A method for parameter identification of permanent magnet synchronous motor based on triangular wave injection

Abstract

The application discloses a permanent magnet synchronous motor parameter identification method based on triangular wave injection and relates to the technical field of motor control. A triangular wave with a direct current bias is injected into the d-axis current of the motor. In the identification equation of the autoregressive least square algorithm, the current differential term in the voltage equation of the permanent magnet synchronous motor is considered. Two groups of input data with low linear correlation are combined into a new equation group. The parameters of the motor are divided into two sections, the new equation group is used for identification, and an identification result is output. The segmented autoregressive least square method parameter identification based on triangular wave injection can realize real-time identification of the resistance, inductance and flux linkage of the motor when the motor is in steady operation, so that the control performance of the double closed loop vector control of the permanent magnet synchronous motor is further improved.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, specifically a method for parameter identification of permanent magnet synchronous motors based on triangular wave injection. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) have been widely used in industrial fields in recent years due to their advantages such as simple structure, high power density, and high efficiency, especially in applications requiring precision control, such as CNC machine tools, aerospace, and electric vehicles. PMSMs typically employ a dual-closed-loop vector control method, in which accurate motor parameters play a crucial role in achieving high-performance control. However, during operation, the parameters of PMSMs may change with temperature, machine aging, and environmental conditions, leading to performance degradation or even system instability. Therefore, accurately identifying the parameters of PMSMs has become a pressing issue.

[0003] Because of its simple structure and ability to provide unbiased estimation of signals with superimposed white noise, the autoregressive least squares algorithm is currently a widely used parameter identification algorithm for permanent magnet synchronous motors. However, the traditional piecewise least squares parameter identification algorithm ignores the d-axis current term and the current differential term. This reduces the accuracy of the voltage model and limits the algorithm to parameter identification only under transient operating conditions. When the motor is in steady-state operation, the equations may become unranked due to excessively high linear correlation between adjacent sets of sampled data, ultimately leading to identification failure. Summary of the Invention

[0004] To address the shortcomings mentioned in the background art, the present invention aims to provide a method for identifying parameters of a permanent magnet synchronous motor based on triangular wave injection. This method improves the accuracy of the identification model and enables successful identification of motor parameters even under steady-state motor operation.

[0005] The objective of this invention can be achieved through the following technical solution: a method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection, the method comprising the following steps:

[0006] A triangular wave with DC bias is injected into the d-axis current of the motor to ensure the accuracy of the current differential term;

[0007] In the identification equation of the autoregressive least squares algorithm, the current differential term in the voltage equation of the permanent magnet synchronous motor is considered to reduce the linear correlation of the voltage equation set.

[0008] Two adjacent sets of input data with low linear correlation are combined into a new set of voltage equations;

[0009] The parameters of the motor are divided into two segments, and the generated new voltage equations are used for identification, and the identification results are output.

[0010] Preferably, the triangular wave with DC bias includes three characteristic quantities: frequency, amplitude, and DC bias. The triangular wave is constructed by adjusting these three characteristic quantities.

[0011] Preferably, the current differential term is obtained by dividing the difference between the actual current value at the next moment and the actual current value at the current moment by the sampling period, using a secant line instead of a tangent line, and the current sampling period is much smaller than the current period.

[0012] Preferably, the voltage equation of the permanent magnet synchronous motor is:

[0013]

[0014] Among them, u d u q Let i be the voltage across the dq axis. d i q Let ω be the dq-axis current. e R is the electric angular velocity. s L is the phase resistance of the motor. d L q Let ψ be the inductance of the motor's dq axis. f For permanent magnet flux linkage in motors;

[0015] For permanent magnet synchronous motors using surface-mounted permanent magnets, there is L d =L q =L s Therefore, the voltage equation of the permanent magnet synchronous motor can be further transformed into the following equation:

[0016]

[0017] Preferably, the parameters of the motor are divided into two segments. The first segment identifies the motor resistance and motor inductance. The second segment treats the motor resistance and motor inductance as known quantities and then identifies the motor flux linkage.

[0018] Preferably, the identification using the newly generated set of equations has the following characteristics: low linear correlation: the two sets of data are not proportional; secondly, the rank of the set of equations formed by the two sets of data that are not proportional is 2, and the number of parameters to be identified in the first segment of the segmented identification is also 2. Therefore, the set of equations is full rank and has a unique solution, thus achieving parameter identification.

[0019] Preferably, the identification process includes the following steps:

[0020] Dual closed-loop vector control is used to start and accelerate the permanent magnet synchronous motor. Once the given speed is reached, the motor begins to operate in a stable state.

[0021] A triangular wave with a certain frequency, amplitude, and DC bias is injected into the d-axis current of the motor.

[0022] The actual values ​​of the dq axis current, the setpoint values ​​of the dq axis voltage, and the electric angular velocity of the motor are sampled. The motor parameter identification program is run, and the motor parameter identification results are exported in real time.

[0023] The identified motor resistance, inductance, and flux linkage are used as new motor parameters, which serve as the basis for subsequent adjustments to the motor control parameters.

[0024] Preferably, the equation for the identification process is as follows:

[0025] First segment: Inject a triangular wave with DC bias to identify the motor resistance R. s With motor inductance L s ;

[0026] The voltage equation used for identification is:

[0027]

[0028] Where ζ is the system noise with a mean of 0, and T s The sampling period is n, where n represents the result of the nth sampling.

[0029] Therefore, the identification matrix for the first segment is obtained from the above equation:

[0030]

[0031] in, d1 is the branch weight matrix for the first segment, and d2 is the expected response matrix for the first segment. Input matrix for the first branch;

[0032] According to the autoregressive least squares algorithm, the motor resistance R can be obtained in real time according to the definition of the above formula. s With inductor L s The identification value;

[0033] Second section: Including the motor resistor R s With inductor L s The identification value is considered a known quantity, and the motor flux linkage ψ is identified. f ;

[0034] The voltage equation used for identification is:

[0035]

[0036] Therefore, the identification matrix for the second segment is obtained from the above equation:

[0037]

[0038] in, d1 is the branch weight matrix for the second segment, and d2 is the expected response matrix for the second segment. Input matrix for the second branch;

[0039] According to the autoregressive least squares algorithm, the motor flux linkage ψ can be obtained in real time according to the definition of the above formula. f Identification value.

[0040] Preferably, an apparatus includes:

[0041] One or more processors;

[0042] Memory, used to store one or more programs;

[0043] When one or more of the programs are executed by one or more of the processors, the one or more processors implement a method for identifying parameters of a permanent magnet synchronous motor based on triangular wave injection as described above.

[0044] Preferably, a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the parameter identification method for a permanent magnet synchronous motor based on triangular wave injection as described above.

[0045] The beneficial effects of this invention are:

[0046] The piecewise autoregressive least squares parameter identification method based on triangular wave injection proposed in this invention can successfully identify the resistance, inductance and flux linkage of the motor in real time even when the motor is running in steady state, thereby further improving the control performance of the dual closed-loop vector control of permanent magnet synchronous motor. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a structural diagram of the permanent magnet synchronous motor parameter identification method of the present invention;

[0049] Figure 2 This is a flowchart of the autoregressive least squares algorithm used in this invention;

[0050] Figure 3 This is a graph showing the relationship between the DC-biased triangular wave injected in this invention and various parameters;

[0051] Figure 4 The waveforms of the identification results for the resistance, inductance, and flux linkage of the permanent magnet synchronous motor are given. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, a parameter identification method for permanent magnet synchronous motors based on triangular wave injection is presented. The method includes the following steps:

[0054] A triangular wave with DC bias is injected into the d-axis current of the motor to ensure the accuracy of the current differential term;

[0055] In the identification equation of the autoregressive least squares algorithm, the current differential term in the voltage equation of the permanent magnet synchronous motor is considered to reduce the linear correlation of the voltage equation set.

[0056] Two adjacent sets of input data with low linear correlation are combined into a new set of voltage equations;

[0057] The parameters of the motor are divided into two segments, and the generated new voltage equations are used for identification, and the identification results are output.

[0058] It should be further explained that, in the specific implementation process, the triangular wave with DC bias includes three characteristic quantities: frequency, amplitude, and DC bias. The triangular wave is constructed by adjusting these three characteristic quantities.

[0059] It should be further explained that, in the specific implementation process, the current differential term is obtained by dividing the difference between the actual current value at the next moment and the actual current value at the current moment by the sampling period, using a secant line instead of a tangent line, and the current sampling period is much smaller than the current period.

[0060] It should be further explained that, in the specific implementation process, the voltage equation of the permanent magnet synchronous motor is:

[0061]

[0062] Among them, u d u q Let i be the voltage across the dq axis. d i q Let ω be the dq-axis current. e R is the electric angular velocity. s L is the phase resistance of the motor. d L q Let ψ be the inductance of the motor's dq axis. f For permanent magnet flux linkage in motors;

[0063] For permanent magnet synchronous motors using surface-mounted permanent magnets, there is L d =L q =L s Therefore, the voltage equation of the permanent magnet synchronous motor can be further transformed into the following equation:

[0064]

[0065] It should be further explained that, in the specific implementation process, the parameters of the motor are divided into two parts. The first part first identifies the motor resistance and motor inductance. The second part treats the motor resistance and motor inductance as known quantities and then identifies the motor flux linkage.

[0066] It needs to be further explained that, in the specific implementation process, the identification using the newly generated set of equations has the following characteristics: the linear correlation is low, meaning the two sets of data are not proportional; secondly, the rank of the set of equations formed by the two sets of data that are not proportional is 2, and the number of parameters to be identified in the first segment of the segmented identification is also 2. Therefore, the set of equations is full rank and has a unique solution, thus achieving parameter identification.

[0067] It should be further explained that, in the specific implementation process, the identification process includes the following steps:

[0068] Dual closed-loop vector control is used to start and accelerate the permanent magnet synchronous motor. Once the given speed is reached, the motor begins to operate in a stable state.

[0069] A triangular wave with a certain frequency, amplitude, and DC bias is injected into the d-axis current of the motor.

[0070] The actual values ​​of the dq axis current, the setpoint values ​​of the dq axis voltage, and the electric angular velocity of the motor are sampled. The motor parameter identification program is run, and the motor parameter identification results are exported in real time.

[0071] The identified motor resistance, inductance, and flux linkage are used as new motor parameters, which serve as the basis for subsequent adjustments to the motor control parameters.

[0072] It should be further explained that, in the specific implementation process, the equation for the identification process is as follows:

[0073] First segment: Inject a triangular wave with DC bias to identify the motor resistance R. s With motor inductance L s ;

[0074] The voltage equation used for identification is:

[0075]

[0076] Where ζ is the system noise with a mean of 0, and Ts The sampling period is n, where n represents the result of the nth sampling.

[0077] Therefore, the identification matrix for the first segment is obtained from the above equation:

[0078]

[0079] in, d1 is the branch weight matrix for the first segment, and d2 is the expected response matrix for the first segment. Input matrix for the first branch;

[0080] According to the autoregressive least squares algorithm, the motor resistance R can be obtained in real time according to the definition of the above formula. s With inductor L s The identification value;

[0081] Second section: Including the motor resistor R s With inductor L s The identification value is considered a known quantity, and the motor flux linkage ψ is identified. f ;

[0082] The voltage equation used for identification is:

[0083]

[0084] Therefore, the identification matrix for the second segment is obtained from the above equation:

[0085]

[0086] in, d1 is the branch weight matrix for the second segment, and d2 is the expected response matrix for the second segment. Input matrix for the second branch;

[0087] According to the autoregressive least squares algorithm, the motor flux linkage ψ can be obtained in real time according to the definition of the above formula. f Identification value.

[0088] exist Figure 1 In the structural diagram of the permanent magnet synchronous motor parameter identification method based on triangular wave injection of the present invention, U dc The inverter uses a voltage-source three-phase full-bridge inverter circuit, and the PMSM is a surface-mounted high-speed permanent magnet synchronous motor. The speed and position detection modules can use either mechanical position sensors or sensorless algorithms. The ASR is a motor speed regulator composed of PI controllers, and the ACR is a motor current regulator composed of PI controllers. The ASR output, after limiting, can be used as the i... q Reference value, i dThe reference value is generally 0. After enabling parameter identification, a triangular wave can be injected here. Using SVPWM can improve DC voltage utilization. The input of the piecewise autoregressive least squares parameter identification module mainly includes the actual value of the dq axis current i. d i q dq axis voltage reference value u dr u qr The electric angular velocity ω of the motor e The output mainly includes the motor resistance R. s Motor inductance L s and motor flux ψ f The parameter identification module acquires current, voltage, and electric angular velocity data during motor operation at a certain sampling frequency, and iteratively solves the motor's voltage equation in two stages, finally outputting the motor's parameter identification results.

[0089] The flowchart of the autoregressive least squares algorithm is as follows: Figure 2 As shown: First, for the correlation matrix P and the branch weight matrix... First, initialize α, where α is a large positive real number, and I represents the identity matrix. Then, use the iterative formula shown in the diagram to obtain the gain matrix k and the prior estimation error matrix ξ, and further obtain the correlation matrix P and branch weight matrix required for the next iteration. The value of λ (where λ is the forgetting factor, usually a positive constant close to 1 and less than or equal to 1); finally, the continuously iteratively corrected branch weight matrix is... The value is used as the final identification result in the output.

[0090] Figure 3 This is a schematic diagram of a triangular wave with DC bias. Its main parameters include frequency f. tri Amplitude A tri With DC bias D tri Among them, the frequency f tri For period T ri The reciprocal of f tri =1 / T ri Injecting a triangular wave with appropriate frequency, amplitude, and DC bias can improve the accuracy of the voltage equation and also increase the value of R in the equation. s i d The signal-to-noise ratio of the item is improved, thereby enhancing the overall recognition accuracy of the recognition algorithm.

[0091] The piecewise autoregressive least squares parameter identification process based on triangular wave injection is as follows: After the motor enters a stable operating state, the injection... Figure 3 The triangular wave shown is used to sample the motor's current, voltage, and electrical angular velocity in real time. Figure 2The autoregressive least squares method shown is used for segmented identification, which can continuously repeat the above process during motor operation to achieve real-time updates of motor parameters and improve motor control performance.

[0092] In this embodiment, the DC bus voltage is 24V, the motor resistance is 0.025Ω, the motor inductance is 12μH, the motor flux linkage is 0.7mWb, the number of motor pole pairs is 1, the friction torque is 0.003N·M, the given speed is 20000rpm, the rated current is 30A, the switching frequency is 20kHz; the parameter identification sampling frequency is 2kHz, the injected triangular wave frequency is 100Hz with an amplitude of 1A, and the DC bias is 1A.

[0093] To demonstrate the effectiveness of the piecewise autoregressive least squares parameter identification method based on triangular wave injection proposed in this invention, the system was simulated in MATLAB / Simulink. Figure 4 The waveforms of the identification results for the resistance, inductance, and flux linkage of the permanent magnet synchronous motor are presented, demonstrating that the identification algorithm maintains high accuracy even during steady-state operation. Therefore, the piecewise autoregressive least squares parameter identification method based on triangular wave injection proposed in this invention can successfully identify the motor's resistance, inductance, and flux linkage in real time even during steady-state operation, thereby further improving the control performance of the dual-closed-loop vector control of the permanent magnet synchronous motor.

[0094] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0095] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0096] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection, characterized in that, The method includes the following steps: A triangular wave with DC bias is injected into the d-axis current of the motor to ensure the accuracy of the current differential term; In the identification equation of the autoregressive least squares algorithm, the current differential term in the voltage equation of the permanent magnet synchronous motor is considered to reduce the linear correlation of the voltage equation set. Two adjacent sets of input data with low linear correlation are combined into a new set of voltage equations; The parameters of the motor are divided into two segments, and the generated new voltage equations are used for identification, and the identification results are output. The equation for the identification process is as follows: First section: Inject a triangular wave with DC bias to identify the motor resistance. R s With motor inductance L s ; The voltage equation used for identification is: （3） in, u dr This is the reference value for the d-axis voltage. ζ The system noise has a mean of 0. T s The sampling period is n Indicates the first n Secondary sampling results; Therefore, the identification matrix for the first segment is obtained from the above equation: （4） in, This is the weight matrix for the first branch. d 1 represents the expected response matrix for the first segment. Input matrix for the first branch; According to the autoregressive least squares algorithm, the motor resistance can be obtained in real time according to the definition of the above formula. R s With inductance L s The identification value; Second section: Motor resistance R s With inductance L s The identification value is considered a known quantity, and the motor flux is identified. ψ f ; The voltage equation used for identification is: （5） u qr This is the reference value for the q-axis voltage. i d , i q for dq shaft current, ω e Electric angular velocity; Therefore, the identification matrix for the second segment is obtained from the above equation: （6） in, This is the branch weight matrix for the second segment. d 2 represents the expected response matrix for the second segment. Input matrix for the second branch; According to the autoregressive least squares algorithm, the motor flux linkage can be obtained in real time according to the definition of the above formula. ψ f Identification value.

2. The method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection according to claim 1, characterized in that, The triangular wave with DC bias includes three characteristic quantities: frequency, amplitude, and DC bias. The triangular wave is constructed by adjusting these three characteristic quantities.

3. The method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection according to claim 1, characterized in that, The current differential term is obtained by dividing the difference between the actual current value at the next moment and the actual current value at the current moment by the sampling period. A secant is used instead of a tangent, and the current sampling period is much smaller than the current period.

4. The method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection according to claim 1, characterized in that, The voltage equation of the permanent magnet synchronous motor is: （1） in, u d , u q for dq shaft voltage, R s For motor phase resistance, L d , L q For motor dq Shaft inductor, ψ f For permanent magnet flux linkage in motors; For permanent magnet synchronous motors using surface-mounted permanent magnets, there are L d = L q = L s Therefore, the voltage equation of the permanent magnet synchronous motor can be further transformed into the following equation: （2）。 5. The method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection according to claim 1, characterized in that, The method of using the newly generated set of equations for identification has the following characteristics: low linear correlation: the two sets of data are not proportional; secondly, the rank of the set of equations formed by the two sets of data that are not proportional is 2, and the number of parameters to be identified in the first segment of the segmented identification is also 2. Therefore, the set of equations is full rank and has a unique solution, thus achieving parameter identification.

6. The method for parameter identification of a permanent magnet synchronous motor based on triangular wave injection according to claim 5, characterized in that, The identification process includes the following steps: Dual closed-loop vector control is used to start and accelerate the permanent magnet synchronous motor. Once the given speed is reached, the motor begins to operate in a stable state. A triangular wave with a certain frequency, amplitude, and DC bias is injected into the d-axis current of the motor. The actual values ​​of the dq axis current, the setpoint values ​​of the dq axis voltage, and the electric angular velocity of the motor are sampled. The motor parameter identification program is run, and the motor parameter identification results are exported in real time. The identified motor resistance, inductance, and flux linkage are used as new motor parameters, which serve as the basis for subsequent adjustments to the motor control parameters.

7. A parameter identification device for permanent magnet synchronous motors based on triangular wave injection, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more of the programs are executed by one or more of the processors, the one or more processors implement a method for identifying parameters of a permanent magnet synchronous motor based on triangular wave injection as described in any one of claims 1-6.

8. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a method for identifying parameters of a permanent magnet synchronous motor based on triangular wave injection as described in any one of claims 1-6.

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