CN113992101B - Current compensation method and device based on vector control of permanent magnet synchronous motor - Google Patents

Current compensation method and device based on vector control of permanent magnet synchronous motor Download PDF

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CN113992101B
CN113992101B CN202111615518.2A CN202111615518A CN113992101B CN 113992101 B CN113992101 B CN 113992101B CN 202111615518 A CN202111615518 A CN 202111615518A CN 113992101 B CN113992101 B CN 113992101B
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CN113992101A (en
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刘冰
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Chengdu Aich Technology Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • H02P25/024—Synchronous motors controlled by supply frequency
    • H02P25/026—Synchronous motors controlled by supply frequency thereby detecting the rotor position
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Ac Motors In General (AREA)
  • Control Of Motors That Do Not Use Commutators (AREA)

Abstract

The invention discloses a current compensation method and device based on permanent magnet synchronous motor vector control, and relates to the technical field of electronics. The current compensation method comprises the following steps: carrying out coordinate transformation processing on the sampled current signal to obtain a current signal to be processed; filtering the current signal to be processed by utilizing an adaptive filtering model of an estimation function determined based on the activation function; carrying out proportional integral control processing on the current signal to be processed after filtering processing to obtain a voltage signal to be processed; the target current signal is determined based on the voltage signal to be processed, the target current signal is injected into the stator current of the motor, the compensation of the stator current is completed, the interference of noise is eliminated, the stability of a current loop is improved due to the performance of an algorithm, the vector control performance of the permanent magnet synchronous motor is improved, the system jitter is reduced, the proportional-integral control processing is performed on the current signal to be processed after the filtering processing, the motor is accelerated to transition to a stable state, and the method has great practical value.

Description

Current compensation method and device based on vector control of permanent magnet synchronous motor
Technical Field
The invention relates to the technical field of electronics, in particular to a current compensation method and device based on permanent magnet synchronous motor vector control.
Background
With the continuous development of control theory and the characteristics of small volume, high power density, long service life, high efficiency, high response speed and the like of the permanent magnet synchronous motor, the permanent magnet synchronous motor is widely applied to the fields of electric automobiles, household appliances, aerospace and the like.
At present, most permanent magnet synchronous motors are controlled by a vector control method, and for a motor control system, obtaining an accurate current sampling value is crucial, and the accuracy of the current sampling value directly determines the performance of the control system. In the vector Control, a current sampling value is usually sent to a Micro Control Unit (MCU) controller for vector Control calculation after passing through a current sensor, an operational amplifier and an analog-to-digital (AD) conversion device. In the current loop sampling stage, interference noise is introduced into the current sensor during sampling, random noise related to the operational amplifier and the resistive circuit thereof is introduced into the conditioning circuit, noise is introduced into factors such as a control circuit in the sampling process, and the performance of the current loop is affected, so that the interference of the noise needs to be eliminated to ensure the performance of the algorithm, and thus the stability of the current loop is improved.
Therefore, a control method is needed to eliminate noise and improve the stability of the current loop.
Disclosure of Invention
The invention aims to provide a current compensation method and a current compensation device based on permanent magnet synchronous motor vector control, and aims to solve the problems that a control method is urgently needed to remove noise and improve the stability of a current loop.
In a first aspect, the present invention provides a current compensation method based on vector control of a permanent magnet synchronous motor, the method comprising:
acquiring a sampling current signal;
carrying out coordinate transformation processing on the sampling current signal to obtain a current signal to be processed;
filtering the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function;
carrying out proportional integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed;
and determining a target current signal based on the voltage signal to be processed, injecting the target current signal into the stator current of the motor, and completing the compensation of the stator current.
Under the condition of adopting the technical scheme, a sampling current signal can be obtained, the sampling current signal is subjected to coordinate transformation processing to obtain a current signal to be processed, the current signal to be processed is subjected to filtering processing by utilizing an adaptive filtering model of an estimation function determined based on an activation function, the current signal to be processed after the filtering processing is subjected to proportional integral control processing to obtain a voltage signal to be processed, a target current signal is determined based on the voltage signal to be processed, the target current signal is injected into the stator current of the motor to complete the compensation of the stator current, the estimation function based on the activation function provided by the invention can redefine a weight value updating formula of an LMS algorithm, can fully utilize information contained in an error signal to eliminate the interference of noise, and ensures the performance of the algorithm so as to improve the stability of a current loop, and then the performance of vector control of the permanent magnet synchronous motor is improved, the system jitter is reduced, and the proportional-integral control processing is carried out on the filtered current signal to be processed, namely a method for dynamically adjusting the proportional-integral control is used, so that the transition of the motor to a stable state is accelerated, and the method has great practical value.
In a possible implementation manner, the performing a filtering process on the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function includes:
determining the adaptive filtering model of the estimation function determined based on the activation function;
and carrying out filtering processing on the current signal to be processed based on the self-adaptive filtering model.
In a possible implementation, the determining an adaptive filtering model of the estimation function determined based on the activation function includes:
obtaining an initial adaptive filtering model;
determining an adaptive weight update function based on a least mean square algorithm, a step factor function and the estimation function determined based on the activation function;
and updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model.
In one possible implementation, the determining an adaptive weight update function based on a least mean square algorithm, a step size factor function, and the estimation function determined based on the activation function includes:
determining the estimation function based on the activation function and an error value of the least mean square algorithm;
determining the step size factor function based on an error value of the least mean square algorithm;
determining the adaptive weight update function based on the step size factor function and the estimation function.
In one possible implementation, the determining a target current signal based on the voltage signal to be processed includes:
performing coordinate inverse transformation processing on the voltage signal to be processed to obtain a reference voltage signal;
determining the target current signal based on the reference voltage signal.
In a possible implementation manner, the performing proportional-integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed includes:
determining a rotating speed error value of the permanent magnet synchronous motor, and adaptively adjusting a proportional coefficient of a proportional integral controller based on the rotating speed error value;
determining a quadrature reference current value based on the scaling factor;
and under the condition that the permanent magnet synchronous motor is in a stable state, performing proportional-integral control processing based on the current signal to be processed after filtering processing and the quadrature axis reference current value to obtain the voltage signal to be processed.
In one possible implementation, the acquiring a sampled current signal includes:
and sampling the three-phase current to obtain the sampling current signal.
In a second aspect, the present invention further provides a current compensation apparatus based on vector control of a permanent magnet synchronous motor, where the apparatus includes:
the acquisition module is used for acquiring a sampling current signal;
the coordinate transformation processing module is used for carrying out coordinate transformation processing on the sampling current signal to obtain a current signal to be processed;
the filtering processing module is used for carrying out filtering processing on the current signal to be processed by utilizing an adaptive filtering model of an estimation function determined based on an activation function;
the control processing module is used for carrying out proportional-integral control processing on the current signal to be processed after filtering processing to obtain a voltage signal to be processed;
and the determining module is used for determining a target current signal based on the voltage signal to be processed, injecting the target current signal into the stator current of the motor and completing the compensation of the stator current.
In one possible implementation manner, the filtering processing module includes:
a first determination submodule for determining the adaptive filtering model of the estimation function determined on the basis of the activation function;
and the filtering processing submodule is used for carrying out filtering processing on the current signal to be processed based on the self-adaptive filtering model.
In one possible implementation, the first determining sub-module includes:
an obtaining unit, configured to obtain an initial adaptive filtering model;
a determining unit, configured to determine an adaptive weight update function based on a least mean square algorithm, a step factor function, and the estimation function determined based on the activation function;
and the updating unit is used for updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model.
In one possible implementation manner, the determining unit includes:
a first determining subunit, configured to determine the estimation function based on the activation function and an error value of the least mean square algorithm;
a second determining subunit, configured to determine the step-size factor function based on an error value of the least-mean-square algorithm;
a third determining subunit, configured to determine the adaptive weight update function based on the step size factor function and the estimation function.
In one possible implementation, the determining module includes:
the coordinate inverse transformation processing submodule is used for carrying out coordinate inverse transformation processing on the voltage signal to be processed to obtain a reference voltage signal;
a second determination submodule to determine the target current signal based on the reference voltage signal.
In one possible implementation, the control processing module includes:
the third determining submodule is used for determining a rotating speed error value of the permanent magnet synchronous motor and adaptively adjusting a proportional coefficient of a proportional integral controller based on the rotating speed error value;
a fourth determining submodule for determining a quadrature reference current value based on the scaling factor;
and the control processing submodule is used for carrying out proportional integral control processing on the basis of the filtered current signal to be processed and the quadrature axis reference current value under the condition that the permanent magnet synchronous motor is in a stable state, so as to obtain the voltage signal to be processed.
In one possible implementation manner, the obtaining module includes:
and the sampling processing submodule is used for sampling the three-phase current to obtain the sampling current signal.
The beneficial effects of the current compensation device based on the vector control of the permanent magnet synchronous motor provided in the second aspect are the same as the beneficial effects of the current compensation method based on the vector control of the permanent magnet synchronous motor described in the first aspect or any one of the possible implementation manners of the first aspect, and are not described herein again.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flowchart illustrating a current compensation method based on vector control of a permanent magnet synchronous motor according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating another current compensation method based on vector control of a permanent magnet synchronous motor according to an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating a vector control system of a permanent magnet synchronous motor according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram based on an adaptive filtering model according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating an embodiment of an adaptive PI control module according to the present disclosure;
fig. 6 shows a schematic structural diagram of a current compensation device based on vector control of a permanent magnet synchronous motor according to an embodiment of the present application;
fig. 7 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Description of the drawings:
01-a current sampling module; 02-a current coordinate transformation module; 03-an adaptive filtering module; 04-an adaptive PI control module; 05-PI control module; 06-a current coordinate inverse transformation module; 07-SVPWM algorithm module; 08-an accumulator; 09-a motor; 010-a position velocity estimation module; 301-an adaptive filter; 303-an estimation function module based on an S-function; 500-an electronic device; 510-a processor; 520-a communication interface; 530-a memory; 540 — communication lines.
Detailed Description
In order to facilitate clear description of technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items having substantially the same functions and actions. For example, the first threshold and the second threshold are only used for distinguishing different thresholds, and the sequence order of the thresholds is not limited. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.
It is to be understood that the terms "exemplary" or "such as" are used herein to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the present invention, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b combination, a and c combination, b and c combination, or a, b and c combination, wherein a, b and c can be single or multiple.
Fig. 1 shows a schematic flowchart of a current compensation method based on vector control of a permanent magnet synchronous motor according to an embodiment of the present application, and as shown in fig. 1, the current compensation method based on vector control of a permanent magnet synchronous motor includes:
step 101: a sampled current signal is acquired.
In this application, the sampling current signal (may be obtained by sampling a three-phase current
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、
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、
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）。
After the sampled current signal is acquired, step 102 is performed.
Step 102: and carrying out coordinate transformation processing on the sampling current signal to obtain a current signal to be processed.
In the application, the coordinate transformation can be carried out on the sampling current signal to obtain the current of the d-q axis of the current loop
Figure 812069DEST_PATH_IMAGE004
、
Figure DEST_PATH_IMAGE005
。
And after the sampling current signal is subjected to coordinate transformation processing to obtain a current signal to be processed, executing step 103.
Step 103: and performing filtering processing on the current signal to be processed by using an adaptive filtering model of an estimation function determined based on the activation function.
Determining the self-adaptive filtering model of the estimation function determined based on the activation function, determining a self-adaptive weight updating function based on a least mean square algorithm, a step factor function and the estimation function determined based on the activation function, and updating the initial self-adaptive filtering model based on the weight updating function to obtain the self-adaptive filtering model; and carrying out filtering processing on the current signal to be processed based on the self-adaptive filtering model.
After the current signal to be processed is filtered using the adaptive filter model of the estimation function determined based on the activation function, step 104 is performed.
Step 104: and carrying out proportional integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed.
In the application, a rotating speed error value of the permanent magnet synchronous motor can be determined, a proportional integral controller proportional coefficient is adjusted in an adaptive mode based on the rotating speed error value, and a quadrature axis (q axis) reference current value is determined based on the proportional coefficient; and under the condition that the permanent magnet synchronous motor is in the stable state, performing proportional-integral control processing based on the current signal to be processed after filtering processing and the q-axis reference current value to obtain the voltage signal to be processed.
And after performing proportional-integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed, executing step 105.
Step 105: and determining a target current signal based on the voltage signal to be processed, injecting the target current signal into the stator current of the motor, and completing the compensation of the stator current.
In the application, the voltage signal to be processed can be subjected to coordinate inverse transformation processing to obtain a reference voltage signal; determining the target current signal based on the reference voltage signal.
To sum up, by using the current compensation method based on vector control of a permanent magnet synchronous motor provided in the embodiment of the present application, a sampled current signal may be obtained, coordinate transformation processing may be performed on the sampled current signal to obtain a current signal to be processed, filtering processing may be performed on the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function, proportional integral control processing may be performed on the filtered current signal to be processed to obtain a voltage signal to be processed, a target current signal may be determined based on the voltage signal to be processed, and the target current signal may be injected into a stator current of a motor to complete compensation of the stator current. The interference to noise is eliminated, the performance of the algorithm is guaranteed, the stability of a current loop is improved, the performance of vector control of the permanent magnet synchronous motor is improved, the jitter of the system is reduced, the proportional-integral control processing is carried out on the current signal to be processed after filtering processing, namely, a method for dynamically adjusting the proportional-integral control is used, the transition of the motor to a stable state is accelerated, and the method has great practical value.
Fig. 2 is a schematic flowchart illustrating another current compensation method based on vector control of a permanent magnet synchronous motor according to an embodiment of the present application, and as shown in fig. 2, the current compensation method based on vector control of a permanent magnet synchronous motor includes:
step 201: a sampled current signal is acquired.
The current compensation method according to the embodiment of the present application may be applied to a Vector control system of a permanent magnet synchronous motor, and fig. 3 illustrates a schematic structural diagram of a Vector control system of a permanent magnet synchronous motor according to the embodiment of the present application, and as shown in fig. 3, the Vector control system of a permanent magnet synchronous motor includes a current sampling module (three-phase bridge) 01, a current coordinate (Clack and Park) transformation module 02, an adaptive filtering module 03, an adaptive PI (proportional integral) control module 04, a plurality of PI control modules 05, a current coordinate inverse transformation (replay) module 06, an SVPWM (Space Vector Pulse Width Modulation) algorithm module 07, a plurality of accumulators 08, a motor 09, and a position and speed estimation module 010, where one accumulator 08 sequentially connects with the adaptive PI control module 04, another accumulator 08, one PI control module 05, the current coordinate inverse transformation (replay) module 06, the SVPWM (Space Vector Pulse Width Modulation) algorithm module 07, The SVPWM algorithm module 07, the motor 09 and the position and speed estimation module 010 complete end-to-end connection to form a closed loop.
Referring to fig. 3, another accumulator 08 is sequentially connected to another PI control module 05, an inverse current coordinate transformation (replay) module 06, an SVPWM algorithm module 07, a current sampling module 01, a current coordinate transformation (Clack and Park) module 02, and an adaptive filtering module 03. One end of the above-mentioned another accumulator is connected to the output end of the adaptive filtering module 03.
In the present application, referring to fig. 3, the sampling current signal can be obtained by sampling the three-phase current: (
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、
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、
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）。
Step 202: and carrying out coordinate transformation processing on the sampling current signal to obtain a current signal to be processed.
Optionally, in this application, referring to fig. 3, the current coordinate transformation module 02 may perform coordinate transformation on the sampled current signal to obtain a current of a current loop direct axis-quadrature axis (d-q axis)
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、
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。
Step 203: determining the adaptive filtering model of the estimation function determined based on the activation function.
Optionally, in this application, the specific implementation process of step 203 may include the following sub-steps:
substep A1: and obtaining an initial adaptive filtering model.
In this application, an adaptive filtering model may be disposed in the adaptive filtering module 03, and fig. 4 shows a structural schematic diagram of an adaptive filtering model provided in an embodiment of the application, and as shown in fig. 4, the adaptive filtering model may include an adaptive filter 301, an accumulator 08, and an S-function-based estimation function module 303. Wherein the output of the adaptive filter 301 is connected to the input of the accumulator, the output of the accumulator is connected to the input of the estimation function, and the output of the estimation function is connected to the input of the adaptive filter, wherein the estimation function module 303 is an estimation function module 303 based on an s (sigmoid) function.
In the adaptive filtering model shown in fig. 4, wherein,
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、
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representing the input and output of the adaptive filtering model,
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which is indicative of a desired output, is,
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the value of the error is represented by,
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and representing the weight, wherein the weight can be regarded as a parameter to be identified in the adaptive algorithm. On the basis of the LMS algorithm, the invention further uses a variable step-size factor and an estimation function based on an S (sigmoid) function to improve the adaptive algorithm.
Substep A2: determining an adaptive weight update function based on a least mean square algorithm, a step factor function, and the estimation function determined based on the activation function.
Specifically, the implementation process of the sub-step a2 may include the following sub-steps:
substep B1: determining the estimation function based on the activation function and an error value of the least mean square algorithm.
In the present application, the adaptive base algorithm used is the LMS algorithm,
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（1）
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、
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a k-th input signal representing an adaptive filtering model and a filtered output signal,
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which is indicative of a desired output, is,
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the value of the error is represented by,
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representing the weight. Based on the LMS algorithm, the invention further uses a step-size factor function and a function based on Sigmoid (A, B, C, EActivation) of an estimation function of a function to improve an algorithm
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The estimation function based on Sigmoid function can be expressed as:
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（2）；
wherein,
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is an s-type function, which is defined
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，
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,
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A value range of
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,
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,
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N has a value range of
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，
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Representing a function taking the median value.
Substep B2: determining the step size factor function based on an error value of the least mean square algorithm.
In the present application, the step size factor at the K-th time may be determined by the following step size factor function:
Figure 79081DEST_PATH_IMAGE034
（3）；
wherein the value range of gamma is 0.0001-1.
Substep B3: determining the adaptive weight update function based on the step size factor function and the estimation function.
In this application, can be used
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，
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Replace weight update function
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In (1)
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And
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the weight updating function of the adaptive algorithm based on the Sigmoid function estimation function can be obtained
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（4）。
Substep A3: and updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model.
According to the formulas (1), (2), (3) and (4), a filter model of a d-q axis of a current loop is designed into
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（5）
Wherein,
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and
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are the input variables of the algorithm and,
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is the q-axis current output by the vector control in real time,
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for the desired value of the q-axis current, by
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After low-pass filtering, obtaining
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To an approximation of this,
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the q-axis current processed by the adaptive filter algorithm is used as the output variable of the algorithm and finally used as the feedback current of a q-axis current loop to carry out subsequent vector control operation.
In the present application, the d-axis current is processed identically to the q-axis current, as shown below
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(6)
Wherein,
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and
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in order to input the variables of the device,
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is vector controlThe d-axis current output in real time is made,
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is a desired value of d-axis current, by
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The low-pass filtering is carried out to obtain,
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the output variable of the algorithm is d-axis current processed by the adaptive filter algorithm and finally used as the operation of feedback subsequent vector control of a d-axis current loop.
Step 204: and carrying out filtering processing on the current signal to be processed based on the self-adaptive filtering model.
In this application, step 205 is performed after the current signal to be processed is filtered based on the adaptive filtering model.
Step 205: and carrying out proportional integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed.
In the present application, the implementation process of step 205 may include the following sub-steps:
substep C1: and determining a rotating speed error value of the permanent magnet synchronous motor, and adaptively adjusting a proportional coefficient of a proportional integral controller based on the rotating speed error value.
In the application, a proportional system can be calculated according to motor parameters based on the PI controller theory
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To number one
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Sub-scale factor
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It can be updated based on the following formula:
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parameter(s)
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And
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，
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is a process of formula (2) wherein
Figure 507436DEST_PATH_IMAGE057
The error between the estimated speed (speed) for the k-th time and the target reference speed (speedref) can be dynamically adjusted by adjusting the proportionality coefficient of the PI controller.
Substep C2: and determining a q-axis reference current value based on the proportional coefficient, and performing proportional integral control processing based on the current signal to be processed after filtering processing and the q-axis reference current value under the condition that the permanent magnet synchronous motor is in the stable state to obtain the voltage signal to be processed.
Step 204 is a current loop optimization process for the kth time, the step 205 is a speed loop PI control process for the kth time, and the above steps 204 and 205 may be continuously repeated from the first proper control calculation until the permanent magnet synchronous motor is in the stable state.
Step 206: and determining a target current signal based on the voltage signal to be processed, injecting the target current signal into the stator current of the motor, and completing the compensation of the stator current.
In the application, the voltage signal to be processed can be subjected to coordinate inverse transformation processing to obtain a reference voltage signal; determining the target current signal based on the reference voltage signal.
The estimation function based on the Sigmoid function redefines a weight value updating formula of an LMS algorithm, can fully utilize information contained in an error signal, eliminates noise interference, ensures the performance of the algorithm, improves the stability of a current loop and further improves the performance of vector control of the permanent magnet synchronous motor. The LMS algorithm is improved by adopting an estimation function of a Sigmoid function, so that the jitter of the system is reduced, and the convergence rate of the adaptive filter can be improved by designing a step factor; the dynamic PI control regulation method is used, the transition of the motor to a stable state is accelerated, and the method has a high practical value.
In the present application, referring to fig. 3, the sampling current signal can be obtained by sampling the three-phase current: (
Figure 331036DEST_PATH_IMAGE058
、
Figure 719292DEST_PATH_IMAGE059
、
Figure 695469DEST_PATH_IMAGE060
). The self-adaptive PI module: and the proportional coefficient of the PI controller is adaptively adjusted by measuring the speed error, so that the speed is controlled. The PI control module is used for filtering the current of the d-q axis
Figure 878189DEST_PATH_IMAGE061
、
Figure 657926DEST_PATH_IMAGE062
Controlling; the current coordinate inverse transformation module: carrying out replay transformation on Vq and Vd output by the PI module to generate reference voltages Va and Vb; the SVPWM algorithm module: the voltage signals Va and Vb are calculated to generate final current signals, and the final current signals are injected into stator currents of the three-phase motor.
Alternatively, referring to fig. 3, the error between the estimated speed (speed) and the target reference speed (speed ref) is input into an accumulator, the q-axis target reference current (Iqref) is output from the adaptive PI module, and the current coordinate transformation (Clack and Park) module 02 may be used to sample the current signal(s) ((r))
Figure DEST_PATH_IMAGE063
、
Figure 115452DEST_PATH_IMAGE064
、
Figure DEST_PATH_IMAGE065
) Performing a Clack transformation to obtain
Figure 978759DEST_PATH_IMAGE066
And
Figure DEST_PATH_IMAGE067
then go right again
Figure 394697DEST_PATH_IMAGE066
And
Figure 943621DEST_PATH_IMAGE067
carrying out Park conversion to obtain
Figure 408101DEST_PATH_IMAGE068
And
Figure 608138DEST_PATH_IMAGE069
and finally outputs a d-axis target reference current (Idref) and a q-axis target reference current (Iqref).
In the present application, the scaling factor may be dynamically adjusted by a Sigmoid error estimation function
Figure 398239DEST_PATH_IMAGE070
Fig. 5 is a schematic diagram illustrating a principle of an adaptive PI control module according to an embodiment of the present application, and as shown in fig. 5, a scaling factor is calculated according to a motor parameter
Figure 667415DEST_PATH_IMAGE070
For the first
Figure DEST_PATH_IMAGE071
Sub-scale factor
Figure 935586DEST_PATH_IMAGE072
It can be updated based on the following formula:
Figure 990129DEST_PATH_IMAGE053
parameter(s)
Figure DEST_PATH_IMAGE073
And
Figure 967444DEST_PATH_IMAGE074
，
Figure DEST_PATH_IMAGE075
is a process of formula (2) wherein
Figure 271386DEST_PATH_IMAGE057
For the k-th estimated speed (speed) error from the target reference speed (speed), the proportional coefficient of the PI controller can be dynamically adjusted, and the proportional coefficient is dynamically adjusted through a Sigmoid error estimation function
Figure 608827DEST_PATH_IMAGE076
And the integral proportional coefficient K obtained by the integral elementiAnd the q-axis target reference current (Iqref) is obtained after passing through the accumulator, is finally applied to the controlled object and is negatively fed back to the previous accumulator so that the previous accumulator can accumulate the target reference speed and the estimated speed, the rotating speed of the motor is adjusted, and the motor is accelerated to transition to a stable state.
To sum up, by using the current compensation method based on vector control of a permanent magnet synchronous motor provided in the embodiment of the present application, a sampled current signal may be obtained, coordinate transformation processing may be performed on the sampled current signal to obtain a current signal to be processed, filtering processing may be performed on the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function, proportional integral control processing may be performed on the filtered current signal to be processed to obtain a voltage signal to be processed, a target current signal may be determined based on the voltage signal to be processed, and the target current signal may be injected into a stator current of a motor to complete compensation of the stator current. The interference to noise is eliminated, the performance of the algorithm is guaranteed, the stability of a current loop is improved, the performance of vector control of the permanent magnet synchronous motor is improved, the jitter of the system is reduced, the proportional-integral control processing is carried out on the current signal to be processed after filtering processing, namely, a method for dynamically adjusting the proportional-integral control is used, the transition of the motor to a stable state is accelerated, and the method has great practical value.
Fig. 6 shows a schematic structural diagram of a current compensation device based on vector control of a permanent magnet synchronous motor according to an embodiment of the present application, and as shown in fig. 6, the current compensation device 400 based on vector control of a permanent magnet synchronous motor includes:
an obtaining module 401, configured to obtain a sampled current signal;
a coordinate transformation processing module 402, configured to perform coordinate transformation processing on the sampling current signal to obtain a current signal to be processed;
a filtering processing module 403, configured to perform filtering processing on the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function;
the control processing module 404 is configured to perform proportional-integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed;
a determining module 405, configured to determine a target current signal based on the voltage signal to be processed, inject the target current signal into a stator current of the motor, and complete compensation of the stator current.
In one possible implementation manner, the filtering processing module includes:
a first determination submodule for determining the adaptive filtering model of the estimation function determined on the basis of the activation function;
and the filtering processing submodule is used for carrying out filtering processing on the current signal to be processed based on the self-adaptive filtering model.
In one possible implementation, the first determining sub-module includes:
an obtaining unit, configured to obtain an initial adaptive filtering model;
a determining unit, configured to determine an adaptive weight update function based on a least mean square algorithm, a step factor function, and the estimation function determined based on the activation function;
and the updating unit is used for updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model.
In one possible implementation manner, the determining unit includes:
a first determining subunit, configured to determine the estimation function based on the activation function and an error value of the least mean square algorithm;
a second determining subunit, configured to determine the step-size factor function based on an error value of the least-mean-square algorithm;
a third determining subunit, configured to determine the adaptive weight update function based on the step size factor function and the estimation function.
In one possible implementation, the determining module includes:
the coordinate inverse transformation processing submodule is used for carrying out coordinate inverse transformation processing on the voltage signal to be processed to obtain a reference voltage signal;
a second determination submodule to determine the target current signal based on the reference voltage signal.
In one possible implementation, the control processing module includes:
the third determining submodule is used for determining a rotating speed error value of the permanent magnet synchronous motor and adaptively adjusting a proportional coefficient of a proportional integral controller based on the rotating speed error value;
a fourth determination submodule for determining a q-axis reference current value based on the scaling factor;
and the control processing submodule is used for carrying out proportional integral control processing on the basis of the filtered current signal to be processed and the q-axis reference current value under the condition that the permanent magnet synchronous motor is in the stable state to obtain the voltage signal to be processed.
In one possible implementation manner, the obtaining module includes:
and the sampling processing submodule is used for sampling the three-phase current to obtain the sampling current signal.
To sum up, with the current compensation device based on vector control of a permanent magnet synchronous motor according to the embodiment of the present application, a sampled current signal may be obtained, coordinate transformation processing may be performed on the sampled current signal to obtain a current signal to be processed, filtering processing may be performed on the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function, proportional integral control processing may be performed on the filtered current signal to be processed to obtain a voltage signal to be processed, a target current signal may be determined based on the voltage signal to be processed, and the target current signal may be injected into a stator current of the motor to complete compensation of the stator current. The interference to noise is eliminated, the performance of the algorithm is guaranteed, the stability of a current loop is improved, the performance of vector control of the permanent magnet synchronous motor is improved, the jitter of the system is reduced, the proportional-integral control processing is carried out on the current signal to be processed after filtering processing, namely, a method for dynamically adjusting the proportional-integral control is used, the transition of the motor to a stable state is accelerated, and the method has great practical value.
Fig. 7 is a schematic diagram illustrating a hardware structure of an electronic device according to an embodiment of the present invention. As shown in fig. 7, the electronic device 500 includes a processor 510.
As shown in fig. 7, the processor 510 may be a general processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more ics for controlling the execution of programs according to the present invention.
As shown in fig. 7, the electronic device 500 may further include a communication line 540. Communication link 540 may include a path to communicate information between the aforementioned components.
Optionally, as shown in fig. 7, the electronic device may further include a communication interface 520. The communication interface 520 may be one or more. The communication interface 520 may use any transceiver or the like for communicating with other devices or communication networks.
Optionally, as shown in fig. 7, the electronic device may further include a memory 530. The memory 530 is used to store computer-executable instructions for performing aspects of the present invention and is controlled for execution by the processor. The processor is used for executing the computer execution instructions stored in the memory, thereby realizing the method provided by the embodiment of the invention.
As shown in fig. 7, the memory 530 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a Random Access Memory (RAM) or other types of dynamic storage devices that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 530 may be separate and coupled to the processor 510 via a communication line 540. Memory 530 may also be integrated with processor 510.
Optionally, the computer-executable instructions in the embodiment of the present invention may also be referred to as application program codes, which is not specifically limited in this embodiment of the present invention.
In particular implementations, as one embodiment, processor 510 may include one or more CPUs, such as CPU0 and CPU1 in fig. 7, as shown in fig. 7.
In one implementation, as shown in fig. 7, a terminal device may include multiple processors, such as processor 510 in fig. 7, for example. Each of the processors may be a single-core processor or a multi-core processor.
In one aspect, a computer-readable storage medium is provided, in which instructions are stored, and when executed, the instructions implement the functions performed by the terminal device in the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the procedures or functions described in the embodiments of the present invention are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a terminal, a user device, or other programmable apparatus. The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; or optical media such as Digital Video Disks (DVDs); it may also be a semiconductor medium, such as a Solid State Drive (SSD).
While the invention has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
While the invention has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the invention. Accordingly, the specification and figures are merely exemplary of the invention as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (4)

1. A current compensation method based on permanent magnet synchronous motor vector control is characterized by comprising the following steps:
acquiring a sampling current signal;
carrying out coordinate transformation processing on the sampling current signal to obtain a current signal to be processed;
filtering the current signal to be processed by using an adaptive filtering model of an estimation function determined based on an activation function;
carrying out proportional integral control processing on the filtered current signal to be processed to obtain a voltage signal to be processed;
determining a target current signal based on the voltage signal to be processed, injecting the target current signal into the stator current of the motor, and completing the compensation of the stator current;
wherein, the filtering the current signal to be processed by using the adaptive filtering model of the estimation function determined based on the activation function comprises:
determining the adaptive filtering model of the estimation function determined based on the activation function;
filtering the current signal to be processed based on the self-adaptive filtering model;
the determining an adaptive filtering model of the estimation function determined based on the activation function includes:
obtaining an initial adaptive filtering model;
determining an adaptive weight update function based on a least mean square algorithm, a step factor function and the estimation function determined based on the activation function;
updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model;
wherein the updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model includes: designing a self-adaptive filter model of a d-q axis of a current loop as follows:
Figure FDA0003515545930000011
iqx(k) and iqd(k) As input variables of the algorithm, iqx(k) Q-axis current, i, being output in real time by vector controlqd(k) For the desired value of q-axis current byqx(k) After low-pass filtering, i is obtainedqd(k) Approximation iqy(k) The output variable of the algorithm is q-axis current processed by the algorithm of the adaptive filter and finally used as feedback electricity of a q-axis current loopThe flow carries out subsequent vector control operation;
the d-axis current is processed the same as the q-axis current,
Figure FDA0003515545930000021
idx(k) and idd(k) As input variables, idx(k) Is the d-axis current output by the vector control in real time,
idd(k) for the desired value of d-axis current bydx(k) Low-pass filtering to obtaindy(k) The output variable of the algorithm is d-axis current processed by the algorithm of the adaptive filter, and finally is used as the operation of feedback subsequent vector control of a d-axis current loop;
the proportional-integral control processing is performed on the filtered current signal to be processed to obtain a voltage signal to be processed, and the method comprises the following steps:
determining a rotating speed error value of the permanent magnet synchronous motor, and adaptively adjusting a proportional coefficient of a proportional integral controller based on the rotating speed error value;
determining a quadrature reference current value based on the scaling factor;
under the condition that the permanent magnet synchronous motor is in a stable state, performing proportional-integral control processing based on the current signal to be processed after filtering processing and the quadrature axis reference current value to obtain a voltage signal to be processed;
the determining of the rotating speed error value of the permanent magnet synchronous motor and the self-adaptive adjustment of the proportional coefficient of the proportional integral controller based on the rotating speed error value comprise the following steps:
based on the theory of proportional-integral controller, the proportional coefficient K is calculated according to the motor parameterspFor the (K +1) th scaling factor Kp(k +1), which can be updated based on the following formula:
Kp(k+1)＝aKp(k)+bε2(espeed(k))；
the parameters 0 < a < 1 and b > 0, epsilon (e)speed(k) Is the estimation functionWherein e isspeed(k) Dynamically adjusting a scaling factor of the proportional-integral controller for the error between the kth estimated speed and the target reference speed;
wherein, the adaptive basic algorithm is LMS algorithm,
Figure FDA0003515545930000031
x (k), y (k) represent the k-th input signal and the filtered output signal of the adaptive filtering model, d (k) represent the desired output, e (k) represent error values, and w (k) represent weight values;
ε (e (k)) is an estimation function based on the Sigmoid function, expressed as:
Figure FDA0003515545930000032
wherein Sigmoid (. cndot.) is an s-type function, which is defined
Figure FDA0003515545930000033
Figure FDA0003515545930000034
Figure FDA0003515545930000041
Figure FDA0003515545930000042
A(k)＝[e2(k)，e2(k-1)，e2(k-2)…e2(k-N+1)]Med (·) denotes a take median function;
determining a step size factor at time K by the step size factor function:
Figure FDA0003515545930000043
wherein, mu (k), epsilon (e) (k) is used to replace mu and e (k) in the weight updating function w (k +1) ═ w (k) +2 mu x (k) e (k)), so as to obtain the weight updating function of the adaptive algorithm based on the Sigmoid function estimation function
w(k+1)＝w(k)+2μ(k)x(k)ε(e(k))。
2. The method of claim 1, wherein determining a target current signal based on the voltage signal to be processed comprises:
performing coordinate inverse transformation processing on the voltage signal to be processed to obtain a reference voltage signal;
determining the target current signal based on the reference voltage signal.
3. The method of claim 1, wherein the obtaining a sampled current signal comprises:
and sampling the three-phase current to obtain the sampling current signal.
4. A current compensation device based on permanent magnet synchronous motor vector control, characterized in that the device comprises:
the acquisition module is used for acquiring a sampling current signal;
the coordinate transformation processing module is used for carrying out coordinate transformation processing on the sampling current signal to obtain a current signal to be processed;
the filtering processing module is used for carrying out filtering processing on the current signal to be processed by utilizing an adaptive filtering model of an estimation function determined based on an activation function;
the control processing module is used for carrying out proportional-integral control processing on the current signal to be processed after filtering processing to obtain a voltage signal to be processed;
the determining module is used for determining a target current signal based on the voltage signal to be processed, injecting the target current signal into the stator current of the motor and completing the compensation of the stator current;
the filtering processing module comprises:
a first determination submodule for determining the adaptive filtering model of the estimation function determined on the basis of the activation function;
the filtering processing submodule is used for carrying out filtering processing on the current signal to be processed based on the self-adaptive filtering model;
the first determination submodule includes:
an obtaining unit, configured to obtain an initial adaptive filtering model;
a determining unit, configured to determine an adaptive weight update function based on a least mean square algorithm, a step factor function, and the estimation function determined based on the activation function;
the updating unit is used for updating the initial adaptive filtering model based on the weight updating function to obtain the adaptive filtering model;
wherein the update unit includes: designing a self-adaptive filter model of a d-q axis of a current loop as follows:
Figure FDA0003515545930000051
wherein iqx(k) And iqd(k) As input variables of the algorithm, iqx(k) Q-axis current, i, being output in real time by vector controlqd(k) For the desired value of q-axis current byqx(k) After low-pass filtering, i is obtainedqd(k) Approximation iqy(k) The q-axis current is processed by the algorithm of the adaptive filter and is finally used as the feedback current of a q-axis current loop to carry out subsequent vector control operation;
the d-axis current is processed the same as the q-axis current,
Figure FDA0003515545930000061
wherein idx(k) And idd(k) As input variables, idx(k) Is a d-axis current, i, output in real time by vector controldd(k) For the desired value of d-axis current bydx(k) Low-pass filtering to obtaindy(k) The output variable of the algorithm is d-axis current processed by the algorithm of the adaptive filter, and finally is used as the operation of feedback subsequent vector control of a d-axis current loop;
the proportional-integral control processing is performed on the filtered current signal to be processed to obtain a voltage signal to be processed, and the method comprises the following steps:
determining a rotating speed error value of the permanent magnet synchronous motor, and adaptively adjusting a proportional coefficient of a proportional integral controller based on the rotating speed error value;
determining a quadrature reference current value based on the scaling factor;
under the condition that the permanent magnet synchronous motor is in a stable state, performing proportional-integral control processing based on the current signal to be processed after filtering processing and the quadrature axis reference current value to obtain a voltage signal to be processed;
the determining of the rotating speed error value of the permanent magnet synchronous motor and the self-adaptive adjustment of the proportional coefficient of the proportional integral controller based on the rotating speed error value comprise the following steps:
based on the theory of proportional-integral controller, the proportional coefficient K is calculated according to the motor parameterspFor the (K +1) th scaling factor Kp(k +1), which can be updated based on the following formula:
Kp(k+1)＝aKp(k)+bε2(espeed(k))；
the parameters 0 < a < 1 and b > 0, epsilon (e)speed(k) Is the estimation function, where espeed(k) Dynamically adjusting a scaling factor of the proportional-integral controller for the error between the kth estimated speed and the target reference speed;
the adaptive base algorithm used is the LMS algorithm,
Figure FDA0003515545930000071
x (k), y (k) represent the k-th input signal and the filtered output signal of the adaptive filtering model, d (k) represent the desired output, e (k) represent error values, and w (k) represent weight values;
ε (e (k)) is an estimation function based on the Sigmoid function, expressed as:
Figure FDA0003515545930000072
wherein Sigmoid (. cndot.) is an s-type function, which is defined
Figure FDA0003515545930000073
Figure FDA0003515545930000074
Figure FDA0003515545930000081
Figure FDA0003515545930000082
A(k)＝[e2(k)，e2(k-1)，e2(k-2)…e2(k-N+1)]Med (·) denotes a take median function;
determining a step size factor at time K by the step size factor function:
Figure FDA0003515545930000083
wherein, mu (k), epsilon (e) (k) is used to replace mu and e (k) in the weight updating function w (k +1) ═ w (k) +2 mu x (k) e (k)), so as to obtain the weight updating function of the adaptive algorithm based on the Sigmoid function estimation function
w(k+1)＝w(k)+2μ(k)X(k)ε(e(k))。
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106160617A (en) * 2016-07-27 2016-11-23 零度智控（北京）智能科技有限公司 The motor control method of aircraft, device, electron speed regulator and aircraft
CN106788077A (en) * 2016-11-28 2017-05-31 广东美芝精密制造有限公司 The control method of motor, device and household electrical appliance
CN108111083A (en) * 2017-12-23 2018-06-01 西安交通大学 Servo control system and Torque Pulsation Control Method based on adaptive-filtering
CN109274308A (en) * 2018-08-13 2019-01-25 瑞声科技(新加坡)有限公司 Parameters of hydraulic motor control system and parameters of hydraulic motor control method
CN109462352A (en) * 2017-08-30 2019-03-12 比亚迪股份有限公司 Motor control method, device and computer readable storage medium
CN112087178A (en) * 2020-09-14 2020-12-15 四川长虹电器股份有限公司 Parameter identification method of permanent magnet synchronous motor
CN113364380A (en) * 2021-08-10 2021-09-07 成都爱旗科技有限公司 Motor parameter determination method and device

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007136828A2 (en) * 2006-05-19 2007-11-29 Siemens Energy & Automation, Inc. Automating tuning of a closed loop controller
CN105871282A (en) * 2016-05-12 2016-08-17 东华大学 Controller PI parameter tuning method based on rotational inertia of motor
CN106849793B (en) * 2017-03-01 2019-03-01 西安交通大学 A kind of Over Electric Motor with PMSM fuzzy Neural Network Control System
CN106887986A (en) * 2017-03-09 2017-06-23 大连理工大学 A kind of permagnetic synchronous motor self-adaptation control method based on RLS algorithm
CN107959456A (en) * 2018-01-08 2018-04-24 齐鲁工业大学 A kind of permanent magnet synchronous motor control strategy of electric automobile
CN109188078A (en) * 2018-10-17 2019-01-11 江苏师范大学 A kind of variable step- size LMS adaptive harmonic current detection method
CN110531138A (en) * 2019-07-08 2019-12-03 江苏科技大学 A kind of Active Power Filter Harmonic Currents detection method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106160617A (en) * 2016-07-27 2016-11-23 零度智控（北京）智能科技有限公司 The motor control method of aircraft, device, electron speed regulator and aircraft
CN106788077A (en) * 2016-11-28 2017-05-31 广东美芝精密制造有限公司 The control method of motor, device and household electrical appliance
CN109462352A (en) * 2017-08-30 2019-03-12 比亚迪股份有限公司 Motor control method, device and computer readable storage medium
CN108111083A (en) * 2017-12-23 2018-06-01 西安交通大学 Servo control system and Torque Pulsation Control Method based on adaptive-filtering
CN109274308A (en) * 2018-08-13 2019-01-25 瑞声科技(新加坡)有限公司 Parameters of hydraulic motor control system and parameters of hydraulic motor control method
CN112087178A (en) * 2020-09-14 2020-12-15 四川长虹电器股份有限公司 Parameter identification method of permanent magnet synchronous motor
CN113364380A (en) * 2021-08-10 2021-09-07 成都爱旗科技有限公司 Motor parameter determination method and device

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