Online multi-parameter identification method and system for permanent magnet synchronous motor

Abstract

The invention discloses an on-line multi-parameter identification method and system for a permanent magnet synchronous motor, and the method comprises the steps: injecting a high-frequency sinusoidal voltage signal into a given voltage outputted by a d-axis and a q-axis during the operation of the permanent magnet synchronous motor, enabling the given voltage to serve as the input of a PWM module, and outputting a PWM signal to drive the permanent magnet synchronous motor; reconstructing a three-phase step voltage of an actual motor port as a voltage signal of a parameter identification strategy; converting the reconstructed three-phase step voltage of the motor port and the three-phase current measured by a sensor into reconstruction voltage and measurement current under d and q axes through coordinates, and then extracting amplitudes and phases of a high-frequency voltage signal and a high-frequency current signal in the reconstruction voltage and the measurement current; substituting into a high-frequency impedance model to obtain d-axis and q-axis inductances at the moment; and in combination with the voltage, current and rotating speed information at the moment, resistance and flux linkage parameters are solved by adopting a least square method with a forgetting factor, finally four parameters of the permanent magnet synchronous motor are obtained, and online multi-parameter identification is realized.

Description

Background Art

[0002] Permanent magnet synchronous motors are widely used in aerospace, intelligent manufacturing, new energy vehicles, household appliances and other fields due to their advantages such as high efficiency, high power density, simple structure and good control performance. In recent years, more and more advanced permanent magnet synchronous motor control strategies have been proposed, but their performance depends largely on the accuracy of motor parameters. However, the parameters of permanent magnet synchronous motors will change with different operating conditions such as load and environmental conditions, resulting in parameter mismatch in the controller, which seriously affects the control accuracy, efficiency and stability of the entire motor drive control system. In addition, the real-time parameters of the motor can also be used for fault diagnosis and reliability detection. Since the stator resistance can reflect the internal winding temperature of the motor, the permanent magnet flux is the embodiment of the health status of the permanent magnet. If the resistance and flux can be estimated in real time, the risk of motor overheating and permanent magnet demagnetization can be prevented. Therefore, accurate identification of permanent magnet synchronous motor parameters is the key to achieving high-performance control strategies.

[0003] In order to solve the problem of decreased control performance due to parameter changes during the operation of permanent magnet synchronous motors, online parameter identification methods for permanent magnet synchronous motors have emerged. Most of the existing online identification schemes are based on voltage equations. By obtaining operating information such as voltage, current, and speed, different convergence algorithms are used to identify parameters, such as recursive least squares method, extended Kalman filter, model reference adaptive system, intelligent algorithm, etc. Since there are four parameters to be identified in permanent magnet synchronous motors and only two voltage equations, online multi-parameter identification generally has the problem of lack of rank. This is because the number of parameters to be identified is greater than the number of identification equations, resulting in the unobservability of the parameters to be identified.

[0004] In order to solve the problem of insufficient rank of the identification equation, one method in the existing technology is to reduce the identification parameters, for example, dividing the identification parameters into two groups, identifying only two parameters each time and using preset values ​​for the other parameters, so that the identification equations of each group are full rank. Obviously, since the preset values ​​are used for other parameters, it will cause larger errors in identification.

[0005] Another method is to increase the identification data, by injecting different DC bias or different reference speed current signals, and collecting multiple sets of voltage, current and speed information under different states, so as to construct multiple sets of identification equations to solve equations of multiple identification parameters. However, this method is complicated and requires the design and collection of multiple sets of data under running conditions, and the parameters to be measured under different states may have changed, resulting in the identified parameters no longer being the parameter values ​​at this time, which in turn causes identification errors.

[0006] In addition, the high-frequency signal injection method can also solve the problem of lack of rank in the identification equation. By injecting high-frequency signals and then extracting high-frequency signals in voltage and current to identify the d and q axis inductances, the number of parameters in solving the voltage equation is reduced. However, since the stator voltage of the actual motor is a step voltage of a PWM waveform, and most drivers do not have a dedicated high-speed voltage acquisition circuit, it is difficult to obtain the input voltage. Therefore, in the prior art, most of the high-frequency signals are extracted from the given voltage signal output from the controller. However, due to control calculation delays, inverter nonlinearity, and DC voltage fluctuations, there is a difference between the given signal in the controller and the actual voltage signal acting on the motor, which leads to inaccurate identification parameters in parameter identification.

[0007] Although relevant modeling and analysis compensation have been carried out for factors such as control calculation delay, inverter nonlinearity, and DC voltage fluctuation in the existing technology, it is difficult to completely compensate for the identification errors caused by all these factors in the field of parameter identification. In addition, the modeling and analysis compensation of these errors are complex in design, technically difficult, difficult to implement, and have poor portability. After adopting other drivers, they need to be re-modeled and analyzed and compensated, which is not conducive to widespread promotion and use.

[0008] In summary, it is of great significance to propose an online multi-parameter identification method for permanent magnet synchronous motor, which can effectively solve the lack-rank problem in the multi-parameter identification equation, does not require changing the motor operating state, does not require designing complex identification algorithms and error compensation algorithms, and can avoid identification errors caused by control calculation delays, inverter nonlinearity, and DC voltage fluctuations. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide a permanent magnet synchronous motor online multi-parameter identification method and system in view of the deficiencies in the above-mentioned prior art, which is used to solve the technical problems existing in the prior art, such as the lack of rank of the multi-parameter identification equation, the complex design of the identification algorithm and the error compensation algorithm, and the online multi-parameter identification error caused by control calculation delay, inverter nonlinearity and DC voltage fluctuation.

[0010] The present invention adopts the following technical solutions: A method for online multi-parameter identification of a permanent magnet synchronous motor comprises the following steps: S1, the given voltage output of the d and q axes when the permanent magnet synchronous motor is running and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output PWM signal to drive the permanent magnet synchronous motor; S2, based on the switching function in the PWM signal obtained in step S1 , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal for the parameter identification strategy; S3, transforming the three-phase step voltage of the motor port reconstructed in step S2 and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extracting the amplitude and phase of the high-frequency voltage signal and the high-frequency current signal in the reconstructed voltage and the measured current through discrete Fourier transform DFT; S4, substituting the amplitude and phase of the high-frequency voltage signal and the high-frequency current signal extracted in step S3 into the high-frequency impedance model, and calculating the d-axis and q-axis inductances at this time; S5. Substitute the d-axis and q-axis inductance values ​​obtained in step S4, as well as the voltage, current, and speed information at this time, into the permanent magnet synchronous motor voltage equation to solve the resistance and flux parameters, and finally obtain four permanent magnet synchronous motor parameters to achieve online multi-parameter identification.

[0011] Preferably, in step S1, a high-frequency sinusoidal voltage signal is injected and for:

[0012] in, and are the amplitudes of the high-frequency sinusoidal voltage injected into the d and q axes, and are the angular frequencies of the high-frequency sinusoidal voltages injected into the d and q axes, and are the initial phases of the high-frequency sinusoidal voltage signals injected into the d and q axes respectively.

[0013] Preferably, in step S2, the reconstructed PWM high-frequency step voltage is:

[0014] in, , and are the reconstructed PWM high-frequency step voltages respectively.

[0015] Preferably, step S3 specifically includes: S301: The three-phase step voltage of the motor port reconstructed in step S2 , and the measured port current and , which is converted into the step voltage signal of the motor port under the d and q axis coordinates after coordinate transformation and , and the port current and ; S302, extracting step voltage signal using DFT algorithm and The high frequency voltage component in and The amplitude and phase of the port current and The high frequency current component in and The amplitude and phase of S303, using a low-pass filter or a moving average filter to remove the noise generated in the DFT process, the amplitude and phase of the high-frequency voltage component after filtering are expressed as , , and , the amplitude and phase of the filtered high-frequency current component are expressed as , , and .

[0016] Preferably, the high-frequency voltage component obtained by DFT and high frequency current components The amplitude and phase of are expressed as:

[0017] in, and is the amplitude of the extracted high-frequency voltage component, and is the phase of the extracted high-frequency voltage component; and is the amplitude of the extracted high-frequency current component, and is the phase of the extracted high-frequency current component, and are the real and imaginary parts of the extracted high-frequency voltage, and are the real and imaginary parts of the extracted high-frequency current respectively.

[0018] Preferably, in step S4, the d-axis and q-axis inductances and The calculation is as follows:

[0019] in, , , and are the amplitude and phase of the high-frequency voltage component after filtering, , , and are the amplitude and phase of the filtered high-frequency current component, and are the angular frequencies of the injected high frequency voltage respectively.

[0020] Preferably, the high frequency impedance model is as follows:

[0021] in, and are the extracted high-frequency voltage components, is the stator resistance.

[0022] Preferably, step S5 is specifically: According to the inductance value obtained in step S4, the other two parameters, namely the stator resistance, are identified through the two voltage equations of the d and q axes. and permanent magnet flux Two parameters, the least square method with genetic factors is used to solve the identification equation to obtain the stator resistance and permanent magnet flux .

[0023] Preferably, the least squares iterative equation with genetic factors is:

[0024] in, is the filter weight matrix, is the input matrix, is the output matrix, and is the gain matrix, is the identity matrix, For the forgetting factor.

[0025] In a second aspect, an embodiment of the present invention provides an online multi-parameter identification system for a permanent magnet synchronous motor, comprising: Signal module, which outputs the given voltage of the d and q axes when the permanent magnet synchronous motor is running and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output PWM signal to drive the permanent magnet synchronous motor; Reconstruction module, based on the switching function in the obtained PWM signal , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal for the parameter identification strategy; The extraction module transforms the reconstructed three-phase step voltage of the motor port and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extracts the amplitude and phase of the high-frequency voltage signal and high-frequency current signal in the reconstructed voltage and measured current through discrete Fourier transform DFT; The calculation module substitutes the amplitude and phase of the extracted high-frequency voltage signal and high-frequency current signal into the high-frequency impedance model to calculate the d-axis and q-axis inductances at this time; The identification module substitutes the obtained d-axis and q-axis inductance values, as well as the voltage, current, and speed information at this time into the permanent magnet synchronous motor voltage equation to solve the resistance and flux parameters, and finally obtains four permanent magnet synchronous motor parameters to realize online multi-parameter identification.

[0026] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for online multi-parameter identification of a permanent magnet synchronous motor when executing the computer program.

[0027] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned permanent magnet synchronous motor online multi-parameter identification method.

[0028] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for online multi-parameter identification of a permanent magnet synchronous motor when executing the computer program.

[0029] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned permanent magnet synchronous motor online multi-parameter identification method.

[0030] Compared with the prior art, the present invention has at least the following beneficial effects: A method for online multi-parameter identification of a permanent magnet synchronous motor is proposed. The inductance is calculated by injecting a high-frequency voltage signal at a given voltage and then extracting the high-frequency voltage and current signal at the motor port. This inductance calculation method does not rely on the motor voltage equation and is not limited by the original motor control strategy. The resistance and flux are then iteratively calculated based on the calculated inductance, which effectively avoids the problem of insufficient rank of the identification equation in full parameter identification. The entire identification process is simple and easy to implement. In order to further improve the identification accuracy, the reconstructed voltage is used instead of the given voltage as the voltage signal for parameter identification, which effectively eliminates the identification error caused by non-ideal factors such as control calculation delay, inverter nonlinearity, and DC voltage fluctuation, improves the parameter identification accuracy, and avoids complex error compensation algorithms and high-speed voltage sampling devices.

[0031] Furthermore, by injecting a high-frequency sinusoidal voltage signal and , which can generate high-frequency components in the motor port voltage and current, so as to facilitate the subsequent calculation of inductance parameters with the help of the information in the high-frequency components of the voltage and current. And when the high-frequency voltage signal is injected, it does not affect or change the original motor control strategy. Therefore, the identification method of the present invention is not limited to the motor control strategy, and can perform multi-parameter identification on any motor control strategy. In the absence of a control strategy, the high-frequency voltage signal can also be injected offline, thereby realizing the inductance identification in offline mode.

[0032] Furthermore, due to non-ideal factors such as control calculation delay, inverter nonlinearity, and DC voltage fluctuation, there is a difference between the given voltage and the actual motor voltage, and the error source is complex, making it difficult to design an error compensation algorithm; the actual PWM voltage of the port is difficult to measure accurately, and an additional high-speed voltage sampling device is required. Therefore, the switch function is used , , And the output high and low levels of the driver switch tube , Reconstructing the PWM high-frequency step voltage can effectively avoid the identification error caused by using a given voltage, significantly improve the identification accuracy, and accurately obtain the motor's port voltage without the need for a high-speed voltage sampling device, nor is there a need to design complex error compensation algorithms based on non-ideal factors.

[0033] Furthermore, the use of discrete Fourier transform (DFT) can more accurately extract the amplitude and phase of high-frequency voltage and current signals in the reconstructed voltage and measured current, reducing the identification error caused by the signal extraction stage. Compared with other high-frequency signal extraction methods such as bandpass filters, the DFT algorithm has a simple structure, does not require complex parameter design, and has a faster dynamic response speed.

[0034] Furthermore, when only high-frequency signals are considered, the motor voltage equation can be simplified to a high-frequency impedance model. The high-frequency impedance model and the extracted high-frequency signal can be used to directly calculate the inductance parameters, avoiding the complex parameter identification algorithm. The calculation of inductance does not depend on the motor voltage equation, and there is no need to change the motor's operating state to achieve full rank of the identification equation. There is no need for multiple sets of motor state data. It is directly based on the original control strategy and extracts the high-frequency signal at this time for direct calculation, effectively avoiding the equation under-rank problem in multi-parameter identification.

[0035] Furthermore, according to the calculated inductance value and the voltage, current, and speed information at this time, the resistance and flux can be iteratively solved. At this time, the identification equation is full rank and the equation has a unique solution. Therefore, the least squares method with a simple structure can also accurately and effectively obtain the parameter values ​​of resistance and flux, and then complete the identification of multiple parameters. The least squares method with a forgetting factor has a simple structure, stable identification results, and a small amount of calculation, which is conducive to the realization and implementation of the identification method of the present invention.

[0036] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0037] In summary, the present invention uses a high-frequency injection method to effectively solve the lack of rank problem in multi-parameter identification, and does not need to design complex identification algorithms and error compensation algorithms. It has a simple structure, small amount of calculation, and is easy to implement. In order to further improve the identification accuracy, the use of PWM reconstruction voltage effectively avoids control delays, inverter nonlinearity, and voltage errors caused by DC voltage fluctuations. Therefore, the identification method in the present invention has higher identification accuracy.

[0038] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic diagram of the process of the online multi-parameter identification method of the present invention; Figure 2 It is a flowchart of the online multi-parameter identification method of the present invention; Figure 3The figure is a comparison diagram of the full parameter identification results under load-addition and load-reduction conditions, wherein (a) is the identification result using a given voltage signal, and (b) is the identification result using the method of the present invention; Figure 4 Surface diagrams of d-axis and q-axis inductance identification results under different currents, where (a) is the d-axis inductance identification result, and (b) is the q-axis inductance identification result; Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 6 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] 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 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.

[0042] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0043] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0045] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0046] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0047] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0048] The present invention provides an online multi-parameter identification method for a permanent magnet synchronous motor. By designing a suitable observer or identification algorithm, more information can be provided for parameter identification while keeping the amount of measured data unchanged, thereby effectively solving the lack-rank problem without changing the motor operating state, simplifying the identification algorithm and error compensation, and reducing the influence of control calculation delay, inverter nonlinearity, and DC voltage fluctuations.

[0049] See also Figure 1 and Figure 2 The present invention provides an online multi-parameter identification method for a permanent magnet synchronous motor, including a high-frequency signal injection link, a port voltage reconstruction link, a high-frequency voltage and current signal extraction link, an AC and DC axis inductance calculation link, and a resistance and flux calculation link. The specific steps are as follows: S1, permanent magnet synchronous motor (PMSM) operates under any motor control strategy, in which the d and q axis outputs a given voltage and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module, and then output PWM signal to drive the control motor; The frequency of the injected high-frequency sinusoidal voltage signal is much higher than the operating frequency of the motor and much lower than the switching frequency of the driver. The injection frequency of the q-axis is generally higher than the injection frequency of the d-axis to avoid coupling between the high-frequency signals of the d-axis and q-axis and affect the extraction of the high-frequency signal. The amplitude of high-frequency voltage injection should be much smaller than the rated voltage of the motor, thereby reducing the impact of high-frequency injection on the system.

[0050] The permanent magnet synchronous motor can be operated under any motor control strategy or in offline mode, that is, only a high-frequency sinusoidal voltage signal is injected without a motor control strategy. and Therefore, the multi-parameter identification method proposed in the present invention is not limited to any motor control strategy, and is an identification operation performed on the basis of any motor control strategy; the injected high-frequency sinusoidal voltage signal and The expression is: (1) in, and are the amplitudes of the high-frequency sinusoidal voltage injected into the d and q axes, and are the angular frequencies of the high-frequency sinusoidal voltages injected into the d and q axes, and are the initial phases of the high-frequency sinusoidal voltage signals injected into the d and q axes respectively.

[0051] In order to reduce the impact of the injected signal on the original control system, the amplitude of the high-frequency sinusoidal voltage injected into the d and q axes is and It should be much smaller than the rated voltage, generally selected as 0.01~0.05 pu.

[0052] (2) in, and is the angular frequency of the high-frequency sinusoidal voltage injected into the d and q axes and The corresponding frequency. In the selection of injection frequency, and It should be as much larger than the motor's operating frequency as possible and much smaller than the driver's switching frequency. In this range, a suitable injection frequency should be selected. Slightly larger than , to prevent the generation of high-frequency signal coupling under the d and q axes, thereby avoiding inaccurate high-frequency voltage and current signals extracted by DFT in step S3.

[0053] After the high-frequency sinusoidal signal is injected, high-frequency voltage and high-frequency current components will be generated in the port voltage and port current of the motor. The port current can be directly measured using a conventional current sensor, while the port voltage is a PWM high-frequency step voltage, which requires an additional special high-speed voltage sampling module to sample, and conventional drivers cannot collect it. Therefore, in step S2, the present invention uses the switch function output by the PWM module , , And the output high and low levels of the driver switch tube , , to reconstruct the PWM high-frequency step voltage of the actual motor port as the voltage signal for the parameter identification strategy.

[0054] The expressions of the motor port stator voltage and current after injecting high-frequency components are expressed as: (3) in, , and The injected high frequency voltage is amplitude, angular frequency and phase. , and Respectively, the high frequency voltage The corresponding high frequency response current amplitude, angular frequency and phase. and are the stator voltage and stator current without injected high-frequency components.

[0055] S2, using the switching function of the PWM module output , , And the output high and low levels of the driver switch tube , , reconstruct the step voltage of the actual motor port as the voltage signal of the parameter identification strategy; Compared with the prior art, which directly adopts the given voltage in the control system (i.e., the output given voltage of the control strategy, i.e., the input of PWM) as the actual port voltage, the present invention reconstructs the step voltage of the actual motor port as the voltage signal of the parameter identification strategy through the switching function of the PWM output and the output high and low levels of the driver switch tube, which can bring higher voltage signal accuracy, enhance the robustness of the system, improve the motor control performance, optimize the parameter identification strategy, and may broaden the application scope of this technology in high-precision requirements.

[0056] Using the switching function of the PWM module output , , And the output high and low levels of the driver switch tube , , to reconstruct the step voltage of the actual motor port as the voltage signal of the parameter identification strategy. , , The value of is 0 and 1. 0 means that the upper switch in the bridge arm of this phase is turned off, the lower switch is turned on, and the output voltage is positive; 1 means that the upper switch in the bridge arm of this phase is turned on, the lower switch is turned off, and the output voltage is negative. The output high and low levels of the driver switch tube , The voltage sensor on the port is used for measurement. No high-speed sampling is required. Only the voltage amplitude at high and low levels is recorded. The expression of the reconstructed PWM high-frequency step voltage is: (4) in, , and They are the reconstructed PWM high-frequency step voltages, which are collectively referred to as .

[0057] S3, transforming the three-phase step voltage of the motor port reconstructed in step S2 and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extracting the amplitude and phase of the high-frequency voltage signal and the high-frequency current signal in the reconstructed voltage and the measured current through discrete Fourier transform DFT; S301: The three-phase step voltage of the motor port reconstructed in step S2 , and the measured port current and , which is converted into the step voltage signal of the motor port under the d and q axis coordinates after coordinate transformation and , and the port current and ; S302, using DFT algorithm to extract the high-frequency voltage component and high frequency current components The amplitude and phase of Because a high-frequency sinusoidal voltage signal with a small amplitude is injected into the given voltage signal in step S1, the step voltage signal at the motor port is obtained. and And the port current and There is a corresponding high-frequency voltage component generated by high-frequency injection and high frequency current components .

[0058] The expression of DFT algorithm is: (5) in, and are the real and imaginary parts of the extracted high-frequency signal, is the amplitude of the mentioned high-frequency signal, and N is the number of data points in the discrete Fourier transform.

[0059] High-frequency voltage components obtained by DFT and high frequency current components The amplitude and phase of are expressed as: (6) in, and is the high frequency voltage component The amplitude of and is the high frequency voltage component The phase of and is the high frequency voltage component The amplitude of and is the high frequency voltage component phase.

[0060] S303, using a low-pass filter (LPF) or a moving average filter (MAF) to remove the noise generated in the DFT process, the amplitude and phase of the high-frequency voltage and current components after filtering can be expressed as , , and .

[0061] S4, substituting the amplitude and phase of the high-frequency voltage signal and the high-frequency current signal extracted in step S3 into the high-frequency impedance model to calculate the d-axis and q-axis inductances at this time; The high frequency impedance model of the motor is determined considering only the injected high frequency signal.

[0062] The voltage equation of a permanent magnet synchronous motor is: (7) in, , , They are the stator voltage, stator current, and d- and q-axis inductances of the PMSM; is the stator resistance; is the flux linkage of the permanent magnet; is the electrical angle of the permanent magnet synchronous motor; is a differential operator.

[0063] Substitute equation (3) into equation (7), eliminate its low-frequency component, and only focus on the high-frequency component in the equation. That is, only focus on the d-axis The frequency component, on the q axis, only the The frequency components, and the injected high frequency frequencies of the d and q axes are different, so there is no d and q axis coupling phenomenon when only considering their respective high frequency components. So the d and q axis high frequency impedance model of PMSM is obtained: (8) According to (8), the inductance identification equation of PMSM is as follows: (9) Substituting the amplitude and phase of the high-frequency voltage and current components into the equation, we obtain the d-axis inductance and q-axis inductance .

[0064] S5. Substitute the d-axis and q-axis inductance values ​​obtained in step S4, as well as the voltage, current, and speed information at this time, into the permanent magnet synchronous motor voltage equation to solve the resistance and flux linkage parameters, and finally obtain four permanent magnet synchronous motor parameters.

[0065] Two equations are used to solve two parameters, so there is a unique solution. Considering the simplicity, speed, and stability of the algorithm, the least square method with genetic factors is used in the identification equation solution algorithm.

[0066] According to the inductance value obtained in step S4, the other two parameters, namely the stator resistance, are identified through the two voltage equations of the d and q axes. and permanent magnet flux Two parameters, there is no problem of lack of rank at this time. The least square method with genetic factors is used to solve the identification equation. The least square method with genetic factors has the advantages of simple structure, small amount of calculation, and numerical stability. Its iterative equation is: (10) in, is the filter weight matrix, is the input matrix, is the output matrix, and is the gain matrix, is the identity matrix, For the forgetting factor, is the inductance value identified by the high frequency injection method.

[0067] Through the iterative calculation of the least squares method, the stator resistance is obtained and permanent magnet flux .

[0068] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "platform".

[0069] In another embodiment of the present invention, a permanent magnet synchronous motor online multi-parameter identification system is provided, which can be used to implement the above-mentioned permanent magnet synchronous motor online multi-parameter identification method. Specifically, the permanent magnet synchronous motor online multi-parameter identification system includes a signal module, a reconstruction module, an extraction module, a calculation module and an identification module.

[0070] Among them, the signal module outputs the given voltage of the d and q axes when the permanent magnet synchronous motor is running. and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output PWM signal to drive the permanent magnet synchronous motor; Reconstruction module, based on the switching function in the obtained PWM signal , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal for the parameter identification strategy; The extraction module transforms the reconstructed three-phase step voltage of the motor port and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extracts the amplitude and phase of the high-frequency voltage signal and high-frequency current signal in the reconstructed voltage and measured current through discrete Fourier transform DFT; The calculation module substitutes the amplitude and phase of the extracted high-frequency voltage signal and high-frequency current signal into the high-frequency impedance model to calculate the d-axis and q-axis inductances at this time; The identification module substitutes the obtained d-axis and q-axis inductance values, as well as the voltage, current, and speed information at this time into the permanent magnet synchronous motor voltage equation to solve the resistance and flux parameters, and finally obtains four permanent magnet synchronous motor parameters to realize online multi-parameter identification.

[0071] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the online multi-parameter identification method of the permanent magnet synchronous motor, including: The given voltage output of the d and q axes when the permanent magnet synchronous motor is running and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output a PWM signal to drive the permanent magnet synchronous motor; based on the switching function in the obtained PWM signal , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal of the parameter identification strategy; transform the reconstructed three-phase step voltage of the motor port and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extract the amplitude and phase of the high-frequency voltage signal and high-frequency current signal in the reconstructed voltage and measured current through discrete Fourier transform DFT; substitute the amplitude and phase of the extracted high-frequency voltage signal and high-frequency current signal into the high-frequency impedance model to calculate the d and q axis inductance at this time; substitute the obtained d and q axis inductance values, as well as the voltage, current and speed information at this time into the permanent magnet synchronous motor voltage equation to solve the resistance and flux linkage parameters, and finally obtain four permanent magnet synchronous motor parameters to realize online multi-parameter identification.

[0072] See also Figure 5 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the online multi-parameter identification method of the permanent magnet synchronous motor in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the online multi-parameter identification system of the permanent magnet synchronous motor in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0073] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0074] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0075] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0076] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0077] See also Figure 6 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0078] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 2 Follow the steps shown in .

[0079] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0080] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0081] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0082] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0083] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0084] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for online multi-parameter identification of a permanent magnet synchronous motor in the above embodiment; the processor may load and execute the following steps: The given voltage output of the d and q axes when the permanent magnet synchronous motor is running and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output a PWM signal to drive the permanent magnet synchronous motor; based on the switching function in the obtained PWM signal , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal of the parameter identification strategy; transform the reconstructed three-phase step voltage of the motor port and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extract the amplitude and phase of the high-frequency voltage signal and high-frequency current signal in the reconstructed voltage and measured current through discrete Fourier transform DFT; substitute the amplitude and phase of the extracted high-frequency voltage signal and high-frequency current signal into the high-frequency impedance model to calculate the d and q axis inductance at this time; substitute the obtained d and q axis inductance values, as well as the voltage, current and speed information at this time into the permanent magnet synchronous motor voltage equation to solve the resistance and flux linkage parameters, and finally obtain four permanent magnet synchronous motor parameters to realize online multi-parameter identification.

[0085] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0086] See also Figure 3 , which is a comparison chart of the full parameter identification results of the prior art and the method described in the present invention under load addition and reduction conditions. The prior art in this embodiment is a method that uses the output given voltage of the motor control strategy as parameter identification, while the method described in the present invention uses the reconstructed high-frequency step voltage as the port voltage for parameter identification. As can be seen from the figure, the parameter identification method proposed in the present invention has higher identification accuracy, the identification error and data waveform are relatively small, and the identification result hardly changes with the change of load, and the identification error does not exceed 1%. The identification result of the prior art will change with the change of load, which is due to the identification error caused by the control delay and the nonlinearity of the inverter. The method described in the present invention greatly reduces the influence caused by the control delay and the nonlinearity of the inverter, so the identification parameters are more accurate and stable.

[0087] See also Figure 4, is a surface diagram of the d-axis and q-axis inductance identification results under different currents. In this embodiment, it is assumed that the inductance parameters are constant, the d-axis inductance is 6.5mH, and the q-axis inductance is 10.5mH. It can be seen from the figure that under different d-axis and q-axis currents, that is, under different operating conditions, the inductance identification results are close to the set parameter values, and the error is controlled within 1%, which verifies the effectiveness of the method described in the present invention.

[0088] In summary, the present invention provides a method and system for online multi-parameter identification of a permanent magnet synchronous motor. By injecting a high-frequency voltage signal into a given voltage in a controller, the high-frequency step voltage on the motor port is reconstructed using the high and low levels of the output of the switch function and the driver switch tube, thereby avoiding the port voltage error caused by factors such as control calculation delay, inverter nonlinearity, and DC voltage fluctuation. Then, the high-frequency voltage and current signals are extracted from the reconstructed port step voltage and measured current using the DFT algorithm, which are substituted into the motor high-frequency impedance model to calculate the d and q axis inductances. The stator resistance and permanent magnet flux are identified based on the obtained inductance value using the least squares method with a forgetting factor, which effectively solves the problem of lack of rank in multi-parameter identification, and the identification process is relatively simple.

[0089] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0090] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0092] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for online multi-parameter identification of a permanent magnet synchronous motor, characterized in that: The following steps are involved: S1, the given voltage output of the d and q axes when the permanent magnet synchronous motor is running and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output PWM signal to drive the permanent magnet synchronous motor; S2, based on the switching function in the PWM signal obtained in step S1 , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal for the parameter identification strategy; S3, transforming the three-phase step voltage of the motor port reconstructed in step S2 and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extracting the amplitude and phase of the high-frequency voltage signal and the high-frequency current signal in the reconstructed voltage and the measured current through discrete Fourier transform DFT; S4, substituting the amplitude and phase of the high-frequency voltage signal and the high-frequency current signal extracted in step S3 into the high-frequency impedance model, and calculating the d-axis and q-axis inductances at this time; S5. Substitute the d-axis and q-axis inductance values ​​obtained in step S4, as well as the voltage, current, and speed information at this time, into the permanent magnet synchronous motor voltage equation to solve the resistance and flux parameters, and finally obtain four permanent magnet synchronous motor parameters to achieve online multi-parameter identification.

2. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 1, characterized in that: In step S1, a high-frequency sinusoidal voltage signal is injected and for: in, and are the amplitudes of the high-frequency sinusoidal voltage injected into the d and q axes, and are the angular frequencies of the high-frequency sinusoidal voltages injected into the d and q axes, and are the initial phases of the high-frequency sinusoidal voltage signals injected into the d and q axes respectively.

3. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 1, characterized in that: In step S2, the reconstructed PWM high-frequency step voltage is: in, , and are the reconstructed PWM high-frequency step voltages respectively.

4. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 1, characterized in that: Step S3 is specifically as follows: S301: The three-phase step voltage of the motor port reconstructed in step S2 , and the measured port current and , which is converted into the step voltage signal of the motor port under the d and q axis coordinates after coordinate transformation and , and the port current and ; S302, extracting step voltage signal using DFT algorithm and The high frequency voltage component in and The amplitude and phase of the port current and The high frequency current component in and The amplitude and phase of S303, using a low-pass filter or a moving average filter to remove the noise generated in the DFT process, the amplitude and phase of the high-frequency voltage component after filtering are expressed as , , and , the amplitude and phase of the filtered high-frequency current component are expressed as , , and .

5. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 4, characterized in that: High-frequency voltage components obtained by DFT and high frequency current components The amplitude and phase of are expressed as: in, and is the amplitude of the extracted high-frequency voltage component, and is the phase of the extracted high-frequency voltage component; and is the amplitude of the extracted high-frequency current component, and is the phase of the extracted high-frequency current component, and are the real and imaginary parts of the extracted high-frequency voltage, and are the real and imaginary parts of the extracted high-frequency current respectively.

6. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 1, characterized in that: In step S4, the d-axis and q-axis inductances and The calculation is as follows: in, , , and are the amplitude and phase of the high-frequency voltage component after filtering, , , and are the amplitude and phase of the filtered high-frequency current component, and are the angular frequencies of the injected high-frequency voltages, respectively.

7. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 6, characterized in that: The high frequency impedance model is as follows: in, and are the extracted high-frequency voltage components, is the stator resistance.

8. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 1, characterized in that: Step S5 is specifically as follows: According to the inductance value obtained in step S4, the other two parameters, namely the stator resistance, are identified through the two voltage equations of the d and q axes. and permanent magnet flux Two parameters, the least square method with genetic factors is used to solve the identification equation to obtain the stator resistance and permanent magnet flux .

9. The method for online multi-parameter identification of a permanent magnet synchronous motor according to claim 8, characterized in that: The least squares iterative equation with genetic factors is: in, is the filter weight matrix, is the input matrix, is the output matrix, and is the gain matrix, is the identity matrix, For the forgetting factor.

10. A permanent magnet synchronous motor online multi-parameter identification system, characterized in that: include: Signal module, which outputs the given voltage of the d and q axes when the permanent magnet synchronous motor is running and Inject high frequency sinusoidal voltage signal and , and then serve as the input of the PWM module to output PWM signal to drive the permanent magnet synchronous motor; Reconstruction module, based on the switching function in the obtained PWM signal , , And the output high and low levels of the driver switch tube , , reconstruct the three-phase step voltage of the actual motor port as the voltage signal for the parameter identification strategy; The extraction module transforms the reconstructed three-phase step voltage of the motor port and the three-phase current measured by the sensor into the reconstructed voltage and measured current under the d and q axes through coordinate transformation, and then extracts the amplitude and phase of the high-frequency voltage signal and high-frequency current signal in the reconstructed voltage and measured current through discrete Fourier transform DFT; The calculation module substitutes the amplitude and phase of the extracted high-frequency voltage signal and high-frequency current signal into the high-frequency impedance model to calculate the d-axis and q-axis inductances at this time; The identification module substitutes the obtained d-axis and q-axis inductance values, as well as the voltage, current, and speed information at this time into the permanent magnet synchronous motor voltage equation to solve the resistance and flux parameters, and finally obtains four permanent magnet synchronous motor parameters to realize online multi-parameter identification.

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