furthermore, the stator current L is obtained according to the permanent magnet synchronous motor voltage equation in the stationary coordinate system under zero vector and effective vector. s Expressed as:

[0021]

[0022] Therefore, the inductance information can be preliminarily identified based on the voltage and current derivatives under the action of the effective vector and the zero vector.

[0023] Furthermore, the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under the zero vector and the effective vector is transformed to decouple the rotor position. The transformed equation group is expressed as:

[0024]

[0025] At the same time, there is only one set of equations, and the parameter coefficient matrix is rank-deficient; when high-frequency current is injected or under dynamic conditions, the set of equations has sufficient independent information for parameter identification.

[0026] Furthermore, the extended Kalman filter algorithm is used for identification, and its state equation and observation equation are expressed as follows:

[0027]

[0028] Stator inductance L s It is identified by the least squares method and input into the equation as a known value.

[0029] Furthermore, the process of extending the Kalman filter algorithm for identification is:

[0030] Linearize and discretize nonlinear systems;

[0031] Taylor expansion is performed on the discretized state equation f(·) and observation equation h(·);

[0032] The current state is predicted based on the state estimate at the previous moment, and the Kalman gain is introduced to correct the predicted value. The new observation value is integrated into the existing state estimate based on the current state prediction covariance and observation noise covariance to complete parameter identification.

[0033] Furthermore, the linearization and discretization process of the nonlinear system can be expressed as:

[0034] x k =f(x k-1 ,u k )+w k-1

[0035] y k =h(x k )+v k

[0036] Among them, x k-1 ,x k Indicates the estimated state value at the previous moment and the current moment, u k represents the control input at time k, y k represents the observed variable at time k; f(·) and h(·) are the state equation and observation equation of the system respectively; w k-1 is the process noise vector, which represents the uncertainty or interference in the state transition process; v k is the observation noise vector, which reflects the error and uncertainty in the measurement process.

[0037] Furthermore, the first-order Taylor expansion of the state equation f(·) and the observation equation h(·) is expressed as:

[0038]

[0039] Where, F k-1 and H k The Jacobian matrices of the state equation and the observation equation are respectively, x k|k-1 Represents the prior state estimate at time k.

[0040] Furthermore, the process of predicting the current state based on the state estimate at the previous moment is expressed as:

[0041]

[0042] The Kalman gain is calculated as follows:

[0043]

[0044] Where, P k|k-1 is the prior state covariance matrix at time k, H k is the Jacobian matrix of the observation equation, R k is the observation noise covariance matrix;

[0045] The predicted value after Kalman gain correction is expressed as:

[0046]

[0047] Where z k -h(x k|k-1 ) is the observation residual, which reflects the difference between the actual observation value and the observation value predicted based on the prior state estimate; the state prediction value and the system measurement value are combined through the Kalman gain to obtain the parameter estimate at the next moment;

[0048] The Kalman gain reflects the uncertainty of the state and the characteristic changes of the observation noise in real time based on the update of the covariance matrix:

[0049]

[0050] P k =(IK k H k )P k|k-1

[0051] Where I is the identity matrix, K k is the Kalman gain at time k, Q k-1 is the process noise covariance matrix; by repeatedly predicting and updating, the true state of the system is gradually approached in the nonlinear system to complete parameter identification.

[0052] The beneficial effects of the present invention are:

[0053] The present invention proposes an online parameter identification method for permanent magnet synchronous motor position decoupling based on extended Kalman filtering, proposes an innovative permanent magnet synchronous motor model that is independent of rotor position, and realizes accurate parameter identification with the help of a nonlinear algorithm.

[0054] By constructing a motor model that is independent of rotor position, this method effectively eliminates the interference of rotor position changes on parameter identification, improving the accuracy and stability of identification. Furthermore, by leveraging the nonlinear processing capabilities of the extended Kalman filter algorithm, it enables real-time and accurate identification of motor electromagnetic parameters under dynamic operating conditions, providing strong support for precise motor control and performance optimization.

[0055] The present invention also has strong robustness and adaptability, and can cope with various uncertainties and interferences during motor operation. Through online parameter identification, changes in motor performance can be detected in a timely manner, providing an important basis for motor fault diagnosis and preventive maintenance.

[0056] The present invention plays an important role in improving the operating efficiency, reliability and safety of permanent magnet synchronous motors and has broad application prospects.

[0057] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0059] Figure 1 Schematic diagram of the overall process of an online parameter identification method for position decoupling of a permanent magnet synchronous motor based on an extended Kalman filter according to an embodiment of the present invention;

[0060] Figure 2 Schematic diagram of the bridge arm conduction state under SVPWM modulation according to an embodiment of the present invention, wherein: Figure 2 (a) is a schematic diagram of the voltage vector space, Figure 2 (b) is a schematic diagram of the 7-segment SVPWM switching state;

[0061] Figure 3 Schematic diagram of resistance identification results of the permanent magnet synchronous motor position decoupling online parameter identification method according to an embodiment of the present invention;

[0062] Figure 4 Schematic diagram of flux linkage identification results of a position decoupling online parameter identification method for a permanent magnet synchronous motor according to an embodiment of the present invention;

[0063] Figure 5 Schematic diagram of inductance identification results of the permanent magnet synchronous motor position decoupling online parameter identification method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0065] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0066] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0067] See also Figures 1 to 5 , which is an online parameter identification method for permanent magnet synchronous motor position decoupling based on extended Kalman filter.

[0068] Example 1

[0069] This embodiment first provides a detailed implementation process of a permanent magnet synchronous motor position decoupling online parameter identification method based on extended Kalman filtering, such as Figure 1 As shown, it specifically includes the following steps:

[0070] S1. Establish the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under zero vector and effective vector respectively;

[0071] S2. Directly read voltage information according to the switching state of the power device of the permanent magnet synchronous motor, and substitute the switching state information into the αβ axis transformation to perform nonlinear compensation on the voltage;

[0072] S3. Obtaining harmonic information of the stator current according to the permanent magnet synchronous motor voltage equation in the stationary coordinate system under the zero vector and the effective vector;

[0073] S4. According to the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under the zero vector and the effective vector, a group of equations containing independent information for decoupling the rotor position is obtained;

[0074] S5. Under high-frequency current injection or dynamic working conditions, the state equation and observation equation of the extended Kalman filter algorithm are established according to the above equation group, and online parameter identification is performed through the extended Kalman filter algorithm.

[0075] In step S1 of this embodiment, the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system is written below the zero vector and the effective vector respectively:

[0076]

[0077] Where u α_z ,u β_z are the zero vector voltages of the α and β axes, i α_z ,i β_z are the zero vector currents of α and β axes respectively; u α_a ,u β_a are the effective vector voltages on the α and β axes, i α_a ,i β_a are the effective vector currents of α and β axes respectively; R s is the stator resistance, L s is the stator inductance, p=d / dt is the differential operator, ω e is the electrical angular velocity of the motor rotor, ψ f is the permanent magnet flux, θ e Indicates the rotor electrical angle.

[0078] Compared with the traditional voltage model, the equations include harmonic information, and the voltage can be directly read from the switching state of the power device, which is independent of the rotor position. Figure 2 The schematic diagram of the bridge arm conduction state under SVPWM modulation is shown in FIG. Figure 2 (a) is a schematic diagram of the voltage vector space, Figure 2 (b) is a schematic diagram of the 7-segment SVPWM switching state. It can be seen from the figure that the instantaneous voltage actually applied to the motor is voltage chopping, and the voltage under the αβ axis is directly related to the bus voltage amplitude.

[0079] In step S2 of this embodiment, the switch state is substituted into the Clark transformation to be expressed as:

[0080]

[0081] Where u a ,u b ,u c Represent the three-phase voltage, u ab ,u ac ,u bc Represent the three-phase line voltage, V dc Indicates bus voltage, S a ,S b ,S c Respectively represent the switching status of the three-phase bridge arms.

[0082] After nonlinear compensation, the voltage estimate can be guaranteed to be independent of the rotor position, and the compensation does not need to consider the inverter dead time and switching delay time, which improves the robustness of the voltage signal compared with the traditional model.

[0083] In step S3 of this embodiment, the voltage drop change on the resistance within one cycle of the stator current is negligible, and the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under the zero vector and the effective vector is solved to obtain:

[0084]

[0085] After obtaining the harmonic information of the current, no other information is needed to identify the inductance. Only the voltage and current derivatives under the effective vector and zero vector are needed.

[0086] In step S4 of this embodiment, in order to decouple the equation from the rotor position, the permanent magnet synchronous motor voltage equation in the stationary coordinate system under the zero vector and the effective vector is phase-shifted and rearranged, and both sides of the equation are squared to obtain:

[0087]

[0088] Although there is only one equation at a time and the parameter coefficient matrix is rank-deficient, the system of equations contains sufficient independent information for parameter identification when injecting high-frequency current or in dynamic conditions.

[0089] In step S5 of this embodiment, the parameters are finally identified online using a nonlinear algorithm. To ensure rapid convergence of the identification results, an extended Kalman filter algorithm is used. The concept is to estimate the state at the next moment based on the system state equation and then correct the estimated value using the observation equation. This method can effectively identify parameters containing nonlinear terms.

[0090] The state equation and observation equation of EKF can be expressed as:

[0091]

[0092] Stator inductance L sIt is identified by the least squares method and input into the equation as a known value.

[0093] Then, the extended Kalman filter algorithm is used for identification, and the process is as follows:

[0094] S51. First, linearize and discretize the nonlinear system:

[0095] x k =f(x k-1 ,u k )+w k-1

[0096] y k =h(x k )+v k

[0097] Among them, x k-1 ,x k Indicates the estimated state value at the previous moment and the current moment, u k represents the control input at time k, y k represents the observed variable at time k; f(·) and h(·) are the state equation and observation equation of the system respectively; w k-1 is the process noise vector, which represents the uncertainty or interference in the state transition process; v k is the observation noise vector, which reflects the error and uncertainty in the measurement process.

[0098] S52. After linearizing the nonlinear system, perform first-order Taylor expansion on f(·) and h(·):

[0099]

[0100] Where, F k-1 and H k The Jacobian matrices of the state equation and the observation equation are respectively, x k|k-1 Represents the prior state estimate at time k.

[0101] S53. After obtaining the linearized equations, the current state can be predicted based on the state estimate at the previous moment:

[0102]

[0103] In order to ensure the correctness of the parameter estimates, the Kalman gain is introduced to correct the predicted values. The Kalman gain is used to determine how to integrate the new observations into the existing state estimates based on the current state prediction covariance and observation noise covariance. The calculation method is as follows:

[0104]

[0105] Where Pk|k-1 is the prior state covariance matrix at time k, H k is the Jacobian matrix of the observation equation, R k is the observation noise covariance matrix.

[0106] The Kalman gain can automatically adjust the update amplitude of the state estimation according to the dynamic characteristics of the system and the quality of the observation data, so that the estimation result is closer to the actual state of the system.

[0107]

[0108] Where z k -h(x k|k-1 ) is the observation residual, reflecting the difference between the actual observed value and the predicted value based on the prior state estimate. During the update phase of the extended Kalman filter, this residual is used to measure the degree of inconsistency between the observed and predicted information during state and covariance updates, and is an important basis for correcting the state estimate. The state prediction value and the system measurement value are combined through the Kalman gain to obtain the parameter estimate for the next moment.

[0109] During system operation, the uncertainty of the state and the characteristics of the observation noise may change. The Kalman gain needs to reflect these changes in real time by updating the covariance matrix:

[0110]

[0111] P k =(IK k H k )P k|k-1

[0112] Where I is the identity matrix, K k is the Kalman gain at time k, Q k-1 is the process noise covariance matrix. By repeating the two stages of prediction and update, the extended Kalman filter can gradually approach the true state of the system in a nonlinear system, providing an effective solution for state estimation of nonlinear systems.

[0113] Example 2

[0114] This embodiment conducts simulation experiments on the relevant contents of the permanent magnet synchronous motor position decoupling online parameter identification method based on extended Kalman filtering given in Example 1.

[0115] A Simulink permanent magnet synchronous motor control system simulation model was built using Matlab. The current differential under different voltage vectors was calculated by oversampling the current. The data used in the model can be obtained from the switching state of the power device and direct measurement. In order to achieve online parameter identification, parameter identification is only performed once per PWM cycle. The final identification results of the parameter identification method are compared with the actual motor data. Figure 3-Figure 5 As shown, Figure 3 Schematic diagram of the resistance identification result of the permanent magnet synchronous motor position decoupling online parameter identification method of the present invention, Figure 4 Schematic diagram of the flux linkage identification result of the permanent magnet synchronous motor position decoupling online parameter identification method of the present invention, Figure 5 Schematic diagram of inductance identification results of the permanent magnet synchronous motor position decoupling online parameter identification method of the present invention.

[0116] from Figure 3-5 As can be seen in the figure, after the motor begins running, the algorithm quickly tracks the actual parameter values and reaches a stable state in approximately 0.05 seconds. The simulation results show that the estimated values of the stator resistance, inductance, and rotor flux after stabilization are 1.015Ω, 1.836mH, and 0.0572Wb, respectively. The maximum identification errors are 1.0%, 2.0%, and 2.2%, respectively. This simulation demonstrates that the parameter identification algorithm can quickly converge to accurate values under the dynamic operating conditions of the motor.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for online parameter identification of position decoupling of a permanent magnet synchronous motor based on an extended Kalman filter, characterized by: The method comprises the following steps: Establish the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under zero vector and effective vector respectively; The voltage information is directly read according to the switching state of the power device of the permanent magnet synchronous motor, and the switching state information is substituted into the αβ axis transformation to perform nonlinear compensation on the voltage. The harmonic information of the stator current is obtained according to the permanent magnet synchronous motor voltage equation in the stationary coordinate system under the zero vector and the effective vector; By changing the voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under zero vector and effective vector, a set of equations containing independent information for decoupling the rotor position is obtained; When injecting high-frequency current or in dynamic working conditions, the state equation and observation equation of the extended Kalman filter algorithm are established according to the above equations, and online parameter identification is performed through the extended Kalman filter algorithm.

2. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on an extended Kalman filter according to claim 1, characterized in that: The established zero vector and effective vector are written as follows: The voltage equation of the permanent magnet synchronous motor in the stationary coordinate system is: Where u α_z ,u β_z are the zero vector voltages of α and β axes, i α_z ,i β_z are the zero vector action currents of α and β axes respectively; u α_a ,u β_a are the effective vector voltages on the α and β axes, i α_a ,i β_a are the effective vector currents of α and β axes respectively; R s is the stator resistance, L s is the stator inductance, p=d / dt is the differential operator, ω e is the electrical angular velocity of the motor rotor, ψ f is the permanent magnet flux, θ e Indicates the rotor electrical angle.

3. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on an extended Kalman filter according to claim 2, characterized in that: Substituting the switching state of the power device into the equation group after the αβ axis transformation is expressed as: Where u a ,u b ,u c Represent the three-phase voltage, u ab ,u ac ,u bc Represent the three-phase line voltage, V dc Indicates bus voltage, S a ,S b ,S c Respectively represent the switching status of the three-phase bridge arms.

4. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on an extended Kalman filter according to claim 3, characterized in that: The stator current L is obtained according to the permanent magnet synchronous motor voltage equation in the stationary coordinate system under zero vector and effective vector s Expressed as: Therefore, the inductance information can be preliminarily identified based on the voltage and current derivatives under the action of the effective vector and the zero vector.

5. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on an extended Kalman filter according to claim 4, characterized in that: The voltage equation of the permanent magnet synchronous motor in the stationary coordinate system under the zero vector and the effective vector is transformed to decouple the rotor position. The transformed equation group is expressed as: At the same time, there is only one set of equations, and the parameter coefficient matrix is rank-deficient; when high-frequency current is injected or under dynamic conditions, the set of equations has sufficient independent information for parameter identification.

6. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on an extended Kalman filter according to claim 5, characterized in that: The extended Kalman filter algorithm is used for identification, and its state equation and observation equation are expressed as follows: Stator inductance L s It is identified by the least squares method and input into the equation as a known value.

7. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on an extended Kalman filter according to claim 6, characterized in that: The identification process of the extended Kalman filter algorithm is as follows: Linearize and discretize nonlinear systems; Taylor expansion is performed on the discretized state equation f(·) and observation equation h(·); The current state is predicted based on the state estimate at the previous moment, and the Kalman gain is introduced to correct the predicted value. The new observation value is integrated into the existing state estimate based on the current state prediction covariance and observation noise covariance to complete parameter identification.

8. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on extended Kalman filtering according to claim 7, characterized in that: The linearization and discretization process of nonlinear systems can be expressed as: x k =f(x k-1 ,u k )+w k-1 y k =h(x k )+v k Among them, x k-1 ,x k Indicates the estimated state value at the previous moment and the current moment, u k represents the control input at time k, y k represents the observed variable at time k; f(·) and h(·) are the state equation and observation equation of the system respectively; w k-1 is the process noise vector, which represents the uncertainty or interference in the state transition process; v k is the observation noise vector, which reflects the error and uncertainty in the measurement process.

9. The method for online parameter identification of a permanent magnet synchronous motor position decoupling based on extended Kalman filtering according to claim 8, characterized in that: The first-order Taylor expansion of the state equation f(·) and the observation equation h(·) is expressed as: Where, F k-1 and H k are the Jacobian matrices of the state equation and the observation equation, respectively, x k|k-1 Represents the prior state estimate at time k.

10. The method for online parameter identification of position decoupling of a permanent magnet synchronous motor based on extended Kalman filtering according to claim 9, characterized in that: The process of predicting the current state based on the state estimate of the previous moment is expressed as: The Kalman gain is calculated as follows: Where, P k|k-1 is the prior state covariance matrix at time k, H k is the Jacobian matrix of the observation equation, R k is the observation noise covariance matrix; The predicted value after Kalman gain correction is expressed as: Where z k -h(x k|k-1 ) is the observation residual, which reflects the difference between the actual observation value and the observation value predicted based on the prior state estimate; the state prediction value and the system measurement value are combined through the Kalman gain to obtain the parameter estimate at the next moment; The Kalman gain reflects the uncertainty of the state and the characteristic changes of the observation noise in real time based on the update of the covariance matrix: P k =(I-K k H k )P k|k-1 Where I is the identity matrix, K k is the Kalman gain at time k, Q k-1 is the process noise covariance matrix; by repeatedly predicting and updating, the true state of the system is gradually approached in the nonlinear system to complete parameter identification.

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