Permanent magnet synchronous motor fundamental current amplitude identification method based on variable forgetting factor

Abstract

The application provides a permanent magnet synchronous motor fundamental wave current amplitude identification method based on a variable forgetting factor, comprising: identifying the fundamental wave current amplitude of the permanent magnet synchronous motor based on a recursive least square method with a fixed forgetting factor; introducing a variable forgetting factor in the recursive least square method with the fixed forgetting factor, and updating the variable forgetting factor using a nonlinear feedback control rate of a active disturbance rejection controller. The application adopts the recursive least square algorithm, and can realize fast and accurate tracking of the fundamental wave current amplitude of the permanent magnet synchronous motor in transient and steady states; compared with the traditional recursive least square algorithm with fixed parameters, the variable forgetting factor recursive least square algorithm provided by the application dynamically adjusts the forgetting factor, so as to realize the stability and rapidity of the fundamental wave current amplitude identification at the same time, and the identification effect is better than that of the fundamental wave current identification method using the recursive least square algorithm with the fixed forgetting factor.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor control technology, and more specifically, to a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are characterized by their simple structure, high power density, strong overload capacity, and ease of maintenance, and are widely used in industrial robots, electric vehicles, aerospace, and other fields. Many PMSM applications require a fundamental current component; however, the three-phase current of a PMSM includes not only the fundamental current component but also higher harmonic components, primarily the fifth and seventh harmonics, making direct extraction of the fundamental current component quite difficult.

[0003] Recursive Least Squares (RLS), as an online identification algorithm, can obtain unbiased estimates of parameters in real time in systems containing white noise and is easy to implement. In the classic Recursive Least Squares with Forgetting Factor (FRLS), the forgetting factor λ is a fixed value, meaning it attenuates the influence of past observations at a fixed rate. If the fixed forgetting factor λ is close to 1, the algorithm has high accuracy but reduced parameter tracking ability. Reducing the fixed forgetting factor λ can improve tracking ability, but at the same time, it will reduce steady-state accuracy.

[0004] To address the problems of existing technologies, this invention provides a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a fundamental current identification method that can achieve fast and accurate tracking of the fundamental current amplitude in both transient and steady-state conditions, and whose identification effect is better than that of the traditional recursive least squares algorithm using a fixed forgetting factor.

[0006] This invention provides a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, the method comprising:

[0007] The fundamental current amplitude of a permanent magnet synchronous motor is identified based on the recursive least squares method with a fixed forgetting factor.

[0008] A variable forgetting factor is introduced into the recursive least squares method with a fixed forgetting factor, and the variable forgetting factor is updated using the nonlinear feedback control law of the active disturbance rejection controller.

[0009] According to an embodiment of the present invention, the fundamental current amplitude of a permanent magnet synchronous motor is identified through the following steps:

[0010] For permanent magnet synchronous motors, a rotor current model for permanent magnet synchronous motors is established;

[0011] The three-phase current formula in the rotor current model of the permanent magnet synchronous motor is rewritten in the form of a least squares relation.

[0012] The fundamental current amplitude of the permanent magnet synchronous motor is identified and calculated using the recursive formula of the recursive least squares method with a fixed forgetting factor.

[0013] According to an embodiment of the present invention, the rotor current model of the permanent magnet synchronous motor is as follows:

[0014]

[0015] Among them, i u i v i w ω represents the three-phase current of the permanent magnet synchronous motor; I1, I5, I7 and θ1, θ5, θ7 represent the amplitude and phase of the fundamental current, the fifth harmonic current and the seventh harmonic current, respectively; ω is the electric angular velocity; t is time.

[0016] According to an embodiment of the present invention, the three-phase current formula in the rotor current model of the permanent magnet synchronous motor is rewritten as y(m)=X T The least squares relation is of the form (m)θ(m)+E(m), where y(m) is the output vector; X T θ(m) is the transpose of the input vector; θ(m) is the parameter value to be identified; E(m) is random noise with zero mean.

[0017] According to one embodiment of the present invention, y(m) = X T The least squares relation of the form (m)θ(m)+E(m) is:

[0018]

[0019] Among them, i u (m), i v (m), i w (m) and θ e (m) represents the three-phase current and electrical angle of the permanent magnet synchronous motor; X(m) is the input vector. It is the estimated parameter; I k θk It represents the amplitude and phase of the k-th harmonic current.

[0020] According to one embodiment of the present invention, the recursive formula of the recursive least squares method with a fixed forgetting factor is as follows:

[0021]

[0022] Where P(m) is the variance matrix; λ is the fixed forgetting factor; K(m) is the correction matrix; X T X(m) is the transpose of the input vector; X(m) is the input vector. y(m) is the estimated parameter; y(m) is the output vector; m is the number of recursive calculations.

[0023] According to one embodiment of the present invention, an update formula for the variable forgetting factor is constructed based on the nonlinear feedback control law of the active disturbance rejection controller using the fal function:

[0024]

[0025] Where, fal function is the nonlinear structure of the extended state observer in the active disturbance rejection controller; λ' is the variable forgetting factor; e is the error rate; Δe is the protected harmonic variation range; α is the nonlinear factor; δ is the filtering factor; k y λ is an intermediate parameter. Max and λ Min It is a limitation setting for the variable forgetting factor; It is the target q-axis current output by the velocity loop; is the estimated parameter; m is the number of recursive calculations.

[0026] According to one embodiment of the present invention, the intermediate parameters are calculated using the following expression:

[0027] k y =(λ Max -λ Min ) / (2△eα)

[0028] Where, k y λ is an intermediate parameter. Max and λ Min Δe is the limiting setting of the variable forgetting factor; Δe is the protected harmonic variation range; α is the nonlinear factor.

[0029] According to another aspect of the invention, a storage medium is also provided, which includes a series of instructions for performing the steps of the method described in any of the preceding claims.

[0030] According to another aspect of the present invention, a device for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor is also provided, which performs the method described in any of the preceding claims, the device comprising:

[0031] The fundamental current amplitude identification module is used to identify the fundamental current amplitude of a permanent magnet synchronous motor based on a recursive least squares method with a fixed forgetting factor.

[0032] A variable forgetting factor update module is used to introduce a variable forgetting factor into the recursive least squares method with a fixed forgetting factor and update the variable forgetting factor using the nonlinear feedback control law of the active disturbance rejection controller.

[0033] This invention provides a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, which has the following advantages compared with the prior art:

[0034] (1) The present invention adopts the recursive least squares algorithm, which can achieve fast and accurate tracking of the fundamental current amplitude of PMSM in both transient and steady state conditions;

[0035] (2) Compared with the traditional recursive least squares algorithm with fixed parameters, the recursive least squares algorithm with variable forgetting factor provided by the present invention dynamically adjusts the forgetting factor λ to simultaneously achieve the stability and speed of fundamental current amplitude identification. The identification effect is better than the fundamental current identification method using the recursive least squares algorithm with fixed forgetting factor.

[0036] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0038] Figure 1 A flowchart illustrating the steps of a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, according to an embodiment of the present invention, is shown.

[0039] Figure 2 A schematic diagram of the steps of a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor according to an embodiment of the present invention is shown.

[0040] In the accompanying drawings, the same parts use the same reference numerals. Also, the drawings are not drawn to scale. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] In existing technologies, in the classic Recursive Least Squares with Forgetting Factor (FRLS), the value of the forgetting factor directly affects the stability of parameter identification. Although different values ​​may not result in significant changes in error, the results demonstrate that the value of the forgetting factor does indeed affect model accuracy. A larger forgetting factor leads to more stable parameter identification results but slower response times, poorer real-time performance, and suboptimal model accuracy. Conversely, a smaller forgetting factor results in more volatile parameter identification results but faster response times and higher model accuracy.

[0043] In the existing technology, there is no universally accepted method for setting a reasonable forgetting factor. That is, there is no universally accepted formula to measure whether the set fixed forgetting factor is reasonable, including whether the identified parameter value corresponding to the fixed forgetting factor is reasonable, and its impact on the model accuracy.

[0044] In summary, to address the problems of existing technologies, this invention provides a fundamental current identification method that can achieve rapid and accurate tracking of the fundamental current amplitude under both transient and steady-state conditions, with better identification performance than the traditional fundamental current identification method using a recursive least squares algorithm with a fixed forgetting factor.

[0045] The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor provided by this invention, compared with the traditional recursive least squares algorithm with a fixed forgetting factor, dynamically adjusts the forgetting factor λ to simultaneously achieve stability and speed in identifying the fundamental current amplitude.

[0046] Figure 1 A flowchart illustrating the steps of a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, according to an embodiment of the present invention, is shown.

[0047] like Figure 1 As shown, in step S101, the fundamental current amplitude of the permanent magnet synchronous motor is identified based on the recursive least squares method with a fixed forgetting factor.

[0048] In one embodiment, the fundamental current amplitude of the permanent magnet synchronous motor is identified through steps S1011-S1013.

[0049] In step S1011, a rotor current model of the permanent magnet synchronous motor is established for the permanent magnet synchronous motor.

[0050] Ideally, a permanent magnet synchronous motor is three-phase balanced and half-wave symmetrical. The phase currents do not contain nth-order (n = 3, 9, 15...) or even-order harmonics. Specifically, in step S1011, considering that the fifth and seventh current harmonics are the main components, the rotor current model of the permanent magnet synchronous motor is as follows:

[0051]

[0052] Among them, i u i v i w ω represents the three-phase current of the permanent magnet synchronous motor; I1, I5, I7 and θ1, θ5, θ7 represent the amplitude and phase of the fundamental current, the fifth harmonic current and the seventh harmonic current, respectively; ω is the electric angular velocity; t is time.

[0053] In step S1012, the three-phase current formula in the rotor current model of the permanent magnet synchronous motor is rewritten into the form of a least squares relation.

[0054] Specifically, in step S1012, the three-phase current formula (1) in the rotor current model of the permanent magnet synchronous motor is rewritten as y(m)=X T The least squares relation is of the form (m)θ(m)+E(m), where y(m) is the output vector and X is the output vector. T θ(m) is the transpose of the input vector; θ(m) is the parameter value to be identified; E(m) is random noise with zero mean; m is the number of recursive calculations. In one embodiment, θ(m) represents the fundamental current amplitude I1; E(m) represents the higher harmonic current.

[0055] Furthermore, based on the three-phase current formula (1) in the rotor current model of a permanent magnet synchronous motor (PMSM), the formula y(m) = X is established. T The least squares relation of the form (m)θ(m)+E(m) is:

[0056]

[0057] Among them, i u (m), i v (m), i w (m) and θ e (m) represents the three-phase current and electrical angle of the permanent magnet synchronous motor; X(m) is the input vector. It is the estimated parameter; I k θ k It represents the amplitude and phase of the k-th harmonic current.

[0058] Specifically, i u (m), i v (m), iw (m) and θ e (m) can come from the sensor's sampling of the permanent magnet synchronous motor (PMSM).

[0059] In step S1013, the fundamental current amplitude of the permanent magnet synchronous motor is identified and calculated using a recursive formula of the recursive least squares method with a fixed forgetting factor.

[0060] Specifically, in step S1013, the recursive formula for the recursive least squares method with a fixed forgetting factor is:

[0061]

[0062] Where P(m) is the variance matrix; λ is the fixed forgetting factor; K(m) is the correction matrix; X T X(m) is the transpose of the input vector; X(m) is the input vector. y(m) is the estimated parameter; y(m) is the output vector; m is the number of recursive calculations.

[0063] like Figure 2 As shown, input i u (m), i v (m), i w (m) and θ e (m), through the recursive formula (3) of the recursive least squares method with a fixed forgetting factor, the fundamental current amplitude (e.g., the average fundamental current) of the permanent magnet synchronous motor is obtained. ( ) to perform identification calculations.

[0064] This invention employs a recursive least squares algorithm, which enables fast and accurate tracking of the fundamental current amplitude of a PMSM in both transient and steady-state conditions.

[0065] like Figure 1 As shown, in step S102, a variable forgetting factor is introduced into the recursive least squares method with a fixed forgetting factor, and the variable forgetting factor is updated using the nonlinear feedback control law of the active disturbance rejection controller.

[0066] An Active Disturbance Rejection Control (ADRC) is a product consisting of a tracking differentiator, an extended state observer, and a nonlinear state error feedback law.

[0067] The tracking differentiator plays a crucial role in arranging the transient response, providing a reasonable control signal, and resolving the conflict between response speed and overshoot. The extended state observer addresses the combined effects of unknown model components and external disturbances on the controlled object. Although called an extended state observer, it differs from a regular state observer. The extended state observer designs an extended state variable to track the influence of unknown model components and external disturbances. It then provides a control variable to compensate for these disturbances, transforming the controlled object into a regular integral-cascaded controlled object. The purpose of designing the extended state observer is to observe the extended state variable to estimate unknown disturbances and the unmodeled parts of the controlled object, achieving feedback linearization of the dynamic system and transforming the controlled object into an integral-cascaded controlled object. The nonlinear error feedback control law provides the control strategy for the controlled object.

[0068] Specifically, in step S102, based on the nonlinear state error feedback law of the active disturbance rejection controller using the fal function, the update formula (4) for the variable forgetting factor is constructed:

[0069]

[0070] Where, fal function is the nonlinear structure of the extended state observer in the active disturbance rejection controller; λ' is the variable forgetting factor; e is the error rate; Δe is the protected harmonic variation range; α is the nonlinear factor; δ is the filtering factor; k y λ is an intermediate parameter. Max and λ Min It is a limitation setting for the variable forgetting factor; It is the target q-axis current output by the velocity loop; is the estimated parameter; m is the number of recursive calculations. In one embodiment, e represents the error rate of the reference q-axis current relative to the amplitude of the fundamental current.

[0071] Further, in step S102, the intermediate parameters are calculated using the following expression (5):

[0072] k y =(λ Max -λ Min ) / (2△e α (5)

[0073] Where, k y λ is an intermediate parameter. Max and λ Min Δe is the limiting setting of the variable forgetting factor; Δe is the protected harmonic variation range; α is the nonlinear factor.

[0074] like Figure 2 As shown, the input λ Max, λ Min , The updated variable forgetting factor λ' is obtained through the update formula (4) and intermediate parameter expression (5) of the variable forgetting factor. The variable forgetting factor λ' is used to replace the fixed forgetting factor λ in the recursive least squares method (FRLS) with a fixed forgetting factor to measure the amplitude of the fundamental current of the permanent magnet synchronous motor (e.g., the average value of the fundamental current). ( ) to perform identification calculations.

[0075] Compared to the traditional recursive least squares algorithm with fixed parameters, the recursive least squares algorithm with variable forgetting factor provided in this invention dynamically adjusts the forgetting factor λ to simultaneously achieve stability and speed in fundamental current amplitude identification. The identification effect is better than the fundamental current identification method using the recursive least squares algorithm with fixed forgetting factor.

[0076] The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor provided by this invention can also be used in conjunction with a computer-readable storage medium. The storage medium stores a computer program, and executing the computer program runs the method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor.

[0077] Computer programs can execute computer instructions, which include computer program code. Computer program code can be in the form of source code, object code, executable files, or some intermediate form.

[0078] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0079] It should be noted that the contents of computer-readable storage media may be appropriately added to or subtracted from the contents according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0080] This invention enables rapid and accurate tracking of the fundamental current amplitude of a permanent magnet synchronous motor (PMSM) under both transient and steady-state conditions, improving the stability and speed of fundamental current amplitude identification. The identification effect is superior to the traditional fundamental current identification method using a recursive least squares algorithm with a fixed forgetting factor, and it has great practical value.

[0081] According to another aspect of the present invention, a device for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor is also provided, which executes a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor. The device includes: a fundamental current amplitude identification module and a variable forgetting factor update module.

[0082] The fundamental current amplitude identification module is used to identify the fundamental current amplitude of a permanent magnet synchronous motor based on the recursive least squares method with a fixed forgetting factor.

[0083] The variable forgetting factor update module is used to introduce a variable forgetting factor into the recursive least squares method with a fixed forgetting factor, and to update the variable forgetting factor using the nonlinear feedback control law of the active disturbance rejection controller.

[0084] In summary, this invention provides a method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, which has the following advantages compared with the prior art:

[0085] (1) The present invention adopts the recursive least squares algorithm, which can achieve fast and accurate tracking of the fundamental current amplitude of PMSM in both transient and steady state conditions;

[0086] (2) Compared with the traditional recursive least squares algorithm with fixed parameters, the recursive least squares algorithm with variable forgetting factor provided by the present invention dynamically adjusts the forgetting factor λ to simultaneously achieve the stability and speed of fundamental current amplitude identification. The identification effect is better than the fundamental current identification method using the recursive least squares algorithm with fixed forgetting factor.

[0087] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0088] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0089] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0090] Certain terms are used throughout this application to refer to specific system components. As those skilled in the art will recognize, the same components may often be referred to by different names, and therefore this application is not intended to distinguish those components that differ only in name and not in function. In this application, the terms “comprise,” “include,” and “have” are used in an open-ended manner and should therefore be interpreted as meaning “including, but not limited to…”. Furthermore, the terms “substantially,” “materially,” or “approximately” as used herein refer to industry-accepted tolerances for the corresponding terms. The term “coupling,” as may be used herein, includes direct coupling and indirect coupling via additional components, elements, circuits, or modules, wherein, for indirect coupling, the intermediate component, element, circuit, or module does not alter the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as “coupling.”

[0091] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0092] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

[0093] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, characterized in that, The method includes: The fundamental current amplitude of a permanent magnet synchronous motor is identified based on the recursive least squares method with a fixed forgetting factor. A variable forgetting factor is introduced into the recursive least squares method with a fixed forgetting factor, and the variable forgetting factor is updated using the nonlinear feedback control law of the active disturbance rejection controller. based on The update formula for the variable forgetting factor is constructed by using the nonlinear feedback control law of the active disturbance rejection controller of the function: in, The function is the nonlinear structure of the extended state observer in the active disturbance rejection controller; It is a variable forgetting factor; It is the error rate; It refers to the protected harmonic variation range; It is a nonlinear factor; It is the filter factor; For intermediate parameters; and It is a limitation setting for the variable forgetting factor; It is the target q-axis current output by the velocity loop; These are estimated parameters; It represents the number of recursive calculations.

2. The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor as described in claim 1, characterized in that, The fundamental current amplitude of a permanent magnet synchronous motor is identified using the following steps: For permanent magnet synchronous motors, a rotor current model for permanent magnet synchronous motors is established; The three-phase current formula in the rotor current model of the permanent magnet synchronous motor is rewritten in the form of a least squares relation. The fundamental current amplitude of the permanent magnet synchronous motor is identified and calculated using the recursive formula of the recursive least squares method with a fixed forgetting factor.

3. The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor as described in claim 2, characterized in that, The rotor current model of the permanent magnet synchronous motor is as follows: in, , , It is the three-phase current of the permanent magnet synchronous motor; , , and , , These are the amplitude and phase of the fundamental current, the fifth harmonic current, and the seventh harmonic current, respectively. It is electric angular velocity; It's time.

4. The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor as described in claim 3, characterized in that, The three-phase current formula in the rotor current model of the permanent magnet synchronous motor is rewritten as follows: The least squares relation of the form, where, It is the output vector; It is the transpose of the input vector; These are the parameter values ​​to be identified; It is random noise with a mean of zero; It represents the number of recursive calculations.

5. The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor as described in claim 4, characterized in that, The least squares relation of the form is: in, , , and These are the three-phase currents and electrical angles of a permanent magnet synchronous motor; It is the input vector; These are estimated parameters; , It represents the amplitude and phase of the k-th harmonic current.

6. A method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, as described in any one of claims 2-5, characterized in that, The recursive formula for the recursive least squares method with a fixed forgetting factor is as follows: in, It is the variance matrix; It is a fixed forgetting factor; It is the correction matrix; It is the transpose of the input vector; It is the input vector; These are estimated parameters; It is the output vector; It represents the number of recursive calculations.

7. The method for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor as described in claim 1, characterized in that, The intermediate parameters are calculated using the following expression: in, For intermediate parameters; and It is a limitation setting for the variable forgetting factor; It refers to the protected harmonic variation range; It is a non-linear factor.

8. A storage medium, characterized in that, It includes a series of instructions for performing the method steps as described in any one of claims 1-7.

9. A device for identifying the fundamental current amplitude of a permanent magnet synchronous motor based on a variable forgetting factor, characterized in that, The apparatus for performing the method as described in any one of claims 1-7 comprises: The fundamental current amplitude identification module is used to identify the fundamental current amplitude of a permanent magnet synchronous motor based on a recursive least squares method with a fixed forgetting factor. A variable forgetting factor update module is used to introduce a variable forgetting factor into the recursive least squares method with a fixed forgetting factor and update the variable forgetting factor using the nonlinear feedback control law of the active disturbance rejection controller.

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