CN117175981A - Permanent magnet synchronous motor position-free control system and method based on image recognition - Google Patents
Permanent magnet synchronous motor position-free control system and method based on image recognition Download PDFInfo
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Abstract
The application provides a permanent magnet synchronous motor position-free control system and method based on image recognition, and relates to the field of motor control. The permanent magnet synchronous motor position-free control system based on image recognition comprises: the current signal acquisition module is used for collecting current data at different rotor positions and generating a training data sample set; the preprocessing module is used for converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current, and generating image data; and the image recognition module is used for extracting the characteristic information associated with the position information from the generated picture in the model training stage by utilizing an image processing technology and a machine learning algorithm, and generating a recognition model according to the deep learning result. The method solves the problems that the permanent magnet synchronous motor with low salient pole ratio fully excavates the position features of the image information by using the image recognition technology, and realizes high-precision and high-robustness position-free estimation.
Description
Technical Field
The application relates to the technical field of motor control, in particular to a permanent magnet synchronous motor position-free control system and method based on image recognition.
Background
The permanent magnet synchronous motor has the advantages of high efficiency, good reliability, small torque pulsation and the like, and is widely applied to motor control. High-performance motor control requires high-precision rotor position information. Typically, the position is measured directly by a mechanical position sensor mounted coaxially with the rotor. Also, installing a mechanical position sensor would result in an increase in the cost and size of the motor system, and also reduce the robustness and reliability of the system due to the additional wiring and connections of the sensor. Even in some special applications there is no way to use mechanical sensors due to space constraints, harsh environments, etc. Sensorless control is a good solution to these problems.
Sensorless control schemes can be broadly divided into two main categories: control based on fundamental frequency mathematical model and control based on motor saliency. The method based on the fundamental frequency mathematical model utilizes the back electromotive force of the permanent magnet synchronous motor to estimate the position, and is only suitable for the medium-high speed range. On the other hand, sensorless control based on motor saliency is adapted to a low speed range, and the rotor position can be obtained even at zero speed. Control of motor saliency can be classified into rotation vector voltage injection and pulse voltage vector injection according to the difference of high frequency injection coordinate systems. The rotary signal injection needs higher saliency, and for a surface-mounted motor with low saliency, a high-frequency rotary voltage injection method is used for position estimation, so that a larger estimation error exists; pulse signal injection can be used to produce additional saturated saliency in low saliency machines, but this approach may lead to system instability problems due to multiple convergence points.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a permanent magnet synchronous motor position-free control system and method based on image recognition, which solve the problems of fully excavating position features in image information and realizing high-precision and high-robustness position-free estimation for a permanent magnet synchronous motor with low salient pole rate by using an image recognition technology.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
in a first aspect, there is provided a permanent magnet synchronous motor position-free control system based on image recognition, including:
the current signal acquisition module is used for collecting current data at different rotor positions and generating a training data sample set;
the preprocessing module is used for converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current, and generating image data;
the image recognition module is used for extracting characteristic information associated with the position information from the generated picture in a model training stage by utilizing an image processing technology and a machine learning algorithm, generating a recognition model according to a deep learning result, and storing model parameters; in the image recognition stage, recognizing the current image data based on the trained recognition model, and outputting a recognition tag value;
and the control module is used for looking up a table according to the label value identified by the image identification module in the image identification stage and outputting a final position estimation result.
Preferably, the current signal acquisition module comprises a sampling circuit and an AD conversion module, and the sampling circuit and the AD conversion module are respectively positioned on the driving board and the control board.
Preferably, the preprocessing module comprises a filtering module and an imaging module, wherein the filtering module is used for suppressing sampling errors, and the imaging module generates an image according to the image template requirement of the training model so as to prepare for the next image recognition.
Preferably, the image recognition module comprises a recognition memory and an image recognition operation module, wherein the recognition memory is used for storing model parameters, and the image recognition operation module is used for recognizing current image data based on a trained recognition model and outputting a recognition tag value.
Preferably, the determining of the identification model specifically includes:
determining position estimation precision and determining classification quantity;
obtaining sample data through experiments, and determining a training sample and a test sample;
adjusting the structure and parameters of the identification model;
training and verifying the recognition model, judging the size of the recognition rate, and determining the structure and parameters of the recognition model.
Preferably, the determining the size of the recognition rate, determining the structure and parameters of the recognition model specifically includes:
and taking the accuracy of the confusion matrix as a reliability evaluation criterion of the recognition model, when the accuracy recognition rate of the confusion matrix is greater than 95%, determining the structure and parameters of the recognition model, and when the accuracy recognition rate of the confusion matrix is less than or equal to 95%, readjusting the structure and parameters of the recognition model until the accuracy recognition rate of the confusion matrix is greater than 95%.
Preferably, the control module comprises a control memory and a logic operation module, wherein the control memory is used for outputting a table look-up value according to the label identified by the image identification module, and the logic operation module is used for determining the final position estimation output.
In a second aspect, a permanent magnet synchronous motor position-free control method based on image recognition is provided, including:
collecting current data at different rotor positions for generating a training data sample set;
converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current for generating image data;
combining the generated image data, extracting characteristic information associated with the position information from the generated picture by utilizing an image processing technology and a machine learning algorithm, generating an identification model according to a deep learning result, and storing model parameters; based on the trained recognition model, recognizing the current image data and outputting a recognition tag value;
and carrying out table lookup according to the identified tag value to output a final position estimation result.
In a second aspect, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
In a third aspect, a computing device is provided, comprising:
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
(III) beneficial effects
(1) The application relates to a permanent magnet synchronous motor position-free control system and a method based on image recognition, which can realize a high single-precision position estimation function by combining an image processing technology and a machine learning algorithm.
(2) The application discloses a permanent magnet synchronous motor position-free control system and method based on image recognition.
(3) The application discloses a permanent magnet synchronous motor position-free control system and method based on image recognition.
(4) According to the position-free control system and method for the permanent magnet synchronous motor based on image recognition, position-free control is realized on the permanent magnet synchronous motor, an additional position sensor or encoder is not needed, and the system cost and volume are greatly reduced; meanwhile, the method for identifying the pattern based on the high-frequency response signal has the capability of detecting and identifying weak position information, can accurately identify the position characteristics and can effectively identify disturbance signals, so that the control with high precision and high robustness is realized, and the method is applicable to all permanent magnet synchronous motor topologies.
Drawings
FIG. 1 is a schematic diagram of a position-free control system framework according to an embodiment of the present application;
FIG. 2 is a flow chart of the extraction of the reconstruction vector in the embodiment of the application;
FIG. 3 is a diagram illustrating a position estimation for pattern recognition according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a pattern recognition model according to an embodiment of the present application;
FIG. 5 is a diagram showing the effect of the initial position estimation result in the embodiment of the present application;
FIG. 6 is a diagram showing the effect of dynamic position estimation according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Examples
As shown in fig. 1, an embodiment of the present application provides a permanent magnet synchronous motor position-free control system based on image recognition, including:
the current signal acquisition module is used for collecting current data at different rotor positions and generating a training data sample set;
the preprocessing module is used for converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current, and generating image data;
the image recognition module is used for extracting characteristic information associated with the position information from the generated picture in a model training stage by utilizing an image processing technology and a machine learning algorithm, generating a recognition model according to a deep learning result, and storing model parameters; in the image recognition stage, recognizing the current image data based on the trained recognition model, and outputting a recognition tag value;
and the control module is used for looking up a table according to the label value identified by the image identification module in the image identification stage and outputting a final position estimation result.
Further, the current signal acquisition module comprises a sampling circuit and an AD conversion module, and the sampling circuit and the AD conversion module are respectively positioned on the driving board and the control board.
Further, the preprocessing module comprises a filtering module and an imaging module, wherein the filtering module is used for suppressing sampling errors, and the imaging module generates images according to the image template requirement of the training model so as to prepare for the next image recognition.
Further, the image recognition module comprises a recognition memory and an image recognition operation module, wherein the recognition memory is used for storing model parameters, and the image recognition operation module is used for recognizing current image data based on the trained recognition model and outputting a recognition tag value.
Referring to fig. 2-6, further, the determining the identification model specifically includes:
determining position estimation precision and determining classification quantity; since the number of classifications is directly related to the position resolution, the number of classifications needs to be selected according to the position control accuracy. By defining a greater number of classifications, a higher accuracy can be obtained. A larger number of classifications may lead to a relatively larger model. In order to ensure the balance between the position precision and the calculated amount, an electric cycle period can be divided into 180 equal parts, and the obtained angle value is used as a corresponding classification label; considering the existence of interference signals, one classification is added on the basis of 180 classifications, so that the finally determined classification number is 181;
obtaining sample data through experiments, and determining a training sample and a test sample; 50 experiments were performed at each location due to nonlinear effects such as non-ideal behavior of the inverter and sensor resolution. To eliminate experimental errors, a carrier frequency of 10 times period is included in each experiment at each location. Randomly selecting 30 groups of data from each type of data for training, establishing nonlinear mapping between the classification labels and the current image, and using the rest 20 groups of data as a test set for model verification;
adjusting the structure and parameters of the identification model; feature extraction is to simplify image data by extracting valuable information in an image and discarding irrelevant information;
training and verifying the recognition model, judging the size of the recognition rate, and determining the structure and parameters of the recognition model.
As shown in fig. 2, after the high-frequency voltage is injected into the motor, the collected current signal is obtained through the current sensor, firstly, the fundamental wave and the high-frequency signal are filtered through a band-pass filter, and then, the high-frequency current with 1-time negative sequence current and 2-time positive sequence current is obtained through a synchronous shafting filter and a band-pass filter;
as shown in fig. 3, the reconstructed vector is obtained in the basic line of fig. 2, the reconstructed vector is drawn along the directions of the horizontal axis and the vertical axis respectively according to the real part and the imaginary part of the vector, the obtained image data is input into the image recognition model, the image recognition model estimates and recognizes the image label corresponding to the current image according to the current image, and finally the image label carries out table lookup according to a pre-designed good table to output a final position estimation value;
as shown in fig. 4, the model determination can be divided into 4 processes, firstly, the final classification number is determined according to the desired estimation accuracy, and in a period of 360 degrees, the more the classification number is, the higher the position estimation accuracy is; the second process is to obtain experimental data, obtain image data of a reconstruction vector according to the steps shown in fig. 2, collect data of a plurality of periods of high-frequency injection at each position as test data in order to eliminate sampling errors of the sensor and experimental errors existing in the test, and sample data at each position should be as much as possible under different working conditions in order to ensure the accuracy of identification, wherein data samples at each position are kept consistent, 60% of the sample data can be selected as training sample data, and 40% of the sample data can be selected as test sample data; thirdly, training, testing and verifying the data obtained on the basis of the second step in a preliminarily designed identification model; the final step is to determine whether the parameters and the structure of the model are reasonable according to the recognition rate of the model, when the final recognition of the model reaches 95%, the parameters and the structure of the model can be considered to meet the estimated requirement, if the recognition rate of the model is lower than 95%, the third step is needed to be returned, the parameters and the structure of the model are readjusted, and then training test is carried out until the recognition rate of the finally trained model parameters reaches more than 95%;
as shown in fig. 5, the error values of the image recognition method in the static state are displayed, the result shows that the position errors in 180 different positions are within 2.5 degrees, most of the estimation errors can be controlled within 1 degree, and the result shows that the control method based on the image recognition provided by the patent can obtain a better estimation effect in the static state;
as shown in fig. 6, the displayed position estimation result of the image recognition method in the low-speed state shows that the estimated position can better follow the change of the actual position in the low-speed operation condition, and the result shows that the control method based on image recognition provided by the patent can obtain a better estimation effect even in the dynamic operation.
Further, judging the size of the recognition rate, and determining the structure and parameters of the recognition model, wherein the method specifically comprises the following steps:
and taking the accuracy of the confusion matrix as a reliability evaluation criterion of the recognition model, when the accuracy recognition rate of the confusion matrix is greater than 95%, determining the structure and parameters of the recognition model, and when the accuracy recognition rate of the confusion matrix is less than or equal to 95%, readjusting the structure and parameters of the recognition model until the accuracy recognition rate of the confusion matrix is greater than 95%.
Further, the control module comprises a control memory and a logic operation module, the control memory is used for outputting a table look-up value according to the label identified by the image identification module, and the logic operation module is used for determining final position estimation output.
The application further provides a permanent magnet synchronous motor position-free control method based on image recognition, which comprises the following steps:
collecting current data at different rotor positions for generating a training data sample set;
converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current for generating image data;
combining the generated image data, extracting characteristic information associated with the position information from the generated picture by utilizing an image processing technology and a machine learning algorithm, generating an identification model according to a deep learning result, and storing model parameters; based on the trained recognition model, recognizing the current image data and outputting a recognition tag value;
and carrying out table lookup according to the identified tag value to output a final position estimation result.
Embodiments of the present application may be provided as a method or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A permanent magnet synchronous motor position-free control system based on image recognition, comprising:
the current signal acquisition module is used for collecting current data at different rotor positions and generating a training data sample set;
the preprocessing module is used for converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current, and generating image data;
the image recognition module is used for extracting characteristic information associated with the position information from the generated picture in a model training stage by utilizing an image processing technology and a machine learning algorithm, generating a recognition model according to a deep learning result, and storing model parameters; in the image recognition stage, recognizing the current image data based on the trained recognition model, and outputting a recognition tag value;
and the control module is used for looking up a table according to the label value identified by the image identification module in the image identification stage and outputting a final position estimation result.
2. The permanent magnet synchronous motor position-free control system based on image recognition according to claim 1, wherein: the current signal acquisition module comprises a sampling circuit and an AD conversion module, and the sampling circuit and the AD conversion module are respectively positioned on the driving board and the control board.
3. The permanent magnet synchronous motor position-free control system based on image recognition according to claim 1, wherein: the preprocessing module comprises a filtering module and an imaging module, wherein the filtering module is used for suppressing sampling errors, and the imaging module generates an image according to the image template requirement of the training model so as to prepare for the next image recognition.
4. A permanent magnet synchronous motor position-free control system based on image recognition according to claim 3, wherein: the image recognition module comprises a recognition memory and an image recognition operation module, wherein the recognition memory is used for storing model parameters, and the image recognition operation module is used for recognizing current image data based on a trained recognition model and outputting a recognition tag value.
5. The permanent magnet synchronous motor position-free control system based on image recognition according to claim 4, wherein: the determination of the identification model specifically comprises the following steps:
determining position estimation precision and determining classification quantity;
obtaining sample data through experiments, and determining a training sample and a test sample;
adjusting the structure and parameters of the identification model;
training and verifying the recognition model, judging the size of the recognition rate, and determining the structure and parameters of the recognition model.
6. The permanent magnet synchronous motor position-free control system based on image recognition according to claim 5, wherein: the step of judging the size of the recognition rate and determining the structure and parameters of the recognition model specifically comprises the following steps:
and taking the accuracy of the confusion matrix as a reliability evaluation criterion of the recognition model, when the accuracy recognition rate of the confusion matrix is greater than 95%, determining the structure and parameters of the recognition model, and when the accuracy recognition rate of the confusion matrix is less than or equal to 95%, readjusting the structure and parameters of the recognition model until the accuracy recognition rate of the confusion matrix is greater than 95%.
7. The permanent magnet synchronous motor position-free control system based on image recognition according to claim 1, wherein: the control module comprises a control memory and a logic operation module, wherein the control memory is used for outputting a table look-up value according to the label identified by the image identification module, and the logic operation module is used for determining final position estimation output.
8. A permanent magnet synchronous motor no-position control method based on image recognition, based on the permanent magnet synchronous motor no-position control system based on image recognition as claimed in any one of claims 1-7, characterized by comprising:
collecting current data at different rotor positions for generating a training data sample set;
converting the collected current data into a synchronous shafting filter and a band-pass filter to extract a negative sequence current and a secondary positive sequence high-frequency current signal of the high-frequency response current for generating image data;
combining the generated image data, extracting characteristic information associated with the position information from the generated picture by utilizing an image processing technology and a machine learning algorithm, generating an identification model according to a deep learning result, and storing model parameters; based on the trained recognition model, recognizing the current image data and outputting a recognition tag value;
and carrying out table lookup according to the identified tag value to output a final position estimation result.
9. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claim 8.
10. A computing device, comprising:
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claim 8.
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