Permanent magnet synchronous motor off-line parameter estimation method and system based on algebraic differential method

Abstract

The invention relates to the technical field of motor control, and particularly discloses a permanent magnet synchronous motor off-line parameter estimation method and system based on an algebraic differential method, and the method comprises the steps: obtaining a speed control hyper-local model and a second-order kinematics equation of a permanent magnet synchronous motor according to an initial electromechanical kinematics equation of the permanent magnet synchronous motor; laplace transformation is carried out on the second-order kinematics equation, an angle low-pass filter is introduced, and a complex frequency domain equation of the permanent magnet synchronous motor is obtained; obtaining a two-dimensional equation set composed of unknown coefficients of the permanent magnet synchronous motor through derivation, time domain conversion and integration of a complex frequency domain equation of the permanent magnet synchronous motor; the permanent magnet synchronous motor is rotated forwards and backwards according to a speed instruction, the obtained angle and q-axis current data are substituted into the two-dimensional equation set for solving, and the value of a non-physical scale factor can be obtained. According to the method, the value of the non-physical scale factor in the model-free speed control of the permanent magnet synchronous motor based on the hyper-local model can be effectively estimated, the dynamic performance of the model-free speed control of the permanent magnet synchronous motor is improved, and the robustness of a control system is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to an off-line parameter estimation method and system for a permanent magnet synchronous motor based on algebraic differentiation. Background Art

[0002] Permanent magnet synchronous motor (PMSM) is gaining more and more popularity in small and medium power applications as well as high voltage and high power applications such as industrial robots and aerospace due to its advantages of high efficiency, high power factor and easy control. The model-free deadbeat predictive speed control of permanent magnet synchronous motor only uses the input and output data of the system, that is, it realizes the control of the motor through real-time observation and prediction of the system state. Model-free deadbeat predictive speed control has the advantages of high-precision control, fast response, no need for precise motor parameters, strong robustness, simple structure and easy implementation. However, if the non-physical proportional factor selection in the model-free deadbeat predictive speed controller is far from the actual motor system parameters, it will affect the control performance, such as poor system dynamic response, large overshoot, or system instability. In many cases, the parameters provided by the motor manufacturer are not accurate or not provided, and it is sometimes difficult to accurately calculate the moment of inertia of the load. In order to ensure the control accuracy and improve the motor performance, it is very necessary to identify the non-physical proportional factor in the model-free deadbeat predictive speed control in engineering practice. There are two main methods for parameter identification: offline identification and online identification. Offline identification mainly includes DC current delay, AC static method and vector control method, but these identification methods need to be performed when the motor is in a stable state. Online identification mainly includes least squares method and its extended algorithm, model reference adaptive method, genetic algorithm and swarm intelligence optimization algorithm, parameter identification method based on artificial neural network, extended Kalman filter method, etc., but this type of identification has high requirements on hardware (such as processor performance) and software, the algorithm is complex and requires real-time calculation. The above parameter identification methods mainly identify the conventional parameters of the motor one by one, which is not suitable for the estimation of non-physical proportional factors. Summary of the invention

[0003] In view of this, the present invention provides an offline parameter estimation method for a permanent magnet synchronous motor based on algebraic differentiation method, which only requires the angle and current information of the motor to accurately estimate the value of the non-physical proportional factor, solves the problem of difficult parameter selection in model-free zero-beat predictive speed control, and improves the control performance of the permanent magnet synchronous motor servo system.

[0004] To achieve the above tasks, the present invention provides the following technical solutions:

[0005] The present invention provides an offline parameter estimation method for a permanent magnet synchronous motor based on algebraic differentiation. By transforming the electromechanical kinematics equation of the permanent magnet synchronous motor, substituting the angle and current information, and solving the linear equations about the motor parameters, an estimated value of the non-physical proportional factor can be obtained. The steps are as follows:

[0006] S1, according to the initial electromechanical kinematics equation of the permanent magnet synchronous motor, the speed control super-local model and the second-order kinematics equation of the permanent magnet synchronous motor are obtained;

[0007] S2, Laplace transform the second-order kinematics equation, introduce an angle low-pass filter, and obtain the complex frequency domain equation of the permanent magnet synchronous motor;

[0008] S3, by derivation, time domain conversion and integration of the complex frequency domain equation of the permanent magnet synchronous motor, a two-dimensional equation group consisting of unknown coefficients of the permanent magnet synchronous motor is obtained;

[0009] S4, according to the speed command, the permanent magnet synchronous motor is rotated forward and reversely, and the obtained angle and q-axis current data are substituted into the two-dimensional equation group for solution to obtain the value of the non-physical proportional factor.

[0010] Furthermore, the step of obtaining the speed control super-local model and the second-order kinematic equation of the permanent magnet synchronous motor according to the initial electromechanical kinematic equation of the permanent magnet synchronous motor in S1 specifically includes the following steps:

[0011] The initial electromechanical kinematics equation of a permanent magnet synchronous motor is:

[0012]

[0013] Where θ is the mechanical angle of the permanent magnet synchronous motor; ω m is the mechanical angular velocity of the permanent magnet synchronous motor; J n is the total moment of inertia of the permanent magnet synchronous motor; T e and T L are electromagnetic torque and load torque respectively; B n is the viscous friction coefficient; d n is a constant disturbance; f t It represents the total disturbance caused by parameter mismatch and other unknown disturbances; n p is the number of magnetic pole pairs of the permanent magnet synchronous motor; is the permanent magnet flux of the permanent magnet synchronous motor; i q is the q-axis stator current.

[0014] The initial electromechanical kinematics equation of the permanent magnet synchronous motor can be rewritten as a second-order kinematics equation about the angle:

[0015]

[0016] Then sort it out:

[0017]

[0018] Among them, the coefficient coefficient Total disturbance And A0 is considered to be a constant.

[0019] Based on the hyperlocal model: y is the system output, u is the system control variable, F is the known and unknown parts of the system, and α is the non-physical proportional factor. The initial electromechanical kinematic equation can be changed to a speed control hyperlocal model:

[0020]

[0021] in, is the non-physical proportional factor of the permanent magnet synchronous motor speed control system, It represents the known and unknown parts of the permanent magnet synchronous motor speed control system. Through the speed control super-local model and the second-order kinematic equation of the permanent magnet synchronous motor, it can be seen that Therefore, the value of the non-physical proportional factor α in the permanent magnet synchronous motor speed control super-local model can be obtained by solving the b parameter.

[0022] Furthermore, the step S2, in which the Laplace transform of the second-order kinematics equation is performed, an angle low-pass filter is introduced, and the complex frequency domain equation of the permanent magnet synchronous motor is obtained, specifically includes the following steps:

[0023] By performing Laplace transform on the second-order kinematic equation of the permanent magnet synchronous motor, we can obtain:

[0024]

[0025] In order to accurately obtain the values ​​of coefficients α and b in the equation, a low-pass filter about the mechanical angle of the permanent magnet synchronous motor is introduced:

[0026]

[0027] Among them, ω c is the cutoff frequency of the low-pass filter, we can get:

[0028]

[0029] Multiplying both sides by s, we can get the complex frequency domain equation of the permanent magnet synchronous motor:

[0030]

[0031] Furthermore, the step of obtaining a two-dimensional equation group consisting of unknown coefficients of the permanent magnet synchronous motor by derivation, time domain conversion and integration of the complex frequency domain equation of the permanent magnet synchronous motor in S3 specifically includes the following steps:

[0032] The complex frequency domain equation of the permanent magnet synchronous motor is derived three times with respect to s, eliminating the initial value θ0, And constant value part A0:

[0033]

[0034] Multiply both sides of the equation by s -4 , we can get:

[0035]

[0036] Since the complex frequency domain d v / ds v and in the time domain (-t) v are equivalent, so we can get:

[0037] aπ1(t)+bπ2(t)=q(t),

[0038] in

[0039] π1(t)=-∫t 3 y+∫ (2) (9t 2 y-ω c t 3 y)+∫ (3) (-18ty+6ω c t 2 y)+∫ (4) (6y-6ω c ty)

[0040] π2(t)=-∫ (4) 3ω c t 2 i q +∫ (3) ω c t 3 i q

[0041] q(t)=t 3 y+∫(-12t 2 y+ω c t 3 y)+∫ (2) (36ty-9ω c t 2 y)+∫ (3) (-24y+18ωcty)-6ω c ∫(4) y

[0042] In the above formula, ∫ (n) φ(t) is expressed as

[0043] Integrating the above formula once, we can get a two-dimensional equation system consisting of unknown coefficients of the permanent magnet synchronous motor:

[0044]

[0045] in:

[0046] P 11 (t)=π1(t),P 12 (t)=π2(t),Q1(t)=q(t)

[0047] P 21 (t)=∫π1(t),P 22 (t)=∫π2(t),Q2(t)=∫q(t)

[0048] Further, the step S4, in which the permanent magnet synchronous motor is rotated forward and reversely according to the speed instruction, the obtained angle and q-axis current data are substituted into the two-dimensional equation group for solving, and the value of the non-physical proportional factor can be obtained, specifically includes the following steps:

[0049] According to the desired speed command, the permanent magnet synchronous motor is rotated forward and reversed. The actual permanent magnet synchronous motor speed and q-axis current are measured and calculated through the motor encoder and current sensor. The values ​​of the matrix elements in the two-dimensional equation group in the interval [0, t] can be calculated:

[0050]

[0051] Then the unknown coefficient a of the permanent magnet synchronous motor can be obtained e and b e Values:

[0052]

[0053] Therefore, the non-physical proportional factor α in the super-local model of permanent magnet synchronous motor can be obtained as follows: e .

[0054] The present invention provides an off-line parameter estimation system for a permanent magnet synchronous motor based on algebraic differentiation method, comprising:

[0055] A data acquisition module, used for acquiring cardiac motion geometry data from existing data files and a permanent magnet synchronous motor servo system;

[0056] A two-dimensional equations building module is used to obtain a speed control super-local model and a second-order kinematics equation of the permanent magnet synchronous motor according to the initial electromechanical kinematics equation of the permanent magnet synchronous motor, perform Laplace transformation on the second-order kinematics equation, introduce an angle low-pass filter, and obtain a complex frequency domain equation of the permanent magnet synchronous motor. A two-dimensional equations group consisting of unknown coefficients of the permanent magnet synchronous motor is established by derivation, time domain conversion, and integration of the complex frequency domain equation of the permanent magnet synchronous motor.

[0057] The parameter estimation module is used to perform operations such as integration on actual data over a period of time and estimate the value of the non-physical proportional factor by solving a quadratic linear equation system.

[0058] The present invention provides a device, which includes a processor and a memory coupled to the processor, wherein the memory stores program instructions for realizing offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method; and the processor is used to execute the program instructions stored in the memory to realize estimation of a non-physical proportional factor.

[0059] The present invention provides a storage medium storing program instructions executable by a processor, wherein the program instructions are used to execute an off-line parameter estimation method for a permanent magnet synchronous motor based on an algebraic differentiation method.

[0060] Compared with the prior art, the beneficial effects of the present invention are: the present invention does not require other information of the motor model, only uses the input and output data of the motor, has the advantages of being simple and easy to implement, having a wide range of applications, and not occupying the hardware resources of the control system, and provides an efficient and fast way to obtain the value of the non-physical proportional factor. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of a flow chart of a method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to Embodiment 1 of the present invention;

[0062] Figure 2 This is a graph of experimental results of angle and current data obtained when the permanent magnet synchronous motor is driven to rotate for the first time according to Embodiment 1 of the present invention;

[0063] Figure 3 This is a diagram showing the experimental results of angle and current data obtained when the permanent magnet synchronous motor is driven to rotate for the second time according to Embodiment 1 of the present invention;

[0064] Figure 4 This is a graph showing the experimental results of angle and current data obtained when the permanent magnet synchronous motor is driven to rotate for the third time according to Embodiment 1 of the present invention;

[0065] Figure 5 This is a diagram of offline parameter estimation results of a permanent magnet synchronous motor based on algebraic differentiation method according to Embodiment 1 of the present invention;

[0066] Figure 6 Schematic diagram of the structure of a permanent magnet synchronous motor offline parameter estimation system based on algebraic differentiation method according to Embodiment 2 of the present invention;

[0067] Figure 7 This is a schematic diagram of the device structure of Embodiment 3 of the present invention;

[0068] Figure 8 This is a schematic diagram of the storage medium structure of Example 4 of the present invention. DETAILED DESCRIPTION

[0069] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Example 1

[0072] Figure 1 This is a flow chart of a method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to Embodiment 1 of the present invention, and the steps include:

[0073] S1, according to the initial electromechanical kinematics equation of the permanent magnet synchronous motor, the speed control super-local model and the second-order kinematics equation of the permanent magnet synchronous motor are obtained.

[0074] In this embodiment, the initial kinematic equation of the permanent magnet synchronous motor is:

[0075]

[0076] Where θ is the mechanical angle of the permanent magnet synchronous motor; ω m is the mechanical angular velocity of the permanent magnet synchronous motor; J n is the total moment of inertia of the permanent magnet synchronous motor; T e and T L are electromagnetic torque and load torque respectively; B n is the viscous friction coefficient; d n is a constant disturbance; f t It represents the total disturbance caused by parameter mismatch and other unknown disturbances;

[0077] For a surface mounted permanent magnet synchronous motor, the electromagnetic torque equation of the permanent magnet synchronous motor is:

[0078]

[0079] Among them, n p is the number of magnetic pole pairs of the permanent magnet synchronous motor; is the permanent magnet flux of the permanent magnet synchronous motor; i d and i q are the stator currents of the d-axis and q-axis respectively, L d =L q are the stator inductances of the d-axis and q-axis respectively.

[0080] The initial kinematic equation and electromagnetic torque equation are rewritten, and the second-order kinematic equation of the permanent magnet synchronous motor is:

[0081]

[0082] Move item:

[0083]

[0084] Then sort it out:

[0085]

[0086] Where, coefficient a = B n / J n ,coefficient Total disturbance A0=(-T L --d n -f t )J n , and A0 is considered to be a constant.

[0087] The expression of the hyperlocal model is:

[0088]

[0089] Among them, y is the system output, u is the system control variable, F is the known and unknown parts of the system, and α is the non-physical proportional factor.

[0090] Based on the hyperlocal model, the electromechanical kinematics equation of the permanent magnet synchronous motor can be rewritten as the hyperlocal model of permanent magnet synchronous motor speed control:

[0091]

[0092] in, is the non-physical proportional factor of the system, F = (-T L -B n ω m -d n -f t ) / J n It represents the known and unknown parts of the system. Through the speed control super-local model and the second-order kinematic equation of the permanent magnet synchronous motor, it can be seen that Therefore, the value of the non-physical proportional factor α in the permanent magnet synchronous motor speed control super-local model can be obtained by solving the b parameter.

[0093] S2, Laplace transform the second-order kinematic equation, introduce an angle low-pass filter, and obtain the complex frequency domain equation of the permanent magnet synchronous motor.

[0094] By performing Laplace transform on the second-order kinematic equation of the permanent magnet synchronous motor with angle and q-axis current as variables, we can obtain:

[0095]

[0096] In order to accurately obtain the values ​​of coefficients a and b in the equation, a low-pass filter about the mechanical angle of the permanent magnet synchronous motor is introduced:

[0097]

[0098] Among them, ω c is the cutoff frequency of the low-pass filter, we can get:

[0099]

[0100] Multiplying both sides of the equation by s, we can get the complex frequency domain equation of the permanent magnet synchronous motor:

[0101]

[0102] S3, by derivation, time domain conversion and integration of the complex frequency domain equation of the permanent magnet synchronous motor, a two-dimensional equation group consisting of unknown coefficients of the permanent magnet synchronous motor is obtained.

[0103] Derivative the complex frequency domain equation of the permanent magnet synchronous motor with respect to s and eliminate A0:

[0104]

[0105] The complex frequency domain equation of the permanent magnet synchronous motor is secondarily differentiated with respect to s to eliminate

[0106]

[0107] The complex frequency domain equation of the permanent magnet synchronous motor is derived three times with respect to s, and the initial value θ0 is finally eliminated. And constant value part A0:

[0108]

[0109] Multiply both sides of the equation by s -4 , we can get:

[0110]

[0111] Since the complex frequency domain d v / ds v and in the time domain (-t) v are equivalent, so we can get:

[0112] aπ1(t)+bπ2(t)=q(t),

[0113] in

[0114] π1(t)=-∫t 3 y+∫ (2) (9t 2 y-ω c t 3 y)+∫ (3) (-18ty+6ω c t 2 y)+∫ (4) (6y-6ω c ty)

[0115] π2(t)=-∫ (4) 3ω c t 2 i q +∫ (3) ω c t 3 i q

[0116] q(t)=t 3 y+∫(-12t 2 y+ω c t 3 y)+∫ (2) (36ty-9ω c t 2 y)+∫ (3) (-24y+18ω c ty)-6ω c ∫ (4) y

[0117] In the above formula, ∫ (n) φ(t) is expressed as in particular

[0118] Integrating the above formula once, we can get the unknown coefficient a of the permanent magnet synchronous motor: e and b e The two-dimensional equation system is:

[0119]

[0120] in:

[0121] P 11 (t)=π1(t),P 12 (t)=π2(t),Q1(t)=q(t)

[0122] P 21 (t)=∫π1(t),P 22 (t)=∫π2(t),Q2(t)=∫q(t)

[0123] S4, according to the speed command, the permanent magnet synchronous motor is rotated forward and reversely, and the obtained angle and q-axis current data are substituted into the two-dimensional equation group for solution to obtain the value of the non-physical proportional factor.

[0124] See also Figure 2 , Figure 3 and Figure 4 , which is the mechanical angle and q-axis current information of the motor obtained after the permanent magnet synchronous motor is rotated forward and reversely according to the speed instruction in Example 1 of the present application. In each figure, the first channel shows the expected speed and actual speed of the permanent magnet synchronous motor, and the permanent magnet synchronous motor drives the load to rotate according to the expected speed requirement; the second channel shows the actual current of the permanent magnet synchronous motor q axis; the third channel shows the actual rotation angle data of the permanent magnet synchronous motor. The difference between the three experiments is that the initial angles of the permanent magnet synchronous motor are different.

[0125] After obtaining the angle and current information, substitute them into the matrix element calculation formula:

[0126]

[0127] The value of each element of the matrix can be obtained.

[0128] By substituting the matrix elements into the following formula, the unknown coefficient a of the permanent magnet synchronous motor can be obtained e and b e Values:

[0129]

[0130] Therefore:

[0131]

[0132] The flux linkage of the permanent magnet synchronous motor in Example 1 of the present application Number of magnetic pole pairs n of permanent magnet synchronous motor p =20, load moment of inertia J n =0.04174Kg.m 2 , so

[0133] See also Figure 5, is the value of the non-physical proportional factor estimated according to the mechanical angle and q-axis current data of the permanent magnet synchronous motor in Example 1 of the present application. It can be seen from the figure that the estimated values ​​of the non-physical proportional factors of the three experiments with different initial angles are all between 39 and 40, which is very close to the theoretical actual value of 39.52, indicating the effectiveness of the permanent magnet synchronous motor offline parameter estimation method based on algebraic differentiation method of the present invention.

[0134] The present invention can effectively estimate the value of the non-physical proportional factor in the model-free speed control of the permanent magnet synchronous motor based on the super-local model, improve the dynamic performance of the model-free speed control of the permanent magnet synchronous motor, and enhance the robustness of the control system.

[0135] Example 2

[0136] See also Figure 6 , is a structural diagram of a permanent magnet synchronous motor offline parameter estimation system 400 based on algebraic differentiation method according to Embodiment 2 of the present application; the specific contents include:

[0137] The data acquisition module 410 is used to acquire angle and current data from existing data files and a permanent magnet synchronous motor servo system;

[0138] The parameter estimation module 420 performs operations such as integration on actual data within a period of time, and estimates the value of the non-physical proportional factor by solving a quadratic linear equation group. The process of establishing the quadratic linear equation group is as follows: according to the initial electromechanical kinematics equation of the permanent magnet synchronous motor, a speed control super-local model and a second-order kinematics equation of the permanent magnet synchronous motor are obtained, the second-order kinematics equation is Laplace transformed, an angle low-pass filter is introduced, and a complex frequency domain equation of the permanent magnet synchronous motor is obtained, and a two-dimensional equation group consisting of unknown coefficients of the permanent magnet synchronous motor is established by derivation, time domain conversion and integration of the complex frequency domain equation of the permanent magnet synchronous motor.

[0139] Example 3

[0140] See also Figure 7 , is a schematic diagram of the device structure of Embodiment 3 of the present application. The device 500 includes a processor 510 and a memory 520 coupled to the processor 510 .

[0141] The memory 520 stores program instructions for implementing the above-mentioned permanent magnet synchronous motor offline parameter estimation method based on algebraic differentiation method.

[0142] The processor 510 is used to execute program instructions stored in the memory 520 to implement offline parameter estimation of the permanent magnet synchronous motor based on algebraic differentiation method.

[0143] The processor 510 may also be referred to as a CPU (Central Processing Unit). The processor 510 may be an integrated circuit chip having signal processing capabilities. The processor 510 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0144] Example 4

[0145] See also Figure 8 , which is a schematic diagram of the structure of the storage medium of Example 4 of the present application. The storage medium of the embodiment of the present application stores a program file 610 that can implement all the above methods, wherein the program file 610 can be stored in the above storage medium in the form of a software product, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer, a server, a mobile phone, a tablet, and other devices.

[0146] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0147] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present invention.

[0148] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0149] Of course, the present invention may have many other implementations. Based on this implementation, other implementations obtained by ordinary technicians in this field without any creative work are all within the scope of protection of the present invention.

Claims

1. A method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method, characterized in that: The electromechanical kinematics equations of the permanent magnet synchronous motor are transformed, and the estimated values ​​of the non-physical proportional factors in the hyperlocal model of the permanent magnet synchronous motor are obtained by solving a set of linear equations about the motor parameters.

2. The method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to claim 1 is characterized in that: The steps include: S1, according to the initial electromechanical kinematics equation of the permanent magnet synchronous motor, the speed control super-local model and the second-order kinematics equation of the permanent magnet synchronous motor are obtained; S2, Laplace transform the second-order kinematics equation, introduce an angle low-pass filter, and obtain the complex frequency domain equation of the permanent magnet synchronous motor; S3, by deriving the complex frequency domain equation of the permanent magnet synchronous motor, converting it into the time domain and integrating it, a two-dimensional equation group consisting of unknown coefficients of the permanent magnet synchronous motor is obtained; S4, forward and reverse the permanent magnet synchronous motor according to the speed command, substitute the obtained angle and q-axis current data into the two-dimensional equation group for solution, and obtain the value of the non-physical proportional factor.

3. The method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to claim 2, characterized in that: The step S1 specifically includes: The initial electromechanical kinematic equations of the permanent magnet synchronous motor are established as: Where θ is the mechanical angle of the permanent magnet synchronous motor; ω m is the mechanical angular velocity of the permanent magnet synchronous motor; J n is the total moment of inertia of the permanent magnet synchronous motor; T e and T L are electromagnetic torque and load torque respectively; B n is the viscous friction coefficient; d n is a constant disturbance; f t It represents the total disturbance caused by parameter mismatch and other unknown disturbances; n p is the number of magnetic pole pairs of the permanent magnet synchronous motor; is the permanent magnet flux of the permanent magnet synchronous motor; i q is the q-axis stator current; The initial electromechanical kinematics equation of the permanent magnet synchronous motor is rewritten as a second-order kinematics equation about the angle: Arranged: Among them, the coefficient coefficient Total disturbance A0 is a constant value; Based on the hyperlocal model, the initial electromechanical kinematic equations are constructed as a speed control hyperlocal model: in, is the non-physical proportional factor of the permanent magnet synchronous motor speed control system, It represents the known and unknown parts of the permanent magnet synchronous motor speed control system.

4. The method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to claim 3 is characterized in that: The hyperlocal model is: y is the system output, u is the system control variable, F is the known and unknown parts of the system, and α is the non-physical proportional factor.

5. The method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to claim 3 is characterized in that: In S2, the second-order kinematics equation is Laplace transformed, an angle low-pass filter is introduced, and a complex frequency domain equation of the permanent magnet synchronous motor is obtained, which specifically includes the following steps: Performing Laplace transform on the second-order kinematic equation of the permanent magnet synchronous motor, we get: Introduce a low-pass filter about the mechanical angle of the permanent magnet synchronous motor: Among them, ω c is the cutoff frequency of the low-pass filter, we get: Multiplying both sides by s, we get the complex frequency domain equation of the permanent magnet synchronous motor:

6. The method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to claim 5, characterized in that: The S3 specifically includes: The complex frequency domain equation of the permanent magnet synchronous motor is derived three times with respect to s, eliminating the initial value θ0, And constant value part A0: Multiply both sides of the equation by s -4 ,have to: Since the complex frequency domain d v / ds v and in the time domain (-t) v are equivalent, so we get: aπ1(t)+bπ2(t)=q(t), in: π1(t)0-∫t 3 y+∫ (2) (9t 2 y-ω c t 3 y)+∫ (3) (-18ty+6ω c t 2 y)+∫ (4) (6y-6ω c ty) π2(t)=-∫ (4) 3ω c t 2 i q +∫ (3) ω c t 3 i q q(t)=t 3 y+∫(-12t 2 y+ω c t 3 y)+∫( 2 )(36ty-9ω c t 2 y)+∫( 3 )(-24y+18ω c ty)-6ω c ∫ (4) y In the above formula, ∫ (n) φ(t) is expressed as Integrating the above equation once, we get a two-dimensional equation system consisting of unknown coefficients of the permanent magnet synchronous motor: in:

7. The method for offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method according to claim 1, characterized in that: The S4 specifically comprises the steps of: According to the desired speed command, the permanent magnet synchronous motor is rotated forward and reversed. The actual permanent magnet synchronous motor speed and q-axis current are measured and calculated through the motor encoder and current sensor, and the values ​​of the matrix elements in the two-dimensional equation group in the interval [0, t] are calculated: Get the unknown coefficient a of the permanent magnet synchronous motor e and b e Values: The non-physical proportional factor α in the super-local model of permanent magnet synchronous motor is obtained as follows: e .

8. A permanent magnet synchronous motor offline parameter estimation system based on algebraic differentiation method for implementing any of the methods described in claims 1-7, characterized in that: include: A data acquisition module is used to obtain angle and current data from existing data files and permanent magnet synchronous motor servo systems; A two-dimensional equations building module is used to obtain a speed control super-local model and a second-order kinematics equation of the permanent magnet synchronous motor according to the initial electromechanical kinematics equation of the permanent magnet synchronous motor, perform Laplace transformation on the second-order kinematics equation, introduce an angle low-pass filter, and obtain a complex frequency domain equation of the permanent magnet synchronous motor. A two-dimensional equations group consisting of unknown coefficients of the permanent magnet synchronous motor is established by derivation, time domain conversion, and integration of the complex frequency domain equation of the permanent magnet synchronous motor. The parameter estimation module is used to perform integration operations on actual data over a period of time and estimate the value of the non-physical proportional factor by solving a two-dimensional system of equations.

9. A device, characterized in that: The device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing offline parameter estimation of a permanent magnet synchronous motor based on algebraic differentiation method as described in any one of claims 1-7; and the processor is used to execute the program instructions stored in the memory to realize estimation of a non-physical proportional factor.

10. A storage medium, characterized in that: Program instructions executable by a processor are stored, and the program instructions are used to execute the steps of the permanent magnet synchronous motor offline parameter estimation method based on algebraic differentiation method as described in any one of claims 1-7.

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